├── .idea ├── alphacat.iml ├── misc.xml ├── modules.xml └── workspace.xml ├── GUI.py ├── GUI.pyc ├── PPyserial .py ├── README.md ├── README0.md ├── camera.py ├── camera.pyc ├── cv2_ ├── face.py ├── face.pyc ├── frozen_inference_graph.pb ├── haarcascade_frontalface_default.xml ├── image ├── fruit.jpg ├── image.jpg └── warm_up_pic.jpg ├── main.py ├── mysql.py ├── mysql.pyc ├── os_ ├── protoc ├── bin │ └── protoc └── include │ └── google │ └── protobuf │ ├── any.proto │ ├── api.proto │ ├── compiler │ └── plugin.proto │ ├── descriptor.proto │ ├── duration.proto │ ├── empty.proto │ ├── field_mask.proto │ ├── source_context.proto │ ├── struct.proto │ ├── timestamp.proto │ ├── type.proto │ └── wrappers.proto ├── readme.txt ├── recognition.py ├── recognition.pyc ├── requests_ ├── research ├── object_detection │ ├── .ipynb_checkpoints │ │ └── object_detection_tutorial-checkpoint.ipynb │ ├── BUILD │ ├── CONTRIBUTING.md │ ├── README.md │ ├── __init__.py │ ├── __init__.pyc │ ├── anchor_generators │ │ ├── BUILD │ │ ├── __init__.py │ │ ├── __init__.pyc │ │ ├── grid_anchor_generator.py │ │ ├── grid_anchor_generator.pyc │ │ ├── grid_anchor_generator_test.py │ │ ├── multiple_grid_anchor_generator.py │ │ ├── multiple_grid_anchor_generator.pyc │ │ └── multiple_grid_anchor_generator_test.py │ ├── box_coders │ │ ├── BUILD │ │ ├── __init__.py │ │ ├── __init__.pyc │ │ ├── faster_rcnn_box_coder.py │ │ ├── faster_rcnn_box_coder.pyc │ │ ├── faster_rcnn_box_coder_test.py │ │ ├── keypoint_box_coder.py │ │ ├── keypoint_box_coder_test.py │ │ ├── mean_stddev_box_coder.py │ │ ├── mean_stddev_box_coder.pyc │ │ ├── mean_stddev_box_coder_test.py │ │ ├── square_box_coder.py │ │ ├── square_box_coder.pyc │ │ └── square_box_coder_test.py │ ├── builders │ │ ├── BUILD │ │ ├── __init__.py │ │ ├── __init__.pyc │ │ ├── anchor_generator_builder.py │ │ ├── anchor_generator_builder.pyc │ │ ├── anchor_generator_builder_test.py │ │ ├── box_coder_builder.py │ │ ├── box_coder_builder.pyc │ │ ├── box_coder_builder_test.py │ │ ├── box_predictor_builder.py │ │ ├── box_predictor_builder.pyc │ │ ├── box_predictor_builder_test.py │ │ ├── hyperparams_builder.py │ │ ├── hyperparams_builder.pyc │ │ ├── hyperparams_builder_test.py │ │ ├── image_resizer_builder.py │ │ ├── image_resizer_builder.pyc │ │ ├── image_resizer_builder_test.py │ │ ├── input_reader_builder.py │ │ ├── input_reader_builder.pyc │ │ ├── input_reader_builder_test.py │ │ ├── losses_builder.py │ │ ├── losses_builder.pyc │ │ ├── losses_builder_test.py │ │ ├── matcher_builder.py │ │ ├── matcher_builder.pyc │ │ ├── matcher_builder_test.py │ │ ├── model_builder.py │ │ ├── model_builder.pyc │ │ ├── model_builder_test.py │ │ ├── optimizer_builder.py │ │ ├── optimizer_builder_test.py │ │ ├── post_processing_builder.py │ │ ├── post_processing_builder.pyc │ │ ├── post_processing_builder_test.py │ │ ├── preprocessor_builder.py │ │ ├── preprocessor_builder_test.py │ │ ├── region_similarity_calculator_builder.py │ │ ├── region_similarity_calculator_builder.pyc │ │ └── region_similarity_calculator_builder_test.py │ ├── core │ │ ├── BUILD │ │ ├── __init__.py │ │ ├── __init__.pyc │ │ ├── anchor_generator.py │ │ ├── anchor_generator.pyc │ │ ├── balanced_positive_negative_sampler.py │ │ ├── balanced_positive_negative_sampler.pyc │ │ ├── balanced_positive_negative_sampler_test.py │ │ ├── batcher.py │ │ ├── batcher_test.py │ │ ├── box_coder.py │ │ ├── box_coder.pyc │ │ ├── box_coder_test.py │ │ ├── box_list.py │ │ ├── box_list.pyc │ │ ├── box_list_ops.py │ │ ├── box_list_ops.pyc │ │ ├── box_list_ops_test.py │ │ ├── box_list_test.py │ │ ├── box_predictor.py │ │ ├── box_predictor.pyc │ │ ├── box_predictor_test.py │ │ ├── data_decoder.py │ │ ├── data_decoder.pyc │ │ ├── keypoint_ops.py │ │ ├── keypoint_ops.pyc │ │ ├── keypoint_ops_test.py │ │ ├── losses.py │ │ ├── losses.pyc │ │ ├── losses_test.py │ │ ├── matcher.py │ │ ├── matcher.pyc │ │ ├── matcher_test.py │ │ ├── minibatch_sampler.py │ │ ├── minibatch_sampler.pyc │ │ ├── minibatch_sampler_test.py │ │ ├── model.py │ │ ├── model.pyc │ │ ├── post_processing.py │ │ ├── post_processing.pyc │ │ ├── post_processing_test.py │ │ ├── prefetcher.py │ │ ├── prefetcher.pyc │ │ ├── prefetcher_test.py │ │ ├── preprocessor.py │ │ ├── preprocessor.pyc │ │ ├── preprocessor_test.py │ │ ├── region_similarity_calculator.py │ │ ├── region_similarity_calculator.pyc │ │ ├── region_similarity_calculator_test.py │ │ ├── standard_fields.py │ │ ├── standard_fields.pyc │ │ ├── target_assigner.py │ │ ├── target_assigner.pyc │ │ └── target_assigner_test.py │ ├── create_pascal_tf_record.py │ ├── create_pascal_tf_record_test.py │ ├── create_pet_tf_record.py │ ├── data │ │ ├── mscoco_label_map.pbtxt │ │ ├── pascal_label_map.pbtxt │ │ └── pet_label_map.pbtxt │ ├── data_decoders │ │ ├── BUILD │ │ ├── __init__.py │ │ ├── __init__.pyc │ │ ├── tf_example_decoder.py │ │ ├── tf_example_decoder.pyc │ │ └── tf_example_decoder_test.py │ ├── eval.py │ ├── eval.pyc │ ├── eval_util.py │ ├── eval_util.pyc │ ├── evaluator.py │ ├── evaluator.pyc │ ├── export_inference_graph.py │ ├── exporter.py │ ├── exporter_test.py │ ├── g3doc │ │ ├── configuring_jobs.md │ │ ├── defining_your_own_model.md │ │ ├── detection_model_zoo.md │ │ ├── exporting_models.md │ │ ├── img │ │ │ ├── dogs_detections_output.jpg │ │ │ ├── example_cat.jpg │ │ │ ├── kites_detections_output.jpg │ │ │ ├── oxford_pet.png │ │ │ ├── tensorboard.png │ │ │ └── tensorboard2.png │ │ ├── installation.md │ │ ├── preparing_inputs.md │ │ ├── running_locally.md │ │ ├── running_notebook.md │ │ ├── running_on_cloud.md │ │ ├── running_pets.md │ │ └── using_your_own_dataset.md │ ├── matchers │ │ ├── BUILD │ │ ├── __init__.py │ │ ├── __init__.pyc │ │ ├── argmax_matcher.py │ │ ├── argmax_matcher.pyc │ │ ├── argmax_matcher_test.py │ │ ├── bipartite_matcher.py │ │ ├── bipartite_matcher.pyc │ │ └── bipartite_matcher_test.py │ ├── meta_architectures │ │ ├── BUILD │ │ ├── __init__.py │ │ ├── __init__.pyc │ │ ├── faster_rcnn_meta_arch.py │ │ ├── faster_rcnn_meta_arch.pyc │ │ ├── faster_rcnn_meta_arch_test.py │ │ ├── faster_rcnn_meta_arch_test_lib.py │ │ ├── rfcn_meta_arch.py │ │ ├── rfcn_meta_arch.pyc │ │ ├── rfcn_meta_arch_test.py │ │ ├── ssd_meta_arch.py │ │ ├── ssd_meta_arch.pyc │ │ └── ssd_meta_arch_test.py │ ├── models │ │ ├── BUILD │ │ ├── __init__.py │ │ ├── __init__.pyc │ │ ├── faster_rcnn_inception_resnet_v2_feature_extractor.py │ │ ├── faster_rcnn_inception_resnet_v2_feature_extractor.pyc │ │ ├── faster_rcnn_inception_resnet_v2_feature_extractor_test.py │ │ ├── faster_rcnn_resnet_v1_feature_extractor.py │ │ ├── faster_rcnn_resnet_v1_feature_extractor.pyc │ │ ├── faster_rcnn_resnet_v1_feature_extractor_test.py │ │ ├── feature_map_generators.py │ │ ├── feature_map_generators.pyc │ │ ├── feature_map_generators_test.py │ │ ├── ssd_feature_extractor_test.py │ │ ├── ssd_inception_v2_feature_extractor.py │ │ ├── ssd_inception_v2_feature_extractor.pyc │ │ ├── ssd_inception_v2_feature_extractor_test.py │ │ ├── ssd_mobilenet_v1_feature_extractor.py │ │ ├── ssd_mobilenet_v1_feature_extractor.pyc │ │ └── ssd_mobilenet_v1_feature_extractor_test.py │ ├── object_detection_tutorial.ipynb │ ├── protos │ │ ├── BUILD │ │ ├── __init__.py │ │ ├── __init__.pyc │ │ ├── anchor_generator.proto │ │ ├── anchor_generator_pb2.py │ │ ├── anchor_generator_pb2.pyc │ │ ├── argmax_matcher.proto │ │ ├── argmax_matcher_pb2.py │ │ ├── argmax_matcher_pb2.pyc │ │ ├── bipartite_matcher.proto │ │ ├── bipartite_matcher_pb2.py │ │ ├── bipartite_matcher_pb2.pyc │ │ ├── box_coder.proto │ │ ├── box_coder_pb2.py │ │ ├── box_coder_pb2.pyc │ │ ├── box_predictor.proto │ │ ├── box_predictor_pb2.py │ │ ├── box_predictor_pb2.pyc │ │ ├── eval.proto │ │ ├── eval_pb2.py │ │ ├── eval_pb2.pyc │ │ ├── faster_rcnn.proto │ │ ├── faster_rcnn_box_coder.proto │ │ ├── faster_rcnn_box_coder_pb2.py │ │ ├── faster_rcnn_box_coder_pb2.pyc │ │ ├── faster_rcnn_pb2.py │ │ ├── faster_rcnn_pb2.pyc │ │ ├── grid_anchor_generator.proto │ │ ├── grid_anchor_generator_pb2.py │ │ ├── grid_anchor_generator_pb2.pyc │ │ ├── hyperparams.proto │ │ ├── hyperparams_pb2.py │ │ ├── hyperparams_pb2.pyc │ │ ├── image_resizer.proto │ │ ├── image_resizer_pb2.py │ │ ├── image_resizer_pb2.pyc │ │ ├── input_reader.proto │ │ ├── input_reader_pb2.py │ │ ├── input_reader_pb2.pyc │ │ ├── losses.proto │ │ ├── losses_pb2.py │ │ ├── losses_pb2.pyc │ │ ├── matcher.proto │ │ ├── matcher_pb2.py │ │ ├── matcher_pb2.pyc │ │ ├── mean_stddev_box_coder.proto │ │ ├── mean_stddev_box_coder_pb2.py │ │ ├── mean_stddev_box_coder_pb2.pyc │ │ ├── model.proto │ │ ├── model_pb2.py │ │ ├── model_pb2.pyc │ │ ├── optimizer.proto │ │ ├── optimizer_pb2.py │ │ ├── optimizer_pb2.pyc │ │ ├── pipeline.proto │ │ ├── pipeline_pb2.py │ │ ├── pipeline_pb2.pyc │ │ ├── post_processing.proto │ │ ├── post_processing_pb2.py │ │ ├── post_processing_pb2.pyc │ │ ├── preprocessor.proto │ │ ├── preprocessor_pb2.py │ │ ├── preprocessor_pb2.pyc │ │ ├── region_similarity_calculator.proto │ │ ├── region_similarity_calculator_pb2.py │ │ ├── region_similarity_calculator_pb2.pyc │ │ ├── square_box_coder.proto │ │ ├── square_box_coder_pb2.py │ │ ├── square_box_coder_pb2.pyc │ │ ├── ssd.proto │ │ ├── ssd_anchor_generator.proto │ │ ├── ssd_anchor_generator_pb2.py │ │ ├── ssd_anchor_generator_pb2.pyc │ │ ├── ssd_pb2.py │ │ ├── ssd_pb2.pyc │ │ ├── string_int_label_map.proto │ │ ├── string_int_label_map_pb2.py │ │ ├── string_int_label_map_pb2.pyc │ │ ├── train.proto │ │ ├── train_pb2.py │ │ └── train_pb2.pyc │ ├── samples │ │ ├── cloud │ │ │ └── cloud.yml │ │ └── configs │ │ │ ├── faster_rcnn_inception_resnet_v2_atrous_coco.config │ │ │ ├── faster_rcnn_inception_resnet_v2_atrous_pets.config │ │ │ ├── faster_rcnn_resnet101_coco.config │ │ │ ├── faster_rcnn_resnet101_pets.config │ │ │ ├── faster_rcnn_resnet101_voc07.config │ │ │ ├── faster_rcnn_resnet152_coco.config │ │ │ ├── faster_rcnn_resnet152_pets.config │ │ │ ├── faster_rcnn_resnet50_coco.config │ │ │ ├── faster_rcnn_resnet50_pets.config │ │ │ ├── rfcn_resnet101_coco.config │ │ │ ├── rfcn_resnet101_pets.config │ │ │ ├── ssd_inception_v2_coco.config │ │ │ ├── ssd_inception_v2_pets.config │ │ │ ├── ssd_mobilenet_v1_coco.config │ │ │ └── ssd_mobilenet_v1_pets.config │ ├── ssd_mobilenet_v1_coco_11_06_2017 │ │ └── frozen_inference_graph.pb │ ├── test_images │ │ ├── image1.jpg │ │ ├── image2.jpg │ │ └── image_info.txt │ ├── train.py │ ├── trainer.py │ ├── trainer_test.py │ └── utils │ │ ├── BUILD │ │ ├── __init__.py │ │ ├── __init__.pyc │ │ ├── category_util.py │ │ ├── category_util_test.py │ │ ├── dataset_util.py │ │ ├── dataset_util_test.py │ │ ├── label_map_util.py │ │ ├── label_map_util.pyc │ │ ├── label_map_util_test.py │ │ ├── learning_schedules.py │ │ ├── learning_schedules_test.py │ │ ├── metrics.py │ │ ├── metrics.pyc │ │ ├── metrics_test.py │ │ ├── np_box_list.py │ │ ├── np_box_list.pyc │ │ ├── np_box_list_ops.py │ │ ├── np_box_list_ops.pyc │ │ ├── np_box_list_ops_test.py │ │ ├── np_box_list_test.py │ │ ├── np_box_ops.py │ │ ├── np_box_ops.pyc │ │ ├── np_box_ops_test.py │ │ ├── object_detection_evaluation.py │ │ ├── object_detection_evaluation.pyc │ │ ├── object_detection_evaluation_test.py │ │ ├── ops.py │ │ ├── ops.pyc │ │ ├── ops_test.py │ │ ├── per_image_evaluation.py │ │ ├── per_image_evaluation.pyc │ │ ├── per_image_evaluation_test.py │ │ ├── shape_utils.py │ │ ├── shape_utils.pyc │ │ ├── shape_utils_test.py │ │ ├── static_shape.py │ │ ├── static_shape.pyc │ │ ├── static_shape_test.py │ │ ├── test_utils.py │ │ ├── test_utils_test.py │ │ ├── variables_helper.py │ │ ├── variables_helper_test.py │ │ ├── visualization_utils.py │ │ ├── visualization_utils.pyc │ │ └── visualization_utils_test.py └── slim │ ├── BUILD │ ├── README.md │ ├── WORKSPACE │ ├── __init__.py │ ├── datasets │ ├── __init__.py │ ├── build_imagenet_data.py │ ├── cifar10.py │ ├── dataset_factory.py │ ├── dataset_utils.py │ ├── download_and_convert_cifar10.py │ ├── download_and_convert_flowers.py │ ├── download_and_convert_imagenet.sh │ ├── download_and_convert_mnist.py │ ├── download_imagenet.sh │ ├── flowers.py │ ├── imagenet.py │ ├── imagenet_2012_validation_synset_labels.txt │ ├── imagenet_lsvrc_2015_synsets.txt │ ├── imagenet_metadata.txt │ ├── mnist.py │ ├── preprocess_imagenet_validation_data.py │ └── process_bounding_boxes.py │ ├── deployment │ ├── __init__.py │ ├── model_deploy.py │ └── model_deploy_test.py │ ├── dist │ └── slim-0.1-py2.7.egg │ ├── download_and_convert_data.py │ ├── eval_image_classifier.py │ ├── export_inference_graph.py │ ├── export_inference_graph_test.py │ ├── nets │ ├── __init__.py │ ├── alexnet.py │ ├── alexnet_test.py │ ├── cifarnet.py │ ├── inception.py │ ├── inception_resnet_v2.py │ ├── inception_resnet_v2_test.py │ ├── inception_utils.py │ ├── inception_v1.py │ ├── inception_v1_test.py │ ├── inception_v2.py │ ├── inception_v2_test.py │ ├── inception_v3.py │ ├── inception_v3_test.py │ ├── inception_v4.py │ ├── inception_v4_test.py │ ├── lenet.py │ ├── mobilenet_v1.md │ ├── mobilenet_v1.png │ ├── mobilenet_v1.py │ ├── mobilenet_v1_test.py │ ├── nets_factory.py │ ├── nets_factory_test.py │ ├── overfeat.py │ ├── overfeat_test.py │ ├── resnet_utils.py │ ├── resnet_v1.py │ ├── resnet_v1_test.py │ ├── resnet_v2.py │ ├── resnet_v2_test.py │ ├── vgg.py │ └── vgg_test.py │ ├── preprocessing │ ├── __init__.py │ ├── cifarnet_preprocessing.py │ ├── inception_preprocessing.py │ ├── lenet_preprocessing.py │ ├── preprocessing_factory.py │ └── vgg_preprocessing.py │ ├── scripts │ ├── export_mobilenet.sh │ ├── finetune_inception_resnet_v2_on_flowers.sh │ ├── finetune_inception_v1_on_flowers.sh │ ├── finetune_inception_v3_on_flowers.sh │ ├── finetune_resnet_v1_50_on_flowers.sh │ ├── train_cifarnet_on_cifar10.sh │ └── train_lenet_on_mnist.sh │ ├── setup.py │ ├── slim.egg-info │ ├── PKG-INFO │ ├── SOURCES.txt │ ├── dependency_links.txt │ └── top_level.txt │ ├── slim_walkthrough.ipynb │ └── train_image_classifier.py ├── test.py ├── time_ └── 蓝牙连接命令 /.idea/alphacat.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 14 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /GUI.py: -------------------------------------------------------------------------------- 1 | # coding:utf-8 2 | 3 | from Tkinter import * 4 | import threading 5 | import time 6 | import Queue 7 | 8 | msgQueue = Queue.Queue() 9 | 10 | def addQueue(canPass): 11 | msgQueue.put(canPass) 12 | 13 | def show(): 14 | def labelSelect(): 15 | while True: 16 | if msgQueue.empty(): 17 | continue 18 | canPass = msgQueue.get() 19 | if canPass == 1: 20 | var1.set(msgQueue.get()) 21 | elif canPass == 2: 22 | var2.set(msgQueue.get()) 23 | elif canPass == 3: 24 | var3.set(msgQueue.get()) 25 | time.sleep(3) 26 | else: 27 | var1.set('欢迎光临') 28 | var2.set('') 29 | var3.set('') 30 | time.sleep(0.01) 31 | 32 | 33 | root = Tk() 34 | 35 | root.title("结算系统") # 窗口标题 36 | root.geometry('1280x720') # 是x 不是* 37 | root.resizable(width=False, height=False) # 宽, 高不可变 38 | 39 | var1 = StringVar() # 设置变量 40 | var2 = StringVar() 41 | var3 = StringVar() 42 | 43 | l1 = Label(root, textvariable=var1, font=("楷体", 50), justify='left', fg='blue', width=500, height=3) # 提示语label 44 | l2 = Label(root, textvariable=var2, font=("楷体", 30), justify='left',width=35, height=3) # 商品信息label 45 | l3 = Label(root, textvariable=var3, font=("楷体", 30), justify='left',width=35, height=3) # 用户信息label 46 | 47 | l1.pack() 48 | l2.pack(side = LEFT) 49 | l3.pack(side = RIGHT) 50 | 51 | th1 = threading.Thread(target=labelSelect,args=()) 52 | th1.setDaemon(True) # 守护线程 53 | th1.start() 54 | 55 | root.mainloop() 56 | 57 | th=threading.Thread(target=show,args=()) 58 | th.setDaemon(True) # 守护线程 59 | th.start() 60 | -------------------------------------------------------------------------------- /GUI.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/GUI.pyc -------------------------------------------------------------------------------- /PPyserial .py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | import serial 4 | import string 5 | import binascii 6 | 7 | ser = serial.Serial('/dev/rfcomm0',9600,timeout=None) 8 | 9 | print (ser.name)#打印设备名称 10 | print (ser.port)#打印设备名 11 | 12 | weight1 = 0.0 13 | weight2 = 0.0 14 | 15 | 16 | while True: 17 | data1 = str(binascii.hexlify(ser.read(1))) 18 | data2 = str(binascii.hexlify(ser.read(1)))#读小数数据 19 | # print data1 20 | # print data2 21 | data1 = '0x'+data1 22 | data2 = '0x'+data2 23 | 24 | data2=int(data2,16)#字符串装16进制 25 | data2=int(data2)#16转10 26 | 27 | data1=int(data1,16) 28 | data1=int(data1) 29 | 30 | weight =data1 + data2*0.01#重量（kg） 31 | 32 | print(weight) 33 | 34 | ser.close() 35 | 36 | 37 | ''' 38 | 实例网址https://blog.csdn.net/u012611644/article/details/79125234 39 | 40 | timeout=None # 永远等待，直到有数据传过来（阻塞） 41 | timeout=0 # 不等待，收不到数据直接退出读取（非阻塞） 42 | timeout=x # 设定等待时间（阻塞，x可以为浮点数） 43 | 44 | ser=serial.Serial("/dev/ttyUSB0",9600,timeout=0.5) #使用USB连接串行口 45 | ser=serial.Serial("/dev/ttyAMA0",9600,timeout=0.5) #使用树莓派的GPIO口连接串行口 46 | ser=serial.Serial(1,9600,timeout=0.5)#winsows系统使用com1口连接串行口 47 | ser=serial.Serial("com1",9600,timeout=0.5)#winsows系统使用com1口连接串行口 48 | ser=serial.Serial("/dev/ttyS1",9600,timeout=0.5)#Linux系统使用com1口连接串行口 49 | print ser.name#打印设备名称 50 | print ser.port#打印设备名 51 | ser.open() 52 | 53 | s = ser.read(10)#从端口读10个字节 54 | ser.write("hello")#向端口些数据 55 | ser.close()#关闭端口 56 | data = ser.read(20) #是读20个字符 57 | 58 | data = ser.readline() #是读一行，以/n结束，要是没有/n就一直读，阻塞。 59 | 60 | data = ser.readlines()和ser.xreadlines()#都需要设置超时时间 61 | 62 | ser.baudrate = 9600 #设置波特率 63 | 64 | ser.isOpen() #看看这个串口是否已经被打开 65 | ''' 66 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 作品简介： 2 | 基于谷歌目标检测API实现的多商品识别系统，用于无人零售解决方案的商品识别 3 | # 作者： 4 | HFUTer郭义臣(https://github.com/mrycguo/) 5 | HFUTer贠博祥(https://github.com/hfut-xc-yun) 6 | -------------------------------------------------------------------------------- /README0.md: -------------------------------------------------------------------------------- 1 | ### 5. 深度学习中的目标检测 2 | 3 | **5.2.1 安装TensorFlow Object Detection API** 4 | 5 | 参考5.2.1小节完成相应操作。 6 | 7 | **5.2.3 训练新的模型** 8 | 9 | 先在地址http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar 下载VOC2012数据集并解压。 10 | 11 | 在项目的object_detection文件夹中新建voc目录，并将解压后的数据集拷贝进来，最终形成的目录为： 12 | 13 | ``` 14 | research/ 15 | object_detection/ 16 | voc/ 17 | VOCdevkit/ 18 | VOC2012/ 19 | JPEGImages/ 20 | 2007_000027.jpg 21 | 2007_000032.jpg 22 | 2007_000033.jpg 23 | 2007_000039.jpg 24 | 2007_000042.jpg 25 | ……………… 26 | Annotations/ 27 | 2007_000027.xml 28 | 2007_000032.xml 29 | 2007_000033.xml 30 | 2007_000039.xml 31 | 2007_000042.xml 32 | ……………… 33 | ……………… 34 | ``` 35 | 36 | 在object_detection目录中执行如下命令将数据集转换为tfrecord： 37 | 38 | ``` 39 | python create_pascal_tf_record.py --data_dir voc/VOCdevkit/ --year=VOC2012 --set=train --output_path=voc/pascal_train.record 40 | python create_pascal_tf_record.py --data_dir voc/VOCdevkit/ --year=VOC2012 --set=val --output_path=voc/pascal_val.record 41 | ``` 42 | 43 | 此外，将pascal_label_map.pbtxt 数据复制到voc 文件夹下： 44 | ``` 45 | cp data/pascal_label_map.pbtxt voc/ 46 | ``` 47 | 48 | 下载模型文件http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_resnet_v2_atrous_coco_11_06_2017.tar.gz 并解压，解压后得到frozen_inference_graph.pb 、graph.pbtxt 、model.ckpt.data-00000-of-00001 、model.ckpt.index、model.ckpt.meta 5 个文件。在voc文件夹中新建一个 49 | pretrained 文件夹，并将这5个文件复制进去。 50 | 51 | 复制一份config文件： 52 | ``` 53 | cp samples/configs/faster_rcnn_inception_resnet_v2_atrous_pets.config \ 54 | voc/voc.config 55 | ``` 56 | 57 | 并在voc/voc.config中修改7处需要重新配置的地方（详见书本）。 58 | 59 | 训练模型的命令： 60 | ``` 61 | python train.py --train_dir voc/train_dir/ --pipeline_config_path voc/voc.config 62 | ``` 63 | 64 | 使用TensorBoard： 65 | ``` 66 | tensorboard --logdir voc/train_dir/ 67 | ``` 68 | 69 | **5.2.4 导出模型并预测单张图片** 70 | 71 | 运行(需要根据voc/train_dir/里实际保存的checkpoint，将1582改为合适的数值)： 72 | ``` 73 | python export_inference_graph.py \ 74 | --input_type image_tensor \ 75 | --pipeline_config_path voc/voc.config \ 76 | --trained_checkpoint_prefix voc/train_dir/model.ckpt-1582 77 | --output_directory voc/export/ 78 | ``` 79 | 80 | 导出的模型是voc/export/frozen_inference_graph.pb 文件。 81 | 82 | #### 拓展阅读 83 | 84 | - 本章提到的R-CNN、SPPNet、Fast R-CNN、Faster R-CNN 都是基于 区域的深度目标检测方法。可以按顺序阅读以下论文了解更多细节： Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation (R-CNN) 、Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition（SPPNet）、Fast R-CNN （Fast R-CNN）、Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks（Faster R-CNN）。 85 | 86 | - 限于篇幅，除了本章提到的这些方法外，还有一些有较高参考价值 的深度学习目标检测方法，这里同样推荐一下相关的论文：R-FCN: Object Detection via Region-based Fully Convolutional Networks （R-FCN）、You Only Look Once: Unified, Real-Time Object Detection （YOLO）、SSD: Single Shot MultiBox Detector（SSD）、YOLO9000: Better, Faster, Stronger（YOLO v2 和YOLO9000）等。 87 | -------------------------------------------------------------------------------- /camera.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | import cv2 4 | def getFaceImage(): 5 | cap = cv2.VideoCapture(0) 6 | ret, frame = cap.read() 7 | cv2.imwrite("image/faceID.jpg", frame) 8 | cap.release() 9 | cv2.destroyAllWindows() 10 | 11 | 12 | def getFruitImage(): 13 | cap = cv2.VideoCapture(1) 14 | ret, frame = cap.read() 15 | cv2.imwrite("image/fruit.jpg", frame) 16 | cap.release() 17 | cv2.destroyAllWindows() 18 | 19 | 20 | 21 | 22 | if __name__ == "__main__": 23 | 24 | 25 | cv2.namedWindow("capture", 0) 26 | cv2.resizeWindow('capture', 1280,960) 27 | cv2.moveWindow('capture',0,0) 28 | while(1): 29 | # get a frame 30 | ret, frame = cap.read() 31 | # show a frame 32 | # 33 | 34 | cv2.imshow("capture",frame) 35 | if cv2.waitKey(1) & 0xFF == ord('q'): 36 | break 37 | ''' 38 | getFruitImage() 39 | 40 | print 'camera main task' 41 | ''' -------------------------------------------------------------------------------- /camera.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/camera.pyc -------------------------------------------------------------------------------- /face.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/face.pyc -------------------------------------------------------------------------------- /frozen_inference_graph.pb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/frozen_inference_graph.pb -------------------------------------------------------------------------------- /image/fruit.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/image/fruit.jpg -------------------------------------------------------------------------------- /image/image.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/image/image.jpg -------------------------------------------------------------------------------- /image/warm_up_pic.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/image/warm_up_pic.jpg -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | # coding:utf-8 2 | 3 | import camera as cam 4 | import recognition as recg 5 | import GUI 6 | import time 7 | 8 | import serial 9 | import binascii 10 | import face 11 | 12 | import sys 13 | reload(sys) 14 | sys.setdefaultencoding('utf8') 15 | 16 | ser = serial.Serial('/dev/rfcomm0',9600,timeout=None) 17 | GUI.addQueue(1) 18 | GUI.addQueue('欢迎光临') 19 | 20 | print (ser.name)#打印设备名称 21 | print (ser.port)#打印设备名 22 | 23 | weight1 = 0.0 24 | weight2 = 0.0 25 | 26 | price = {'apple':9,'banana':12,'orange':10,'chair':20,'dog':100} 27 | wait_flag = False # 当同时识别到多种水果时 置位该flag 等待用户重新称重 28 | while True: 29 | data1 = str(binascii.hexlify(ser.read(1))) 30 | data2 = str(binascii.hexlify(ser.read(1)))#读小数数据 31 | # print data1 32 | # print data2 33 | data1 = '0x'+data1 34 | data2 = '0x'+data2 35 | 36 | data2=int(data2,16)#字符串装16进制 37 | data2=int(data2)#16转10 38 | 39 | data1=int(data1,16) 40 | data1=int(data1) 41 | 42 | weight =data1 + data2*0.01#重量（kg） 43 | if weight >20.0: # 噪声滤出 44 | continue 45 | print(weight) 46 | 47 | weight1 = weight2 48 | weight2 = weight 49 | if weight2 < 0.03 and abs(weight1 - weight2)<0.02 : 50 | wait_flag = False 51 | if wait_flag: 52 | GUI.addQueue(1) 53 | GUI.addQueue('请提走您的商品') 54 | print '等待用户' 55 | continue 56 | else: 57 | GUI.addQueue(0) 58 | 59 | if abs(weight1 - weight2)<0.02 and weight2 >0.03: 60 | print 'w1-w2: '+str(weight1 - weight2)+ ' w2: '+str(weight2) 61 | cam.getFruitImage() 62 | fruit_type = recg.recg() 63 | print 'start ' + fruit_type + ' end' 64 | 65 | if fruit_type!