├── helpers ├── __init__.py ├── pascal_map.npy ├── helpers.py └── annotools.py ├── networks ├── __init__.py ├── classifiers.py ├── dextr.py └── resnet.py ├── doc ├── dextr.png └── github_teaser.gif ├── sample_images ├── bear.jpg ├── people.jpg ├── plane.jpg ├── skater.jpg ├── dog-cat.jpg └── tea_boxes.jpg ├── mypath.py ├── models └── download_dextr_model.sh ├── anno_cfg.yml ├── .gitignore ├── conda_env.yml ├── README.md ├── annotate.py └── LICENSE /helpers/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /networks/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /doc/dextr.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/karan-shr/DEXTR-AnnoTool/HEAD/doc/dextr.png -------------------------------------------------------------------------------- /doc/github_teaser.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/karan-shr/DEXTR-AnnoTool/HEAD/doc/github_teaser.gif -------------------------------------------------------------------------------- /helpers/pascal_map.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/karan-shr/DEXTR-AnnoTool/HEAD/helpers/pascal_map.npy -------------------------------------------------------------------------------- /sample_images/bear.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/karan-shr/DEXTR-AnnoTool/HEAD/sample_images/bear.jpg -------------------------------------------------------------------------------- /sample_images/people.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/karan-shr/DEXTR-AnnoTool/HEAD/sample_images/people.jpg -------------------------------------------------------------------------------- /sample_images/plane.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/karan-shr/DEXTR-AnnoTool/HEAD/sample_images/plane.jpg -------------------------------------------------------------------------------- /sample_images/skater.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/karan-shr/DEXTR-AnnoTool/HEAD/sample_images/skater.jpg -------------------------------------------------------------------------------- /sample_images/dog-cat.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/karan-shr/DEXTR-AnnoTool/HEAD/sample_images/dog-cat.jpg -------------------------------------------------------------------------------- /mypath.py: -------------------------------------------------------------------------------- 1 | 2 | class Path(object): 3 | @staticmethod 4 | def models_dir(): 5 | return 'models/' 6 | -------------------------------------------------------------------------------- /sample_images/tea_boxes.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/karan-shr/DEXTR-AnnoTool/HEAD/sample_images/tea_boxes.jpg -------------------------------------------------------------------------------- /models/download_dextr_model.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # Model trained on PASCAL + SBD 4 | wget https://data.vision.ee.ethz.ch/csergi/share/DEXTR/dextr_pascal-sbd.h5 5 | -------------------------------------------------------------------------------- /anno_cfg.yml: -------------------------------------------------------------------------------- 1 | #-----------------------------------------------anno_cfg.yml------------------------------------------------------------ 2 | # Summary: 3 | # - this is the YAML configuration file where the settings for annotation are stored. 4 | # - please edit the file accordingly to your annotation setup. 5 | #----------------------------------------------------------------------------------------------------------------------- 6 | 7 | # Required parameters to be set by user 8 | required: 9 | source: ./sample_images # path to the dir containing images to be annotated 10 | destination: ./sample_images/segMasks # path to the dir where the generated masks should be saved 11 | 12 | # Optional parameters 13 | optional: 14 | update: true # set to True if more images were added to source folder after initial run 15 | mask_suffix: _mask # e.g. if source file: img_01.jpg then mask file: img_01_mask.jpg 16 | save_format: png # supports npy and png, (jpg not ideal) 17 | 18 | # ADMIN parameters (CAUTION: Don't edit unless you are sure what you are doing) 19 | admin: 20 | progress_file: .anno_prog # file used for tracking annotation progress, saved in the source folder 21 | reset_progress: false # set True to reset annotation progress i.e. to start over. 22 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # User specified 2 | models/ 3 | 4 | 5 | # Byte-compiled / optimized / DLL files 6 | __pycache__/ 7 | *.py[cod] 8 | *$py.class 9 | *.pth 10 | 11 | # C extensions 12 | *.so 13 | 14 | # Distribution / packaging 15 | .Python 16 | env/ 17 | build/ 18 | develop-eggs/ 19 | dist/ 20 | downloads/ 21 | eggs/ 22 | .eggs/ 23 | lib/ 24 | lib64/ 25 | parts/ 26 | sdist/ 27 | var/ 28 | wheels/ 29 | *.egg-info/ 30 | .installed.cfg 31 | *.egg 32 | 33 | # PyInstaller 34 | # Usually these files are written by a python script from a template 35 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 36 | *.manifest 37 | *.spec 38 | 39 | # Installer logs 40 | pip-log.txt 41 | pip-delete-this-directory.txt 42 | 43 | # Unit test / coverage reports 44 | htmlcov/ 45 | .tox/ 46 | .coverage 47 | .coverage.* 48 | .cache 49 | nosetests.xml 50 | coverage.xml 51 | *.cover 52 | .hypothesis/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | 62 | # Flask stuff: 63 | instance/ 64 | .webassets-cache 65 | 66 | # Scrapy stuff: 67 | .scrapy 68 | 69 | # Sphinx documentation 70 | docs/_build/ 71 | 72 | # PyBuilder 73 | target/ 74 | 75 | # Jupyter Notebook 76 | .ipynb_checkpoints 77 | 78 | # pyenv 79 | .python-version 80 | 81 | # celery beat schedule file 82 | celerybeat-schedule 83 | 84 | # SageMath parsed files 85 | *.sage.py 86 | 87 | # dotenv 88 | .env 89 | 90 | # virtualenv 91 | .venv 92 | venv/ 93 | ENV/ 94 | 95 | # Spyder project settings 96 | .spyderproject 97 | .spyproject 98 | 99 | # Pycharm project settings 100 | .idea 101 | 102 | # Rope project settings 103 | .ropeproject 104 | 105 | # mkdocs documentation 106 | /site 107 | 108 | # mypy 109 | .mypy_cache/ 110 | -------------------------------------------------------------------------------- /conda_env.yml: -------------------------------------------------------------------------------- 1 | name: dextr_annotool 2 | channels: 3 | - conda-forge 4 | - defaults 5 | dependencies: 6 | - _tflow_select=2.1.0=gpu 7 | - absl-py=0.7.1=py36_0 8 | - astor=0.7.1=py36_0 9 | - blas=1.0=mkl 10 | - bzip2=1.0.6=h14c3975_5 11 | - c-ares=1.15.0=h7b6447c_1 12 | - ca-certificates=2019.5.15=0 13 | - cairo=1.14.12=h8948797_3 14 | - certifi=2019.3.9=py36_0 15 | - cudatoolkit=9.0=h13b8566_0 16 | - cudnn=7.6.0=cuda9.0_0 17 | - cupti=9.0.176=0 18 | - cycler=0.10.0=py36_0 19 | - dbus=1.13.6=h746ee38_0 20 | - expat=2.2.6=he6710b0_0 21 | - ffmpeg=4.0=hcdf2ecd_0 22 | - fontconfig=2.13.0=h9420a91_0 23 | - freeglut=3.0.0=hf484d3e_5 24 | - freetype=2.9.1=h8a8886c_1 25 | - gast=0.2.2=py36_0 26 | - glib=2.56.2=hd408876_0 27 | - graphite2=1.3.13=h23475e2_0 28 | - grpcio=1.16.1=py36hf8bcb03_1 29 | - gst-plugins-base=1.14.0=hbbd80ab_1 30 | - gstreamer=1.14.0=hb453b48_1 31 | - h5py=2.8.0=py36h989c5e5_3 32 | - harfbuzz=1.8.8=hffaf4a1_0 33 | - hdf5=1.10.2=hba1933b_1 34 | - icu=58.2=h9c2bf20_1 35 | - intel-openmp=2019.4=243 36 | - jasper=2.0.14=h07fcdf6_1 37 | - jpeg=9b=h024ee3a_2 38 | - keras=2.2.4=0 39 | - keras-applications=1.0.8=py_0 40 | - keras-base=2.2.4=py36_0 41 | - keras-preprocessing=1.1.0=py_1 42 | - kiwisolver=1.1.0=py36he6710b0_0 43 | - libedit=3.1.20181209=hc058e9b_0 44 | - libffi=3.2.1=hd88cf55_4 45 | - libgcc-ng=9.1.0=hdf63c60_0 46 | - libgfortran-ng=7.3.0=hdf63c60_0 47 | - libglu=9.0.0=hf484d3e_1 48 | - libopencv=3.4.2=hb342d67_1 49 | - libopus=1.3=h7b6447c_0 50 | - libpng=1.6.37=hbc83047_0 51 | - libprotobuf=3.8.0=hd408876_0 52 | - libstdcxx-ng=9.1.0=hdf63c60_0 53 | - libtiff=4.0.10=h2733197_2 54 | - libuuid=1.0.3=h1bed415_2 55 | - libvpx=1.7.0=h439df22_0 56 | - libxcb=1.13=h1bed415_1 57 | - libxml2=2.9.9=he19cac6_0 58 | - markdown=3.1.1=py36_0 59 | - matplotlib=3.1.0=py36h5429711_0 60 | - mkl=2019.4=243 61 | - mkl_fft=1.0.12=py36ha843d7b_0 62 | - mkl_random=1.0.2=py36hd81dba3_0 63 | - mock=3.0.5=py36_0 64 | - ncurses=6.1=he6710b0_1 65 | - numpy=1.16.4=py36h7e9f1db_0 66 | - numpy-base=1.16.4=py36hde5b4d6_0 67 | - olefile=0.46=py36_0 68 | - opencv=3.4.2=py36h6fd60c2_1 69 | - openssl=1.1.1c=h7b6447c_1 70 | - pcre=8.43=he6710b0_0 