='': 66 | if fruit_type == 'error': 67 | GUI.addQueue(1) 68 | GUI.addQueue('系统检测到多种商品，请分开称重') 69 | print('系统检测到多种水果，请分开称重') 70 | time.sleep(5) 71 | else: 72 | GUI.addQueue(2) 73 | GUI.addQueue( '商品名称：'+fruit_type+'\n' + '单价(元/kg)：'+str(price[fruit_type])+'\n' + '重量(kg)：'+str(weight2)) 74 | print fruit_type 75 | print '水果重量为：', 76 | print weight2 77 | print '正准备支付' 78 | GUI.addQueue(1) 79 | GUI.addQueue('请正对摄像头以完成支付') 80 | user_id,balance = face.pay_check(price[fruit_type]*weight2) #返回余额 81 | GUI.addQueue(1) 82 | GUI.addQueue('支付成功，欢迎下次光临') 83 | print user_id,balance 84 | GUI.addQueue(3) 85 | GUI.addQueue(('会员名：' + user_id + '\n') 86 | + ('本次消费(元)：' + str(price[fruit_type]*weight2) + '\n') 87 | + ('账户余额(元)：' + str(balance))) 88 | wait_flag = True 89 | 90 | 91 | ser.close() 92 | recg.re 93 | 94 | 95 | 96 | -------------------------------------------------------------------------------- /mysql.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Mon Jun 4 19:48:39 2018 4 | 5 | @author: lenovo 6 | """ 7 | import pymysql.cursors 8 | 9 | connect = pymysql.Connect( 10 | host='192.168.43.192',#MySQL服务器地址 11 | port=3306,#MySQL服务器端口号 12 | user='myuser', 13 | passwd='123456', 14 | db='mysql',#数据库名称 15 | charset='utf8'#连接编码 16 | ) 17 | 18 | # 获取游标 19 | cursor = connect.cursor() 20 | 21 | # 插入数据 22 | def insert_data(): 23 | sql = "INSERT INTO SC (user_id, user_money) VALUES ( '%s', '%d')" 24 | data = ('Mr.guo', 1500) 25 | cursor.execute(sql % data) 26 | connect.commit() 27 | print('成功插入', cursor.rowcount, '条数据') 28 | 29 | # 修改数据 30 | def change_data1(money,user_id): 31 | sql = "UPDATE SC SET user_money = %d WHERE user_id = '%s' " 32 | data = (money, user_id) 33 | cursor.execute(sql % data) 34 | connect.commit() 35 | print('成功修改', cursor.rowcount, '条数据') 36 | 37 | def change_data(user_num,user_id): 38 | sql = "UPDATE SC SET user_num = %d WHERE user_id = '%s' " 39 | data = (user_num, user_id) 40 | cursor.execute(sql % data) 41 | connect.commit() 42 | print(u'成功修改', cursor.rowcount, u'条数据') 43 | 44 | # 查询数据 45 | #sql = "SELECT user_money FROM SC WHERE user_id = '%s' " 46 | def check_data(): 47 | sql = "SELECT user_num FROM SC" 48 | cursor.execute(sql) 49 | return cursor.fetchall() 50 | #for row in cursor.fetchall(): 51 | #print("Money:%.2f" % row) 52 | 53 | def check_data1(user_id): 54 | sql = sql = "SELECT user_money FROM SC WHERE user_id = '%s' " 55 | data = (user_id) 56 | cursor.execute(sql % data) 57 | return cursor.fetchall() 58 | 59 | if __name__ == "__main__": 60 | 61 | money = check_data1('Mr.guo') 62 | print money 63 | 64 | change_data1(money[0][0]-1,'Mr.guo') 65 | 66 | money = check_data1('Mr.guo') 67 | print money 68 | 69 | 70 | 71 | 72 | ''' 73 | change_data(1,'Jone Snow') 74 | row = check_data() 75 | sum = 0 76 | for i in row[1:]: 77 | sum = i[0]+sum 78 | print(sum) 79 | cursor.close() 80 | connect.close() 81 | ''' 82 | -------------------------------------------------------------------------------- /mysql.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/mysql.pyc -------------------------------------------------------------------------------- /protoc/bin/protoc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/protoc/bin/protoc -------------------------------------------------------------------------------- /protoc/include/google/protobuf/empty.proto: -------------------------------------------------------------------------------- 1 | // Protocol Buffers - Google's data interchange format 2 | // Copyright 2008 Google Inc. All rights reserved. 3 | // https://developers.google.com/protocol-buffers/ 4 | // 5 | // Redistribution and use in source and binary forms, with or without 6 | // modification, are permitted provided that the following conditions are 7 | // met: 8 | // 9 | // * Redistributions of source code must retain the above copyright 10 | // notice, this list of conditions and the following disclaimer. 11 | // * Redistributions in binary form must reproduce the above 12 | // copyright notice, this list of conditions and the following disclaimer 13 | // in the documentation and/or other materials provided with the 14 | // distribution. 15 | // * Neither the name of Google Inc. nor the names of its 16 | // contributors may be used to endorse or promote products derived from 17 | // this software without specific prior written permission. 18 | // 19 | // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 20 | // "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 21 | // LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR 22 | // A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT 23 | // OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, 24 | // SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT 25 | // LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, 26 | // DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY 27 | // THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT 28 | // (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 29 | // OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 30 | 31 | syntax = "proto3"; 32 | 33 | package google.protobuf; 34 | 35 | option csharp_namespace = "Google.Protobuf.WellKnownTypes"; 36 | option go_package = "github.com/golang/protobuf/ptypes/empty"; 37 | option java_package = "com.google.protobuf"; 38 | option java_outer_classname = "EmptyProto"; 39 | option java_multiple_files = true; 40 | option objc_class_prefix = "GPB"; 41 | option cc_enable_arenas = true; 42 | 43 | // A generic empty message that you can re-use to avoid defining duplicated 44 | // empty messages in your APIs. A typical example is to use it as the request 45 | // or the response type of an API method. For instance: 46 | // 47 | // service Foo { 48 | // rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); 49 | // } 50 | // 51 | // The JSON representation for `Empty` is empty JSON object `{}`. 52 | message Empty {} 53 | -------------------------------------------------------------------------------- /protoc/include/google/protobuf/source_context.proto: -------------------------------------------------------------------------------- 1 | // Protocol Buffers - Google's data interchange format 2 | // Copyright 2008 Google Inc. All rights reserved. 3 | // https://developers.google.com/protocol-buffers/ 4 | // 5 | // Redistribution and use in source and binary forms, with or without 6 | // modification, are permitted provided that the following conditions are 7 | // met: 8 | // 9 | // * Redistributions of source code must retain the above copyright 10 | // notice, this list of conditions and the following disclaimer. 11 | // * Redistributions in binary form must reproduce the above 12 | // copyright notice, this list of conditions and the following disclaimer 13 | // in the documentation and/or other materials provided with the 14 | // distribution. 15 | // * Neither the name of Google Inc. nor the names of its 16 | // contributors may be used to endorse or promote products derived from 17 | // this software without specific prior written permission. 18 | // 19 | // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 20 | // "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 21 | // LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR 22 | // A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT 23 | // OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, 24 | // SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT 25 | // LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, 26 | // DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY 27 | // THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT 28 | // (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 29 | // OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 30 | 31 | syntax = "proto3"; 32 | 33 | package google.protobuf; 34 | 35 | option csharp_namespace = "Google.Protobuf.WellKnownTypes"; 36 | option java_package = "com.google.protobuf"; 37 | option java_outer_classname = "SourceContextProto"; 38 | option java_multiple_files = true; 39 | option objc_class_prefix = "GPB"; 40 | option go_package = "google.golang.org/genproto/protobuf/source_context;source_context"; 41 | 42 | // `SourceContext` represents information about the source of a 43 | // protobuf element, like the file in which it is defined. 44 | message SourceContext { 45 | // The path-qualified name of the .proto file that contained the associated 46 | // protobuf element. For example: `"google/protobuf/source_context.proto"`. 47 | string file_name = 1; 48 | } 49 | -------------------------------------------------------------------------------- /readme.txt: -------------------------------------------------------------------------------- 1 | Protocol Buffers - Google's data interchange format 2 | Copyright 2008 Google Inc. 3 | https://developers.google.com/protocol-buffers/ 4 | 5 | This package contains a precompiled binary version of the protocol buffer 6 | compiler (protoc). This binary is intended for users who want to use Protocol 7 | Buffers in languages other than C++ but do not want to compile protoc 8 | themselves. To install, simply place this binary somewhere in your PATH. 9 | 10 | If you intend to use the included well known types then don't forget to 11 | copy the contents of the 'include' directory somewhere as well, for example 12 | into '/usr/local/include/'. 13 | 14 | Please refer to our official github site for more installation instructions: 15 | https://github.com/google/protobuf 16 | -------------------------------------------------------------------------------- /recognition.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/recognition.pyc -------------------------------------------------------------------------------- /research/object_detection/CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contributing to the Tensorflow Object Detection API 2 | 3 | Patches to Tensorflow Object Detection API are welcome! 4 | 5 | We require contributors to fill out either the individual or corporate 6 | Contributor License Agreement (CLA). 7 | 8 | * If you are an individual writing original source code and you're sure you own the intellectual property, then you'll need to sign an [individual CLA](http://code.google.com/legal/individual-cla-v1.0.html). 9 | * If you work for a company that wants to allow you to contribute your work, then you'll need to sign a [corporate CLA](http://code.google.com/legal/corporate-cla-v1.0.html). 10 | 11 | Please follow the 12 | [Tensorflow contributing guidelines](https://github.com/tensorflow/tensorflow/blob/master/CONTRIBUTING.md) 13 | when submitting pull requests. 14 | -------------------------------------------------------------------------------- /research/object_detection/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/__init__.py -------------------------------------------------------------------------------- /research/object_detection/__init__.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/__init__.pyc -------------------------------------------------------------------------------- /research/object_detection/anchor_generators/BUILD: -------------------------------------------------------------------------------- 1 | # Tensorflow Object Detection API: Anchor Generator implementations. 2 | 3 | package( 4 | default_visibility = ["//visibility:public"], 5 | ) 6 | 7 | licenses(["notice"]) 8 | 9 | # Apache 2.0 10 | py_library( 11 | name = "grid_anchor_generator", 12 | srcs = [ 13 | "grid_anchor_generator.py", 14 | ], 15 | deps = [ 16 | "//tensorflow", 17 | "//tensorflow_models/object_detection/core:anchor_generator", 18 | "//tensorflow_models/object_detection/core:box_list", 19 | "//tensorflow_models/object_detection/utils:ops", 20 | ], 21 | ) 22 | 23 | py_test( 24 | name = "grid_anchor_generator_test", 25 | srcs = [ 26 | "grid_anchor_generator_test.py", 27 | ], 28 | deps = [ 29 | ":grid_anchor_generator", 30 | "//tensorflow", 31 | ], 32 | ) 33 | 34 | py_library( 35 | name = "multiple_grid_anchor_generator", 36 | srcs = [ 37 | "multiple_grid_anchor_generator.py", 38 | ], 39 | deps = [ 40 | ":grid_anchor_generator", 41 | "//tensorflow", 42 | "//tensorflow_models/object_detection/core:anchor_generator", 43 | "//tensorflow_models/object_detection/core:box_list_ops", 44 | ], 45 | ) 46 | 47 | py_test( 48 | name = "multiple_grid_anchor_generator_test", 49 | srcs = [ 50 | "multiple_grid_anchor_generator_test.py", 51 | ], 52 | deps = [ 53 | ":multiple_grid_anchor_generator", 54 | "//third_party/py/numpy", 55 | ], 56 | ) 57 | -------------------------------------------------------------------------------- /research/object_detection/anchor_generators/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/anchor_generators/__init__.py -------------------------------------------------------------------------------- /research/object_detection/anchor_generators/__init__.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/anchor_generators/__init__.pyc -------------------------------------------------------------------------------- /research/object_detection/anchor_generators/grid_anchor_generator.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/anchor_generators/grid_anchor_generator.pyc -------------------------------------------------------------------------------- /research/object_detection/anchor_generators/grid_anchor_generator_test.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Tests for object_detection.grid_anchor_generator.""" 17 | 18 | import tensorflow as tf 19 | 20 | from object_detection.anchor_generators import grid_anchor_generator 21 | 22 | 23 | class GridAnchorGeneratorTest(tf.test.TestCase): 24 | 25 | def test_construct_single_anchor(self): 26 | """Builds a 1x1 anchor grid to test the size of the output boxes.""" 27 | scales = [0.5, 1.0, 2.0] 28 | aspect_ratios = [0.25, 1.0, 4.0] 29 | anchor_offset = [7, -3] 30 | exp_anchor_corners = [[-121, -35, 135, 29], [-249, -67, 263, 61], 31 | [-505, -131, 519, 125], [-57, -67, 71, 61], 32 | [-121, -131, 135, 125], [-249, -259, 263, 253], 33 | [-25, -131, 39, 125], [-57, -259, 71, 253], 34 | [-121, -515, 135, 509]] 35 | 36 | anchor_generator = grid_anchor_generator.GridAnchorGenerator( 37 | scales, aspect_ratios, 38 | anchor_offset=anchor_offset) 39 | anchors = anchor_generator.generate(feature_map_shape_list=[(1, 1)]) 40 | anchor_corners = anchors.get() 41 | 42 | with self.test_session(): 43 | anchor_corners_out = anchor_corners.eval() 44 | self.assertAllClose(anchor_corners_out, exp_anchor_corners) 45 | 46 | def test_construct_anchor_grid(self): 47 | base_anchor_size = [10, 10] 48 | anchor_stride = [19, 19] 49 | anchor_offset = [0, 0] 50 | scales = [0.5, 1.0, 2.0] 51 | aspect_ratios = [1.0] 52 | 53 | exp_anchor_corners = [[-2.5, -2.5, 2.5, 2.5], [-5., -5., 5., 5.], 54 | [-10., -10., 10., 10.], [-2.5, 16.5, 2.5, 21.5], 55 | [-5., 14., 5, 24], [-10., 9., 10, 29], 56 | [16.5, -2.5, 21.5, 2.5], [14., -5., 24, 5], 57 | [9., -10., 29, 10], [16.5, 16.5, 21.5, 21.5], 58 | [14., 14., 24, 24], [9., 9., 29, 29]] 59 | 60 | anchor_generator = grid_anchor_generator.GridAnchorGenerator( 61 | scales, 62 | aspect_ratios, 63 | base_anchor_size=base_anchor_size, 64 | anchor_stride=anchor_stride, 65 | anchor_offset=anchor_offset) 66 | 67 | anchors = anchor_generator.generate(feature_map_shape_list=[(2, 2)]) 68 | anchor_corners = anchors.get() 69 | 70 | with self.test_session(): 71 | anchor_corners_out = anchor_corners.eval() 72 | self.assertAllClose(anchor_corners_out, exp_anchor_corners) 73 | 74 | 75 | if __name__ == '__main__': 76 | tf.test.main() 77 | -------------------------------------------------------------------------------- /research/object_detection/anchor_generators/multiple_grid_anchor_generator.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/anchor_generators/multiple_grid_anchor_generator.pyc -------------------------------------------------------------------------------- /research/object_detection/box_coders/BUILD: -------------------------------------------------------------------------------- 1 | # Tensorflow Object Detection API: Box Coder implementations. 2 | 3 | package( 4 | default_visibility = ["//visibility:public"], 5 | ) 6 | 7 | licenses(["notice"]) 8 | 9 | # Apache 2.0 10 | py_library( 11 | name = "faster_rcnn_box_coder", 12 | srcs = [ 13 | "faster_rcnn_box_coder.py", 14 | ], 15 | deps = [ 16 | "//tensorflow_models/object_detection/core:box_coder", 17 | "//tensorflow_models/object_detection/core:box_list", 18 | ], 19 | ) 20 | 21 | py_test( 22 | name = "faster_rcnn_box_coder_test", 23 | srcs = [ 24 | "faster_rcnn_box_coder_test.py", 25 | ], 26 | deps = [ 27 | ":faster_rcnn_box_coder", 28 | "//tensorflow", 29 | "//tensorflow_models/object_detection/core:box_list", 30 | ], 31 | ) 32 | 33 | py_library( 34 | name = "keypoint_box_coder", 35 | srcs = [ 36 | "keypoint_box_coder.py", 37 | ], 38 | deps = [ 39 | "//tensorflow_models/object_detection/core:box_coder", 40 | "//tensorflow_models/object_detection/core:box_list", 41 | "//tensorflow_models/object_detection/core:standard_fields", 42 | ], 43 | ) 44 | 45 | py_test( 46 | name = "keypoint_box_coder_test", 47 | srcs = [ 48 | "keypoint_box_coder_test.py", 49 | ], 50 | deps = [ 51 | ":keypoint_box_coder", 52 | "//tensorflow", 53 | "//tensorflow_models/object_detection/core:box_list", 54 | "//tensorflow_models/object_detection/core:standard_fields", 55 | ], 56 | ) 57 | 58 | py_library( 59 | name = "mean_stddev_box_coder", 60 | srcs = [ 61 | "mean_stddev_box_coder.py", 62 | ], 63 | deps = [ 64 | "//tensorflow_models/object_detection/core:box_coder", 65 | "//tensorflow_models/object_detection/core:box_list", 66 | ], 67 | ) 68 | 69 | py_test( 70 | name = "mean_stddev_box_coder_test", 71 | srcs = [ 72 | "mean_stddev_box_coder_test.py", 73 | ], 74 | deps = [ 75 | ":mean_stddev_box_coder", 76 | "//tensorflow", 77 | "//tensorflow_models/object_detection/core:box_list", 78 | ], 79 | ) 80 | 81 | py_library( 82 | name = "square_box_coder", 83 | srcs = [ 84 | "square_box_coder.py", 85 | ], 86 | deps = [ 87 | "//tensorflow_models/object_detection/core:box_coder", 88 | "//tensorflow_models/object_detection/core:box_list", 89 | ], 90 | ) 91 | 92 | py_test( 93 | name = "square_box_coder_test", 94 | srcs = [ 95 | "square_box_coder_test.py", 96 | ], 97 | deps = [ 98 | ":square_box_coder", 99 | "//tensorflow", 100 | "//tensorflow_models/object_detection/core:box_list", 101 | ], 102 | ) 103 | -------------------------------------------------------------------------------- /research/object_detection/box_coders/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/box_coders/__init__.py -------------------------------------------------------------------------------- /research/object_detection/box_coders/__init__.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/box_coders/__init__.pyc -------------------------------------------------------------------------------- /research/object_detection/box_coders/faster_rcnn_box_coder.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/box_coders/faster_rcnn_box_coder.pyc -------------------------------------------------------------------------------- /research/object_detection/box_coders/mean_stddev_box_coder.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Mean stddev box coder. 17 | 18 | This box coder use the following coding schema to encode boxes: 19 | rel_code = (box_corner - anchor_corner_mean) / anchor_corner_stddev. 20 | """ 21 | from object_detection.core import box_coder 22 | from object_detection.core import box_list 23 | 24 | 25 | class MeanStddevBoxCoder(box_coder.BoxCoder): 26 | """Mean stddev box coder.""" 27 | 28 | @property 29 | def code_size(self): 30 | return 4 31 | 32 | def _encode(self, boxes, anchors): 33 | """Encode a box collection with respect to anchor collection. 34 | 35 | Args: 36 | boxes: BoxList holding N boxes to be encoded. 37 | anchors: BoxList of N anchors. We assume that anchors has an associated 38 | stddev field. 39 | 40 | Returns: 41 | a tensor representing N anchor-encoded boxes 42 | Raises: 43 | ValueError: if the anchors BoxList does not have a stddev field 44 | """ 45 | if not anchors.has_field('stddev'): 46 | raise ValueError('anchors must have a stddev field') 47 | box_corners = boxes.get() 48 | means = anchors.get() 49 | stddev = anchors.get_field('stddev') 50 | return (box_corners - means) / stddev 51 | 52 | def _decode(self, rel_codes, anchors): 53 | """Decode. 54 | 55 | Args: 56 | rel_codes: a tensor representing N anchor-encoded boxes. 57 | anchors: BoxList of anchors. We assume that anchors has an associated 58 | stddev field. 59 | 60 | Returns: 61 | boxes: BoxList holding N bounding boxes 62 | Raises: 63 | ValueError: if the anchors BoxList does not have a stddev field 64 | """ 65 | if not anchors.has_field('stddev'): 66 | raise ValueError('anchors must have a stddev field') 67 | means = anchors.get() 68 | stddevs = anchors.get_field('stddev') 69 | box_corners = rel_codes * stddevs + means 70 | return box_list.BoxList(box_corners) 71 | -------------------------------------------------------------------------------- /research/object_detection/box_coders/mean_stddev_box_coder.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/box_coders/mean_stddev_box_coder.pyc -------------------------------------------------------------------------------- /research/object_detection/box_coders/mean_stddev_box_coder_test.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Tests for object_detection.box_coder.mean_stddev_boxcoder.""" 17 | 18 | import tensorflow as tf 19 | 20 | from object_detection.box_coders import mean_stddev_box_coder 21 | from object_detection.core import box_list 22 | 23 | 24 | class MeanStddevBoxCoderTest(tf.test.TestCase): 25 | 26 | def testGetCorrectRelativeCodesAfterEncoding(self): 27 | box_corners = [[0.0, 0.0, 0.5, 0.5], [0.0, 0.0, 0.5, 0.5]] 28 | boxes = box_list.BoxList(tf.constant(box_corners)) 29 | expected_rel_codes = [[0.0, 0.0, 0.0, 0.0], [-5.0, -5.0, -5.0, -3.0]] 30 | prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 1.0, 0.8]]) 31 | prior_stddevs = tf.constant(2 * [4 * [.1]]) 32 | priors = box_list.BoxList(prior_means) 33 | priors.add_field('stddev', prior_stddevs) 34 | 35 | coder = mean_stddev_box_coder.MeanStddevBoxCoder() 36 | rel_codes = coder.encode(boxes, priors) 37 | with self.test_session() as sess: 38 | rel_codes_out = sess.run(rel_codes) 39 | self.assertAllClose(rel_codes_out, expected_rel_codes) 40 | 41 | def testGetCorrectBoxesAfterDecoding(self): 42 | rel_codes = tf.constant([[0.0, 0.0, 0.0, 0.0], [-5.0, -5.0, -5.0, -3.0]]) 43 | expected_box_corners = [[0.0, 0.0, 0.5, 0.5], [0.0, 0.0, 0.5, 0.5]] 44 | prior_means = tf.constant([[0.0, 0.0, 0.5, 0.5], [0.5, 0.5, 1.0, 0.8]]) 45 | prior_stddevs = tf.constant(2 * [4 * [.1]]) 46 | priors = box_list.BoxList(prior_means) 47 | priors.add_field('stddev', prior_stddevs) 48 | 49 | coder = mean_stddev_box_coder.MeanStddevBoxCoder() 50 | decoded_boxes = coder.decode(rel_codes, priors) 51 | decoded_box_corners = decoded_boxes.get() 52 | with self.test_session() as sess: 53 | decoded_out = sess.run(decoded_box_corners) 54 | self.assertAllClose(decoded_out, expected_box_corners) 55 | 56 | 57 | if __name__ == '__main__': 58 | tf.test.main() 59 | -------------------------------------------------------------------------------- /research/object_detection/box_coders/square_box_coder.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/box_coders/square_box_coder.pyc -------------------------------------------------------------------------------- /research/object_detection/builders/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/builders/__init__.py -------------------------------------------------------------------------------- /research/object_detection/builders/__init__.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/builders/__init__.pyc -------------------------------------------------------------------------------- /research/object_detection/builders/anchor_generator_builder.