71 | - pillow=6.0.0=py36h34e0f95_0 72 | - pip=19.1.1=py36_0 73 | - pixman=0.38.0=h7b6447c_0 74 | - protobuf=3.8.0=py36he6710b0_0 75 | - py-opencv=3.4.2=py36hb342d67_1 76 | - pyparsing=2.4.0=py_0 77 | - pyqt=5.9.2=py36h05f1152_2 78 | - python=3.6.8=h0371630_0 79 | - python-dateutil=2.8.0=py36_0 80 | - pytz=2019.1=py_0 81 | - pyyaml=5.1=py36h7b6447c_0 82 | - qt=5.9.7=h5867ecd_1 83 | - readline=7.0=h7b6447c_5 84 | - ruamel=1.0=py36_0 85 | - ruamel.yaml=0.15.96=py36h516909a_0 86 | - scipy=1.2.1=py36h7c811a0_0 87 | - setuptools=41.0.1=py36_0 88 | - sip=4.19.8=py36hf484d3e_0 89 | - six=1.12.0=py36_0 90 | - sqlite=3.28.0=h7b6447c_0 91 | - tensorboard=1.9.0=py36hf484d3e_0 92 | - tensorflow=1.9.0=gpu_py36h02c5d5e_1 93 | - tensorflow-base=1.9.0=gpu_py36h6ecc378_0 94 | - tensorflow-estimator=1.13.0=py_0 95 | - tensorflow-gpu=1.9.0=hf154084_0 96 | - termcolor=1.1.0=py36_1 97 | - tk=8.6.8=hbc83047_0 98 | - tornado=6.0.2=py36h7b6447c_0 99 | - werkzeug=0.15.4=py_0 100 | - wheel=0.33.4=py36_0 101 | - xz=5.2.4=h14c3975_4 102 | - yaml=0.1.7=had09818_2 103 | - zlib=1.2.11=h7b6447c_3 104 | - zstd=1.3.7=h0b5b093_0 105 | 106 | 107 | -------------------------------------------------------------------------------- /networks/classifiers.py: -------------------------------------------------------------------------------- 1 | from math import ceil 2 | 3 | from keras.layers.merge import Concatenate, Add 4 | from keras.layers import AveragePooling2D, ZeroPadding2D 5 | 6 | from keras.layers import Activation 7 | from keras.layers import Conv2D 8 | from keras.layers import Layer 9 | 10 | from networks import resnet 11 | import keras.backend as K 12 | from keras.backend import tf as ktf 13 | 14 | 15 | class Upsampling(Layer): 16 | 17 | def __init__(self, new_size, **kwargs): 18 | self.new_size = new_size 19 | super(Upsampling, self).__init__(**kwargs) 20 | 21 | def build(self, input_shape): 22 | super(Upsampling, self).build(input_shape) 23 | 24 | def call(self, inputs, **kwargs): 25 | new_height, new_width = self.new_size 26 | resized = ktf.image.resize_images(inputs, [new_height, new_width], 27 | align_corners=True) 28 | return resized 29 | 30 | def compute_output_shape(self, input_shape): 31 | return tuple([None, self.new_size[0], self.new_size[1], input_shape[3]]) 32 | 33 | def get_config(self): 34 | config = super(Upsampling, self).get_config() 35 | config['new_size'] = self.new_size 36 | return config 37 | 38 | 39 | def psp_block(prev_layer, level, feature_map_shape, input_shape): 40 | if input_shape == (512, 512): 41 | kernel_strides_map = {1: [64, 64], 42 | 2: [32, 32], 43 | 3: [22, 21], 44 | 6: [11, 9]} # TODO: Level 6: Kernel correct, but stride not exactly the same as Pytorch 45 | else: 46 | raise ValueError("Pooling parameters for input shape " + input_shape + " are not defined.") 47 | 48 | if K.image_data_format() == 'channels_last': 49 | bn_axis = 3 50 | else: 51 | bn_axis = 1 52 | 53 | names = [ 54 | "class_psp_" + str(level) + "_conv", 55 | "class_psp_" + str(level) + "_bn" 56 | ] 57 | kernel = (kernel_strides_map[level][0], kernel_strides_map[level][0]) 58 | strides = (kernel_strides_map[level][1], kernel_strides_map[level][1]) 59 | prev_layer = AveragePooling2D(kernel, strides=strides)(prev_layer) 60 | prev_layer = Conv2D(512, (1, 1), strides=(1, 1), name=names[0], use_bias=False)(prev_layer) 61 | prev_layer = resnet.BN(bn_axis, name=names[1])(prev_layer) 62 | prev_layer = Activation('relu')(prev_layer) 63 | prev_layer = Upsampling(feature_map_shape)(prev_layer) 64 | return prev_layer 65 | 66 | 67 | def build_pyramid_pooling_module(res, input_shape, nb_classes, sigmoid=False, output_size=None): 68 | """Build the Pyramid Pooling Module.""" 69 | # ---PSPNet concat layers with Interpolation 70 | feature_map_size = tuple(int(ceil(input_dim / 8.0)) for input_dim in input_shape) 71 | if K.image_data_format() == 'channels_last': 72 | bn_axis = 3 73 | else: 74 | bn_axis = 1 75 | print("PSP module will interpolate to a final feature map size of %s" % 76 | (feature_map_size, )) 77 | 78 | interp_block1 = psp_block(res, 1, feature_map_size, input_shape) 79 | interp_block2 = psp_block(res, 2, feature_map_size, input_shape) 80 | interp_block3 = psp_block(res, 3, feature_map_size, input_shape) 81 | interp_block6 = psp_block(res, 6, feature_map_size, input_shape) 82 | 83 | # concat all these layers. resulted 84 | res = Concatenate()([interp_block1, 85 | interp_block2, 86 | interp_block3, 87 | interp_block6, 88 | res]) 89 | x = Conv2D(512, (1, 1), strides=(1, 1), padding="same", name="class_psp_reduce_conv", use_bias=False)(res) 90 | x = resnet.BN(bn_axis, name="class_psp_reduce_bn")(x) 91 | x = Activation('relu')(x) 92 | 93 | x = Conv2D(nb_classes, (1, 1), strides=(1, 1), name="class_psp_final_conv")(x) 94 | 95 | if output_size: 96 | x = Upsampling(output_size)(x) 97 | 98 | if sigmoid: 99 | x = Activation('sigmoid')(x) 100 | return x 101 | -------------------------------------------------------------------------------- /networks/dextr.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | from os.path import splitext, join 3 | import numpy as np 4 | from scipy import misc 5 | from keras import backend as K 6 | 7 | import tensorflow as tf 8 | from networks import resnet 9 | 10 | from mypath import Path 11 | 12 | 13 | class DEXTR(object): 14 | """Pyramid Scene Parsing Network by Hengshuang Zhao et al 2017""" 15 | 16 | def __init__(self, nb_classes, resnet_layers, input_shape, weights, num_input_channels=4, 17 | classifier='psp', use_numpy=False, sigmoid=False): 18 | self.input_shape = input_shape 19 | self.num_input_channels = num_input_channels 20 | self.sigmoid = sigmoid 21 | self.model = resnet.build_network(nb_classes=nb_classes, resnet_layers=resnet_layers, num_input_channels=num_input_channels, 22 | input_shape=self.input_shape, classifier=classifier, sigmoid=self.sigmoid, output_size=self.input_shape) 23 | if use_numpy: 24 | print("No Keras model & weights found, import from npy weights.") 25 | self.set_npy_weights(weights) 26 | else: 27 | print("Loading weights from H5 file.") 28 | h5_path = join(Path.models_dir(), weights + '.h5') 29 | self.model.load_weights(h5_path) 30 | 31 | def predict(self, img): 32 | # Preprocess 33 | img = misc.imresize(img, self.input_shape) 34 | img = img.astype('float32') 35 | probs = self.feed_forward(img) 36 | return probs 37 | 38 | def feed_forward(self, data): 39 | print("Predicting...") 40 | assert data.shape == (self.input_shape[0], self.input_shape[1], self.num_input_channels) 41 | prediction = self.model.predict(np.expand_dims(data, 0))[0] 42 | print("Finished prediction...") 43 | return prediction 44 | 45 | def set_npy_weights(self, weights_path): 46 | npy_weights_path = join("weights", "npy", weights_path + ".npy") 47 | h5_path = join(Path.models_dir(), weights_path + '.h5') 48 | # h5_path_model = join("models", weights_path + ".h5") 49 | 50 | print("Importing weights from %s" % npy_weights_path) 51 | weights = np.load(npy_weights_path, encoding='bytes').item() 52 | for layer in self.model.layers: 53 | # print('{}'.format(layer.name)) 54 | if layer.name[:2] == 'bn' or layer.name[-2:] == 'bn': 55 | print('{}'.format(layer.name)) 56 | # print('{} {}'.format(layer.name, layer.get_weights()[0].shape)) 57 | gamma = weights[layer.name]['gamma'] 58 | beta = weights[layer.name]['beta'] 59 | moving_mean = weights[layer.name]['moving_mean'] 60 | moving_variance = weights[layer.name]['moving_variance'] 61 | 62 | self.model.get_layer(layer.name).set_weights([gamma, beta, moving_mean, moving_variance]) 63 | 64 | elif layer.name[:3] == 'res' or layer.name[-4:] == 'conv' or layer.name[:4] == 'conv': 65 | print('{}'.format(layer.name)) 66 | # print('{} {}'.format(layer.name, layer.get_weights()[0].shape)) 67 | if len(self.model.get_layer(layer.name).get_weights()) == 2: 68 | weight = weights[layer.name]['weights'] 69 | biases = weights[layer.name]['biases'] 70 | if biases is None: 71 | raise ValueError('Bias inconsistency') 72 | self.model.get_layer(layer.name).set_weights([weight, biases]) 73 | else: 74 | weight = weights[layer.name]['weights'] 75 | self.model.get_layer(layer.name).set_weights([weight]) 76 | 77 | print('Finished importing weights.') 