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/builders/anchor_generator_builder.pyc -------------------------------------------------------------------------------- /research/object_detection/builders/box_coder_builder.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """A function to build an object detection box coder from configuration.""" 17 | from object_detection.box_coders import faster_rcnn_box_coder 18 | from object_detection.box_coders import mean_stddev_box_coder 19 | from object_detection.box_coders import square_box_coder 20 | from object_detection.protos import box_coder_pb2 21 | 22 | 23 | def build(box_coder_config): 24 | """Builds a box coder object based on the box coder config. 25 | 26 | Args: 27 | box_coder_config: A box_coder.proto object containing the config for the 28 | desired box coder. 29 | 30 | Returns: 31 | BoxCoder based on the config. 32 | 33 | Raises: 34 | ValueError: On empty box coder proto. 35 | """ 36 | if not isinstance(box_coder_config, box_coder_pb2.BoxCoder): 37 | raise ValueError('box_coder_config not of type box_coder_pb2.BoxCoder.') 38 | 39 | if box_coder_config.WhichOneof('box_coder_oneof') == 'faster_rcnn_box_coder': 40 | return faster_rcnn_box_coder.FasterRcnnBoxCoder(scale_factors=[ 41 | box_coder_config.faster_rcnn_box_coder.y_scale, 42 | box_coder_config.faster_rcnn_box_coder.x_scale, 43 | box_coder_config.faster_rcnn_box_coder.height_scale, 44 | box_coder_config.faster_rcnn_box_coder.width_scale 45 | ]) 46 | if (box_coder_config.WhichOneof('box_coder_oneof') == 47 | 'mean_stddev_box_coder'): 48 | return mean_stddev_box_coder.MeanStddevBoxCoder() 49 | if box_coder_config.WhichOneof('box_coder_oneof') == 'square_box_coder': 50 | return square_box_coder.SquareBoxCoder(scale_factors=[ 51 | box_coder_config.square_box_coder.y_scale, 52 | box_coder_config.square_box_coder.x_scale, 53 | box_coder_config.square_box_coder.length_scale 54 | ]) 55 | raise ValueError('Empty box coder.') 56 | -------------------------------------------------------------------------------- /research/object_detection/builders/box_coder_builder.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/builders/box_coder_builder.pyc -------------------------------------------------------------------------------- /research/object_detection/builders/box_predictor_builder.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/builders/box_predictor_builder.pyc -------------------------------------------------------------------------------- /research/object_detection/builders/hyperparams_builder.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/builders/hyperparams_builder.pyc -------------------------------------------------------------------------------- /research/object_detection/builders/image_resizer_builder.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Builder function for image resizing operations.""" 17 | import functools 18 | 19 | from object_detection.core import preprocessor 20 | from object_detection.protos import image_resizer_pb2 21 | 22 | 23 | def build(image_resizer_config): 24 | """Builds callable for image resizing operations. 25 | 26 | Args: 27 | image_resizer_config: image_resizer.proto object containing parameters for 28 | an image resizing operation. 29 | 30 | Returns: 31 | image_resizer_fn: Callable for image resizing. This callable always takes 32 | a rank-3 image tensor (corresponding to a single image) and returns a 33 | rank-3 image tensor, possibly with new spatial dimensions. 34 | 35 | Raises: 36 | ValueError: if `image_resizer_config` is of incorrect type. 37 | ValueError: if `image_resizer_config.image_resizer_oneof` is of expected 38 | type. 39 | ValueError: if min_dimension > max_dimension when keep_aspect_ratio_resizer 40 | is used. 41 | """ 42 | if not isinstance(image_resizer_config, image_resizer_pb2.ImageResizer): 43 | raise ValueError('image_resizer_config not of type ' 44 | 'image_resizer_pb2.ImageResizer.') 45 | 46 | if image_resizer_config.WhichOneof( 47 | 'image_resizer_oneof') == 'keep_aspect_ratio_resizer': 48 | keep_aspect_ratio_config = image_resizer_config.keep_aspect_ratio_resizer 49 | if not (keep_aspect_ratio_config.min_dimension 50 | <= keep_aspect_ratio_config.max_dimension): 51 | raise ValueError('min_dimension > max_dimension') 52 | return functools.partial( 53 | preprocessor.resize_to_range, 54 | min_dimension=keep_aspect_ratio_config.min_dimension, 55 | max_dimension=keep_aspect_ratio_config.max_dimension) 56 | if image_resizer_config.WhichOneof( 57 | 'image_resizer_oneof') == 'fixed_shape_resizer': 58 | fixed_shape_resizer_config = image_resizer_config.fixed_shape_resizer 59 | return functools.partial(preprocessor.resize_image, 60 | new_height=fixed_shape_resizer_config.height, 61 | new_width=fixed_shape_resizer_config.width) 62 | raise ValueError('Invalid image resizer option.') 63 | -------------------------------------------------------------------------------- /research/object_detection/builders/image_resizer_builder.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/builders/image_resizer_builder.pyc -------------------------------------------------------------------------------- /research/object_detection/builders/image_resizer_builder_test.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Tests for object_detection.builders.image_resizer_builder.""" 17 | import tensorflow as tf 18 | from google.protobuf import text_format 19 | from object_detection.builders import image_resizer_builder 20 | from object_detection.protos import image_resizer_pb2 21 | 22 | 23 | class ImageResizerBuilderTest(tf.test.TestCase): 24 | 25 | def _shape_of_resized_random_image_given_text_proto( 26 | self, input_shape, text_proto): 27 | image_resizer_config = image_resizer_pb2.ImageResizer() 28 | text_format.Merge(text_proto, image_resizer_config) 29 | image_resizer_fn = image_resizer_builder.build(image_resizer_config) 30 | images = tf.to_float(tf.random_uniform( 31 | input_shape, minval=0, maxval=255, dtype=tf.int32)) 32 | resized_images = image_resizer_fn(images) 33 | with self.test_session() as sess: 34 | return sess.run(resized_images).shape 35 | 36 | def test_built_keep_aspect_ratio_resizer_returns_expected_shape(self): 37 | image_resizer_text_proto = """ 38 | keep_aspect_ratio_resizer { 39 | min_dimension: 10 40 | max_dimension: 20 41 | } 42 | """ 43 | input_shape = (50, 25, 3) 44 | expected_output_shape = (20, 10, 3) 45 | output_shape = self._shape_of_resized_random_image_given_text_proto( 46 | input_shape, image_resizer_text_proto) 47 | self.assertEqual(output_shape, expected_output_shape) 48 | 49 | def test_built_fixed_shape_resizer_returns_expected_shape(self): 50 | image_resizer_text_proto = """ 51 | fixed_shape_resizer { 52 | height: 10 53 | width: 20 54 | } 55 | """ 56 | input_shape = (50, 25, 3) 57 | expected_output_shape = (10, 20, 3) 58 | output_shape = self._shape_of_resized_random_image_given_text_proto( 59 | input_shape, image_resizer_text_proto) 60 | self.assertEqual(output_shape, expected_output_shape) 61 | 62 | def test_raises_error_on_invalid_input(self): 63 | invalid_input = 'invalid_input' 64 | with self.assertRaises(ValueError): 65 | image_resizer_builder.build(invalid_input) 66 | 67 | 68 | if __name__ == '__main__': 69 | tf.test.main() 70 | 71 | -------------------------------------------------------------------------------- /research/object_detection/builders/input_reader_builder.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Input reader builder. 17 | 18 | Creates data sources for DetectionModels from an InputReader config. See 19 | input_reader.proto for options. 20 | 21 | Note: If users wishes to also use their own InputReaders with the Object 22 | Detection configuration framework, they should define their own builder function 23 | that wraps the build function. 24 | """ 25 | 26 | import tensorflow as tf 27 | 28 | from object_detection.data_decoders import tf_example_decoder 29 | from object_detection.protos import input_reader_pb2 30 | 31 | parallel_reader = tf.contrib.slim.parallel_reader 32 | 33 | 34 | def build(input_reader_config): 35 | """Builds a tensor dictionary based on the InputReader config. 36 | 37 | Args: 38 | input_reader_config: A input_reader_pb2.InputReader object. 39 | 40 | Returns: 41 | A tensor dict based on the input_reader_config. 42 | 43 | Raises: 44 | ValueError: On invalid input reader proto. 45 | """ 46 | if not isinstance(input_reader_config, input_reader_pb2.InputReader): 47 | raise ValueError('input_reader_config not of type ' 48 | 'input_reader_pb2.InputReader.') 49 | 50 | if input_reader_config.WhichOneof('input_reader') == 'tf_record_input_reader': 51 | config = input_reader_config.tf_record_input_reader 52 | _, string_tensor = parallel_reader.parallel_read( 53 | config.input_path, 54 | reader_class=tf.TFRecordReader, 55 | num_epochs=(input_reader_config.num_epochs 56 | if input_reader_config.num_epochs else None), 57 | num_readers=input_reader_config.num_readers, 58 | shuffle=input_reader_config.shuffle, 59 | dtypes=[tf.string, tf.string], 60 | capacity=input_reader_config.queue_capacity, 61 | min_after_dequeue=input_reader_config.min_after_dequeue) 62 | 63 | return tf_example_decoder.TfExampleDecoder().decode(string_tensor) 64 | 65 | raise ValueError('Unsupported input_reader_config.') 66 | -------------------------------------------------------------------------------- /research/object_detection/builders/input_reader_builder.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/builders/input_reader_builder.pyc -------------------------------------------------------------------------------- /research/object_detection/builders/losses_builder.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/builders/losses_builder.pyc -------------------------------------------------------------------------------- /research/object_detection/builders/matcher_builder.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """A function to build an object detection matcher from configuration.""" 17 | 18 | from object_detection.matchers import argmax_matcher 19 | from object_detection.matchers import bipartite_matcher 20 | from object_detection.protos import matcher_pb2 21 | 22 | 23 | def build(matcher_config): 24 | """Builds a matcher object based on the matcher config. 25 | 26 | Args: 27 | matcher_config: A matcher.proto object containing the config for the desired 28 | Matcher. 29 | 30 | Returns: 31 | Matcher based on the config. 32 | 33 | Raises: 34 | ValueError: On empty matcher proto. 35 | """ 36 | if not isinstance(matcher_config, matcher_pb2.Matcher): 37 | raise ValueError('matcher_config not of type matcher_pb2.Matcher.') 38 | if matcher_config.WhichOneof('matcher_oneof') == 'argmax_matcher': 39 | matcher = matcher_config.argmax_matcher 40 | matched_threshold = unmatched_threshold = None 41 | if not matcher.ignore_thresholds: 42 | matched_threshold = matcher.matched_threshold 43 | unmatched_threshold = matcher.unmatched_threshold 44 | return argmax_matcher.ArgMaxMatcher( 45 | matched_threshold=matched_threshold, 46 | unmatched_threshold=unmatched_threshold, 47 | negatives_lower_than_unmatched=matcher.negatives_lower_than_unmatched, 48 | force_match_for_each_row=matcher.force_match_for_each_row) 49 | if matcher_config.WhichOneof('matcher_oneof') == 'bipartite_matcher': 50 | return bipartite_matcher.GreedyBipartiteMatcher() 51 | raise ValueError('Empty matcher.') 52 | -------------------------------------------------------------------------------- /research/object_detection/builders/matcher_builder.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/builders/matcher_builder.pyc -------------------------------------------------------------------------------- /research/object_detection/builders/model_builder.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/builders/model_builder.pyc -------------------------------------------------------------------------------- /research/object_detection/builders/post_processing_builder.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/builders/post_processing_builder.pyc -------------------------------------------------------------------------------- /research/object_detection/builders/post_processing_builder_test.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Tests for post_processing_builder.""" 17 | 18 | import tensorflow as tf 19 | from google.protobuf import text_format 20 | from object_detection.builders import post_processing_builder 21 | from object_detection.protos import post_processing_pb2 22 | 23 | 24 | class PostProcessingBuilderTest(tf.test.TestCase): 25 | 26 | def test_build_non_max_suppressor_with_correct_parameters(self): 27 | post_processing_text_proto = """ 28 | batch_non_max_suppression { 29 | score_threshold: 0.7 30 | iou_threshold: 0.6 31 | max_detections_per_class: 100 32 | max_total_detections: 300 33 | } 34 | """ 35 | post_processing_config = post_processing_pb2.PostProcessing() 36 | text_format.Merge(post_processing_text_proto, post_processing_config) 37 | non_max_suppressor, _ = post_processing_builder.build( 38 | post_processing_config) 39 | self.assertEqual(non_max_suppressor.keywords['max_size_per_class'], 100) 40 | self.assertEqual(non_max_suppressor.keywords['max_total_size'], 300) 41 | self.assertAlmostEqual(non_max_suppressor.keywords['score_thresh'], 0.7) 42 | self.assertAlmostEqual(non_max_suppressor.keywords['iou_thresh'], 0.6) 43 | 44 | def test_build_identity_score_converter(self): 45 | post_processing_text_proto = """ 46 | score_converter: IDENTITY 47 | """ 48 | post_processing_config = post_processing_pb2.PostProcessing() 49 | text_format.Merge(post_processing_text_proto, post_processing_config) 50 | _, score_converter = post_processing_builder.build(post_processing_config) 51 | self.assertEqual(score_converter, tf.identity) 52 | 53 | def test_build_sigmoid_score_converter(self): 54 | post_processing_text_proto = """ 55 | score_converter: SIGMOID 56 | """ 57 | post_processing_config = post_processing_pb2.PostProcessing() 58 | text_format.Merge(post_processing_text_proto, post_processing_config) 59 | _, score_converter = post_processing_builder.build(post_processing_config) 60 | self.assertEqual(score_converter, tf.sigmoid) 61 | 62 | def test_build_softmax_score_converter(self): 63 | post_processing_text_proto = """ 64 | score_converter: SOFTMAX 65 | """ 66 | post_processing_config = post_processing_pb2.PostProcessing() 67 | text_format.Merge(post_processing_text_proto, post_processing_config) 68 | _, score_converter = post_processing_builder.build(post_processing_config) 69 | self.assertEqual(score_converter, tf.nn.softmax) 70 | 71 | 72 | if __name__ == '__main__': 73 | tf.test.main() 74 | -------------------------------------------------------------------------------- /research/object_detection/builders/region_similarity_calculator_builder.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Builder for region similarity calculators.""" 17 | 18 | from object_detection.core import region_similarity_calculator 19 | from object_detection.protos import region_similarity_calculator_pb2 20 | 21 | 22 | def build(region_similarity_calculator_config): 23 | """Builds region similarity calculator based on the configuration. 24 | 25 | Builds one of [IouSimilarity, IoaSimilarity, NegSqDistSimilarity] objects. See 26 | core/region_similarity_calculator.proto for details. 27 | 28 | Args: 29 | region_similarity_calculator_config: RegionSimilarityCalculator 30 | configuration proto. 31 | 32 | Returns: 33 | region_similarity_calculator: RegionSimilarityCalculator object. 34 | 35 | Raises: 36 | ValueError: On unknown region similarity calculator. 37 | """ 38 | 39 | if not isinstance( 40 | region_similarity_calculator_config, 41 | region_similarity_calculator_pb2.RegionSimilarityCalculator): 42 | raise ValueError( 43 | 'region_similarity_calculator_config not of type ' 44 | 'region_similarity_calculator_pb2.RegionsSimilarityCalculator') 45 | 46 | similarity_calculator = region_similarity_calculator_config.WhichOneof( 47 | 'region_similarity') 48 | if similarity_calculator == 'iou_similarity': 49 | return region_similarity_calculator.IouSimilarity() 50 | if similarity_calculator == 'ioa_similarity': 51 | return region_similarity_calculator.IoaSimilarity() 52 | if similarity_calculator == 'neg_sq_dist_similarity': 53 | return region_similarity_calculator.NegSqDistSimilarity() 54 | 55 | raise ValueError('Unknown region similarity calculator.') 56 | 57 | -------------------------------------------------------------------------------- /research/object_detection/builders/region_similarity_calculator_builder.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/builders/region_similarity_calculator_builder.pyc -------------------------------------------------------------------------------- /research/object_detection/builders/region_similarity_calculator_builder_test.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Tests for region_similarity_calculator_builder.""" 17 | 18 | import tensorflow as tf 19 | 20 | from google.protobuf import text_format 21 | from object_detection.builders import region_similarity_calculator_builder 22 | from object_detection.core import region_similarity_calculator 23 | from object_detection.protos import region_similarity_calculator_pb2 as sim_calc_pb2 24 | 25 | 26 | class RegionSimilarityCalculatorBuilderTest(tf.test.TestCase): 27 | 28 | def testBuildIoaSimilarityCalculator(self): 29 | similarity_calc_text_proto = """ 30 | ioa_similarity { 31 | } 32 | """ 33 | similarity_calc_proto = sim_calc_pb2.RegionSimilarityCalculator() 34 | text_format.Merge(similarity_calc_text_proto, similarity_calc_proto) 35 | similarity_calc = region_similarity_calculator_builder.build( 36 | similarity_calc_proto) 37 | self.assertTrue(isinstance(similarity_calc, 38 | region_similarity_calculator.IoaSimilarity)) 39 | 40 | def testBuildIouSimilarityCalculator(self): 41 | similarity_calc_text_proto = """ 42 | iou_similarity { 43 | } 44 | """ 45 | similarity_calc_proto = sim_calc_pb2.RegionSimilarityCalculator() 46 | text_format.Merge(similarity_calc_text_proto, similarity_calc_proto) 47 | similarity_calc = region_similarity_calculator_builder.build( 48 | similarity_calc_proto) 49 | self.assertTrue(isinstance(similarity_calc, 50 | region_similarity_calculator.IouSimilarity)) 51 | 52 | def testBuildNegSqDistSimilarityCalculator(self): 53 | similarity_calc_text_proto = """ 54 | neg_sq_dist_similarity { 55 | } 56 | """ 57 | similarity_calc_proto = sim_calc_pb2.RegionSimilarityCalculator() 58 | text_format.Merge(similarity_calc_text_proto, similarity_calc_proto) 59 | similarity_calc = region_similarity_calculator_builder.build( 60 | similarity_calc_proto) 61 | self.assertTrue(isinstance(similarity_calc, 62 | region_similarity_calculator. 63 | NegSqDistSimilarity)) 64 | 65 | 66 | if __name__ == '__main__': 67 | tf.test.main() 68 | -------------------------------------------------------------------------------- /research/object_detection/core/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/core/__init__.py -------------------------------------------------------------------------------- /research/object_detection/core/__init__.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/core/__init__.pyc -------------------------------------------------------------------------------- /research/object_detection/core/anchor_generator.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/core/anchor_generator.pyc -------------------------------------------------------------------------------- /research/object_detection/core/balanced_positive_negative_sampler.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/core/balanced_positive_negative_sampler.pyc -------------------------------------------------------------------------------- /research/object_detection/core/box_coder.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/core/box_coder.pyc -------------------------------------------------------------------------------- /research/object_detection/core/box_coder_test.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Tests for object_detection.core.box_coder.""" 17 | 18 | import tensorflow as tf 19 | 20 | from object_detection.core import box_coder 21 | from object_detection.core import box_list 22 | 23 | 24 | class MockBoxCoder(box_coder.BoxCoder): 25 | """Test BoxCoder that encodes/decodes using the multiply-by-two function.""" 26 | 27 | def code_size(self): 28 | return 4 29 | 30 | def _encode(self, boxes, anchors): 31 | return 2.0 * boxes.get() 32 | 33 | def _decode(self, rel_codes, anchors): 34 | return box_list.BoxList(rel_codes / 2.0) 35 | 36 | 37 | class BoxCoderTest(tf.test.TestCase): 38 | 39 | def test_batch_decode(self): 40 | mock_anchor_corners = tf.constant( 41 | [[0, 0.1, 0.2, 0.3], [0.2, 0.4, 0.4, 0.6]], tf.float32) 42 | mock_anchors = box_list.BoxList(mock_anchor_corners) 43 | mock_box_coder = MockBoxCoder() 44 | 45 | expected_boxes = [[[0.0, 0.1, 0.5, 0.6], [0.5, 0.6, 0.7, 0.8]], 46 | [[0.1, 0.2, 0.3, 0.4], [0.7, 0.8, 0.9, 1.0]]] 47 | 48 | encoded_boxes_list = [mock_box_coder.encode( 49 | box_list.BoxList(tf.constant(boxes)), mock_anchors) 50 | for boxes in expected_boxes] 51 | encoded_boxes = tf.stack(encoded_boxes_list) 52 | decoded_boxes = box_coder.batch_decode( 53 | encoded_boxes, mock_box_coder, mock_anchors) 54 | 55 | with self.test_session() as sess: 56 | decoded_boxes_result = sess.run(decoded_boxes) 57 | self.assertAllClose(expected_boxes, decoded_boxes_result) 58 | 59 | 60 | if __name__ == '__main__': 61 | tf.test.main() 62 | -------------------------------------------------------------------------------- /research/object_detection/core/box_list.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/core/box_list.pyc -------------------------------------------------------------------------------- /research/object_detection/core/box_list_ops.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/core/box_list_ops.pyc -------------------------------------------------------------------------------- /research/object_detection/core/box_predictor.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/core/box_predictor.pyc -------------------------------------------------------------------------------- /research/object_detection/core/data_decoder.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Interface for data decoders. 17 | 18 | Data decoders decode the input data and return a dictionary of tensors keyed by 19 | the entries in core.reader.Fields. 20 | """ 21 | from abc import ABCMeta 22 | from abc import abstractmethod 23 | 24 | 25 | class DataDecoder(object): 26 | """Interface for data decoders.""" 27 | __metaclass__ = ABCMeta 28 | 29 | @abstractmethod 30 | def decode(self, data): 31 | """Return a single image and associated labels. 32 | 33 | Args: 34 | data: a string tensor holding a serialized protocol buffer corresponding 35 | to data for a single image. 36 | 37 | Returns: 38 | tensor_dict: a dictionary containing tensors. Possible keys are defined in 39 | reader.Fields. 40 | """ 41 | pass 42 | -------------------------------------------------------------------------------- /research/object_detection/core/data_decoder.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/core/data_decoder.pyc -------------------------------------------------------------------------------- /research/object_detection/core/keypoint_ops.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/core/keypoint_ops.pyc -------------------------------------------------------------------------------- /research/object_detection/core/losses.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/core/losses.pyc -------------------------------------------------------------------------------- /research/object_detection/core/matcher.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/core/matcher.pyc -------------------------------------------------------------------------------- /research/object_detection/core/minibatch_sampler.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/core/minibatch_sampler.pyc -------------------------------------------------------------------------------- /research/object_detection/core/model.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/core/model.pyc -------------------------------------------------------------------------------- /research/object_detection/core/post_processing.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/core/post_processing.pyc -------------------------------------------------------------------------------- /research/object_detection/core/prefetcher.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Provides functions to prefetch tensors to feed into models.""" 17 | import tensorflow as tf 18 | 19 | 20 | def prefetch(tensor_dict, capacity): 21 | """Creates a prefetch queue for tensors. 22 | 23 | Creates a FIFO queue to asynchronously enqueue tensor_dicts and returns a 24 | dequeue op that evaluates to a tensor_dict. This function is useful in 25 | prefetching preprocessed tensors so that the data is readily available for 26 | consumers. 27 | 28 | Example input pipeline when you don't need batching: 29 | ---------------------------------------------------- 30 | key, string_tensor = slim.parallel_reader.parallel_read(...) 31 | tensor_dict = decoder.decode(string_tensor) 32 | tensor_dict = preprocessor.preprocess(tensor_dict, ...) 33 | prefetch_queue = prefetcher.prefetch(tensor_dict, capacity=20) 34 | tensor_dict = prefetch_queue.dequeue() 35 | outputs = Model(tensor_dict) 36 | ... 37 | ---------------------------------------------------- 38 | 39 | For input pipelines with batching, refer to core/batcher.py 40 | 41 | Args: 42 | tensor_dict: a dictionary of tensors to prefetch. 43 | capacity: the size of the prefetch queue. 44 | 45 | Returns: 46 | a FIFO prefetcher queue 47 | """ 48 | names = list(tensor_dict.keys()) 49 | dtypes = [t.dtype for t in tensor_dict.values()] 50 | shapes = [t.get_shape() for t in tensor_dict.values()] 51 | prefetch_queue = tf.PaddingFIFOQueue(capacity, dtypes=dtypes, 52 | shapes=shapes, 53 | names=names, 54 | name='prefetch_queue') 55 | enqueue_op = prefetch_queue.enqueue(tensor_dict) 56 | tf.train.queue_runner.add_queue_runner(tf.train.queue_runner.QueueRunner( 57 | prefetch_queue, [enqueue_op])) 58 | tf.summary.scalar('queue/%s/fraction_of_%d_full' % (prefetch_queue.name, 59 | capacity), 60 | tf.to_float(prefetch_queue.size()) * (1. / capacity)) 61 | return prefetch_queue 62 | -------------------------------------------------------------------------------- /research/object_detection/core/prefetcher.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/core/prefetcher.pyc -------------------------------------------------------------------------------- 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'bottle' 24 | } 25 | 26 | item { 27 | id: 6 28 | name: 'bus' 29 | } 30 | 31 | item { 32 | id: 7 33 | name: 'car' 34 | } 35 | 36 | item { 37 | id: 8 38 | name: 'cat' 39 | } 40 | 41 | item { 42 | id: 9 43 | name: 'chair' 44 | } 45 | 46 | item { 47 | id: 10 48 | name: 'cow' 49 | } 50 | 51 | item { 52 | id: 11 53 | name: 'diningtable' 54 | } 55 | 56 | item { 57 | id: 12 58 | name: 'dog' 59 | } 60 | 61 | item { 62 | id: 13 63 | name: 'horse' 64 | } 65 | 66 | item { 67 | id: 14 68 | name: 'motorbike' 69 | } 70 | 71 | item { 72 | id: 15 73 | name: 'person' 74 | } 75 | 76 | item { 77 | id: 16 78 | name: 'pottedplant' 79 | } 80 | 81 | item { 82 | id: 17 83 | name: 'sheep' 84 | } 85 | 86 | item { 87 | id: 18 88 | name: 'sofa' 89 | } 90 | 91 | item { 92 | id: 19 93 | name: 'train' 94 | } 95 | 96 | item { 97 | id: 20 98 | name: 'tvmonitor' 99 | } 100 | -------------------------------------------------------------------------------- /research/object_detection/data/pet_label_map.pbtxt: -------------------------------------------------------------------------------- 1 | item { 2 | id: 1 3 | name: 'Abyssinian' 4 | } 5 | 6 | item { 7 | id: 2 8 | name: 'american_bulldog' 9 | } 10 | 11 | item { 12 | id: 3 13 | name: 'american_pit_bull_terrier' 14 | } 15 | 16 | item { 17 | id: 4 18 | name: 'basset_hound' 19 | } 20 | 21 | item { 22 | id: 5 23 | name: 'beagle' 24 | } 25 | 26 | item { 27 | id: 6 28 | name: 'Bengal' 29 | } 30 | 31 | item { 32 | id: 7 33 | name: 'Birman' 34 | } 35 | 36 | item { 37 | id: 8 38 | name: 'Bombay' 39 | } 40 | 41 | item { 42 | id: 9 43 | name: 'boxer' 44 | } 45 | 46 | item { 47 | id: 10 48 | name: 'British_Shorthair' 49 | } 50 | 51 | item { 52 | id: 11 53 | name: 'chihuahua' 54 | } 55 | 56 | item { 57 | id: 12 58 | name: 'Egyptian_Mau' 59 | } 60 | 61 | item { 62 | id: 13 63 | name: 'english_cocker_spaniel' 64 | } 65 | 66 | item { 67 | id: 14 68 | name: 'english_setter' 69 | } 70 | 71 | item { 72 | id: 15 73 | name: 'german_shorthaired' 74 | } 75 | 76 | item { 77 | id: 16 78 | name: 'great_pyrenees' 79 | } 80 | 81 | item { 82 | id: 17 83 | name: 'havanese' 84 | } 85 | 86 | item { 87 | id: 18 88 | name: 'japanese_chin' 89 | } 90 | 91 | item { 92 | id: 19 93 | name: 'keeshond' 94 | } 95 | 96 | item { 97 | id: 20 98 | name: 'leonberger' 99 | } 100 | 101 | item { 102 | id: 21 103 | name: 'Maine_Coon' 104 | } 105 | 106 | item { 107 | id: 22 108 | name: 'miniature_pinscher' 109 | } 110 | 111 | item { 112 | id: 23 113 | name: 'newfoundland' 114 | } 115 | 116 | item { 117 | id: 24 118 | name: 'Persian' 119 | } 120 | 121 | item { 122 | id: 25 123 | name: 'pomeranian' 124 | } 125 | 126 | item { 127 | id: 26 128 | name: 'pug' 129 | } 130 | 131 | item { 132 | id: 27 133 | name: 'Ragdoll' 134 | } 135 | 136 | item { 137 | id: 28 138 | name: 'Russian_Blue' 139 | } 140 | 141 | item { 142 | id: 29 143 | name: 'saint_bernard' 144 | } 145 | 146 | item { 147 | id: 30 148 | name: 'samoyed' 149 | } 150 | 151 | item { 152 | id: 31 153 | name: 'scottish_terrier' 154 | } 155 | 156 | item { 157 | id: 32 158 | name: 'shiba_inu' 159 | } 160 | 161 | item { 162 | id: 33 163 | name: 'Siamese' 164 | } 165 | 166 | item { 167 | id: 34 168 | name: 'Sphynx' 169 | } 170 | 171 | item { 172 | id: 35 173 | name: 'staffordshire_bull_terrier' 174 | } 175 | 176 | item { 177 | id: 36 178 | name: 'wheaten_terrier' 179 | } 180 | 181 | item { 182 | id: 37 183 | name: 'yorkshire_terrier' 184 | } 185 | -------------------------------------------------------------------------------- /research/object_detection/data_decoders/BUILD: -------------------------------------------------------------------------------- 1 | # Tensorflow Object Detection API: data decoders. 2 | 3 | package( 4 | default_visibility = ["//visibility:public"], 5 | ) 6 | 7 | licenses(["notice"]) 8 | # Apache 2.0 9 | 10 | py_library( 11 | name = "tf_example_decoder", 12 | srcs = ["tf_example_decoder.py"], 13 | deps = [ 14 | "//tensorflow", 15 | "//tensorflow_models/object_detection/core:data_decoder", 16 | "//tensorflow_models/object_detection/core:standard_fields", 17 | ], 18 | ) 19 | 20 | py_test( 21 | name = "tf_example_decoder_test", 22 | srcs = ["tf_example_decoder_test.py"], 23 | deps = [ 24 | ":tf_example_decoder", 25 | "//tensorflow", 26 | "//tensorflow_models/object_detection/core:standard_fields", 27 | ], 28 | ) 29 | -------------------------------------------------------------------------------- /research/object_detection/data_decoders/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/data_decoders/__init__.py -------------------------------------------------------------------------------- /research/object_detection/data_decoders/__init__.pyc: -------------------------------------------------------------------------------- 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/research/object_detection/eval_util.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/eval_util.pyc -------------------------------------------------------------------------------- /research/object_detection/evaluator.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/evaluator.pyc -------------------------------------------------------------------------------- /research/object_detection/g3doc/detection_model_zoo.md: -------------------------------------------------------------------------------- 1 | # Tensorflow detection model zoo 2 | 3 | We provide a collection of detection models pre-trained on the 4 | [COCO dataset](http://mscoco.org). 5 | These models can be useful for out-of-the-box inference if you are interested 6 | in categories already in COCO (e.g., humans, cars, etc). 7 | They are also useful for initializing your models when training on novel 8 | datasets. 9 | 10 | In the table below, we list each such pre-trained model including: 11 | 12 | * a model name that corresponds to a config file that was used to train this 13 | model in the `samples/configs` directory, 14 | * a download link to a tar.gz file containing the pre-trained model, 15 | * model speed (one of {slow, medium, fast}), 16 | * detector performance on COCO data as measured by the COCO mAP measure. 17 | Here, higher is better, and we only report bounding box mAP rounded to the 18 | nearest integer. 19 | * Output types (currently only `Boxes`) 20 | 21 | You can un-tar each tar.gz file via, e.g.,: 22 | 23 | ``` 24 | tar -xzvf ssd_mobilenet_v1_coco.tar.gz 25 | ``` 26 | 27 | Inside the un-tar'ed directory, you will find: 28 | 29 | * a graph proto (`graph.pbtxt`) 30 | * a checkpoint 31 | (`model.ckpt.data-00000-of-00001`, `model.ckpt.index`, `model.ckpt.meta`) 32 | * a frozen graph proto with weights baked into the graph as constants 33 | (`frozen_inference_graph.pb`) to be used for out of the box inference 34 | (try this out in the Jupyter notebook!) 35 | 36 | | Model name | Speed | COCO mAP | Outputs | 37 | | ------------ | :--------------: | :--------------: | :-------------: | 38 | | [ssd_mobilenet_v1_coco](http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_11_06_2017.tar.gz) | fast | 21 | Boxes | 39 | | [ssd_inception_v2_coco](http://download.tensorflow.org/models/object_detection/ssd_inception_v2_coco_11_06_2017.tar.gz) | fast | 24 | Boxes | 40 | | [rfcn_resnet101_coco](http://download.tensorflow.org/models/object_detection/rfcn_resnet101_coco_11_06_2017.tar.gz) | medium | 30 | Boxes | 41 | | [faster_rcnn_resnet101_coco](http://download.tensorflow.org/models/object_detection/faster_rcnn_resnet101_coco_11_06_2017.tar.gz) | medium | 32 | Boxes | 42 | | [faster_rcnn_inception_resnet_v2_atrous_coco](http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_resnet_v2_atrous_coco_11_06_2017.tar.gz) | slow | 37 | Boxes | 43 | -------------------------------------------------------------------------------- /research/object_detection/g3doc/exporting_models.md: -------------------------------------------------------------------------------- 1 | # Exporting a trained model for inference 2 | 3 | After your model has been trained, you should export it to a Tensorflow 4 | graph proto. A checkpoint will typically consist of three files: 5 | 6 | * model.ckpt-${CHECKPOINT_NUMBER}.data-00000-of-00001, 7 | * model.ckpt-${CHECKPOINT_NUMBER}.index 8 | * model.ckpt-${CHECKPOINT_NUMBER}.meta 9 | 10 | After you've identified a candidate checkpoint to export, run the following 11 | command from tensorflow/models/research/object_detection: 12 | 13 | ``` bash 14 | # From tensorflow/models/research/ 15 | python object_detection/export_inference_graph.py \ 16 | --input_type image_tensor \ 17 | --pipeline_config_path ${PIPELINE_CONFIG_PATH} \ 18 | --trained_checkpoint_prefix ${TRAIN_PATH} \ 19 | --output_directory output_inference_graph.pb 20 | ``` 21 | 22 | Afterwards, you should see a graph named output_inference_graph.pb. 23 | -------------------------------------------------------------------------------- /research/object_detection/g3doc/img/dogs_detections_output.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/g3doc/img/dogs_detections_output.jpg -------------------------------------------------------------------------------- /research/object_detection/g3doc/img/example_cat.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/g3doc/img/example_cat.jpg -------------------------------------------------------------------------------- /research/object_detection/g3doc/img/kites_detections_output.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/g3doc/img/kites_detections_output.jpg -------------------------------------------------------------------------------- /research/object_detection/g3doc/img/oxford_pet.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/g3doc/img/oxford_pet.png -------------------------------------------------------------------------------- /research/object_detection/g3doc/img/tensorboard.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/g3doc/img/tensorboard.png -------------------------------------------------------------------------------- /research/object_detection/g3doc/img/tensorboard2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/g3doc/img/tensorboard2.png -------------------------------------------------------------------------------- /research/object_detection/g3doc/installation.md: -------------------------------------------------------------------------------- 1 | # Installation 2 | 3 | ## Dependencies 4 | 5 | Tensorflow Object Detection API depends on the following libraries: 6 | 7 | * Protobuf 2.6 8 | * Pillow 1.0 9 | * lxml 10 | * tf Slim (which is included in the "tensorflow/models/research/" checkout) 11 | * Jupyter notebook 12 | * Matplotlib 13 | * Tensorflow 14 | 15 | For detailed steps to install Tensorflow, follow the 16 | [Tensorflow installation instructions](https://www.tensorflow.org/install/). 17 | A typically user can install Tensorflow using one of the following commands: 18 | 19 | ``` bash 20 | # For CPU 21 | pip install tensorflow 22 | # For GPU 23 | pip install tensorflow-gpu 24 | ``` 25 | 26 | The remaining libraries can be installed on Ubuntu 16.04 using via apt-get: 27 | 28 | ``` bash 29 | sudo apt-get install protobuf-compiler python-pil python-lxml 30 | sudo pip install jupyter 31 | sudo pip install matplotlib 32 | ``` 33 | 34 | Alternatively, users can install dependencies using pip: 35 | 36 | ``` bash 37 | sudo pip install pillow 38 | sudo pip install lxml 39 | sudo pip install jupyter 40 | sudo pip install matplotlib 41 | ``` 42 | 43 | ## Protobuf Compilation 44 | 45 | The Tensorflow Object Detection API uses Protobufs to configure model and 46 | training parameters. Before the framework can be used, the Protobuf libraries 47 | must be compiled. This should be done by running the following command from 48 | the tensorflow/models/research/ directory: 49 | 50 | 51 | ``` bash 52 | # From tensorflow/models/research/ 53 | protoc object_detection/protos/*.proto --python_out=. 54 | ``` 55 | 56 | ## Add Libraries to PYTHONPATH 57 | 58 | When running locally, the tensorflow/models/research/ and slim directories 59 | should be appended to PYTHONPATH. This can be done by running the following from 60 | tensorflow/models/research/: 61 | 62 | 63 | ``` bash 64 | # From tensorflow/models/research/ 65 | export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim 66 | ``` 67 | 68 | Note: This command needs to run from every new terminal you start. If you wish 69 | to avoid running this manually, you can add it as a new line to the end of your 70 | ~/.bashrc file. 71 | 72 | # Testing the Installation 73 | 74 | You can test that you have correctly installed the Tensorflow Object Detection\ 75 | API by running the following command: 76 | 77 | ```bash 78 | python object_detection/builders/model_builder_test.py 79 | ``` 80 | -------------------------------------------------------------------------------- /research/object_detection/g3doc/preparing_inputs.md: -------------------------------------------------------------------------------- 1 | # Preparing Inputs 2 | 3 | Tensorflow Object Detection API reads data using the TFRecord file format. Two 4 | sample scripts (`create_pascal_tf_record.py` and `create_pet_tf_record.py`) are 5 | provided to convert from the PASCAL VOC dataset and Oxford-IIIT Pet dataset to 6 | TFRecords. 7 | 8 | ## Generating the PASCAL VOC TFRecord files. 9 | 10 | The raw 2012 PASCAL VOC data set is located 11 | [here](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar). 12 | To download, extract and convert it to TFRecords, run the following commands 13 | below: 14 | 15 | ```bash 16 | # From tensorflow/models/research/ 17 | wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar 18 | tar -xvf VOCtrainval_11-May-2012.tar 19 | python object_detection/create_pascal_tf_record.py \ 20 | --label_map_path=object_detection/data/pascal_label_map.pbtxt \ 21 | --data_dir=VOCdevkit --year=VOC2012 --set=train \ 22 | --output_path=pascal_train.record 23 | python object_detection/create_pascal_tf_record.py \ 24 | --label_map_path=object_detection/data/pascal_label_map.pbtxt \ 25 | --data_dir=VOCdevkit --year=VOC2012 --set=val \ 26 | --output_path=pascal_val.record 27 | ``` 28 | 29 | You should end up with two TFRecord files named `pascal_train.record` and 30 | `pascal_val.record` in the `tensorflow/models/research/` directory. 31 | 32 | The label map for the PASCAL VOC data set can be found at 33 | `object_detection/data/pascal_label_map.pbtxt`. 34 | 35 | ## Generating the Oxford-IIIT Pet TFRecord files. 36 | 37 | The Oxford-IIIT Pet data set is located 38 | [here](http://www.robots.ox.ac.uk/~vgg/data/pets/). To download, extract and 39 | convert it to TFRecrods, run the following commands below: 40 | 41 | ```bash 42 | # From tensorflow/models/research/ 43 | wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz 44 | wget http://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz 45 | tar -xvf annotations.tar.gz 46 | tar -xvf images.tar.gz 47 | python object_detection/create_pet_tf_record.py \ 48 | --label_map_path=object_detection/data/pet_label_map.pbtxt \ 49 | --data_dir=`pwd` \ 50 | --output_dir=`pwd` 51 | ``` 52 | 53 | You should end up with two TFRecord files named `pet_train.record` and 54 | `pet_val.record` in the `tensorflow/models/research/` directory. 55 | 56 | The label map for the Pet dataset can be found at 57 | `object_detection/data/pet_label_map.pbtxt`. 58 | -------------------------------------------------------------------------------- /research/object_detection/g3doc/running_locally.md: -------------------------------------------------------------------------------- 1 | # Running Locally 2 | 3 | This page walks through the steps required to train an object detection model 4 | on a local machine. It assumes the reader has completed the 5 | following prerequisites: 6 | 7 | 1. The Tensorflow Object Detection API has been installed as documented in the 8 | [installation instructions](installation.md). This includes installing library 9 | dependencies, compiling the configuration protobufs and setting up the Python 10 | environment. 11 | 2. A valid data set has been created. See [this page](preparing_inputs.md) for 12 | instructions on how to generate a dataset for the PASCAL VOC challenge or the 13 | Oxford-IIIT Pet dataset. 14 | 3. A Object Detection pipeline configuration has been written. See 15 | [this page](configuring_jobs.md) for details on how to write a pipeline configuration. 16 | 17 | ## Recommended Directory Structure for Training and Evaluation 18 | 19 | ``` 20 | +data 21 | -label_map file 22 | -train TFRecord file 23 | -eval TFRecord file 24 | +models 25 | + model 26 | -pipeline config file 27 | +train 28 | +eval 29 | ``` 30 | 31 | ## Running the Training Job 32 | 33 | A local training job can be run with the following command: 34 | 35 | ```bash 36 | # From the tensorflow/models/research/ directory 37 | python object_detection/train.py \ 38 | --logtostderr \ 39 | --pipeline_config_path=${PATH_TO_YOUR_PIPELINE_CONFIG} \ 40 | --train_dir=${PATH_TO_TRAIN_DIR} 41 | ``` 42 | 43 | where `${PATH_TO_YOUR_PIPELINE_CONFIG}` points to the pipeline config and 44 | `${PATH_TO_TRAIN_DIR}` points to the directory in which training checkpoints 45 | and events will be written to. By default, the training job will 46 | run indefinitely until the user kills it. 47 | 48 | ## Running the Evaluation Job 49 | 50 | Evaluation is run as a separate job. The eval job will periodically poll the 51 | train directory for new checkpoints and evaluate them on a test dataset. The 52 | job can be run using the following command: 53 | 54 | ```bash 55 | # From the tensorflow/models/research/ directory 56 | python object_detection/eval.py \ 57 | --logtostderr \ 58 | --pipeline_config_path=${PATH_TO_YOUR_PIPELINE_CONFIG} \ 59 | --checkpoint_dir=${PATH_TO_TRAIN_DIR} \ 60 | --eval_dir=${PATH_TO_EVAL_DIR} 61 | ``` 62 | 63 | where `${PATH_TO_YOUR_PIPELINE_CONFIG}` points to the pipeline config, 64 | `${PATH_TO_TRAIN_DIR}` points to the directory in which training checkpoints 65 | were saved (same as the training job) and `${PATH_TO_EVAL_DIR}` points to the 66 | directory in which evaluation events will be saved. As with the training job, 67 | the eval job run until terminated by default. 68 | 69 | ## Running Tensorboard 70 | 71 | Progress for training and eval jobs can be inspected using Tensorboard. If 72 | using the recommended directory structure, Tensorboard can be run using the 73 | following command: 74 | 75 | ```bash 76 | tensorboard --logdir=${PATH_TO_MODEL_DIRECTORY} 77 | ``` 78 | 79 | where `${PATH_TO_MODEL_DIRECTORY}` points to the directory that contains the 80 | train and eval directories. Please note it may take Tensorboard a couple minutes 81 | to populate with data. 82 | -------------------------------------------------------------------------------- /research/object_detection/g3doc/running_notebook.md: -------------------------------------------------------------------------------- 1 | # Quick Start: Jupyter notebook for off-the-shelf inference 2 | 3 | If you'd like to hit the ground running and run detection on a few example 4 | images right out of the box, we recommend trying out the Jupyter notebook demo. 5 | To run the Jupyter notebook, run the following command from 6 | `tensorflow/models/research/object_detection`: 7 | 8 | ``` 9 | # From tensorflow/models/research/object_detection 10 | jupyter notebook 11 | ``` 12 | 13 | The notebook should open in your favorite web browser. Click the 14 | [`object_detection_tutorial.ipynb`](../object_detection_tutorial.ipynb) link to 15 | open the demo. 16 | -------------------------------------------------------------------------------- /research/object_detection/matchers/BUILD: -------------------------------------------------------------------------------- 1 | # Tensorflow Object Detection API: Matcher implementations. 2 | 3 | package( 4 | default_visibility = ["//visibility:public"], 5 | ) 6 | 7 | licenses(["notice"]) 8 | 9 | # Apache 2.0 10 | py_library( 11 | name = "argmax_matcher", 12 | srcs = [ 13 | "argmax_matcher.py", 14 | ], 15 | deps = [ 16 | "//tensorflow", 17 | "//tensorflow_models/object_detection/core:matcher", 18 | ], 19 | ) 20 | 21 | py_test( 22 | name = "argmax_matcher_test", 23 | srcs = ["argmax_matcher_test.py"], 24 | deps = [ 25 | ":argmax_matcher", 26 | "//tensorflow", 27 | ], 28 | ) 29 | 30 | py_library( 31 | name = "bipartite_matcher", 32 | srcs = [ 33 | "bipartite_matcher.py", 34 | ], 35 | deps = [ 36 | "//tensorflow", 37 | "//tensorflow/contrib/image:image_py", 38 | "//tensorflow_models/object_detection/core:matcher", 39 | ], 40 | ) 41 | 42 | py_test( 43 | name = "bipartite_matcher_test", 44 | srcs = [ 45 | "bipartite_matcher_test.py", 46 | ], 47 | deps = [ 48 | ":bipartite_matcher", 49 | "//tensorflow", 50 | ], 51 | ) 52 | -------------------------------------------------------------------------------- /research/object_detection/matchers/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/matchers/__init__.py -------------------------------------------------------------------------------- /research/object_detection/matchers/__init__.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/matchers/__init__.pyc -------------------------------------------------------------------------------- /research/object_detection/matchers/argmax_matcher.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/matchers/argmax_matcher.pyc -------------------------------------------------------------------------------- /research/object_detection/matchers/bipartite_matcher.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Bipartite matcher implementation.""" 