78 | 79 | print("Writing keras weights") 80 | self.model.save_weights(h5_path) 81 | # models.save_model(self.model, h5_path_model, include_optimizer=False) 82 | 83 | print("Finished writing Keras model & weights") 84 | 85 | 86 | if __name__ == "__main__": 87 | classifier = 'psp' 88 | input_size = 512 89 | num_input_channels = 4 90 | resnet_size = 101 91 | image = 'ims/dog_512.png' 92 | extreme_points = 'ims/dog_512_extreme.png' 93 | 94 | input_type = 'bbox' if num_input_channels == 3 else 'extreme' 95 | 96 | model = 'dextr_pascal-sbd' 97 | 98 | # Handle input and output args 99 | sess = tf.Session() 100 | K.set_session(sess) 101 | 102 | with sess.as_default(): 103 | dextr = DEXTR(nb_classes=1, resnet_layers=resnet_size, input_shape=(input_size, input_size), weights=model, 104 | num_input_channels=num_input_channels, classifier=classifier, use_numpy=False, sigmoid=True) 105 | 106 | img = misc.imread(image, mode='RGB') 107 | if num_input_channels == 4: 108 | extreme = misc.imread(extreme_points) 109 | 110 | img_extreme = np.zeros((input_size, input_size, 4)) 111 | img_extreme[:, :, :3] = img 112 | img_extreme[:, :, 3] = extreme 113 | img_extreme = np.expand_dims(img_extreme.astype('float32'), 0) 114 | else: 115 | img_extreme = np.expand_dims(img.astype('float32'), 0) 116 | 117 | pred = dextr.model.predict(img_extreme)[0, :, :, 0] 118 | 119 | mask = pred > 0.8 120 | 121 | filename, ext = splitext(image) 122 | 123 | misc.imsave(filename + "_seg_"+ classifier + ext, mask.astype(np.uint8)*255) 124 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Deep Extreme Cut (DEXTR) based Annotation Tool 2 | is a simple matplotlib-based annotation User Interface (UI) that can be used for extracting segmentation masks for images. The main advantage of using this tool is the speed of annotation, as even for complex objects (e.g. the animals in the following image) the segmentation masks can be acquired by annotating only the four extreme points (left, right, top and bottom) for that object. 3 | 4 |

5 | 6 | This repository builds on the original work of [scaelles/DEXTR-KerasTensorflowPyTorch](https://github.com/scaelles/DEXTR-KerasTensorflow) by providing extra code that extends their demo code into a simple and elegant UI for annotating segmentation masks. The tool can be invoked from the command line and offers tracking of annotation progress between subsequent runs (i.e., for a given source images folder, it can track which images are already annotated, not annotated or skipped). The resulting segmentation masks can be saved either as *png* or as *numpy* array (.npy) files. 7 | 8 | 9 | ## Deep Extreme Cut (DEXTR) 10 | The `DEXTR` code used in this fork is a Keras+TensorFlow based re-implementation of original [PyTorch](https://github.com/scaelles/DEXTR-PyTorch) code by the same authors. More information on `DEXTR` can be found on the associated [project page](http://www.vision.ee.ethz.ch/~cvlsegmentation/dextr). A short summary is presented as follows: 11 | 12 | ![DEXTR](doc/dextr.png) 13 | 14 | #### Summary 15 | `DEXTR` is a deep-learning based approach to obtain precise object segmentation in images and videos. To achieve the same, it follows a semiautomatic approach where (usually) user-defined object extreme points are added as an extra channel to an image before it is input into a Convolutional Neural Network (CNN). The CNN learns to transform this information into a segmentation of an object that matches those extreme points. This approach is thus highly useful for guided segmentation (grabcut-style), interactive segmentation, video object segmentation, and dense segmentation annotation. 16 | 17 | 18 | ## Annotation Tool 19 | The annotation tool is a simple but powerful matplotlib-based UI that allows users to easily and interactively annotate images to obtain object segmentation masks for the same. Some of the main features of this tool are as follows: 20 | - easy modification of settings using a YAML-based configuration file. 21 | - ability to annotate extreme points for multiple objects in a image. 22 | - visualisation of results for each object during annotation. 23 | - ability to redo annotation if object results are not correct or if wrong points were selected. 24 | - ability to skip annotation if object results are unsatisfactory/wrong. 25 | - multiple images can be annotated in a session. 26 | - progress tracking across sessions. Thus, skipped or already annotated images are not shown during subsequent runs. 27 | 28 | More details on the different settings and using the annotation tool are provided in the later sections. 29 | 30 | 31 | ## Setup 32 | It is good practice to work with virtual environments when trying out new code. So please setup a virtual environment using either Python directly or *anaconda*, as you prefer. The code here was developed and tested using [Anaconda](https://docs.anaconda.com/anaconda/) with Python version 3.6, so we suggest you to do the same. The simplest way to get started is to follow the instructions below. 33 | 34 | 0. Clone this repo: 35 | ```Shell 36 | git clone https://github.com/karan-shr/DEXTR-AnnoTool 37 | cd DEXTR-AnnoTool 38 | ``` 39 | 40 | 1. Setting up the environment: 41 | ```Shell 42 | conda env create -n dextr_annotool -f conda_env.yml 43 | ``` 44 | Where `dextr_annotool` is the name of the environment (change if required) and `conda_env.yml` is the accompanying *conda* environment file. If you are using *pip* or installing packages manually, pay special attention to the package versions. 45 | 46 | 2. Download the model by running the script inside ```models/```: 47 | ```Shell 48 | cd models/ 49 | chmod +x download_dextr_model.sh 50 | ./download_dextr_model.sh 51 | cd .. 52 | ``` 53 | The default model is trained on PASCAL VOC Segmentation train + SBD (10582 images). To download models trained on PASCAL VOC Segmentation train or COCO, please visit the [project page](http://www.vision.ee.ethz.ch/~cvlsegmentation/dextr/#downloads). You can also manually download the models and place them in the models directory. 54 | 55 | 3. To demo the annotation UI using the provided sample images, please run: 56 | ```Shell 57 | python annotate.py -c anno_cfg.yml 58 | ``` 59 | If you have multiple GPUs, you can specify which one should be used (for example gpu with id 0): 60 | ```Shell 61 | CUDA_VISIBLE_DEVICES=0 python annotate.py -c anno_cfg.yml 62 | ``` 63 | 64 | 65 | ## Usage 66 | 67 | To get started annotating your dataset, 68 | 69 | 1. Modify the `anno_cfg.yml` configuration file: The comments in this file explain the purpose of the various setting parameters that the user can modify. Nevertheless, a brief description of the parameters whose purpose might not be clear at first sight is as follows: 70 | * `update` - when the program runs the first time it creates an annotation progress tracking file (more on this later) by scanning the source folder for all *png* and *jpeg* files and adding them to this file. The `update` parameter allows the user to specify if they would like to update the filenames cache every time the program runs. There are advantages to both approaches e.g., if the user added new data to the source folder but wants the annotation tool to only show the preexisting files, she can do so by setting `update: false`. 71 | * `admin` settings - typically the user shouldn't have to change these settings. Here the `progress_file: .anno_prog` is just the name for the annotation progress tracking file and `reset_progress: false` allows you to wipe the progress tracking history by making a new progress tracking file. 72 | 73 | 2. Once you have modified the `anno_cfg.yml` file to your liking, just undertake step 3 of the previous section to start annotating your data. Enjoy :thumbsup: 74 | 75 | Lastly, if you encounter any problems please open an issue. 