17 | 18 | import tensorflow as tf 19 | 20 | from tensorflow.contrib.image.python.ops import image_ops 21 | from object_detection.core import matcher 22 | 23 | 24 | class GreedyBipartiteMatcher(matcher.Matcher): 25 | """Wraps a Tensorflow greedy bipartite matcher.""" 26 | 27 | def _match(self, similarity_matrix, num_valid_rows=-1): 28 | """Bipartite matches a collection rows and columns. A greedy bi-partite. 29 | 30 | TODO: Add num_valid_columns options to match only that many columns with 31 | all the rows. 32 | 33 | Args: 34 | similarity_matrix: Float tensor of shape [N, M] with pairwise similarity 35 | where higher values mean more similar. 36 | num_valid_rows: A scalar or a 1-D tensor with one element describing the 37 | number of valid rows of similarity_matrix to consider for the bipartite 38 | matching. If set to be negative, then all rows from similarity_matrix 39 | are used. 40 | 41 | Returns: 42 | match_results: int32 tensor of shape [M] with match_results[i]=-1 43 | meaning that column i is not matched and otherwise that it is matched to 44 | row match_results[i]. 45 | """ 46 | # Convert similarity matrix to distance matrix as tf.image.bipartite tries 47 | # to find minimum distance matches. 48 | distance_matrix = -1 * similarity_matrix 49 | _, match_results = image_ops.bipartite_match( 50 | distance_matrix, num_valid_rows) 51 | match_results = tf.reshape(match_results, [-1]) 52 | match_results = tf.cast(match_results, tf.int32) 53 | return match_results 54 | -------------------------------------------------------------------------------- /research/object_detection/matchers/bipartite_matcher.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/matchers/bipartite_matcher.pyc -------------------------------------------------------------------------------- /research/object_detection/matchers/bipartite_matcher_test.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Tests for object_detection.core.bipartite_matcher.""" 17 | 18 | import tensorflow as tf 19 | 20 | from object_detection.matchers import bipartite_matcher 21 | 22 | 23 | class GreedyBipartiteMatcherTest(tf.test.TestCase): 24 | 25 | def test_get_expected_matches_when_all_rows_are_valid(self): 26 | similarity_matrix = tf.constant([[0.50, 0.1, 0.8], [0.15, 0.2, 0.3]]) 27 | num_valid_rows = 2 28 | expected_match_results = [-1, 1, 0] 29 | 30 | matcher = bipartite_matcher.GreedyBipartiteMatcher() 31 | match = matcher.match(similarity_matrix, num_valid_rows=num_valid_rows) 32 | with self.test_session() as sess: 33 | match_results_out = sess.run(match._match_results) 34 | self.assertAllEqual(match_results_out, expected_match_results) 35 | 36 | def test_get_expected_matches_with_valid_rows_set_to_minus_one(self): 37 | similarity_matrix = tf.constant([[0.50, 0.1, 0.8], [0.15, 0.2, 0.3]]) 38 | num_valid_rows = -1 39 | expected_match_results = [-1, 1, 0] 40 | 41 | matcher = bipartite_matcher.GreedyBipartiteMatcher() 42 | match = matcher.match(similarity_matrix, num_valid_rows=num_valid_rows) 43 | with self.test_session() as sess: 44 | match_results_out = sess.run(match._match_results) 45 | self.assertAllEqual(match_results_out, expected_match_results) 46 | 47 | def test_get_no_matches_with_zero_valid_rows(self): 48 | similarity_matrix = tf.constant([[0.50, 0.1, 0.8], [0.15, 0.2, 0.3]]) 49 | num_valid_rows = 0 50 | expected_match_results = [-1, -1, -1] 51 | 52 | matcher = bipartite_matcher.GreedyBipartiteMatcher() 53 | match = matcher.match(similarity_matrix, num_valid_rows=num_valid_rows) 54 | with self.test_session() as sess: 55 | match_results_out = sess.run(match._match_results) 56 | self.assertAllEqual(match_results_out, expected_match_results) 57 | 58 | def test_get_expected_matches_with_only_one_valid_row(self): 59 | similarity_matrix = tf.constant([[0.50, 0.1, 0.8], [0.15, 0.2, 0.3]]) 60 | num_valid_rows = 1 61 | expected_match_results = [-1, -1, 0] 62 | 63 | matcher = bipartite_matcher.GreedyBipartiteMatcher() 64 | match = matcher.match(similarity_matrix, num_valid_rows=num_valid_rows) 65 | with self.test_session() as sess: 66 | match_results_out = sess.run(match._match_results) 67 | self.assertAllEqual(match_results_out, expected_match_results) 68 | 69 | 70 | if __name__ == '__main__': 71 | tf.test.main() 72 | -------------------------------------------------------------------------------- /research/object_detection/meta_architectures/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/meta_architectures/__init__.py -------------------------------------------------------------------------------- /research/object_detection/meta_architectures/__init__.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/meta_architectures/__init__.pyc -------------------------------------------------------------------------------- /research/object_detection/meta_architectures/faster_rcnn_meta_arch.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/meta_architectures/faster_rcnn_meta_arch.pyc -------------------------------------------------------------------------------- /research/object_detection/meta_architectures/rfcn_meta_arch.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/meta_architectures/rfcn_meta_arch.pyc -------------------------------------------------------------------------------- /research/object_detection/meta_architectures/rfcn_meta_arch_test.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Tests for object_detection.meta_architectures.rfcn_meta_arch.""" 17 | 18 | import tensorflow as tf 19 | 20 | from object_detection.meta_architectures import faster_rcnn_meta_arch_test_lib 21 | from object_detection.meta_architectures import rfcn_meta_arch 22 | 23 | 24 | class RFCNMetaArchTest( 25 | faster_rcnn_meta_arch_test_lib.FasterRCNNMetaArchTestBase): 26 | 27 | def _get_second_stage_box_predictor_text_proto(self): 28 | box_predictor_text_proto = """ 29 | rfcn_box_predictor { 30 | conv_hyperparams { 31 | op: CONV 32 | activation: NONE 33 | regularizer { 34 | l2_regularizer { 35 | weight: 0.0005 36 | } 37 | } 38 | initializer { 39 | variance_scaling_initializer { 40 | factor: 1.0 41 | uniform: true 42 | mode: FAN_AVG 43 | } 44 | } 45 | } 46 | } 47 | """ 48 | return box_predictor_text_proto 49 | 50 | def _get_model(self, box_predictor, **common_kwargs): 51 | return rfcn_meta_arch.RFCNMetaArch( 52 | second_stage_rfcn_box_predictor=box_predictor, **common_kwargs) 53 | 54 | 55 | if __name__ == '__main__': 56 | tf.test.main() 57 | -------------------------------------------------------------------------------- /research/object_detection/meta_architectures/ssd_meta_arch.pyc: -------------------------------------------------------------------------------- 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/research/object_detection/protos/anchor_generator.proto: -------------------------------------------------------------------------------- 1 | syntax = "proto2"; 2 | 3 | package object_detection.protos; 4 | 5 | import "object_detection/protos/grid_anchor_generator.proto"; 6 | import "object_detection/protos/ssd_anchor_generator.proto"; 7 | 8 | // Configuration proto for the anchor generator to use in the object detection 9 | // pipeline. See core/anchor_generator.py for details. 10 | message AnchorGenerator { 11 | oneof anchor_generator_oneof { 12 | GridAnchorGenerator grid_anchor_generator = 1; 13 | SsdAnchorGenerator ssd_anchor_generator = 2; 14 | } 15 | } 16 | -------------------------------------------------------------------------------- /research/object_detection/protos/anchor_generator_pb2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/protos/anchor_generator_pb2.pyc -------------------------------------------------------------------------------- /research/object_detection/protos/argmax_matcher.proto: -------------------------------------------------------------------------------- 1 | syntax = "proto2"; 2 | 3 | package object_detection.protos; 4 | 5 | // Configuration proto for ArgMaxMatcher. See 6 | // matchers/argmax_matcher.py for details. 7 | message ArgMaxMatcher { 8 | // Threshold for positive matches. 9 | optional float matched_threshold = 1 [default = 0.5]; 10 | 11 | // Threshold for negative matches. 12 | optional float unmatched_threshold = 2 [default = 0.5]; 13 | 14 | // Whether to construct ArgMaxMatcher without thresholds. 15 | optional bool ignore_thresholds = 3 [default = false]; 16 | 17 | // If True then negative matches are the ones below the unmatched_threshold, 18 | // whereas ignored matches are in between the matched and umatched 19 | // threshold. If False, then negative matches are in between the matched 20 | // and unmatched threshold, and everything lower than unmatched is ignored. 21 | optional bool negatives_lower_than_unmatched = 4 [default = true]; 22 | 23 | // Whether to ensure each row is matched to at least one column. 24 | optional bool force_match_for_each_row = 5 [default = false]; 25 | } 26 | -------------------------------------------------------------------------------- /research/object_detection/protos/argmax_matcher_pb2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/protos/argmax_matcher_pb2.pyc -------------------------------------------------------------------------------- /research/object_detection/protos/bipartite_matcher.proto: -------------------------------------------------------------------------------- 1 | syntax = "proto2"; 2 | 3 | package object_detection.protos; 4 | 5 | // Configuration proto for bipartite matcher. See 6 | // matchers/bipartite_matcher.py for details. 7 | message BipartiteMatcher { 8 | } 9 | -------------------------------------------------------------------------------- /research/object_detection/protos/bipartite_matcher_pb2.py: -------------------------------------------------------------------------------- 1 | # Generated by the protocol buffer compiler. DO NOT EDIT! 2 | # source: object_detection/protos/bipartite_matcher.proto 3 | 4 | import sys 5 | _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) 6 | from google.protobuf import descriptor as _descriptor 7 | from google.protobuf import message as _message 8 | from google.protobuf import reflection as _reflection 9 | from google.protobuf import symbol_database as _symbol_database 10 | from google.protobuf import descriptor_pb2 11 | # @@protoc_insertion_point(imports) 12 | 13 | _sym_db = _symbol_database.Default() 14 | 15 | 16 | 17 | 18 | DESCRIPTOR = _descriptor.FileDescriptor( 19 | name='object_detection/protos/bipartite_matcher.proto', 20 | package='object_detection.protos', 21 | syntax='proto2', 22 | serialized_pb=_b('\n/object_detection/protos/bipartite_matcher.proto\x12\x17object_detection.protos\"\x12\n\x10\x42ipartiteMatcher') 23 | ) 24 | 25 | 26 | 27 | 28 | _BIPARTITEMATCHER = _descriptor.Descriptor( 29 | name='BipartiteMatcher', 30 | full_name='object_detection.protos.BipartiteMatcher', 31 | filename=None, 32 | file=DESCRIPTOR, 33 | containing_type=None, 34 | fields=[ 35 | ], 36 | extensions=[ 37 | ], 38 | nested_types=[], 39 | enum_types=[ 40 | ], 41 | options=None, 42 | is_extendable=False, 43 | syntax='proto2', 44 | extension_ranges=[], 45 | oneofs=[ 46 | ], 47 | serialized_start=76, 48 | serialized_end=94, 49 | ) 50 | 51 | DESCRIPTOR.message_types_by_name['BipartiteMatcher'] = _BIPARTITEMATCHER 52 | _sym_db.RegisterFileDescriptor(DESCRIPTOR) 53 | 54 | BipartiteMatcher = _reflection.GeneratedProtocolMessageType('BipartiteMatcher', (_message.Message,), dict( 55 | DESCRIPTOR = _BIPARTITEMATCHER, 56 | __module__ = 'object_detection.protos.bipartite_matcher_pb2' 57 | # @@protoc_insertion_point(class_scope:object_detection.protos.BipartiteMatcher) 58 | )) 59 | _sym_db.RegisterMessage(BipartiteMatcher) 60 | 61 | 62 | # @@protoc_insertion_point(module_scope) 63 | -------------------------------------------------------------------------------- /research/object_detection/protos/bipartite_matcher_pb2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/protos/bipartite_matcher_pb2.pyc -------------------------------------------------------------------------------- /research/object_detection/protos/box_coder.proto: -------------------------------------------------------------------------------- 1 | syntax = "proto2"; 2 | 3 | package object_detection.protos; 4 | 5 | import "object_detection/protos/faster_rcnn_box_coder.proto"; 6 | import "object_detection/protos/mean_stddev_box_coder.proto"; 7 | import "object_detection/protos/square_box_coder.proto"; 8 | 9 | // Configuration proto for the box coder to be used in the object detection 10 | // pipeline. See core/box_coder.py for details. 11 | message BoxCoder { 12 | oneof box_coder_oneof { 13 | FasterRcnnBoxCoder faster_rcnn_box_coder = 1; 14 | MeanStddevBoxCoder mean_stddev_box_coder = 2; 15 | SquareBoxCoder square_box_coder = 3; 16 | } 17 | } 18 | -------------------------------------------------------------------------------- /research/object_detection/protos/box_coder_pb2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/protos/box_coder_pb2.pyc -------------------------------------------------------------------------------- /research/object_detection/protos/box_predictor_pb2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/protos/box_predictor_pb2.pyc -------------------------------------------------------------------------------- /research/object_detection/protos/eval.proto: -------------------------------------------------------------------------------- 1 | syntax = "proto2"; 2 | 3 | package object_detection.protos; 4 | 5 | // Message for configuring DetectionModel evaluation jobs (eval.py). 6 | message EvalConfig { 7 | // Number of visualization images to generate. 8 | optional uint32 num_visualizations = 1 [default=10]; 9 | 10 | // Number of examples to process of evaluation. 11 | optional uint32 num_examples = 2 [default=5000]; 12 | 13 | // How often to run evaluation. 14 | optional uint32 eval_interval_secs = 3 [default=300]; 15 | 16 | // Maximum number of times to run evaluation. If set to 0, will run forever. 17 | optional uint32 max_evals = 4 [default=0]; 18 | 19 | // Whether the TensorFlow graph used for evaluation should be saved to disk. 20 | optional bool save_graph = 5 [default=false]; 21 | 22 | // Path to directory to store visualizations in. If empty, visualization 23 | // images are not exported (only shown on Tensorboard). 24 | optional string visualization_export_dir = 6 [default=""]; 25 | 26 | // BNS name of the TensorFlow master. 27 | optional string eval_master = 7 [default=""]; 28 | 29 | // Type of metrics to use for evaluation. Currently supports only Pascal VOC 30 | // detection metrics. 31 | optional string metrics_set = 8 [default="pascal_voc_metrics"]; 32 | 33 | // Path to export detections to COCO compatible JSON format. 34 | optional string export_path = 9 [default='']; 35 | 36 | // Option to not read groundtruth labels and only export detections to 37 | // COCO-compatible JSON file. 38 | optional bool ignore_groundtruth = 10 [default=false]; 39 | 40 | // Use exponential moving averages of variables for evaluation. 41 | // TODO: When this is false make sure the model is constructed 42 | // without moving averages in restore_fn. 43 | optional bool use_moving_averages = 11 [default=false]; 44 | 45 | // Whether to evaluate instance masks. 46 | optional bool eval_instance_masks = 12 [default=false]; 47 | } 48 | -------------------------------------------------------------------------------- /research/object_detection/protos/eval_pb2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/protos/eval_pb2.pyc -------------------------------------------------------------------------------- /research/object_detection/protos/faster_rcnn_box_coder.proto: -------------------------------------------------------------------------------- 1 | syntax = "proto2"; 2 | 3 | package object_detection.protos; 4 | 5 | // Configuration proto for FasterRCNNBoxCoder. See 6 | // box_coders/faster_rcnn_box_coder.py for details. 7 | message FasterRcnnBoxCoder { 8 | // Scale factor for anchor encoded box center. 9 | optional float y_scale = 1 [default = 10.0]; 10 | optional float x_scale = 2 [default = 10.0]; 11 | 12 | // Scale factor for anchor encoded box height. 13 | optional float height_scale = 3 [default = 5.0]; 14 | 15 | // Scale factor for anchor encoded box width. 16 | optional float width_scale = 4 [default = 5.0]; 17 | } 18 | -------------------------------------------------------------------------------- /research/object_detection/protos/faster_rcnn_box_coder_pb2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/protos/faster_rcnn_box_coder_pb2.pyc -------------------------------------------------------------------------------- /research/object_detection/protos/faster_rcnn_pb2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/protos/faster_rcnn_pb2.pyc -------------------------------------------------------------------------------- /research/object_detection/protos/grid_anchor_generator.proto: -------------------------------------------------------------------------------- 1 | syntax = "proto2"; 2 | 3 | package object_detection.protos; 4 | 5 | // Configuration proto for GridAnchorGenerator. See 6 | // anchor_generators/grid_anchor_generator.py for details. 7 | message GridAnchorGenerator { 8 | // Anchor height in pixels. 9 | optional int32 height = 1 [default = 256]; 10 | 11 | // Anchor width in pixels. 12 | optional int32 width = 2 [default = 256]; 13 | 14 | // Anchor stride in height dimension in pixels. 15 | optional int32 height_stride = 3 [default = 16]; 16 | 17 | // Anchor stride in width dimension in pixels. 18 | optional int32 width_stride = 4 [default = 16]; 19 | 20 | // Anchor height offset in pixels. 21 | optional int32 height_offset = 5 [default = 0]; 22 | 23 | // Anchor width offset in pixels. 24 | optional int32 width_offset = 6 [default = 0]; 25 | 26 | // At any given location, len(scales) * len(aspect_ratios) anchors are 27 | // generated with all possible combinations of scales and aspect ratios. 28 | 29 | // List of scales for the anchors. 30 | repeated float scales = 7; 31 | 32 | // List of aspect ratios for the anchors. 33 | repeated float aspect_ratios = 8; 34 | } 35 | -------------------------------------------------------------------------------- /research/object_detection/protos/grid_anchor_generator_pb2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/protos/grid_anchor_generator_pb2.pyc -------------------------------------------------------------------------------- /research/object_detection/protos/hyperparams_pb2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/protos/hyperparams_pb2.pyc -------------------------------------------------------------------------------- /research/object_detection/protos/image_resizer.proto: -------------------------------------------------------------------------------- 1 | syntax = "proto2"; 2 | 3 | package object_detection.protos; 4 | 5 | // Configuration proto for image resizing operations. 6 | // See builders/image_resizer_builder.py for details. 7 | message ImageResizer { 8 | oneof image_resizer_oneof { 9 | KeepAspectRatioResizer keep_aspect_ratio_resizer = 1; 10 | FixedShapeResizer fixed_shape_resizer = 2; 11 | } 12 | } 13 | 14 | 15 | // Configuration proto for image resizer that keeps aspect ratio. 16 | message KeepAspectRatioResizer { 17 | // Desired size of the smaller image dimension in pixels. 18 | optional int32 min_dimension = 1 [default = 600]; 19 | 20 | // Desired size of the larger image dimension in pixels. 21 | optional int32 max_dimension = 2 [default = 1024]; 22 | } 23 | 24 | 25 | // Configuration proto for image resizer that resizes to a fixed shape. 26 | message FixedShapeResizer { 27 | // Desired height of image in pixels. 28 | optional int32 height = 1 [default = 300]; 29 | 30 | // Desired width of image in pixels. 31 | optional int32 width = 2 [default = 300]; 32 | } 33 | -------------------------------------------------------------------------------- /research/object_detection/protos/image_resizer_pb2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/protos/image_resizer_pb2.pyc -------------------------------------------------------------------------------- /research/object_detection/protos/input_reader.proto: -------------------------------------------------------------------------------- 1 | syntax = "proto2"; 2 | 3 | package object_detection.protos; 4 | 5 | // Configuration proto for defining input readers that generate Object Detection 6 | // Examples from input sources. Input readers are expected to generate a 7 | // dictionary of tensors, with the following fields populated: 8 | // 9 | // 'image': an [image_height, image_width, channels] image tensor that detection 10 | // will be run on. 11 | // 'groundtruth_classes': a [num_boxes] int32 tensor storing the class 12 | // labels of detected boxes in the image. 13 | // 'groundtruth_boxes': a [num_boxes, 4] float tensor storing the coordinates of 14 | // detected boxes in the image. 15 | // 'groundtruth_instance_masks': (Optional), a [num_boxes, image_height, 16 | // image_width] float tensor storing binary mask of the objects in boxes. 17 | 18 | message InputReader { 19 | // Path to StringIntLabelMap pbtxt file specifying the mapping from string 20 | // labels to integer ids. 21 | optional string label_map_path = 1 [default=""]; 22 | 23 | // Whether data should be processed in the order they are read in, or 24 | // shuffled randomly. 25 | optional bool shuffle = 2 [default=true]; 26 | 27 | // Maximum number of records to keep in reader queue. 28 | optional uint32 queue_capacity = 3 [default=2000]; 29 | 30 | // Minimum number of records to keep in reader queue. A large value is needed 31 | // to generate a good random shuffle. 32 | optional uint32 min_after_dequeue = 4 [default=1000]; 33 | 34 | // The number of times a data source is read. If set to zero, the data source 35 | // will be reused indefinitely. 36 | optional uint32 num_epochs = 5 [default=0]; 37 | 38 | // Number of reader instances to create. 39 | optional uint32 num_readers = 6 [default=8]; 40 | 41 | // Whether to load groundtruth instance masks. 42 | optional bool load_instance_masks = 7 [default = false]; 43 | 44 | oneof input_reader { 45 | TFRecordInputReader tf_record_input_reader = 8; 46 | ExternalInputReader external_input_reader = 9; 47 | } 48 | } 49 | 50 | // An input reader that reads TF Example protos from local TFRecord files. 51 | message TFRecordInputReader { 52 | // Path to TFRecordFile. 53 | optional string input_path = 1 [default=""]; 54 | } 55 | 56 | // An externally defined input reader. Users may define an extension to this 57 | // proto to interface their own input readers. 58 | message ExternalInputReader { 59 | extensions 1 to 999; 60 | } 61 | -------------------------------------------------------------------------------- /research/object_detection/protos/input_reader_pb2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/protos/input_reader_pb2.pyc -------------------------------------------------------------------------------- /research/object_detection/protos/losses_pb2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/protos/losses_pb2.pyc -------------------------------------------------------------------------------- /research/object_detection/protos/matcher.proto: -------------------------------------------------------------------------------- 1 | syntax = "proto2"; 2 | 3 | package object_detection.protos; 4 | 5 | import "object_detection/protos/argmax_matcher.proto"; 6 | import "object_detection/protos/bipartite_matcher.proto"; 7 | 8 | // Configuration proto for the matcher to be used in the object detection 9 | // pipeline. See core/matcher.py for details. 10 | message Matcher { 11 | oneof matcher_oneof { 12 | ArgMaxMatcher argmax_matcher = 1; 13 | BipartiteMatcher bipartite_matcher = 2; 14 | } 15 | } 16 | -------------------------------------------------------------------------------- /research/object_detection/protos/matcher_pb2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/protos/matcher_pb2.pyc -------------------------------------------------------------------------------- /research/object_detection/protos/mean_stddev_box_coder.proto: -------------------------------------------------------------------------------- 1 | syntax = "proto2"; 2 | 3 | package object_detection.protos; 4 | 5 | // Configuration proto for MeanStddevBoxCoder. See 6 | // box_coders/mean_stddev_box_coder.py for details. 