76 | 77 | 78 | ## More information related to DEXTR 79 | 80 | ### Pre-trained models for DEXTR 81 | We provide the following DEXTR models, pre-trained on: 82 | * [PASCAL + SBD](https://data.vision.ee.ethz.ch/csergi/share/DEXTR/dextr_pascal-sbd.h5), trained on PASCAL VOC Segmentation train + SBD (10582 images). Achieves mIoU of 91.5% on PASCAL VOC Segmentation val. 83 | * [PASCAL](https://data.vision.ee.ethz.ch/csergi/share/DEXTR/dextr_pascal.h5), trained on PASCAL VOC Segmentation train (1464 images). Achieves mIoU of 90.5% on PASCAL VOC Segmentation val. 84 | * [COCO](https://data.vision.ee.ethz.ch/csergi/share/DEXTR/dextr_coco.h5), trained on COCO train 2014 (82783 images). Achieves mIoU of 87.8% on PASCAL VOC Segmentation val. 85 | 86 | ### Citation 87 | This annotation tool wouldn't have been possible without the excellent work on DEXTR by the team at ETH-Zurich. Please consider citing their following work if you use this tool: 88 | 89 | @Inproceedings{Man+18, 90 | Title = {Deep Extreme Cut: From Extreme Points to Object Segmentation}, 91 | Author = {K.K. Maninis and S. Caelles and J. Pont-Tuset and L. {Van Gool}}, 92 | Booktitle = {Computer Vision and Pattern Recognition (CVPR)}, 93 | Year = {2018} 94 | } 95 | 96 | @InProceedings{Pap+17, 97 | Title = {Extreme clicking for efficient object annotation}, 98 | Author = {D.P. Papadopoulos and J. Uijlings and F. Keller and V. Ferrari}, 99 | Booktitle = {ICCV}, 100 | Year = {2017} 101 | } 102 | 103 | 104 | -------------------------------------------------------------------------------- /networks/resnet.py: -------------------------------------------------------------------------------- 1 | from keras.layers import Input 2 | from keras import layers 3 | from keras.layers import Activation 4 | from keras.layers import Conv2D 5 | from keras.layers import MaxPooling2D 6 | from keras.layers import ZeroPadding2D 7 | from keras.layers import BatchNormalization 8 | from keras.models import Model 9 | 10 | import keras.backend as K 11 | 12 | from networks.classifiers import build_pyramid_pooling_module 13 | 14 | 15 | def BN(axis, name=""): 16 | return BatchNormalization(axis=axis, momentum=0.1, name=name, epsilon=1e-5) 17 | 18 | 19 | def identity_block(input_tensor, kernel_size, filters, stage, block, dilation=1): 20 | """The identity block is the block that has no conv layer at shortcut. 21 | 22 | # Arguments 23 | input_tensor: input tensor 24 | kernel_size: defualt 3, the kernel size of middle conv layer at main path 25 | filters: list of integers, the filterss of 3 conv layer at main path 26 | stage: integer, current stage label, used for generating layer names 27 | block: 'a','b'..., current block label, used for generating layer names 28 | dilation: dilation of the intermediate convolution 29 | 30 | # Returns 31 | Output tensor for the block. 32 | """ 33 | filters1, filters2, filters3 = filters 34 | if K.image_data_format() == 'channels_last': 35 | bn_axis = 3 36 | else: 37 | bn_axis = 1 38 | conv_name_base = 'res' + str(stage) + block + '_branch' 39 | bn_name_base = 'bn' + str(stage) + block + '_branch' 40 | 41 | x = Conv2D(filters1, (1, 1), name=conv_name_base + '2a', use_bias=False)(input_tensor) 42 | x = BN(axis=bn_axis, name=bn_name_base + '2a')(x) 43 | x = Activation('relu')(x) 44 | 45 | x = Conv2D(filters2, kernel_size, padding='same', name=conv_name_base + '2b', use_bias=False, dilation_rate=dilation)(x) 46 | x = BN(axis=bn_axis, name=bn_name_base + '2b')(x) 47 | x = Activation('relu')(x) 48 | 49 | x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c', use_bias=False)(x) 50 | x = BN(axis=bn_axis, name=bn_name_base + '2c')(x) 51 | 52 | x = layers.add([x, input_tensor]) 53 | x = Activation('relu')(x) 54 | return x 55 | 56 | 57 | def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(1, 1), dilation=1): 58 | """conv_block is the block that has a conv layer at shortcut 59 | 60 | # Arguments 61 | input_tensor: input tensor 62 | kernel_size: defualt 3, the kernel size of middle conv layer at main path 63 | filters: list of integers, the filterss of 3 conv layer at main path 64 | stage: integer, current stage label, used for generating layer names 65 | block: 'a','b'..., current block label, used for generating layer names 66 | 67 | # Returns 68 | Output tensor for the block. 69 | 70 | Note that from stage 3, the first conv layer at main path is with strides=(2,2) 71 | And the shortcut should have strides=(2,2) as well 72 | """ 73 | filters1, filters2, filters3 = filters 74 | if K.image_data_format() == 'channels_last': 75 | bn_axis = 3 76 | else: 77 | bn_axis = 1 78 | conv_name_base = 'res' + str(stage) + block + '_branch' 79 | bn_name_base = 'bn' + str(stage) + block + '_branch' 80 | 81 | x = Conv2D(filters1, (1, 1), strides=strides, name=conv_name_base + '2a', use_bias=False)(input_tensor) 82 | x = BN(axis=bn_axis, name=bn_name_base + '2a')(x) 83 | x = Activation('relu')(x) 84 | 85 | x = Conv2D(filters2, kernel_size, padding='same', name=conv_name_base + '2b', use_bias=False, dilation_rate=dilation)(x) 86 | x = BN(axis=bn_axis, name=bn_name_base + '2b')(x) 87 | x = Activation('relu')(x) 88 | 89 | x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c', use_bias=False)(x) 90 | x = BN(axis=bn_axis, name=bn_name_base + '2c')(x) 91 | 92 | shortcut = Conv2D(filters3, (1, 1), strides=strides, name=conv_name_base + '1', use_bias=False)(input_tensor) 93 | shortcut = BN(axis=bn_axis, name=bn_name_base + '1')(shortcut) 94 | 95 | x = layers.add([x, shortcut]) 96 | x = Activation('relu')(x) 97 | return x 98 | 99 | 100 | def ResNet101(input_tensor=None): 101 | 102 | img_input = input_tensor 103 | if K.image_data_format() == 'channels_last': 104 | bn_axis = 3 105 | else: 106 | bn_axis = 1 107 | 108 | x = ZeroPadding2D((3, 3))(img_input) 109 | x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1', use_bias=False)(x) 110 | x = BN(axis=bn_axis, name='bn_conv1')(x) 111 | x = Activation('relu')(x) 112 | x = ZeroPadding2D((1, 1))(x) 113 | x = MaxPooling2D((3, 3), strides=(2, 2), padding='valid')(x) 114 | 115 | x = conv_block(x, 3, [64, 64, 256], stage=2, block='a') 116 | x = identity_block(x, 3, [64, 64, 256], stage=2, block='b') 117 | x = identity_block(x, 3, [64, 64, 256], stage=2, block='c') 118 | 119 | x = conv_block(x, 3, [128, 128, 512], stage=3, block='a', strides=(2, 2)) 120 | x = identity_block(x, 3, [128, 128, 512], stage=3, block='b') 121 | x = identity_block(x, 3, [128, 128, 512], stage=3, block='c') 122 | x = identity_block(x, 3, [128, 128, 512], stage=3, block='d') 123 | 124 | x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a', dilation=2) 125 | x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b', dilation=2) 126 | x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c', dilation=2) 127 | x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d', dilation=2) 128 | x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e', dilation=2) 129 | x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f', dilation=2) 130 | x = identity_block(x, 3, [256, 256, 1024], stage=4, block='g', dilation=2) 131 | x = identity_block(x, 3, [256, 256, 1024], stage=4, block='h', dilation=2) 132 | x = identity_block(x, 3, [256, 256, 1024], stage=4, block='i', dilation=2) 133 | x = identity_block(x, 3, [256, 256, 1024], stage=4, block='j', dilation=2) 134 | x = identity_block(x, 3, [256, 256, 1024], stage=4, block='k', dilation=2) 135 | x = identity_block(x, 3, [256, 256, 1024], stage=4, block='l', dilation=2) 136 | x = identity_block(x, 3, [256, 256, 1024], stage=4, block='m', dilation=2) 137 | x = identity_block(x, 3, [256, 256, 1024], stage=4, block='n', dilation=2) 138 | x = identity_block(x, 3, [256, 256, 1024], stage=4, block='o', dilation=2) 139 | x = identity_block(x, 3, [256, 256, 1024], stage=4, block='p', dilation=2) 140 | x = identity_block(x, 3, [256, 256, 1024], stage=4, block='q', dilation=2) 141 | x = identity_block(x, 3, [256, 256, 1024], stage=4, block='r', dilation=2) 142 | x = identity_block(x, 3, [256, 256, 1024], stage=4, block='s', dilation=2) 143 | x = identity_block(x, 3, [256, 256, 1024], stage=4, block='t', dilation=2) 144 | x = identity_block(x, 3, [256, 256, 1024], stage=4, block='u', dilation=2) 145 | x = identity_block(x, 3, [256, 256, 1024], stage=4, block='v', dilation=2) 146 | x = identity_block(x, 3, [256, 256, 1024], stage=4, block='w', dilation=2) 147 | 148 | x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a', dilation=4) 149 | x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b', dilation=4) 150 | x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c', dilation=4) 151 | 152 | return x 153 | 154 | 155 | def build_network(nb_classes, input_shape, resnet_layers=101, classifier='psp', sigmoid=False, output_size=None, 156 | num_input_channels=4): 157 | """Build Network""" 158 | inp = Input((input_shape[0], input_shape[1], num_input_channels)) 159 | if resnet_layers == 101: 160 | res = ResNet101(inp) 161 | else: 162 | ValueError('Resnet {} does not exist'.format(resnet_layers)) 163 | if classifier == 'psp': 164 | print("Building network based on ResNet %i and PSP module expecting inputs of shape %s predicting %i classes" % ( 165 | resnet_layers, input_shape, nb_classes)) 166 | x = build_pyramid_pooling_module(res, input_shape, nb_classes, sigmoid=sigmoid, output_size=output_size) 167 | else: 168 | raise ValueError('Classifier not implemented.') 