7 | message MeanStddevBoxCoder { 8 | } 9 | -------------------------------------------------------------------------------- /research/object_detection/protos/mean_stddev_box_coder_pb2.py: -------------------------------------------------------------------------------- 1 | # Generated by the protocol buffer compiler. DO NOT EDIT! 2 | # source: object_detection/protos/mean_stddev_box_coder.proto 3 | 4 | import sys 5 | _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) 6 | from google.protobuf import descriptor as _descriptor 7 | from google.protobuf import message as _message 8 | from google.protobuf import reflection as _reflection 9 | from google.protobuf import symbol_database as _symbol_database 10 | from google.protobuf import descriptor_pb2 11 | # @@protoc_insertion_point(imports) 12 | 13 | _sym_db = _symbol_database.Default() 14 | 15 | 16 | 17 | 18 | DESCRIPTOR = _descriptor.FileDescriptor( 19 | name='object_detection/protos/mean_stddev_box_coder.proto', 20 | package='object_detection.protos', 21 | syntax='proto2', 22 | serialized_pb=_b('\n3object_detection/protos/mean_stddev_box_coder.proto\x12\x17object_detection.protos\"\x14\n\x12MeanStddevBoxCoder') 23 | ) 24 | 25 | 26 | 27 | 28 | _MEANSTDDEVBOXCODER = _descriptor.Descriptor( 29 | name='MeanStddevBoxCoder', 30 | full_name='object_detection.protos.MeanStddevBoxCoder', 31 | filename=None, 32 | file=DESCRIPTOR, 33 | containing_type=None, 34 | fields=[ 35 | ], 36 | extensions=[ 37 | ], 38 | nested_types=[], 39 | enum_types=[ 40 | ], 41 | options=None, 42 | is_extendable=False, 43 | syntax='proto2', 44 | extension_ranges=[], 45 | oneofs=[ 46 | ], 47 | serialized_start=80, 48 | serialized_end=100, 49 | ) 50 | 51 | DESCRIPTOR.message_types_by_name['MeanStddevBoxCoder'] = _MEANSTDDEVBOXCODER 52 | _sym_db.RegisterFileDescriptor(DESCRIPTOR) 53 | 54 | MeanStddevBoxCoder = _reflection.GeneratedProtocolMessageType('MeanStddevBoxCoder', (_message.Message,), dict( 55 | DESCRIPTOR = _MEANSTDDEVBOXCODER, 56 | __module__ = 'object_detection.protos.mean_stddev_box_coder_pb2' 57 | # @@protoc_insertion_point(class_scope:object_detection.protos.MeanStddevBoxCoder) 58 | )) 59 | _sym_db.RegisterMessage(MeanStddevBoxCoder) 60 | 61 | 62 | # @@protoc_insertion_point(module_scope) 63 | -------------------------------------------------------------------------------- /research/object_detection/protos/mean_stddev_box_coder_pb2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/protos/mean_stddev_box_coder_pb2.pyc -------------------------------------------------------------------------------- /research/object_detection/protos/model.proto: -------------------------------------------------------------------------------- 1 | syntax = "proto2"; 2 | 3 | package object_detection.protos; 4 | 5 | import "object_detection/protos/faster_rcnn.proto"; 6 | import "object_detection/protos/ssd.proto"; 7 | 8 | // Top level configuration for DetectionModels. 9 | message DetectionModel { 10 | oneof model { 11 | FasterRcnn faster_rcnn = 1; 12 | Ssd ssd = 2; 13 | } 14 | } 15 | -------------------------------------------------------------------------------- /research/object_detection/protos/model_pb2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/protos/model_pb2.pyc -------------------------------------------------------------------------------- /research/object_detection/protos/optimizer.proto: -------------------------------------------------------------------------------- 1 | syntax = "proto2"; 2 | 3 | package object_detection.protos; 4 | 5 | // Messages for configuring the optimizing strategy for training object 6 | // detection models. 7 | 8 | // Top level optimizer message. 9 | message Optimizer { 10 | oneof optimizer { 11 | RMSPropOptimizer rms_prop_optimizer = 1; 12 | MomentumOptimizer momentum_optimizer = 2; 13 | AdamOptimizer adam_optimizer = 3; 14 | } 15 | optional bool use_moving_average = 4 [default=true]; 16 | optional float moving_average_decay = 5 [default=0.9999]; 17 | } 18 | 19 | // Configuration message for the RMSPropOptimizer 20 | // See: https://www.tensorflow.org/api_docs/python/tf/train/RMSPropOptimizer 21 | message RMSPropOptimizer { 22 | optional LearningRate learning_rate = 1; 23 | optional float momentum_optimizer_value = 2 [default=0.9]; 24 | optional float decay = 3 [default=0.9]; 25 | optional float epsilon = 4 [default=1.0]; 26 | } 27 | 28 | // Configuration message for the MomentumOptimizer 29 | // See: https://www.tensorflow.org/api_docs/python/tf/train/MomentumOptimizer 30 | message MomentumOptimizer { 31 | optional LearningRate learning_rate = 1; 32 | optional float momentum_optimizer_value = 2 [default=0.9]; 33 | } 34 | 35 | // Configuration message for the AdamOptimizer 36 | // See: https://www.tensorflow.org/api_docs/python/tf/train/AdamOptimizer 37 | message AdamOptimizer { 38 | optional LearningRate learning_rate = 1; 39 | } 40 | 41 | // Configuration message for optimizer learning rate. 42 | message LearningRate { 43 | oneof learning_rate { 44 | ConstantLearningRate constant_learning_rate = 1; 45 | ExponentialDecayLearningRate exponential_decay_learning_rate = 2; 46 | ManualStepLearningRate manual_step_learning_rate = 3; 47 | } 48 | } 49 | 50 | // Configuration message for a constant learning rate. 51 | message ConstantLearningRate { 52 | optional float learning_rate = 1 [default=0.002]; 53 | } 54 | 55 | // Configuration message for an exponentially decaying learning rate. 56 | // See https://www.tensorflow.org/versions/master/api_docs/python/train/ \ 57 | // decaying_the_learning_rate#exponential_decay 58 | message ExponentialDecayLearningRate { 59 | optional float initial_learning_rate = 1 [default=0.002]; 60 | optional uint32 decay_steps = 2 [default=4000000]; 61 | optional float decay_factor = 3 [default=0.95]; 62 | optional bool staircase = 4 [default=true]; 63 | } 64 | 65 | // Configuration message for a manually defined learning rate schedule. 66 | message ManualStepLearningRate { 67 | optional float initial_learning_rate = 1 [default=0.002]; 68 | message LearningRateSchedule { 69 | optional uint32 step = 1; 70 | optional float learning_rate = 2 [default=0.002]; 71 | } 72 | repeated LearningRateSchedule schedule = 2; 73 | } 74 | -------------------------------------------------------------------------------- /research/object_detection/protos/optimizer_pb2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/protos/optimizer_pb2.pyc -------------------------------------------------------------------------------- /research/object_detection/protos/pipeline.proto: -------------------------------------------------------------------------------- 1 | syntax = "proto2"; 2 | 3 | package object_detection.protos; 4 | 5 | import "object_detection/protos/eval.proto"; 6 | import "object_detection/protos/input_reader.proto"; 7 | import "object_detection/protos/model.proto"; 8 | import "object_detection/protos/train.proto"; 9 | 10 | // Convenience message for configuring a training and eval pipeline. Allows all 11 | // of the pipeline parameters to be configured from one file. 12 | message TrainEvalPipelineConfig { 13 | optional DetectionModel model = 1; 14 | optional TrainConfig train_config = 2; 15 | optional InputReader train_input_reader = 3; 16 | optional EvalConfig eval_config = 4; 17 | optional InputReader eval_input_reader = 5; 18 | } 19 | -------------------------------------------------------------------------------- /research/object_detection/protos/pipeline_pb2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/protos/pipeline_pb2.pyc -------------------------------------------------------------------------------- /research/object_detection/protos/post_processing.proto: -------------------------------------------------------------------------------- 1 | syntax = "proto2"; 2 | 3 | package object_detection.protos; 4 | 5 | // Configuration proto for non-max-suppression operation on a batch of 6 | // detections. 7 | message BatchNonMaxSuppression { 8 | // Scalar threshold for score (low scoring boxes are removed). 9 | optional float score_threshold = 1 [default = 0.0]; 10 | 11 | // Scalar threshold for IOU (boxes that have high IOU overlap 12 | // with previously selected boxes are removed). 13 | optional float iou_threshold = 2 [default = 0.6]; 14 | 15 | // Maximum number of detections to retain per class. 16 | optional int32 max_detections_per_class = 3 [default = 100]; 17 | 18 | // Maximum number of detections to retain across all classes. 19 | optional int32 max_total_detections = 5 [default = 100]; 20 | } 21 | 22 | // Configuration proto for post-processing predicted boxes and 23 | // scores. 24 | message PostProcessing { 25 | // Non max suppression parameters. 26 | optional BatchNonMaxSuppression batch_non_max_suppression = 1; 27 | 28 | // Enum to specify how to convert the detection scores. 29 | enum ScoreConverter { 30 | // Input scores equals output scores. 31 | IDENTITY = 0; 32 | 33 | // Applies a sigmoid on input scores. 34 | SIGMOID = 1; 35 | 36 | // Applies a softmax on input scores 37 | SOFTMAX = 2; 38 | } 39 | 40 | // Score converter to use. 41 | optional ScoreConverter score_converter = 2 [default = IDENTITY]; 42 | } 43 | -------------------------------------------------------------------------------- /research/object_detection/protos/post_processing_pb2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/protos/post_processing_pb2.pyc -------------------------------------------------------------------------------- /research/object_detection/protos/preprocessor_pb2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/protos/preprocessor_pb2.pyc -------------------------------------------------------------------------------- /research/object_detection/protos/region_similarity_calculator.proto: -------------------------------------------------------------------------------- 1 | syntax = "proto2"; 2 | 3 | package object_detection.protos; 4 | 5 | // Configuration proto for region similarity calculators. See 6 | // core/region_similarity_calculator.py for details. 7 | message RegionSimilarityCalculator { 8 | oneof region_similarity { 9 | NegSqDistSimilarity neg_sq_dist_similarity = 1; 10 | IouSimilarity iou_similarity = 2; 11 | IoaSimilarity ioa_similarity = 3; 12 | } 13 | } 14 | 15 | // Configuration for negative squared distance similarity calculator. 16 | message NegSqDistSimilarity { 17 | } 18 | 19 | // Configuration for intersection-over-union (IOU) similarity calculator. 20 | message IouSimilarity { 21 | } 22 | 23 | // Configuration for intersection-over-area (IOA) similarity calculator. 24 | message IoaSimilarity { 25 | } 26 | -------------------------------------------------------------------------------- /research/object_detection/protos/region_similarity_calculator_pb2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/protos/region_similarity_calculator_pb2.pyc -------------------------------------------------------------------------------- /research/object_detection/protos/square_box_coder.proto: -------------------------------------------------------------------------------- 1 | syntax = "proto2"; 2 | 3 | package object_detection.protos; 4 | 5 | // Configuration proto for SquareBoxCoder. See 6 | // box_coders/square_box_coder.py for details. 7 | message SquareBoxCoder { 8 | // Scale factor for anchor encoded box center. 9 | optional float y_scale = 1 [default = 10.0]; 10 | optional float x_scale = 2 [default = 10.0]; 11 | 12 | // Scale factor for anchor encoded box length. 13 | optional float length_scale = 3 [default = 5.0]; 14 | } 15 | -------------------------------------------------------------------------------- /research/object_detection/protos/square_box_coder_pb2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/protos/square_box_coder_pb2.pyc -------------------------------------------------------------------------------- /research/object_detection/protos/ssd.proto: -------------------------------------------------------------------------------- 1 | syntax = "proto2"; 2 | package object_detection.protos; 3 | 4 | import "object_detection/protos/anchor_generator.proto"; 5 | import "object_detection/protos/box_coder.proto"; 6 | import "object_detection/protos/box_predictor.proto"; 7 | import "object_detection/protos/hyperparams.proto"; 8 | import "object_detection/protos/image_resizer.proto"; 9 | import "object_detection/protos/matcher.proto"; 10 | import "object_detection/protos/losses.proto"; 11 | import "object_detection/protos/post_processing.proto"; 12 | import "object_detection/protos/region_similarity_calculator.proto"; 13 | 14 | // Configuration for Single Shot Detection (SSD) models. 15 | message Ssd { 16 | 17 | // Number of classes to predict. 18 | optional int32 num_classes = 1; 19 | 20 | // Image resizer for preprocessing the input image. 21 | optional ImageResizer image_resizer = 2; 22 | 23 | // Feature extractor config. 24 | optional SsdFeatureExtractor feature_extractor = 3; 25 | 26 | // Box coder to encode the boxes. 27 | optional BoxCoder box_coder = 4; 28 | 29 | // Matcher to match groundtruth with anchors. 30 | optional Matcher matcher = 5; 31 | 32 | // Region similarity calculator to compute similarity of boxes. 33 | optional RegionSimilarityCalculator similarity_calculator = 6; 34 | 35 | // Box predictor to attach to the features. 36 | optional BoxPredictor box_predictor = 7; 37 | 38 | // Anchor generator to compute anchors. 39 | optional AnchorGenerator anchor_generator = 8; 40 | 41 | // Post processing to apply on the predictions. 42 | optional PostProcessing post_processing = 9; 43 | 44 | // Whether to normalize the loss by number of groundtruth boxes that match to 45 | // the anchors. 46 | optional bool normalize_loss_by_num_matches = 10 [default=true]; 47 | 48 | // Loss configuration for training. 49 | optional Loss loss = 11; 50 | } 51 | 52 | 53 | message SsdFeatureExtractor { 54 | // Type of ssd feature extractor. 55 | optional string type = 1; 56 | 57 | // The factor to alter the depth of the channels in the feature extractor. 58 | optional float depth_multiplier = 2 [default=1.0]; 59 | 60 | // Minimum number of the channels in the feature extractor. 61 | optional int32 min_depth = 3 [default=16]; 62 | 63 | // Hyperparameters for the feature extractor. 64 | optional Hyperparams conv_hyperparams = 4; 65 | } 66 | -------------------------------------------------------------------------------- /research/object_detection/protos/ssd_anchor_generator.proto: -------------------------------------------------------------------------------- 1 | syntax = "proto2"; 2 | 3 | package object_detection.protos; 4 | 5 | // Configuration proto for SSD anchor generator described in 6 | // https://arxiv.org/abs/1512.02325. See 7 | // anchor_generators/multiple_grid_anchor_generator.py for details. 8 | message SsdAnchorGenerator { 9 | // Number of grid layers to create anchors for. 10 | optional int32 num_layers = 1 [default = 6]; 11 | 12 | // Scale of anchors corresponding to finest resolution. 13 | optional float min_scale = 2 [default = 0.2]; 14 | 15 | // Scale of anchors corresponding to coarsest resolution 16 | optional float max_scale = 3 [default = 0.95]; 17 | 18 | // Aspect ratios for anchors at each grid point. 19 | repeated float aspect_ratios = 4; 20 | 21 | // Whether to use the following aspect ratio and scale combination for the 22 | // layer with the finest resolution : (scale=0.1, aspect_ratio=1.0), 23 | // (scale=min_scale, aspect_ration=2.0), (scale=min_scale, aspect_ratio=0.5). 24 | optional bool reduce_boxes_in_lowest_layer = 5 [default = true]; 25 | } 26 | -------------------------------------------------------------------------------- /research/object_detection/protos/ssd_anchor_generator_pb2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/protos/ssd_anchor_generator_pb2.pyc -------------------------------------------------------------------------------- /research/object_detection/protos/ssd_pb2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/protos/ssd_pb2.pyc -------------------------------------------------------------------------------- /research/object_detection/protos/string_int_label_map.proto: -------------------------------------------------------------------------------- 1 | // Message to store the mapping from class label strings to class id. Datasets 2 | // use string labels to represent classes while the object detection framework 3 | // works with class ids. This message maps them so they can be converted back 4 | // and forth as needed. 5 | syntax = "proto2"; 6 | 7 | package object_detection.protos; 8 | 9 | message StringIntLabelMapItem { 10 | // String name. The most common practice is to set this to a MID or synsets 11 | // id. 12 | optional string name = 1; 13 | 14 | // Integer id that maps to the string name above. Label ids should start from 15 | // 1. 16 | optional int32 id = 2; 17 | 18 | // Human readable string label. 19 | optional string display_name = 3; 20 | }; 21 | 22 | message StringIntLabelMap { 23 | repeated StringIntLabelMapItem item = 1; 24 | }; 25 | -------------------------------------------------------------------------------- /research/object_detection/protos/string_int_label_map_pb2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/protos/string_int_label_map_pb2.pyc -------------------------------------------------------------------------------- /research/object_detection/protos/train.proto: -------------------------------------------------------------------------------- 1 | syntax = "proto2"; 2 | 3 | package object_detection.protos; 4 | 5 | import "object_detection/protos/optimizer.proto"; 6 | import "object_detection/protos/preprocessor.proto"; 7 | 8 | // Message for configuring DetectionModel training jobs (train.py). 9 | message TrainConfig { 10 | // Input queue batch size. 11 | optional uint32 batch_size = 1 [default=32]; 12 | 13 | // Data augmentation options. 14 | repeated PreprocessingStep data_augmentation_options = 2; 15 | 16 | // Whether to synchronize replicas during training. 17 | optional bool sync_replicas = 3 [default=false]; 18 | 19 | // How frequently to keep checkpoints. 20 | optional uint32 keep_checkpoint_every_n_hours = 4 [default=1000]; 21 | 22 | // Optimizer used to train the DetectionModel. 23 | optional Optimizer optimizer = 5; 24 | 25 | // If greater than 0, clips gradients by this value. 26 | optional float gradient_clipping_by_norm = 6 [default=0.0]; 27 | 28 | // Checkpoint to restore variables from. Typically used to load feature 29 | // extractor variables trained outside of object detection. 30 | optional string fine_tune_checkpoint = 7 [default=""]; 31 | 32 | // Specifies if the finetune checkpoint is from an object detection model. 33 | // If from an object detection model, the model being trained should have 34 | // the same parameters with the exception of the num_classes parameter. 35 | // If false, it assumes the checkpoint was a object classification model. 36 | optional bool from_detection_checkpoint = 8 [default=false]; 37 | 38 | // Number of steps to train the DetectionModel for. If 0, will train the model 39 | // indefinitely. 40 | optional uint32 num_steps = 9 [default=0]; 41 | 42 | // Number of training steps between replica startup. 43 | // This flag must be set to 0 if sync_replicas is set to true. 44 | optional float startup_delay_steps = 10 [default=15]; 45 | 46 | // If greater than 0, multiplies the gradient of bias variables by this 47 | // amount. 48 | optional float bias_grad_multiplier = 11 [default=0]; 49 | 50 | // Variables that should not be updated during training. 51 | repeated string freeze_variables = 12; 52 | 53 | // Number of replicas to aggregate before making parameter updates. 54 | optional int32 replicas_to_aggregate = 13 [default=1]; 55 | 56 | // Maximum number of elements to store within a queue. 57 | optional int32 batch_queue_capacity = 14 [default=600]; 58 | 59 | // Number of threads to use for batching. 60 | optional int32 num_batch_queue_threads = 15 [default=8]; 61 | 62 | // Maximum capacity of the queue used to prefetch assembled batches. 63 | optional int32 prefetch_queue_capacity = 16 [default=10]; 64 | } 65 | -------------------------------------------------------------------------------- /research/object_detection/protos/train_pb2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/protos/train_pb2.pyc -------------------------------------------------------------------------------- /research/object_detection/samples/cloud/cloud.yml: -------------------------------------------------------------------------------- 1 | trainingInput: 2 | runtimeVersion: "1.0" 3 | scaleTier: CUSTOM 4 | masterType: standard_gpu 5 | workerCount: 5 6 | workerType: standard_gpu 7 | parameterServerCount: 3 8 | parameterServerType: standard 9 | 10 | 11 | 12 | -------------------------------------------------------------------------------- /research/object_detection/ssd_mobilenet_v1_coco_11_06_2017/frozen_inference_graph.pb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/ssd_mobilenet_v1_coco_11_06_2017/frozen_inference_graph.pb -------------------------------------------------------------------------------- /research/object_detection/test_images/image1.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/test_images/image1.jpg -------------------------------------------------------------------------------- /research/object_detection/test_images/image2.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/test_images/image2.jpg -------------------------------------------------------------------------------- /research/object_detection/test_images/image_info.txt: -------------------------------------------------------------------------------- 1 | 2 | Image provenance: 3 | image1.jpg: https://commons.wikimedia.org/wiki/File:Baegle_dwa.jpg 4 | image2.jpg: Michael Miley, 5 | https://www.flickr.com/photos/mike_miley/4678754542/in/photolist-88rQHL-88oBVp-88oC2B-88rS6J-88rSqm-88oBLv-88oBC4 6 | 7 | -------------------------------------------------------------------------------- /research/object_detection/utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/utils/__init__.py -------------------------------------------------------------------------------- /research/object_detection/utils/__init__.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/utils/__init__.pyc -------------------------------------------------------------------------------- /research/object_detection/utils/category_util.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Functions for importing/exporting Object Detection categories.""" 17 | import csv 18 | 19 | import tensorflow as tf 20 | 21 | 22 | def load_categories_from_csv_file(csv_path): 23 | """Loads categories from a csv file. 24 | 25 | The CSV file should have one comma delimited numeric category id and string 26 | category name pair per line. For example: 27 | 28 | 0,"cat" 29 | 1,"dog" 30 | 2,"bird" 31 | ... 32 | 33 | Args: 34 | csv_path: Path to the csv file to be parsed into categories. 35 | Returns: 36 | categories: A list of dictionaries representing all possible categories. 37 | The categories will contain an integer 'id' field and a string 38 | 'name' field. 39 | Raises: 40 | ValueError: If the csv file is incorrectly formatted. 41 | """ 42 | categories = [] 43 | 44 | with tf.gfile.Open(csv_path, 'r') as csvfile: 45 | reader = csv.reader(csvfile, delimiter=',', quotechar='"') 46 | for row in reader: 47 | if not row: 48 | continue 49 | 50 | if len(row) != 2: 51 | raise ValueError('Expected 2 fields per row in csv: %s' % ','.join(row)) 52 | 53 | category_id = int(row[0]) 54 | category_name = row[1] 55 | categories.append({'id': category_id, 'name': category_name}) 56 | 57 | return categories 58 | 59 | 60 | def save_categories_to_csv_file(categories, csv_path): 61 | """Saves categories to a csv file. 62 | 63 | Args: 64 | categories: A list of dictionaries representing categories to save to file. 65 | Each category must contain an 'id' and 'name' field. 66 | csv_path: Path to the csv file to be parsed into categories. 67 | """ 68 | categories.sort(key=lambda x: x['id']) 69 | with tf.gfile.Open(csv_path, 'w') as csvfile: 70 | writer = csv.writer(csvfile, delimiter=',', quotechar='"') 71 | for category in categories: 72 | writer.writerow([category['id'], category['name']]) 73 | -------------------------------------------------------------------------------- /research/object_detection/utils/category_util_test.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Tests for object_detection.utils.category_util.""" 17 | import os 18 | 19 | import tensorflow as tf 20 | 21 | from object_detection.utils import category_util 22 | 23 | 24 | class EvalUtilTest(tf.test.TestCase): 25 | 26 | def test_load_categories_from_csv_file(self): 27 | csv_data = """ 28 | 0,"cat" 29 | 1,"dog" 30 | 2,"bird" 31 | """.strip(' ') 32 | csv_path = os.path.join(self.get_temp_dir(), 'test.csv') 33 | with tf.gfile.Open(csv_path, 'wb') as f: 34 | f.write(csv_data) 35 | 36 | categories = category_util.load_categories_from_csv_file(csv_path) 37 | self.assertTrue({'id': 0, 'name': 'cat'} in categories) 38 | self.assertTrue({'id': 1, 'name': 'dog'} in categories) 39 | self.assertTrue({'id': 2, 'name': 'bird'} in categories) 40 | 41 | def test_save_categories_to_csv_file(self): 42 | categories = [ 43 | {'id': 0, 'name': 'cat'}, 44 | {'id': 1, 'name': 'dog'}, 45 | {'id': 2, 'name': 'bird'}, 46 | ] 47 | csv_path = os.path.join(self.get_temp_dir(), 'test.csv') 48 | category_util.save_categories_to_csv_file(categories, csv_path) 49 | saved_categories = category_util.load_categories_from_csv_file(csv_path) 50 | self.assertEqual(saved_categories, categories) 51 | 52 | 53 | if __name__ == '__main__': 54 | tf.test.main() 55 | -------------------------------------------------------------------------------- /research/object_detection/utils/dataset_util.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Utility functions for creating TFRecord data sets.""" 17 | 18 | import tensorflow as tf 19 | 20 | 21 | def int64_feature(value): 22 | return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) 23 | 24 | 25 | def int64_list_feature(value): 26 | return tf.train.Feature(int64_list=tf.train.Int64List(value=value)) 27 | 28 | 29 | def bytes_feature(value): 30 | return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) 31 | 32 | 33 | def bytes_list_feature(value): 34 | return tf.train.Feature(bytes_list=tf.train.BytesList(value=value)) 35 | 36 | 37 | def float_list_feature(value): 38 | return tf.train.Feature(float_list=tf.train.FloatList(value=value)) 39 | 40 | 41 | def read_examples_list(path): 42 | """Read list of training or validation examples. 43 | 44 | The file is assumed to contain a single example per line where the first 45 | token in the line is an identifier that allows us to find the image and 46 | annotation xml for that example. 47 | 48 | For example, the line: 49 | xyz 3 50 | would allow us to find files xyz.jpg and xyz.xml (the 3 would be ignored). 51 | 52 | Args: 53 | path: absolute path to examples list file. 54 | 55 | Returns: 56 | list of example identifiers (strings). 57 | """ 58 | with tf.gfile.GFile(path) as fid: 59 | lines = fid.readlines() 60 | return [line.strip().split(' ')[0] for line in lines] 61 | 62 | 63 | def recursive_parse_xml_to_dict(xml): 64 | """Recursively parses XML contents to python dict. 65 | 66 | We assume that `object` tags are the only ones that can appear 67 | multiple times at the same level of a tree. 68 | 69 | Args: 70 | xml: xml tree obtained by parsing XML file contents using lxml.etree 71 | 72 | Returns: 73 | Python dictionary holding XML contents. 74 | """ 75 | if not xml: 76 | return {xml.tag: xml.text} 77 | result = {} 78 | for child in xml: 79 | child_result = recursive_parse_xml_to_dict(child) 80 | if child.tag != 'object': 81 | result[child.tag] = child_result[child.tag] 82 | else: 83 | if child.tag not in result: 84 | result[child.tag] = [] 85 | result[child.tag].append(child_result[child.tag]) 86 | return {xml.tag: result} 87 | -------------------------------------------------------------------------------- /research/object_detection/utils/dataset_util_test.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Tests for object_detection.utils.dataset_util.""" 17 | 18 | import os 19 | import tensorflow as tf 20 | 21 | from object_detection.utils import dataset_util 22 | 23 | 24 | class DatasetUtilTest(tf.test.TestCase): 25 | 26 | def test_read_examples_list(self): 27 | example_list_data = """example1 1\nexample2 2""" 28 | example_list_path = os.path.join(self.get_temp_dir(), 'examples.txt') 29 | with tf.gfile.Open(example_list_path, 'wb') as f: 30 | f.write(example_list_data) 31 | 32 | examples = dataset_util.read_examples_list(example_list_path) 33 | self.assertListEqual(['example1', 'example2'], examples) 34 | 35 | 36 | if __name__ == '__main__': 37 | tf.test.main() 38 | -------------------------------------------------------------------------------- /research/object_detection/utils/label_map_util.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/utils/label_map_util.pyc -------------------------------------------------------------------------------- /research/object_detection/utils/learning_schedules_test.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Tests for object_detection.utils.learning_schedules.""" 17 | import tensorflow as tf 18 | 19 | from object_detection.utils import learning_schedules 20 | 21 | 22 | class LearningSchedulesTest(tf.test.TestCase): 23 | 24 | def testExponentialDecayWithBurnin(self): 25 | global_step = tf.placeholder(tf.int32, []) 26 | learning_rate_base = 1.0 27 | learning_rate_decay_steps = 3 28 | learning_rate_decay_factor = .1 29 | burnin_learning_rate = .5 30 | burnin_steps = 2 31 | exp_rates = [.5, .5, 1, .1, .1, .1, .01, .01] 32 | learning_rate = learning_schedules.exponential_decay_with_burnin( 33 | global_step, learning_rate_base, learning_rate_decay_steps, 34 | learning_rate_decay_factor, burnin_learning_rate, burnin_steps) 35 | with self.test_session() as sess: 36 | output_rates = [] 37 | for input_global_step in range(8): 38 | output_rate = sess.run(learning_rate, 39 | feed_dict={global_step: input_global_step}) 40 | output_rates.append(output_rate) 41 | self.assertAllClose(output_rates, exp_rates) 42 | 43 | def testManualStepping(self): 44 | global_step = tf.placeholder(tf.int64, []) 45 | boundaries = [2, 3, 7] 46 | rates = [1.0, 2.0, 3.0, 4.0] 47 | exp_rates = [1.0, 1.0, 2.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0] 48 | learning_rate = learning_schedules.manual_stepping(global_step, boundaries, 49 | rates) 50 | with self.test_session() as sess: 51 | output_rates = [] 52 | for input_global_step in range(10): 53 | output_rate = sess.run(learning_rate, 54 | feed_dict={global_step: input_global_step}) 55 | output_rates.append(output_rate) 56 | self.assertAllClose(output_rates, exp_rates) 57 | 58 | if __name__ == '__main__': 59 | tf.test.main() 60 | -------------------------------------------------------------------------------- /research/object_detection/utils/metrics.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/utils/metrics.pyc -------------------------------------------------------------------------------- /research/object_detection/utils/np_box_list.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/utils/np_box_list.pyc -------------------------------------------------------------------------------- /research/object_detection/utils/np_box_list_ops.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/utils/np_box_list_ops.pyc -------------------------------------------------------------------------------- /research/object_detection/utils/np_box_ops.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/utils/np_box_ops.pyc -------------------------------------------------------------------------------- /research/object_detection/utils/np_box_ops_test.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Tests for object_detection.np_box_ops.""" 17 | 18 | import numpy as np 19 | import tensorflow as tf 20 | 21 | from object_detection.utils import np_box_ops 22 | 23 | 24 | class BoxOpsTests(tf.test.TestCase): 25 | 26 | def setUp(self): 27 | boxes1 = np.array([[4.0, 3.0, 7.0, 5.0], [5.0, 6.0, 10.0, 7.0]], 28 | dtype=float) 29 | boxes2 = np.array([[3.0, 4.0, 6.0, 8.0], [14.0, 14.0, 15.0, 15.0], 30 | [0.0, 0.0, 20.0, 20.0]], 31 | dtype=float) 32 | self.boxes1 = boxes1 33 | self.boxes2 = boxes2 34 | 35 | def testArea(self): 36 | areas = np_box_ops.area(self.boxes1) 37 | expected_areas = np.array([6.0, 5.0], dtype=float) 38 | self.assertAllClose(expected_areas, areas) 39 | 40 | def testIntersection(self): 41 | intersection = np_box_ops.intersection(self.boxes1, self.boxes2) 42 | expected_intersection = np.array([[2.0, 0.0, 6.0], [1.0, 0.0, 5.0]], 43 | dtype=float) 44 | self.assertAllClose(intersection, expected_intersection) 45 | 46 | def testIOU(self): 47 | iou = np_box_ops.iou(self.boxes1, self.boxes2) 48 | expected_iou = np.array([[2.0 / 16.0, 0.0, 6.0 / 400.0], 49 | [1.0 / 16.0, 0.0, 5.0 / 400.0]], 50 | dtype=float) 51 | self.assertAllClose(iou, expected_iou) 52 | 53 | def testIOA(self): 54 | boxes1 = np.array([[0.25, 0.25, 0.75, 0.75], 55 | [0.0, 0.0, 0.5, 0.75]], 56 | dtype=np.float32) 57 | boxes2 = np.array([[0.5, 0.25, 1.0, 1.0], 58 | [0.0, 0.0, 1.0, 1.0]], 59 | dtype=np.float32) 60 | ioa21 = np_box_ops.ioa(boxes2, boxes1) 61 | expected_ioa21 = np.array([[0.5, 0.0], 62 | [1.0, 1.0]], 63 | dtype=np.float32) 64 | self.assertAllClose(ioa21, expected_ioa21) 65 | 66 | 67 | if __name__ == '__main__': 68 | tf.test.main() 69 | -------------------------------------------------------------------------------- /research/object_detection/utils/object_detection_evaluation.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/utils/object_detection_evaluation.pyc -------------------------------------------------------------------------------- /research/object_detection/utils/ops.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/utils/ops.pyc -------------------------------------------------------------------------------- /research/object_detection/utils/per_image_evaluation.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/utils/per_image_evaluation.pyc -------------------------------------------------------------------------------- /research/object_detection/utils/shape_utils.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/utils/shape_utils.pyc -------------------------------------------------------------------------------- /research/object_detection/utils/static_shape.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Helper functions to access TensorShape values. 17 | 18 | The rank 4 tensor_shape must be of the form [batch_size, height, width, depth]. 19 | """ 20 | 21 | 22 | def get_batch_size(tensor_shape): 23 | """Returns batch size from the tensor shape. 24 | 25 | Args: 26 | tensor_shape: A rank 4 TensorShape. 27 | 28 | Returns: 29 | An integer representing the batch size of the tensor. 30 | """ 31 | tensor_shape.assert_has_rank(rank=4) 32 | return tensor_shape[0].value 33 | 34 | 35 | def get_height(tensor_shape): 36 | """Returns height from the tensor shape. 37 | 38 | Args: 39 | tensor_shape: A rank 4 TensorShape. 40 | 41 | Returns: 42 | An integer representing the height of the tensor. 43 | """ 44 | tensor_shape.assert_has_rank(rank=4) 45 | return tensor_shape[1].value 46 | 47 | 48 | def get_width(tensor_shape): 49 | """Returns width from the tensor shape. 50 | 51 | Args: 52 | tensor_shape: A rank 4 TensorShape. 53 | 54 | Returns: 55 | An integer representing the width of the tensor. 56 | """ 57 | tensor_shape.assert_has_rank(rank=4) 58 | return tensor_shape[2].value 59 | 60 | 61 | def get_depth(tensor_shape): 62 | """Returns depth from the tensor shape. 63 | 64 | Args: 65 | tensor_shape: A rank 4 TensorShape. 66 | 67 | Returns: 68 | An integer representing the depth of the tensor. 69 | """ 70 | tensor_shape.assert_has_rank(rank=4) 71 | return tensor_shape[3].value 72 | -------------------------------------------------------------------------------- /research/object_detection/utils/static_shape.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/utils/static_shape.pyc -------------------------------------------------------------------------------- /research/object_detection/utils/static_shape_test.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Tests for object_detection.utils.static_shape.""" 17 | 18 | import tensorflow as tf 19 | 20 | from object_detection.utils import static_shape 21 | 22 | 23 | class StaticShapeTest(tf.test.TestCase): 24 | 25 | def test_return_correct_batchSize(self): 26 | tensor_shape = tf.TensorShape(dims=[32, 299, 384, 3]) 27 | self.assertEqual(32, static_shape.get_batch_size(tensor_shape)) 28 | 29 | def test_return_correct_height(self): 30 | tensor_shape = tf.TensorShape(dims=[32, 299, 384, 3]) 31 | self.assertEqual(299, static_shape.get_height(tensor_shape)) 32 | 33 | def test_return_correct_width(self): 34 | tensor_shape = tf.TensorShape(dims=[32, 299, 384, 3]) 35 | self.assertEqual(384, static_shape.get_width(tensor_shape)) 36 | 37 | def test_return_correct_depth(self): 38 | tensor_shape = tf.TensorShape(dims=[32, 299, 384, 3]) 39 | self.assertEqual(3, static_shape.get_depth(tensor_shape)) 40 | 41 | def test_die_on_tensor_shape_with_rank_three(self): 42 | tensor_shape = tf.TensorShape(dims=[32, 299, 384]) 43 | with self.assertRaises(ValueError): 44 | static_shape.get_batch_size(tensor_shape) 45 | static_shape.get_height(tensor_shape) 46 | static_shape.get_width(tensor_shape) 47 | static_shape.get_depth(tensor_shape) 48 | 49 | if __name__ == '__main__': 50 | tf.test.main() 51 | -------------------------------------------------------------------------------- /research/object_detection/utils/test_utils_test.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Tests for object_detection.utils.test_utils.""" 17 | 18 | import numpy as np 19 | import tensorflow as tf 20 | 21 | from object_detection.utils import test_utils 22 | 23 | 24 | class TestUtilsTest(tf.test.TestCase): 25 | 26 | def test_diagonal_gradient_image(self): 27 | """Tests if a good pyramid image is created.""" 28 | pyramid_image = test_utils.create_diagonal_gradient_image(3, 4, 2) 29 | 30 | # Test which is easy to understand. 31 | expected_first_channel = np.array([[3, 2, 1, 0], 32 | [4, 3, 2, 1], 33 | [5, 4, 3, 2]], dtype=np.float32) 34 | self.assertAllEqual(np.squeeze(pyramid_image[:, :, 0]), 35 | expected_first_channel) 36 | 37 | # Actual test. 38 | expected_image = np.array([[[3, 30], 39 | [2, 20], 40 | [1, 10], 41 | [0, 0]], 42 | [[4, 40], 43 | [3, 30], 44 | [2, 20], 45 | [1, 10]], 46 | [[5, 50], 47 | [4, 40], 48 | [3, 30], 49 | [2, 20]]], dtype=np.float32) 50 | 51 | self.assertAllEqual(pyramid_image, expected_image) 52 | 53 | def test_random_boxes(self): 54 | """Tests if valid random boxes are created.""" 55 | num_boxes = 1000 56 | max_height = 3 57 | max_width = 5 58 | boxes = test_utils.create_random_boxes(num_boxes, 59 | max_height, 60 | max_width) 61 | 62 | true_column = np.ones(shape=(num_boxes)) == 1 63 | self.assertAllEqual(boxes[:, 0] < boxes[:, 2], true_column) 64 | self.assertAllEqual(boxes[:, 1] < boxes[:, 3], true_column) 65 | 66 | self.assertTrue(boxes[:, 0].min() >= 0) 67 | self.assertTrue(boxes[:, 1].min() >= 0) 68 | self.assertTrue(boxes[:, 2].max() <= max_height) 69 | self.assertTrue(boxes[:, 3].max() <= max_width) 70 | 71 | 72 | if __name__ == '__main__': 73 | tf.test.main() 74 | -------------------------------------------------------------------------------- /research/object_detection/utils/visualization_utils.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/object_detection/utils/visualization_utils.pyc -------------------------------------------------------------------------------- /research/slim/WORKSPACE: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/slim/WORKSPACE -------------------------------------------------------------------------------- /research/slim/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/slim/__init__.py -------------------------------------------------------------------------------- /research/slim/datasets/__init__.py: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /research/slim/datasets/dataset_factory.py: -------------------------------------------------------------------------------- 1 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | """A factory-pattern class which returns classification image/label pairs.""" 16 | 17 | from __future__ import absolute_import 18 | from __future__ import division 19 | from __future__ import print_function 20 | 21 | from datasets import cifar10 22 | from datasets import flowers 23 | from datasets import imagenet 24 | from datasets import mnist 25 | 26 | datasets_map = { 27 | 'cifar10': cifar10, 28 | 'flowers': flowers, 29 | 'imagenet': imagenet, 30 | 'mnist': mnist, 31 | } 32 | 33 | 34 | def get_dataset(name, split_name, dataset_dir, file_pattern=None, reader=None): 35 | """Given a dataset name and a split_name returns a Dataset. 36 | 37 | Args: 38 | name: String, the name of the dataset. 39 | split_name: A train/test split name. 40 | dataset_dir: The directory where the dataset files are stored. 41 | file_pattern: The file pattern to use for matching the dataset source files. 42 | reader: The subclass of tf.ReaderBase. If left as `None`, then the default 43 | reader defined by each dataset is used. 44 | 45 | Returns: 46 | A `Dataset` class. 47 | 48 | Raises: 49 | ValueError: If the dataset `name` is unknown. 50 | """ 51 | if name not in datasets_map: 52 | raise ValueError('Name of dataset unknown %s' % name) 53 | return datasets_map[name].get_split( 54 | split_name, 55 | dataset_dir, 56 | file_pattern, 57 | reader) 58 | -------------------------------------------------------------------------------- /research/slim/deployment/__init__.py: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /research/slim/dist/slim-0.1-py2.7.egg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/slim/dist/slim-0.1-py2.7.egg -------------------------------------------------------------------------------- /research/slim/download_and_convert_data.py: -------------------------------------------------------------------------------- 1 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | r"""Downloads and converts a particular dataset. 16 | 17 | Usage: 18 | ```shell 19 | 20 | $ python download_and_convert_data.py \ 21 | --dataset_name=mnist \ 22 | --dataset_dir=/tmp/mnist 23 | 24 | $ python download_and_convert_data.py \ 25 | --dataset_name=cifar10 \ 26 | --dataset_dir=/tmp/cifar10 27 | 28 | $ python download_and_convert_data.py \ 29 | --dataset_name=flowers \ 30 | --dataset_dir=/tmp/flowers 31 | ``` 32 | """ 33 | from __future__ import absolute_import 34 | from __future__ import division 35 | from __future__ import print_function 36 | 37 | import tensorflow as tf 38 | 39 | from datasets import download_and_convert_cifar10 40 | from datasets import download_and_convert_flowers 41 | from datasets import download_and_convert_mnist 42 | 43 | FLAGS = tf.app.flags.FLAGS 44 | 45 | tf.app.flags.DEFINE_string( 46 | 'dataset_name', 47 | None, 48 | 'The name of the dataset to convert, one of "cifar10", "flowers", "mnist".') 49 | 50 | tf.app.flags.DEFINE_string( 51 | 'dataset_dir', 52 | None, 53 | 'The directory where the output TFRecords and temporary files are saved.') 54 | 55 | 56 | def main(_): 57 | if not FLAGS.dataset_name: 58 | raise ValueError('You must supply the dataset name with --dataset_name') 59 | if not FLAGS.dataset_dir: 60 | raise ValueError('You must supply the dataset directory with --dataset_dir') 61 | 62 | if FLAGS.dataset_name == 'cifar10': 63 | download_and_convert_cifar10.run(FLAGS.dataset_dir) 64 | elif FLAGS.dataset_name == 'flowers': 65 | download_and_convert_flowers.run(FLAGS.dataset_dir) 66 | elif FLAGS.dataset_name == 'mnist': 67 | download_and_convert_mnist.run(FLAGS.dataset_dir) 68 | else: 69 | raise ValueError( 70 | 'dataset_name [%s] was not recognized.' % FLAGS.dataset_name) 71 | 72 | if __name__ == '__main__': 73 | tf.app.run() 74 | -------------------------------------------------------------------------------- /research/slim/export_inference_graph_test.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Tests for export_inference_graph.""" 17 | 18 | from __future__ import absolute_import 19 | from __future__ import division 20 | from __future__ import print_function 21 | 22 | import os 23 | 24 | 25 | import tensorflow as tf 26 | 27 | from tensorflow.python.platform import gfile 28 | import export_inference_graph 29 | 30 | 31 | class ExportInferenceGraphTest(tf.test.TestCase): 32 | 33 | def testExportInferenceGraph(self): 34 | tmpdir = self.get_temp_dir() 35 | output_file = os.path.join(tmpdir, 'inception_v3.pb') 36 | flags = tf.app.flags.FLAGS 37 | flags.output_file = output_file 38 | flags.model_name = 'inception_v3' 39 | flags.dataset_dir = tmpdir 40 | export_inference_graph.main(None) 41 | self.assertTrue(gfile.Exists(output_file)) 42 | 43 | if __name__ == '__main__': 44 | tf.test.main() 45 | -------------------------------------------------------------------------------- /research/slim/nets/__init__.py: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /research/slim/nets/inception.py: -------------------------------------------------------------------------------- 1 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | """Brings all inception models under one namespace.""" 16 | 17 | from __future__ import absolute_import 18 | from __future__ import division 19 | from __future__ import print_function 20 | 21 | # pylint: disable=unused-import 22 | from nets.inception_resnet_v2 import inception_resnet_v2 23 | from nets.inception_resnet_v2 import inception_resnet_v2_arg_scope 24 | from nets.inception_resnet_v2 import inception_resnet_v2_base 25 | from nets.inception_v1 import inception_v1 26 | from nets.inception_v1 import inception_v1_arg_scope 27 | from nets.inception_v1 import inception_v1_base 28 | from nets.inception_v2 import inception_v2 29 | from nets.inception_v2 import inception_v2_arg_scope 30 | from nets.inception_v2 import inception_v2_base 31 | from nets.inception_v3 import inception_v3 32 | from nets.inception_v3 import inception_v3_arg_scope 33 | from nets.inception_v3 import inception_v3_base 34 | from nets.inception_v4 import inception_v4 35 | from nets.inception_v4 import inception_v4_arg_scope 36 | from nets.inception_v4 import inception_v4_base 37 | # pylint: enable=unused-import 38 | -------------------------------------------------------------------------------- /research/slim/nets/inception_utils.py: -------------------------------------------------------------------------------- 1 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | """Contains common code shared by all inception models. 16 | 17 | Usage of arg scope: 18 | with slim.arg_scope(inception_arg_scope()): 19 | logits, end_points = inception.inception_v3(images, num_classes, 20 | is_training=is_training) 21 | 22 | """ 23 | from __future__ import absolute_import 24 | from __future__ import division 25 | from __future__ import print_function 26 | 27 | import tensorflow as tf 28 | 29 | slim = tf.contrib.slim 30 | 31 | 32 | def inception_arg_scope(weight_decay=0.00004, 33 | use_batch_norm=True, 34 | batch_norm_decay=0.9997, 35 | batch_norm_epsilon=0.001): 36 | """Defines the default arg scope for inception models. 37 | 38 | Args: 39 | weight_decay: The weight decay to use for regularizing the model. 40 | use_batch_norm: "If `True`, batch_norm is applied after each convolution. 41 | batch_norm_decay: Decay for batch norm moving average. 42 | batch_norm_epsilon: Small float added to variance to avoid dividing by zero 43 | in batch norm. 44 | 45 | Returns: 46 | An `arg_scope` to use for the inception models. 47 | """ 48 | batch_norm_params = { 49 | # Decay for the moving averages. 50 | 'decay': batch_norm_decay, 51 | # epsilon to prevent 0s in variance. 