169 | model = Model(inputs=inp, outputs=x) 170 | 171 | return model 172 | -------------------------------------------------------------------------------- /helpers/helpers.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | import cv2 4 | import random 5 | import numpy as np 6 | 7 | 8 | def tens2image(im): 9 | if im.size()[0] == 1: 10 | tmp = np.squeeze(im.numpy(), axis=0) 11 | else: 12 | tmp = im.numpy() 13 | if tmp.ndim == 2: 14 | return tmp 15 | else: 16 | return tmp.transpose((1, 2, 0)) 17 | 18 | 19 | def crop2fullmask(crop_mask, bbox, im=None, im_size=None, zero_pad=False, relax=0, mask_relax=True, 20 | interpolation=cv2.INTER_CUBIC, scikit=False): 21 | if scikit: 22 | from skimage.transform import resize as sk_resize 23 | assert(not(im is None and im_size is None)), 'You have to provide an image or the image size' 24 | if im is None: 25 | im_si = im_size 26 | else: 27 | im_si = im.shape 28 | # Borers of image 29 | bounds = (0, 0, im_si[1] - 1, im_si[0] - 1) 30 | 31 | # Valid bounding box locations as (x_min, y_min, x_max, y_max) 32 | bbox_valid = (max(bbox[0], bounds[0]), 33 | max(bbox[1], bounds[1]), 34 | min(bbox[2], bounds[2]), 35 | min(bbox[3], bounds[3])) 36 | 37 | # Bounding box of initial mask 38 | bbox_init = (bbox[0] + relax, 39 | bbox[1] + relax, 40 | bbox[2] - relax, 41 | bbox[3] - relax) 42 | 43 | if zero_pad: 44 | # Offsets for x and y 45 | offsets = (-bbox[0], -bbox[1]) 46 | else: 47 | assert((bbox == bbox_valid).all()) 48 | offsets = (-bbox_valid[0], -bbox_valid[1]) 49 | 50 | # Simple per element addition in the tuple 51 | inds = tuple(map(sum, zip(bbox_valid, offsets + offsets))) 52 | 53 | if scikit: 54 | crop_mask = sk_resize(crop_mask, (bbox[3] - bbox[1] + 1, bbox[2] - bbox[0] + 1), order=0, mode='constant').astype(crop_mask.dtype) 55 | else: 56 | crop_mask = cv2.resize(crop_mask, (bbox[2] - bbox[0] + 1, bbox[3] - bbox[1] + 1), interpolation=interpolation) 57 | result_ = np.zeros(im_si) 58 | result_[bbox_valid[1]:bbox_valid[3] + 1, bbox_valid[0]:bbox_valid[2] + 1] = \ 59 | crop_mask[inds[1]:inds[3] + 1, inds[0]:inds[2] + 1] 60 | 61 | result = np.zeros(im_si) 62 | if mask_relax: 63 | result[bbox_init[1]:bbox_init[3]+1, bbox_init[0]:bbox_init[2]+1] = \ 64 | result_[bbox_init[1]:bbox_init[3]+1, bbox_init[0]:bbox_init[2]+1] 65 | else: 66 | result = result_ 67 | 68 | return result 69 | 70 | 71 | def overlay_mask(im, ma, colors=None, alpha=0.5): 72 | assert np.max(im) <= 1.0 73 | if colors is None: 74 | colors = np.load(os.path.join(os.path.dirname(__file__), 'pascal_map.npy'))/255. 75 | else: 76 | colors = np.append([[0.,0.,0.]], colors, axis=0); 77 | 78 | if ma.ndim == 3: 79 | assert len(colors) >= ma.shape[0], 'Not enough colors' 80 | ma = ma.astype(np.bool) 81 | im = im.astype(np.float32) 82 | 83 | if ma.ndim == 2: 84 | fg = im * alpha+np.ones(im.shape) * (1 - alpha) * colors[1, :3] # np.array([0,0,255])/255.0 85 | else: 86 | fg = [] 87 | for n in range(ma.ndim): 88 | fg.append(im * alpha + np.ones(im.shape) * (1 - alpha) * colors[1+n, :3]) 89 | # Whiten background 90 | bg = im.copy() 91 | if ma.ndim == 2: 92 | bg[ma == 0] = im[ma == 0] 93 | bg[ma == 1] = fg[ma == 1] 94 | total_ma = ma 95 | else: 96 | total_ma = np.zeros([ma.shape[1], ma.shape[2]]) 97 | for n in range(ma.shape[0]): 98 | tmp_ma = ma[n, :, :] 99 | total_ma = np.logical_or(tmp_ma, total_ma) 100 | tmp_fg = fg[n] 101 | bg[tmp_ma == 1] = tmp_fg[tmp_ma == 1] 102 | bg[total_ma == 0] = im[total_ma == 0] 103 | 104 | # [-2:] is s trick to be compatible both with opencv 2 and 3 105 | contours = cv2.findContours(total_ma.copy().astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2:] 106 | cv2.drawContours(bg, contours[0], -1, (0.0, 0.0, 0.0), 1) 107 | 108 | return bg 109 | 110 | 111 | def overlay_masks(im, masks, alpha=0.5): 112 | colors = np.load(os.path.join(os.path.dirname(__file__), 'pascal_map.npy'))/255. 113 | 114 | if isinstance(masks, np.ndarray): 115 | masks = [masks] 116 | 117 | assert len(colors) >= len(masks), 'Not enough colors' 118 | 119 | ov = im.copy() 120 | im = im.astype(np.float32) 121 | total_ma = np.zeros([im.shape[0], im.shape[1]]) 122 | i = 1 123 | for ma in masks: 124 | ma = ma.astype(np.bool) 125 | fg = im * alpha+np.ones(im.shape) * (1 - alpha) * colors[i, :3] # np.array([0,0,255])/255.0 126 | i = i + 1 127 | ov[ma == 1] = fg[ma == 1] 128 | total_ma += ma 129 | 130 | # [-2:] is s trick to be compatible both with opencv 2 and 3 131 | contours = cv2.findContours(ma.copy().astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2:] 132 | cv2.drawContours(ov, contours[0], -1, (0.0, 0.0, 0.0), 1) 133 | ov[total_ma == 0] = im[total_ma == 0] 134 | 135 | return ov 136 | 137 | 138 | def extreme_points(mask, pert): 139 | def find_point(id_x, id_y, ids): 140 | sel_id = ids[0][random.randint(0, len(ids[0]) - 1)] 141 | return [id_x[sel_id], id_y[sel_id]] 142 | 143 | # List of coordinates of the mask 144 | inds_y, inds_x = np.where(mask > 0.5) 145 | 146 | # Find extreme points 147 | return np.array([find_point(inds_x, inds_y, np.where(inds_x <= np.min(inds_x)+pert)), # left 148 | find_point(inds_x, inds_y, np.where(inds_x >= np.max(inds_x)-pert)), # right 149 | find_point(inds_x, inds_y, np.where(inds_y <= np.min(inds_y)+pert)), # top 150 | find_point(inds_x, inds_y, np.where(inds_y >= np.max(inds_y)-pert)) # bottom 151 | ]) 152 | 153 | 154 | def get_bbox(mask, points=None, pad=0, zero_pad=False): 155 | if points is not None: 156 | inds = np.flip(points.transpose(), axis=0) 157 | else: 158 | inds = np.where(mask > 0) 159 | 160 | if inds[0].shape[0] == 0: 161 | return None 162 | 163 | if zero_pad: 164 | x_min_bound = -np.inf 165 | y_min_bound = -np.inf 166 | x_max_bound = np.inf 167 | y_max_bound = np.inf 168 | else: 169 | x_min_bound = 0 170 | y_min_bound = 0 171 | x_max_bound = mask.shape[1] - 1 172 | y_max_bound = mask.shape[0] - 1 173 | 174 | x_min = max(inds[1].min() - pad, x_min_bound) 175 | y_min = max(inds[0].min() - pad, y_min_bound) 176 | x_max = min(inds[1].max() + pad, x_max_bound) 177 | y_max = min(inds[0].max() + pad, y_max_bound) 178 | 179 | return x_min, y_min, x_max, y_max 180 | 181 | 182 | def crop_from_bbox(img, bbox, zero_pad=False): 183 | # Borders of image 184 | bounds = (0, 0, img.shape[1] - 1, img.shape[0] - 1) 185 | 186 | # Valid bounding box locations as (x_min, y_min, x_max, y_max) 187 | bbox_valid = (max(bbox[0], bounds[0]), 188 | max(bbox[1], bounds[1]), 189 | min(bbox[2], bounds[2]), 190 | min(bbox[3], bounds[3])) 191 | 192 | if zero_pad: 193 | # Initialize crop size (first 2 dimensions) 194 | crop = np.zeros((bbox[3] - bbox[1] + 1, bbox[2] - bbox[0] + 1), dtype=img.dtype) 195 | 196 | # Offsets for x and y 197 | offsets = (-bbox[0], -bbox[1]) 198 | 199 | else: 200 | assert(bbox == bbox_valid) 201 | crop = np.zeros((bbox_valid[3] - bbox_valid[1] + 1, bbox_valid[2] - bbox_valid[0] + 1), dtype=img.dtype) 202 | offsets = (-bbox_valid[0], -bbox_valid[1]) 203 | 204 | # Simple per element addition in the tuple 205 | inds = tuple(map(sum, zip(bbox_valid, offsets + offsets))) 206 | 207 | img = np.squeeze(img) 208 | if img.ndim == 2: 209 | crop[inds[1]:inds[3] + 1, inds[0]:inds[2] + 1] = \ 210 | img[bbox_valid[1]:bbox_valid[3] + 1, bbox_valid[0]:bbox_valid[2] + 1] 211 | else: 212 | crop = np.tile(crop[:, :, np.newaxis], [1, 1, 3]) # Add 3 RGB Channels 213 | crop[inds[1]:inds[3] + 1, inds[0]:inds[2] + 1, :] = \ 214 | img[bbox_valid[1]:bbox_valid[3] + 1, bbox_valid[0]:bbox_valid[2] + 1, :] 215 | 216 | return crop 217 | 218 | 219 | def fixed_resize(sample, resolution, flagval=None): 220 | 221 | if flagval is None: 222 | if ((sample == 0) | (sample == 1)).all(): 223 | flagval = cv2.INTER_NEAREST 224 | else: 225 | flagval = cv2.INTER_CUBIC 226 | 227 | if isinstance(resolution, int): 228 | tmp = [resolution, resolution] 229 | tmp[np.argmax(sample.shape[:2])] = int(round(float(resolution)/np.min(sample.shape[:2])*np.max(sample.shape[:2]))) 230 | resolution = tuple(tmp) 231 | 232 | if sample.ndim == 2 or (sample.ndim == 3 and sample.shape[2] == 3): 233 | sample = cv2.resize(sample, resolution[::-1], interpolation=flagval) 234 | else: 235 | tmp = sample 236 | sample = np.zeros(np.append(resolution, tmp.shape[2]), dtype=np.float32) 237 | for ii in range(sample.shape[2]): 238 | sample[:, :, ii] = cv2.resize(tmp[:, :, ii], resolution[::-1], interpolation=flagval) 239 | return sample 240 | 241 | 242 | def crop_from_mask(img, mask, relax=0, zero_pad=False): 243 | if mask.shape[:2] != img.shape[:2]: 244 | mask = cv2.resize(mask, dsize=tuple(reversed(img.shape[:2])), interpolation=cv2.INTER_NEAREST) 245 | 246 | assert(mask.shape[:2] == img.shape[:2]) 247 | 248 | bbox = get_bbox(mask, pad=relax, zero_pad=zero_pad) 249 | 250 | if bbox is None: 251 | return None 252 | 253 | crop = crop_from_bbox(img, bbox, zero_pad) 254 | 255 | return crop 256 | 257 | 258 | def make_gaussian(size, sigma=10, center=None, d_type=np.float64): 259 | """ Make a square gaussian kernel. 