52 | 'epsilon': batch_norm_epsilon, 53 | # collection containing update_ops. 54 | 'updates_collections': tf.GraphKeys.UPDATE_OPS, 55 | } 56 | if use_batch_norm: 57 | normalizer_fn = slim.batch_norm 58 | normalizer_params = batch_norm_params 59 | else: 60 | normalizer_fn = None 61 | normalizer_params = {} 62 | # Set weight_decay for weights in Conv and FC layers. 63 | with slim.arg_scope([slim.conv2d, slim.fully_connected], 64 | weights_regularizer=slim.l2_regularizer(weight_decay)): 65 | with slim.arg_scope( 66 | [slim.conv2d], 67 | weights_initializer=slim.variance_scaling_initializer(), 68 | activation_fn=tf.nn.relu, 69 | normalizer_fn=normalizer_fn, 70 | normalizer_params=normalizer_params) as sc: 71 | return sc 72 | -------------------------------------------------------------------------------- /research/slim/nets/mobilenet_v1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mrycguo/CommodityRecognitionSystem/58c8c9261e0052319889982c1a1dd2dcd01cbf27/research/slim/nets/mobilenet_v1.png -------------------------------------------------------------------------------- /research/slim/nets/nets_factory_test.py: -------------------------------------------------------------------------------- 1 | # Copyright 2016 Google Inc. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Tests for slim.inception.""" 17 | 18 | from __future__ import absolute_import 19 | from __future__ import division 20 | from __future__ import print_function 21 | 22 | 23 | import tensorflow as tf 24 | 25 | from nets import nets_factory 26 | 27 | 28 | class NetworksTest(tf.test.TestCase): 29 | 30 | def testGetNetworkFnFirstHalf(self): 31 | batch_size = 5 32 | num_classes = 1000 33 | for net in nets_factory.networks_map.keys()[:10]: 34 | with tf.Graph().as_default() as g, self.test_session(g): 35 | net_fn = nets_factory.get_network_fn(net, num_classes) 36 | # Most networks use 224 as their default_image_size 37 | image_size = getattr(net_fn, 'default_image_size', 224) 38 | inputs = tf.random_uniform((batch_size, image_size, image_size, 3)) 39 | logits, end_points = net_fn(inputs) 40 | self.assertTrue(isinstance(logits, tf.Tensor)) 41 | self.assertTrue(isinstance(end_points, dict)) 42 | self.assertEqual(logits.get_shape().as_list()[0], batch_size) 43 | self.assertEqual(logits.get_shape().as_list()[-1], num_classes) 44 | 45 | def testGetNetworkFnSecondHalf(self): 46 | batch_size = 5 47 | num_classes = 1000 48 | for net in nets_factory.networks_map.keys()[10:]: 49 | with tf.Graph().as_default() as g, self.test_session(g): 50 | net_fn = nets_factory.get_network_fn(net, num_classes) 51 | # Most networks use 224 as their default_image_size 52 | image_size = getattr(net_fn, 'default_image_size', 224) 53 | inputs = tf.random_uniform((batch_size, image_size, image_size, 3)) 54 | logits, end_points = net_fn(inputs) 55 | self.assertTrue(isinstance(logits, tf.Tensor)) 56 | self.assertTrue(isinstance(end_points, dict)) 57 | self.assertEqual(logits.get_shape().as_list()[0], batch_size) 58 | self.assertEqual(logits.get_shape().as_list()[-1], num_classes) 59 | 60 | if __name__ == '__main__': 61 | tf.test.main() 62 | -------------------------------------------------------------------------------- /research/slim/preprocessing/__init__.py: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /research/slim/preprocessing/lenet_preprocessing.py: -------------------------------------------------------------------------------- 1 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | """Provides utilities for preprocessing.""" 16 | 17 | from __future__ import absolute_import 18 | from __future__ import division 19 | from __future__ import print_function 20 | 21 | import tensorflow as tf 22 | 23 | slim = tf.contrib.slim 24 | 25 | 26 | def preprocess_image(image, output_height, output_width, is_training): 27 | """Preprocesses the given image. 28 | 29 | Args: 30 | image: A `Tensor` representing an image of arbitrary size. 31 | output_height: The height of the image after preprocessing. 32 | output_width: The width of the image after preprocessing. 33 | is_training: `True` if we're preprocessing the image for training and 34 | `False` otherwise. 35 | 36 | Returns: 37 | A preprocessed image. 38 | """ 39 | image = tf.to_float(image) 40 | image = tf.image.resize_image_with_crop_or_pad( 41 | image, output_width, output_height) 42 | image = tf.subtract(image, 128.0) 43 | image = tf.div(image, 128.0) 44 | return image 45 | -------------------------------------------------------------------------------- /research/slim/preprocessing/preprocessing_factory.py: -------------------------------------------------------------------------------- 1 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | """Contains a factory for building various models.""" 16 | 17 | from __future__ import absolute_import 18 | from __future__ import division 19 | from __future__ import print_function 20 | 21 | import tensorflow as tf 22 | 23 | from preprocessing import cifarnet_preprocessing 24 | from preprocessing import inception_preprocessing 25 | from preprocessing import lenet_preprocessing 26 | from preprocessing import vgg_preprocessing 27 | 28 | slim = tf.contrib.slim 29 | 30 | 31 | def get_preprocessing(name, is_training=False): 32 | """Returns preprocessing_fn(image, height, width, **kwargs). 33 | 34 | Args: 35 | name: The name of the preprocessing function. 36 | is_training: `True` if the model is being used for training and `False` 37 | otherwise. 38 | 39 | Returns: 40 | preprocessing_fn: A function that preprocessing a single image (pre-batch). 41 | It has the following signature: 42 | image = preprocessing_fn(image, output_height, output_width, ...). 43 | 44 | Raises: 45 | ValueError: If Preprocessing `name` is not recognized. 46 | """ 47 | preprocessing_fn_map = { 48 | 'cifarnet': cifarnet_preprocessing, 49 | 'inception': inception_preprocessing, 50 | 'inception_v1': inception_preprocessing, 51 | 'inception_v2': inception_preprocessing, 52 | 'inception_v3': inception_preprocessing, 53 | 'inception_v4': inception_preprocessing, 54 | 'inception_resnet_v2': inception_preprocessing, 55 | 'lenet': lenet_preprocessing, 56 | 'mobilenet_v1': inception_preprocessing, 57 | 'resnet_v1_50': vgg_preprocessing, 58 | 'resnet_v1_101': vgg_preprocessing, 59 | 'resnet_v1_152': vgg_preprocessing, 60 | 'resnet_v1_200': vgg_preprocessing, 61 | 'resnet_v2_50': vgg_preprocessing, 62 | 'resnet_v2_101': vgg_preprocessing, 63 | 'resnet_v2_152': vgg_preprocessing, 64 | 'resnet_v2_200': vgg_preprocessing, 65 | 'vgg': vgg_preprocessing, 66 | 'vgg_a': vgg_preprocessing, 67 | 'vgg_16': vgg_preprocessing, 68 | 'vgg_19': vgg_preprocessing, 69 | } 70 | 71 | if name not in preprocessing_fn_map: 72 | raise ValueError('Preprocessing name [%s] was not recognized' % name) 73 | 74 | def preprocessing_fn(image, output_height, output_width, **kwargs): 75 | return preprocessing_fn_map[name].preprocess_image( 76 | image, output_height, output_width, is_training=is_training, **kwargs) 77 | 78 | return preprocessing_fn 79 | -------------------------------------------------------------------------------- /research/slim/scripts/finetune_inception_v1_on_flowers.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # 3 | # This script performs the following operations: 4 | # 1. Downloads the Flowers dataset 5 | # 2. Fine-tunes an InceptionV1 model on the Flowers training set. 6 | # 3. Evaluates the model on the Flowers validation set. 7 | # 8 | # Usage: 9 | # cd slim 10 | # ./slim/scripts/finetune_inception_v1_on_flowers.sh 11 | set -e 12 | 13 | # Where the pre-trained InceptionV1 checkpoint is saved to. 14 | PRETRAINED_CHECKPOINT_DIR=/tmp/checkpoints 15 | 16 | # Where the training (fine-tuned) checkpoint and logs will be saved to. 17 | TRAIN_DIR=/tmp/flowers-models/inception_v1 18 | 19 | # Where the dataset is saved to. 20 | DATASET_DIR=/tmp/flowers 21 | 22 | # Download the pre-trained checkpoint. 23 | if [ ! -d "$PRETRAINED_CHECKPOINT_DIR" ]; then 24 | mkdir ${PRETRAINED_CHECKPOINT_DIR} 25 | fi 26 | if [ ! -f ${PRETRAINED_CHECKPOINT_DIR}/inception_v1.ckpt ]; then 27 | wget http://download.tensorflow.org/models/inception_v1_2016_08_28.tar.gz 28 | tar -xvf inception_v1_2016_08_28.tar.gz 29 | mv inception_v1.ckpt ${PRETRAINED_CHECKPOINT_DIR}/inception_v1.ckpt 30 | rm inception_v1_2016_08_28.tar.gz 31 | fi 32 | 33 | # Download the dataset 34 | python download_and_convert_data.py \ 35 | --dataset_name=flowers \ 36 | --dataset_dir=${DATASET_DIR} 37 | 38 | # Fine-tune only the new layers for 2000 steps. 39 | python train_image_classifier.py \ 40 | --train_dir=${TRAIN_DIR} \ 41 | --dataset_name=flowers \ 42 | --dataset_split_name=train \ 43 | --dataset_dir=${DATASET_DIR} \ 44 | --model_name=inception_v1 \ 45 | --checkpoint_path=${PRETRAINED_CHECKPOINT_DIR}/inception_v1.ckpt \ 46 | --checkpoint_exclude_scopes=InceptionV1/Logits \ 47 | --trainable_scopes=InceptionV1/Logits \ 48 | --max_number_of_steps=3000 \ 49 | --batch_size=32 \ 50 | --learning_rate=0.01 \ 51 | --save_interval_secs=60 \ 52 | --save_summaries_secs=60 \ 53 | --log_every_n_steps=100 \ 54 | --optimizer=rmsprop \ 55 | --weight_decay=0.00004 56 | 57 | # Run evaluation. 58 | python eval_image_classifier.py \ 59 | --checkpoint_path=${TRAIN_DIR} \ 60 | --eval_dir=${TRAIN_DIR} \ 61 | --dataset_name=flowers \ 62 | --dataset_split_name=validation \ 63 | --dataset_dir=${DATASET_DIR} \ 64 | --model_name=inception_v1 65 | 66 | # Fine-tune all the new layers for 1000 steps. 67 | python train_image_classifier.py \ 68 | --train_dir=${TRAIN_DIR}/all \ 69 | --dataset_name=flowers \ 70 | --dataset_split_name=train \ 71 | --dataset_dir=${DATASET_DIR} \ 72 | --checkpoint_path=${TRAIN_DIR} \ 73 | --model_name=inception_v1 \ 74 | --max_number_of_steps=1000 \ 75 | --batch_size=32 \ 76 | --learning_rate=0.001 \ 77 | --save_interval_secs=60 \ 78 | --save_summaries_secs=60 \ 79 | --log_every_n_steps=100 \ 80 | --optimizer=rmsprop \ 81 | --weight_decay=0.00004 82 | 83 | # Run evaluation. 84 | python eval_image_classifier.py \ 85 | --checkpoint_path=${TRAIN_DIR}/all \ 86 | --eval_dir=${TRAIN_DIR}/all \ 87 | --dataset_name=flowers \ 88 | --dataset_split_name=validation \ 89 | --dataset_dir=${DATASET_DIR} \ 90 | --model_name=inception_v1 91 | -------------------------------------------------------------------------------- /research/slim/scripts/finetune_inception_v3_on_flowers.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # 3 | # This script performs the following operations: 4 | # 1. Downloads the Flowers dataset 5 | # 2. Fine-tunes an InceptionV3 model on the Flowers training set. 6 | # 3. Evaluates the model on the Flowers validation set. 7 | # 8 | # Usage: 9 | # cd slim 10 | # ./slim/scripts/finetune_inception_v3_on_flowers.sh 11 | set -e 12 | 13 | # Where the pre-trained InceptionV3 checkpoint is saved to. 14 | PRETRAINED_CHECKPOINT_DIR=/tmp/checkpoints 15 | 16 | # Where the training (fine-tuned) checkpoint and logs will be saved to. 17 | TRAIN_DIR=/tmp/flowers-models/inception_v3 18 | 19 | # Where the dataset is saved to. 20 | DATASET_DIR=/tmp/flowers 21 | 22 | # Download the pre-trained checkpoint. 23 | if [ ! -d "$PRETRAINED_CHECKPOINT_DIR" ]; then 24 | mkdir ${PRETRAINED_CHECKPOINT_DIR} 25 | fi 26 | if [ ! -f ${PRETRAINED_CHECKPOINT_DIR}/inception_v3.ckpt ]; then 27 | wget http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz 28 | tar -xvf inception_v3_2016_08_28.tar.gz 29 | mv inception_v3.ckpt ${PRETRAINED_CHECKPOINT_DIR}/inception_v3.ckpt 30 | rm inception_v3_2016_08_28.tar.gz 31 | fi 32 | 33 | # Download the dataset 34 | python download_and_convert_data.py \ 35 | --dataset_name=flowers \ 36 | --dataset_dir=${DATASET_DIR} 37 | 38 | # Fine-tune only the new layers for 1000 steps. 39 | python train_image_classifier.py \ 40 | --train_dir=${TRAIN_DIR} \ 41 | --dataset_name=flowers \ 42 | --dataset_split_name=train \ 43 | --dataset_dir=${DATASET_DIR} \ 44 | --model_name=inception_v3 \ 45 | --checkpoint_path=${PRETRAINED_CHECKPOINT_DIR}/inception_v3.ckpt \ 46 | --checkpoint_exclude_scopes=InceptionV3/Logits,InceptionV3/AuxLogits \ 47 | --trainable_scopes=InceptionV3/Logits,InceptionV3/AuxLogits \ 48 | --max_number_of_steps=1000 \ 49 | --batch_size=32 \ 50 | --learning_rate=0.01 \ 51 | --learning_rate_decay_type=fixed \ 52 | --save_interval_secs=60 \ 53 | --save_summaries_secs=60 \ 54 | --log_every_n_steps=100 \ 55 | --optimizer=rmsprop \ 56 | --weight_decay=0.00004 57 | 58 | # Run evaluation. 59 | python eval_image_classifier.py \ 60 | --checkpoint_path=${TRAIN_DIR} \ 61 | --eval_dir=${TRAIN_DIR} \ 62 | --dataset_name=flowers \ 63 | --dataset_split_name=validation \ 64 | --dataset_dir=${DATASET_DIR} \ 65 | --model_name=inception_v3 66 | 67 | # Fine-tune all the new layers for 500 steps. 68 | python train_image_classifier.py \ 69 | --train_dir=${TRAIN_DIR}/all \ 70 | --dataset_name=flowers \ 71 | --dataset_split_name=train \ 72 | --dataset_dir=${DATASET_DIR} \ 73 | --model_name=inception_v3 \ 74 | --checkpoint_path=${TRAIN_DIR} \ 75 | --max_number_of_steps=500 \ 76 | --batch_size=32 \ 77 | --learning_rate=0.0001 \ 78 | --learning_rate_decay_type=fixed \ 79 | --save_interval_secs=60 \ 80 | --save_summaries_secs=60 \ 81 | --log_every_n_steps=10 \ 82 | --optimizer=rmsprop \ 83 | --weight_decay=0.00004 84 | 85 | # Run evaluation. 86 | python eval_image_classifier.py \ 87 | --checkpoint_path=${TRAIN_DIR}/all \ 88 | --eval_dir=${TRAIN_DIR}/all \ 89 | --dataset_name=flowers \ 90 | --dataset_split_name=validation \ 91 | --dataset_dir=${DATASET_DIR} \ 92 | --model_name=inception_v3 93 | -------------------------------------------------------------------------------- /research/slim/scripts/finetune_resnet_v1_50_on_flowers.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # 3 | # This script performs the following operations: 4 | # 1. Downloads the Flowers dataset 5 | # 2. Fine-tunes a ResNetV1-50 model on the Flowers training set. 6 | # 3. Evaluates the model on the Flowers validation set. 7 | # 8 | # Usage: 9 | # cd slim 10 | # ./slim/scripts/finetune_resnet_v1_50_on_flowers.sh 11 | set -e 12 | 13 | # Where the pre-trained ResNetV1-50 checkpoint is saved to. 14 | PRETRAINED_CHECKPOINT_DIR=/tmp/checkpoints 15 | 16 | # Where the training (fine-tuned) checkpoint and logs will be saved to. 17 | TRAIN_DIR=/tmp/flowers-models/resnet_v1_50 18 | 19 | # Where the dataset is saved to. 20 | DATASET_DIR=/tmp/flowers 21 | 22 | # Download the pre-trained checkpoint. 23 | if [ ! -d "$PRETRAINED_CHECKPOINT_DIR" ]; then 24 | mkdir ${PRETRAINED_CHECKPOINT_DIR} 25 | fi 26 | if [ ! -f ${PRETRAINED_CHECKPOINT_DIR}/resnet_v1_50.ckpt ]; then 27 | wget http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz 28 | tar -xvf resnet_v1_50_2016_08_28.tar.gz 29 | mv resnet_v1_50.ckpt ${PRETRAINED_CHECKPOINT_DIR}/resnet_v1_50.ckpt 30 | rm resnet_v1_50_2016_08_28.tar.gz 31 | fi 32 | 33 | # Download the dataset 34 | python download_and_convert_data.py \ 35 | --dataset_name=flowers \ 36 | --dataset_dir=${DATASET_DIR} 37 | 38 | # Fine-tune only the new layers for 3000 steps. 39 | python train_image_classifier.py \ 40 | --train_dir=${TRAIN_DIR} \ 41 | --dataset_name=flowers \ 42 | --dataset_split_name=train \ 43 | --dataset_dir=${DATASET_DIR} \ 44 | --model_name=resnet_v1_50 \ 45 | --checkpoint_path=${PRETRAINED_CHECKPOINT_DIR}/resnet_v1_50.ckpt \ 46 | --checkpoint_exclude_scopes=resnet_v1_50/logits \ 47 | --trainable_scopes=resnet_v1_50/logits \ 48 | --max_number_of_steps=3000 \ 49 | --batch_size=32 \ 50 | --learning_rate=0.01 \ 51 | --save_interval_secs=60 \ 52 | --save_summaries_secs=60 \ 53 | --log_every_n_steps=100 \ 54 | --optimizer=rmsprop \ 55 | --weight_decay=0.00004 56 | 57 | # Run evaluation. 58 | python eval_image_classifier.py \ 59 | --checkpoint_path=${TRAIN_DIR} \ 60 | --eval_dir=${TRAIN_DIR} \ 61 | --dataset_name=flowers \ 62 | --dataset_split_name=validation \ 63 | --dataset_dir=${DATASET_DIR} \ 64 | --model_name=resnet_v1_50 65 | 66 | # Fine-tune all the new layers for 1000 steps. 67 | python train_image_classifier.py \ 68 | --train_dir=${TRAIN_DIR}/all \ 69 | --dataset_name=flowers \ 70 | --dataset_split_name=train \ 71 | --dataset_dir=${DATASET_DIR} \ 72 | --checkpoint_path=${TRAIN_DIR} \ 73 | --model_name=resnet_v1_50 \ 74 | --max_number_of_steps=1000 \ 75 | --batch_size=32 \ 76 | --learning_rate=0.001 \ 77 | --save_interval_secs=60 \ 78 | --save_summaries_secs=60 \ 79 | --log_every_n_steps=100 \ 80 | --optimizer=rmsprop \ 81 | --weight_decay=0.00004 82 | 83 | # Run evaluation. 84 | python eval_image_classifier.py \ 85 | --checkpoint_path=${TRAIN_DIR}/all \ 86 | --eval_dir=${TRAIN_DIR}/all \ 87 | --dataset_name=flowers \ 88 | --dataset_split_name=validation \ 89 | --dataset_dir=${DATASET_DIR} \ 90 | --model_name=resnet_v1_50 91 | -------------------------------------------------------------------------------- /research/slim/scripts/train_cifarnet_on_cifar10.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # 3 | # This script performs the following operations: 4 | # 1. Downloads the Cifar10 dataset 5 | # 2. Trains a CifarNet model on the Cifar10 training set. 6 | # 3. Evaluates the model on the Cifar10 testing set. 7 | # 8 | # Usage: 9 | # cd slim 10 | # ./scripts/train_cifarnet_on_cifar10.sh 11 | set -e 12 | 13 | # Where the checkpoint and logs will be saved to. 14 | TRAIN_DIR=/tmp/cifarnet-model 15 | 16 | # Where the dataset is saved to. 17 | DATASET_DIR=/tmp/cifar10 18 | 19 | # Download the dataset 20 | python download_and_convert_data.py \ 21 | --dataset_name=cifar10 \ 22 | --dataset_dir=${DATASET_DIR} 23 | 24 | # Run training. 25 | python train_image_classifier.py \ 26 | --train_dir=${TRAIN_DIR} \ 27 | --dataset_name=cifar10 \ 28 | --dataset_split_name=train \ 29 | --dataset_dir=${DATASET_DIR} \ 30 | --model_name=cifarnet \ 31 | --preprocessing_name=cifarnet \ 32 | --max_number_of_steps=100000 \ 33 | --batch_size=128 \ 34 | --save_interval_secs=120 \ 35 | --save_summaries_secs=120 \ 36 | --log_every_n_steps=100 \ 37 | --optimizer=sgd \ 38 | --learning_rate=0.1 \ 39 | --learning_rate_decay_factor=0.1 \ 40 | --num_epochs_per_decay=200 \ 41 | --weight_decay=0.004 42 | 43 | # Run evaluation. 44 | python eval_image_classifier.py \ 45 | --checkpoint_path=${TRAIN_DIR} \ 46 | --eval_dir=${TRAIN_DIR} \ 47 | --dataset_name=cifar10 \ 48 | --dataset_split_name=test \ 49 | --dataset_dir=${DATASET_DIR} \ 50 | --model_name=cifarnet 51 | -------------------------------------------------------------------------------- /research/slim/scripts/train_lenet_on_mnist.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # 3 | # This script performs the following operations: 4 | # 1. Downloads the MNIST dataset 5 | # 2. Trains a LeNet model on the MNIST training set. 6 | # 3. Evaluates the model on the MNIST testing set. 7 | # 8 | # Usage: 9 | # cd slim 10 | # ./slim/scripts/train_lenet_on_mnist.sh 11 | set -e 12 | 13 | # Where the checkpoint and logs will be saved to. 14 | TRAIN_DIR=/tmp/lenet-model 15 | 16 | # Where the dataset is saved to. 17 | DATASET_DIR=/tmp/mnist 18 | 19 | # Download the dataset 20 | python download_and_convert_data.py \ 21 | --dataset_name=mnist \ 22 | --dataset_dir=${DATASET_DIR} 23 | 24 | # Run training. 25 | python train_image_classifier.py \ 26 | --train_dir=${TRAIN_DIR} \ 27 | --dataset_name=mnist \ 28 | --dataset_split_name=train \ 29 | --dataset_dir=${DATASET_DIR} \ 30 | --model_name=lenet \ 31 | --preprocessing_name=lenet \ 32 | --max_number_of_steps=20000 \ 33 | --batch_size=50 \ 34 | --learning_rate=0.01 \ 35 | --save_interval_secs=60 \ 36 | --save_summaries_secs=60 \ 37 | --log_every_n_steps=100 \ 38 | --optimizer=sgd \ 39 | --learning_rate_decay_type=fixed \ 40 | --weight_decay=0 41 | 42 | # Run evaluation. 43 | python eval_image_classifier.py \ 44 | --checkpoint_path=${TRAIN_DIR} \ 45 | --eval_dir=${TRAIN_DIR} \ 46 | --dataset_name=mnist \ 47 | --dataset_split_name=test \ 48 | --dataset_dir=${DATASET_DIR} \ 49 | --model_name=lenet 50 | -------------------------------------------------------------------------------- /research/slim/setup.py: -------------------------------------------------------------------------------- 1 | """Setup script for slim.""" 2 | 3 | from setuptools import find_packages 4 | from setuptools import setup 5 | 6 | 7 | setup( 8 | name='slim', 9 | version='0.1', 10 | include_package_data=True, 11 | packages=find_packages(), 12 | description='tf-slim', 13 | ) 14 | -------------------------------------------------------------------------------- /research/slim/slim.egg-info/PKG-INFO: -------------------------------------------------------------------------------- 1 | Metadata-Version: 1.0 2 | Name: slim 3 | Version: 0.1 4 | Summary: tf-slim 5 | Home-page: UNKNOWN 6 | Author: UNKNOWN 7 | Author-email: UNKNOWN 8 | License: UNKNOWN 9 | Description: UNKNOWN 10 | Platform: UNKNOWN 11 | -------------------------------------------------------------------------------- /research/slim/slim.egg-info/SOURCES.txt: -------------------------------------------------------------------------------- 1 | setup.py 2 | datasets/__init__.py 3 | datasets/build_imagenet_data.py 4 | datasets/cifar10.py 5 | datasets/dataset_factory.py 6 | datasets/dataset_utils.py 7 | datasets/download_and_convert_cifar10.py 8 | datasets/download_and_convert_flowers.py 9 | datasets/download_and_convert_mnist.py 10 | datasets/flowers.py 11 | datasets/imagenet.py 12 | datasets/mnist.py 13 | datasets/preprocess_imagenet_validation_data.py 14 | datasets/process_bounding_boxes.py 15 | deployment/__init__.py 16 | deployment/model_deploy.py 17 | deployment/model_deploy_test.py 18 | nets/__init__.py 19 | nets/alexnet.py 20 | nets/alexnet_test.py 21 | nets/cifarnet.py 22 | nets/inception.py 23 | nets/inception_resnet_v2.py 24 | nets/inception_resnet_v2_test.py 25 | nets/inception_utils.py 26 | nets/inception_v1.py 27 | nets/inception_v1_test.py 28 | nets/inception_v2.py 29 | nets/inception_v2_test.py 30 | nets/inception_v3.py 31 | nets/inception_v3_test.py 32 | nets/inception_v4.py 33 | nets/inception_v4_test.py 34 | nets/lenet.py 35 | nets/mobilenet_v1.py 36 | nets/mobilenet_v1_test.py 37 | nets/nets_factory.py 38 | nets/nets_factory_test.py 39 | nets/overfeat.py 40 | nets/overfeat_test.py 41 | nets/resnet_utils.py 42 | nets/resnet_v1.py 43 | nets/resnet_v1_test.py 44 | nets/resnet_v2.py 45 | nets/resnet_v2_test.py 46 | nets/vgg.py 47 | nets/vgg_test.py 48 | preprocessing/__init__.py 49 | preprocessing/cifarnet_preprocessing.py 50 | preprocessing/inception_preprocessing.py 51 | preprocessing/lenet_preprocessing.py 52 | preprocessing/preprocessing_factory.py 53 | preprocessing/vgg_preprocessing.py 54 | slim.egg-info/PKG-INFO 55 | slim.egg-info/SOURCES.txt 56 | slim.egg-info/dependency_links.txt 57 | slim.egg-info/top_level.txt -------------------------------------------------------------------------------- /research/slim/slim.egg-info/dependency_links.txt: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /research/slim/slim.egg-info/top_level.txt: -------------------------------------------------------------------------------- 1 | datasets 2 | deployment 3 | nets 4 | preprocessing 5 | -------------------------------------------------------------------------------- /test.py: -------------------------------------------------------------------------------- 1 | # coding:utf-8 2 | 3 | import GUI 4 | import time 5 | # 提示语 6 | # 欢迎光临 11 7 | # 支付成功 12 8 | # 水果重复 13 9 | 10 | # 商品信息 2（商品名 21 单价 22 重量 23 ） 11 | # 用户信息 3（userid 31 余额 32） 12 | 13 | price = {'apple':9,'banana':12,'orange':10,'chair':20,'dog':100} 14 | i=0 15 | while True: 16 | print i 17 | GUI.addQueue(1) 18 | GUI.addQueue('欢迎光临') 19 | 20 | GUI.addQueue(2) 21 | fruit_type = 'apple' 22 | weight2 = 1.23 23 | 24 | GUI.addQueue('商品名称：' + fruit_type + '\n' + '单价(元/kg)：' + str(price[fruit_type]) + '\n' + '重量(kg)：' + str(weight2)) 25 | 26 | time.sleep(0.5) 27 | 28 | -------------------------------------------------------------------------------- /蓝牙连接命令: -------------------------------------------------------------------------------- 1 | sudo rfcomm bind /dev/rfcomm0 20:17:01:10:28:64 2 | sudo mknod /dev/rfcomm0 c 216 0 3 | sudo chmod 777 /dev/rfcomm0 4 | sudo hciconfig hci0 noauth 5 | --------------------------------------------------------------------------------