260 | size: is the dimensions of the output gaussian 261 | sigma: is full-width-half-maximum, which 262 | can be thought of as an effective radius. 263 | """ 264 | 265 | x = np.arange(0, size[1], 1, float) 266 | y = np.arange(0, size[0], 1, float) 267 | y = y[:, np.newaxis] 268 | 269 | if center is None: 270 | x0 = y0 = size[0] // 2 271 | else: 272 | x0 = center[0] 273 | y0 = center[1] 274 | 275 | return np.exp(-4 * np.log(2) * ((x - x0) ** 2 + (y - y0) ** 2) / sigma ** 2).astype(d_type) 276 | 277 | 278 | def make_gt(img, labels, sigma=10, one_mask_per_point=False): 279 | """ Make the ground-truth for landmark. 280 | img: the original color image 281 | labels: label with the Gaussian center(s) [[x0, y0],[x1, y1],...] 282 | sigma: sigma of the Gaussian. 283 | one_mask_per_point: masks for each point in different channels? 284 | """ 285 | h, w = img.shape[:2] 286 | if labels is None: 287 | gt = make_gaussian((h, w), center=(h//2, w//2), sigma=sigma) 288 | else: 289 | labels = np.array(labels) 290 | if labels.ndim == 1: 291 | labels = labels[np.newaxis] 292 | if one_mask_per_point: 293 | gt = np.zeros(shape=(h, w, labels.shape[0])) 294 | for ii in range(labels.shape[0]): 295 | gt[:, :, ii] = make_gaussian((h, w), center=labels[ii, :], sigma=sigma) 296 | else: 297 | gt = np.zeros(shape=(h, w), dtype=np.float64) 298 | for ii in range(labels.shape[0]): 299 | gt = np.maximum(gt, make_gaussian((h, w), center=labels[ii, :], sigma=sigma)) 300 | 301 | gt = gt.astype(dtype=img.dtype) 302 | 303 | return gt 304 | 305 | 306 | def cstm_normalize(im, max_value): 307 | """ 308 | Normalize image to range 0 - max_value 309 | """ 310 | imn = max_value*(im - im.min()) / max((im.max() - im.min()), 1e-8) 311 | return imn 312 | 313 | 314 | def generate_param_report(logfile, param): 315 | log_file = open(logfile, 'w') 316 | for key, val in param.items(): 317 | log_file.write(key+':'+str(val)+'\n') 318 | log_file.close() 319 | -------------------------------------------------------------------------------- /helpers/annotools.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import io 3 | from ruamel.yaml import YAML 4 | import ruamel.yaml.util as ymlutil 5 | from os import path, makedirs, listdir 6 | import warnings 7 | from PIL import Image 8 | import numpy as np 9 | 10 | 11 | # configure ruamel.yaml 12 | yaml = YAML() 13 | yaml.default_flow_style = False 14 | 15 | 16 | # setup custom exception classes 17 | class ConfigFileWarning(UserWarning): 18 | pass 19 | 20 | 21 | class ConfigFileError(Exception): 22 | pass 23 | 24 | 25 | # ----------------------------------------General helper functions------------------------------------------------------ 26 | 27 | def _query_yes_no(question, default="no"): 28 | """Ask a yes/no question via raw_input() and return their answer. 29 | 30 | "question" is a string that is presented to the user. 31 | "default" is the presumed answer if the user just hits . 32 | It must be "yes" (the default), "no" or None (meaning 33 | an answer is required of the user). 34 | 35 | The "answer" return value is True for "yes" or False for "no". 36 | """ 37 | valid = {"yes": True, "y": True, "ye": True, "no": False, "n": False} 38 | if default is None: 39 | prompt = " [y/n] " 40 | elif default == "yes": 41 | prompt = " [Y/n] " 42 | elif default == "no": 43 | prompt = " [y/N] " 44 | else: 45 | raise ValueError("invalid default answer: '%s'" % default) 46 | 47 | while True: 48 | sys.stdout.write(question + prompt) 49 | choice = input().lower() 50 | if default is not None and choice == '': 51 | return valid[default] 52 | elif choice in valid: 53 | return valid[choice] 54 | else: 55 | sys.stdout.write("Please respond with 'yes' or 'no' (or 'y' or 'n').\n") 56 | 57 | 58 | def _files_to_annotate(source): 59 | dir_contains = listdir(source) 60 | to_annotate = [] 61 | for name in dir_contains: 62 | if name.endswith('.jpg') or name.endswith('.png'): 63 | to_annotate.append(name) 64 | 65 | return to_annotate 66 | 67 | 68 | # -------------------------------------Functions for progress tracking-------------------------------------------------- 69 | 70 | def _update_filecache(config, filename): 71 | # parse file names already in the progress logging file 72 | with io.open(filename, 'r') as progress_file: 73 | _progress = yaml.load(progress_file) 74 | 75 | curr_files = list(_progress['annotated']) 76 | all_files = _files_to_annotate(config['required']['source']) 77 | # make new dict based on files not in currently listed files (curr_files) 78 | new_files = [f for f in all_files if f not in curr_files] 79 | new_files.sort() # sort the list 80 | tmp_extra_dict = {f: 'no' for f in new_files} 81 | # append this new dict to current dict 82 | _progress['annotated'].update(tmp_extra_dict) 83 | 84 | # write this to file 85 | with io.open(filename, 'w+') as progress_file: 86 | yaml.dump(_progress, progress_file) 87 | 88 | 89 | def _get_to_annotate(filename): 90 | # load progress file again and return list of keys (filenames) from annotated section 91 | with io.open(filename, 'r') as progfile: 92 | progress = yaml.load(progfile) 93 | 94 | files_anno = [] # to hold file names for files to be annotated 95 | for file, status in list(progress['annotated'].items()): 96 | if status.lower() == 'no': 97 | files_anno.append(file) 98 | 99 | return files_anno 100 | 101 | 102 | def _prog_update_done(config, source_filename): 103 | 104 | # open and edit the progress file 105 | progressfile_path = path.join(config['required']['source'], config['admin']['progress_file']) 106 | 107 | with io.open(progressfile_path, 'r') as progress_file: 108 | progress = yaml.load(progress_file) 109 | 110 | progress['annotated'][source_filename] = 'yes' 111 | 112 | # save update to progress file 113 | with io.open(progressfile_path, 'w+') as progress_file: 114 | yaml.dump(progress, progress_file) 115 | 116 | 117 | def prog_update_skip(config, source_filename): 118 | 119 | # open and edit the progress file 120 | progressfile_path = path.join(config['required']['source'], config['admin']['progress_file']) 121 | 122 | with io.open(progressfile_path, 'r') as progress_file: 123 | progress = yaml.load(progress_file) 124 | 125 | progress['annotated'][source_filename] = 'skip' 126 | 127 | # save update to progress file 128 | with io.open(progressfile_path, 'w+') as progress_file: 129 | yaml.dump(progress, progress_file) 130 | 131 | 132 | def _make_progfile(config, filename): 133 | # parse the folder and add filename to a dict 134 | files = _files_to_annotate(config['required']['source']) 135 | files.sort() # return sorted list 136 | 137 | if not files: 138 | raise ConfigFileError("No jpg or png files found in the source folder specified in the config file. Aborting " 139 | "creation of progress file and exiting...") 140 | 141 | # make a temporary dict to store file names and annotation status 142 | tmp_anno_dict = {f: 'no' for f in files} 143 | # make a temporary dict to get come info from the config file dict with the the same section headers 144 | tmp_info_dict = config['required'] 145 | 146 | # join the dicts into common dict to be saved into the progress tracking file 147 | _progress = {'required': tmp_info_dict, 'annotated': tmp_anno_dict} 148 | 149 | with io.open(filename, 'w') as progfile: 150 | yaml.dump(_progress, progfile) 151 | 152 | print("Annotation progress tracking file created.\n\n" 153 | "********************************************************************************\n" 154 | "Tip: if the source directory changes (i.e. if files are added/removed), in config\n" 155 | "file set 'update: True' to update the file-names cache in the progress tracker.\n" 156 | "********************************************************************************") 157 | 158 | 159 | def config_prog_log(config, cfg_file): 160 | 161 | # progress file settings 162 | _progress_filename = path.join(config['required']['source'], config['admin']['progress_file']) 163 | 164 | # when progress file doesn't exist 165 | if not path.isfile(_progress_filename): 166 | # idiot check 167 | if config['admin']['reset_progress']: 168 | warnings.warn("You are resetting the progress file before it was setup. Ignoring 'reset: True' and setting" 169 | "'reset: False' in config file to prevent any errors later.", ConfigFileWarning) 170 | sys.stderr.flush() # to print warning immediately 171 | _modify_config(cfg_file, 'reset_progress', False) 172 | if config['optional']['update']: 173 | warnings.warn("You are updating the progress file before it was setup. Ignoring 'update: True' and" 174 | " continuing to make the progress file. If required, please change 'update:' status in the" 175 | " config file.", ConfigFileWarning) 176 | sys.stderr.flush() # to print warning immediately 177 | 178 | # prompt user that file will be created 179 | print("Progress file that tracks your annotation history doesn't exist. So:\n" 180 | "(1) either this is the first time you are annotating files in the provided source folder, or\n" 181 | "(2) you deleted the file. Please recover it if you want to continue from last annotated image.\n" 182 | "In either case, will try to create a new progress tracking file now...") 183 | _make_progfile(config, _progress_filename) 184 | 185 | # when progress file exists 186 | elif path.isfile(_progress_filename): 187 | # idiot check 188 | if config['admin']['reset_progress'] and config['optional']['update']: 189 | raise ConfigFileError("You've set 'reset: True' and 'update: True'. What do you want? If you want to...\n" 190 | "- reset annotation progress, then set 'reset: True' and 'update: False'.\n" 191 | "- update annotation progress file-cache, then set 'update: True' and 'reset: False'.\n" 192 | "Exiting now...") 193 | elif config['admin']['reset_progress']: 194 | reset_prompt = _query_yes_no("In config file 'reset: True'. Thus, you'll lose your annotation progress so " 195 | "far.\n Are you sure? Enter 'yes' to continue resetting or 'no' otherwise") 196 | if reset_prompt: 197 | print("Okay then. Remember you've been warned. Resetting...") 198 | _make_progfile(config, _progress_filename) 199 | elif not reset_prompt: 200 | print("Phew...be careful don't modify stuff you don't understand. Ignoring resetting...") 201 | elif config['optional']['update']: 202 | print("In config file 'update: True', so source folder will be scanned again and new files will be added to" 203 | " progress tracking cache.") 204 | _update_filecache(config, _progress_filename) 205 | 206 | # load config file again in case any modifications were made 207 | with io.open(cfg_file, 'r') as conffile: 208 | updated_config = yaml.load(conffile) 209 | 210 | to_annotate = _get_to_annotate(_progress_filename) 211 | 212 | return updated_config, to_annotate 213 | 214 | 215 | # -------------------------------------Configuration file operations---------------------------------------------------- 216 | 217 | def _modify_config(cfg_file, param, param_new_value=False): 218 | # open file 219 | config, ind, bsi = ymlutil.load_yaml_guess_indent(open(cfg_file)) 220 | 221 | if param is 'reset_progress': 222 | config['admin']['reset_progress'] = param_new_value 223 | elif param is 'update': 224 | config['optional']['update'] = param_new_value 225 | 226 | with io.open(cfg_file, 'w') as conffile: 227 | yaml.dump(config, conffile) 228 | 229 | 230 | def load_config(cfg_file): 231 | 232 | # load config from specified yaml file 233 | with io.open(cfg_file, 'r') as conffile: 234 | config = yaml.load(conffile) 235 | 236 | # source folder exists? 237 | if not path.exists(config['required']['source']): 238 | raise ConfigFileError("The source folder doesn't exist. Please properly set folder in config file. Exiting...") 239 | 240 | # destination folder exists? No, make it. 241 | if not path.exists(config['required']['destination']): 242 | warnings.warn("The specified destination folder for segmasks doesn't exist, making it.", ConfigFileWarning) 243 | sys.stderr.flush() # to print warning immediately 244 | makedirs(config['required']['destination']) 245 | 246 | return config 247 | 248 | 249 | # --------------------------------Functions for generating and saving mask images--------------------------------------- 250 | 251 | def get_img_segmasks(im, masks): 252 | orig_img = im.copy() 253 | # not sure if this is required 254 | if isinstance(masks, np.ndarray): 255 | masks = [masks] 256 | # full image mask 257 | img_mask = np.zeros([im.shape[0], im.shape[1]]) # init full image mask with zeros 258 | for n_mask in range(len(masks)): 259 | curr_mask = masks[n_mask] 260 | img_mask[curr_mask] = n_mask + 1 261 | 262 | return img_mask 263 | 264 | 265 | def save_mask(mask_array, config, source_filename): 266 | # get the mask image name 267 | source_file_initial, _ = source_filename.split('.') 268 | mask_file_name = source_file_initial + config['optional']['mask_suffix'] 269 | 270 | # make mask image from mask array and save it to disk 271 | mask_array_mod = mask_array.astype(np.uint8) # convert array to uint8 before saving 272 | 273 | if config['optional']['save_format'] != 'png' and config['optional']['save_format'] != 'npy': 274 | raise ConfigFileError("Mask save format can only be 'png' or 'npy'. Change setting in config file.") 275 | elif config['optional']['save_format'] == 'png': 276 | mask_img = Image.fromarray(mask_array_mod, mode='L') # mode L is for 8-bit pixels, black and white 277 | mask_name_full = mask_file_name + '.png' 278 | mask_save_path = path.join(config['required']['destination'], mask_name_full) 279 | mask_img.save(mask_save_path) 280 | elif config['optional']['save_format'] == 'npy': 281 | mask_save_path = path.join(config['required']['destination'], mask_file_name) 282 | np.save(mask_save_path, mask_array_mod) 283 | 284 | # now update the progress file 285 | _prog_update_done(config, source_filename) 286 | 287 | return mask_array_mod 288 | -------------------------------------------------------------------------------- /annotate.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | 3 | import argparse 4 | from os import path 5 | 6 | from PIL import Image 7 | import numpy as np 8 | from matplotlib import pyplot as plt 9 | from matplotlib.widgets import Button 10 | 11 | import tensorflow as tf 12 | from keras import backend as K 13 | 14 | # ---user-defined modules--- 15 | # repo defined 16 | from networks.dextr import DEXTR 17 | from mypath import Path 18 | from helpers import helpers as helpers 19 | from helpers import annotools as tools 20 | 21 | modelName = 'dextr_pascal-sbd' 22 | pad = 50 23 | thres = 0.8 24 | gpu_id = 0 25 | 26 | 27 | if __name__ == "__main__": 28 | 29 | # parse command line arguments 30 | parser = argparse.ArgumentParser(description='annotation tool based on DEXTR for annotating segmentation masks for' 31 | ' images') 32 | required_arguments = parser.add_argument_group('Required arguments') 33 | required_arguments.add_argument('-c', '--config', type=str, help='path to the yaml file that holds the' 34 | ' configuration', required=True) 35 | args = parser.parse_args() 36 | 37 | # ---------------------------------Setting up based on yaml config-------------------------------------------------- 38 | 39 | # load (and check) the user defined config 40 | cfg = tools.load_config(args.config) 41 | # setup progress file: check config to detect what should be done, return files that need to be annotated 42 | cfg, files_to_annotate = tools.config_prog_log(cfg, args.config) 43 | 44 | if not files_to_annotate: 45 | print("Yippie...finished annotating all files in the source folder. Exiting...") 46 | exit(0) 47 | 48 | # ------------------------------------Setup TensorFlow session------------------------------------------------------ 49 | # Handle input and output args 50 | sess = tf.Session() 51 | K.set_session(sess) 52 | 53 | with sess.as_default(): 54 | net = DEXTR(nb_classes=1, resnet_layers=101, input_shape=(512, 512), weights=modelName, 55 | num_input_channels=4, classifier='psp', sigmoid=True) 56 | 57 | # -----------------------------------Start annotation interface------------------------------------------------- 58 | _KEEPannotating = True # annotation status flag 59 | 60 | # Initialise the UI 61 | plt.switch_backend('TkAgg') # force 'TkAgg' backend 62 | ui = plt.figure('Annotation Interface') 63 | manager = plt.get_current_fig_manager() 64 | manager.resize(*manager.window.maxsize()) # maximize window 65 | plt.ion() # setup interactive mode 66 | 67 | for filename in files_to_annotate: 68 | 69 | if _KEEPannotating: 70 | 71 | # read image 72 | imagepath = path.join(cfg['required']['source'], filename) 73 | image = np.array(Image.open(imagepath)) 74 | 75 | # image level status flags 76 | _ANNOinit = False 77 | _lastANNOwrong = False 78 | _SKIP_IMAGE = False 79 | 80 | # -----Setup figure for annotating ----- 81 | ui.clear() 82 | 83 | gs = ui.add_gridspec(2, 2, width_ratios=[5, 1], height_ratios=[11, 1]) 84 | # setup window for images 85 | ui_img = ui.add_subplot(gs[0]) 86 | ui_img.axis('off') 87 | ui_img.imshow(image) 88 | # window for annotation instructions 89 | ui_instruct = ui.add_subplot(gs[1], sharex=ui_img, sharey=ui_img) 90 | ui_instruct.axis('off') 91 | instruct_msg = ui_instruct.text(0, 0, 92 | 'Instructions:\n' 93 | '1. Use the left mouse button to select the\n' 94 | 'following four extreme points for an object:\n' 95 | '- left,\n' 96 | '- right,\n' 97 | '- top, and\n' 98 | '- bottom.\n\n' 99 | '2. In case of misselection, use right mouse\n' 100 | 'button to undo.\n\n' 101 | '3. When the 4 points are selected, the\n' 102 | 'resulting masks are overlayed.\n\n' 103 | '4. Status messages are displayed at bottom\n' 104 | 'of the image; provide input based on them.\n\n' 105 | '5. Close window to exit.', 106 | fontsize=12, wrap=True, va='top', ha='left') 107 | # window for displaying status 108 | ui_status = ui.add_subplot(gs[2], sharey=ui_img) # window for displaying status 109 | ui_status.axis('off') 110 | 111 | # hold predictions from image 112 | image_results = [] 113 | 114 | # keep running till user says he had finished annotating all objects in an image 115 | while True: 116 | 117 | if not _ANNOinit: 118 | ui_msg = ui_status.text(0, 0, 'Start annotation...', color='green', wrap=True, fontsize=14, 119 | va='top', ha='left') 120 | _ANNOinit = True 121 | 122 | elif _ANNOinit: 123 | if not _lastANNOwrong: 124 | ui_msg = ui_status.text(0, 0, 'Continue annotation...', color='green', wrap=True, 125 | fontsize=14, va='top', ha='left') 126 | elif _lastANNOwrong: 127 | ui_img.imshow(helpers.overlay_masks(image / 255, image_results)) 128 | ui_msg = ui_status.text(0, 0, 'Ok. try again...', color='green', wrap=True, 129 | fontsize=14, va='top', ha='left') 130 | 131 | 132 | # user input required 133 | extreme_points_ori = np.array(plt.ginput(4, timeout=0, mouse_add=1)).astype(np.int) 134 | 135 | ui_msg.remove() 136 | 137 | # Crop image to the bounding box from the extreme points and resize 138 | bbox = helpers.get_bbox(image, points=extreme_points_ori, pad=pad, zero_pad=True) 139 | crop_image = helpers.crop_from_bbox(image, bbox, zero_pad=True) 140 | resize_image = helpers.fixed_resize(crop_image, (512, 512)).astype(np.float32) 141 | 142 | # Generate extreme point heat map normalized to image values 143 | extreme_points = extreme_points_ori - [np.min(extreme_points_ori[:, 0]), 144 | np.min(extreme_points_ori[:, 1])] + [pad, pad] 145 | extreme_points = (512 * extreme_points * [1 / crop_image.shape[1], 1 / crop_image.shape[0]]).astype( 146 | np.int) 147 | extreme_heatmap = helpers.make_gt(resize_image, extreme_points, sigma=10) 148 | extreme_heatmap = helpers.cstm_normalize(extreme_heatmap, 255) 149 | 150 | # Concatenate inputs and convert to tensor 151 | input_dextr = np.concatenate((resize_image, extreme_heatmap[:, :, np.newaxis]), axis=2) 152 | 153 | # Run a forward pass 154 | pred = net.model.predict(input_dextr[np.newaxis, ...])[0, :, :, 0] 155 | result = helpers.crop2fullmask(pred, bbox, im_size=image.shape[:2], zero_pad=True, relax=pad) > thres 156 | 157 | # Mask correct or not 158 | image_results.append(result) # add result to list of results even if incorrect for vis purposes 159 | plot_mask = ui_img.imshow(helpers.overlay_masks(image / 255, image_results)) 160 | 161 | if _lastANNOwrong: 162 | ui_msg = ui_status.text(0, 0, 'Is the mask now correct? Click left mouse for yes, press \'n\' ' 163 | 'for no.', 164 | color='orange', wrap=True, fontsize=14, va='top', ha='left') 165 | usr_choice = plt.waitforbuttonpress() 166 | if usr_choice: 167 | ui_msg.remove() 168 | ui_msg = ui_status.text(0, 0, 'Okay, to try annotating one more time click left mouse or ' 169 | 'press \'n\' to skip to the new image.', 170 | color='red', wrap=True, fontsize=14, va='top', ha='left') 171 | usr_choice_skip = plt.waitforbuttonpress() 172 | if usr_choice_skip: 173 | ui_msg.remove() 174 | _SKIP_IMAGE = True 175 | _lastANNOwrong = False 176 | break 177 | else: 178 | ui_msg.remove() 179 | wrong_anno_mask = image_results.pop() 180 | _lastANNOwrong = True 181 | continue 182 | else: 183 | _lastANNOwrong = False 184 | ui_msg.remove() 185 | else: 186 | ui_msg = ui_status.text(0, 0, 'Is the generated mask correct? Click left mouse for yes, press' 187 | ' \'r\' to redo.', 188 | color='orange', wrap=True, fontsize=14, va='top', ha='left') 189 | not_anno_correct = plt.waitforbuttonpress() 190 | ui_msg.remove() 191 | if not_anno_correct: 192 | wrong_anno_mask = image_results.pop() 193 | _lastANNOwrong = True 194 | continue 195 | 196 | # Add extreme points to show annotation done for the object 197 | ui_img.plot(extreme_points_ori[:, 0], extreme_points_ori[:, 1], 'gx') 198 | # Check what user wants to do next 199 | ui_msg = ui_status.text(0, 0, 'Click left mouse to continue annotating this image or press \'d\'' 200 | ' if done.', 201 | color='orange', wrap=True, fontsize=14, va='top', ha='left') 202 | usr_choice = plt.waitforbuttonpress() 203 | ui_msg.remove() 204 | 205 | if usr_choice: # if user presses d then break 206 | ui_msg = ui_status.text(0, 0, 'Annotation of this image stopped, mask image saved and progress' 207 | ' updated', 208 | color='red', wrap=True, fontsize=14, va='top', ha='left') 209 | break 210 | 211 | if _SKIP_IMAGE: # check if the break was initialised by skipping image 212 | tools.prog_update_skip(cfg, filename) # update prog file 213 | continue # if so, then continue on to next image 214 | 215 | # Show and save the generated masks 216 | generated_mask = tools.get_img_segmasks(image, image_results) 217 | mask_uint8 = tools.save_mask(generated_mask, cfg, filename) 218 | 219 | # -----Setup figure for showing annotation results ----- 220 | ui.clear() 221 | # update figure to show subplots 222 | gs_res = ui.add_gridspec(2, 2, height_ratios=[11, 1]) 223 | # subplot-1: image 224 | ui_res_img = ui.add_subplot(gs_res[0]) 225 | ui_res_img.axis('off') 226 | ui_res_img.imshow(image) 227 | ui_res_img.set_title('Original Image') 228 | # subplot-2: mask image 229 | ui_res_mask = ui.add_subplot(gs_res[1]) 230 | ui_res_mask.axis('off') 231 | ui_res_mask.imshow(mask_uint8, cmap='gray') 232 | ui_res_mask.set_title('Mask Image\n Note: Mask value scaled to [0 255] for visualisation') 233 | # subplot-3: status message 234 | ui_res_status = ui.add_subplot(gs_res[2], sharey=ui_res_img) # window for displaying status 235 | ui_res_status.axis('off') 236 | ui_res_msg = ui_res_status.text(0.0, 0.0, 'Annotate next image? Click left mouse for yes or press \'e\'' 237 | ' to exit annotation and resume later', 238 | color='orange', wrap=True, fontsize=14, va='top', ha='left') 239 | 240 | # user input required 241 | usr_choice = plt.waitforbuttonpress() 242 | if usr_choice: # if user presses e then break 243 | _KEEPannotating = False 244 | ui_res_msg.remove() 245 | else: 246 | print("Annotation of images stopped. Your progress has been saved. Run script again to resume " 247 | "annotation. Exiting...") 248 | exit(0) 249 | 250 | print("Well Done...Finished annotating all files in the source folder. Exiting...") 251 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. By contrast, 15 | the GNU General Public License is intended to guarantee your freedom to 16 | share and change all versions of a program--to make sure it remains free 17 | software for all its users. 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Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------