├── public └── .gitignore ├── .gitignore ├── content ├── valid │ ├── 1.PNG │ ├── 10.PNG │ ├── 11.PNG │ ├── 12.PNG │ ├── 13.PNG │ ├── 14.PNG │ ├── 15.PNG │ ├── 16.PNG │ ├── 17.PNG │ ├── 18.PNG │ ├── 19.PNG │ ├── 2.PNG │ ├── 20.PNG │ ├── 21.PNG │ ├── 22.PNG │ ├── 23.PNG │ ├── 24.PNG │ ├── 25.PNG │ ├── 26.PNG │ ├── 27.PNG │ ├── 28.PNG │ ├── 29.PNG │ ├── 3.PNG │ ├── 30.PNG │ ├── 31.PNG │ ├── 32.PNG │ ├── 33.PNG │ ├── 34.PNG │ ├── 35.PNG │ ├── 36.PNG │ ├── 37.PNG │ ├── 38.PNG │ ├── 39.PNG │ ├── 4.PNG │ ├── 40.PNG │ ├── 41.PNG │ ├── 42.PNG │ ├── 43.PNG │ ├── 44.PNG │ ├── 45.PNG │ ├── 46.PNG │ ├── 47.PNG │ ├── 48.PNG │ ├── 49.PNG │ ├── 5.PNG │ ├── 50.PNG │ ├── 6.PNG │ ├── 7.PNG │ ├── 8.PNG │ ├── 9.PNG │ └── _annotations.coco.json └── train │ ├── z197.PNG │ ├── z198.PNG │ ├── z199.PNG │ ├── z200.PNG │ ├── 3i (47).PNG │ ├── 3i (48).PNG │ ├── 3i (49).PNG │ ├── 3i (51).PNG │ ├── 3i (53).PNG │ ├── 3i (54).PNG │ ├── 3i (55).PNG │ ├── 3i (56).PNG │ ├── 3i (57).PNG │ ├── 3i (58).PNG │ ├── 3i (59).PNG │ ├── 3i (61).PNG │ ├── 3i (63).PNG │ ├── 3i (65).PNG │ ├── 3i (66).PNG │ ├── 3i (67).PNG │ ├── 3i (69).PNG │ ├── 3i (71).PNG │ ├── 3i (72).PNG │ ├── 3i (73).PNG │ ├── 3i (74).PNG │ ├── 3i (75).PNG │ ├── 3i (76).PNG │ ├── 3i (77).PNG │ ├── 3i (79).PNG │ ├── 3i (81).PNG │ ├── 3i (82).PNG │ ├── 3i (83).PNG │ ├── 3i (84).PNG │ ├── 3i (85).PNG │ ├── 3i (86).PNG │ ├── 3i (91).PNG │ ├── 3i (92).PNG │ ├── 3i (94).PNG │ ├── 3i (96).PNG │ ├── 3i (98).PNG │ ├── q (1).PNG │ ├── q (10).PNG │ ├── q (11).PNG │ ├── q (12).PNG │ ├── q (13).PNG │ ├── q (14).PNG │ ├── q (15).PNG │ ├── q (16).PNG │ ├── q (17).PNG │ ├── q (18).PNG │ ├── q (19).PNG │ ├── q (2).PNG │ ├── q (20).PNG │ ├── q (21).PNG │ ├── q (22).PNG │ ├── q (23).PNG │ ├── q (24).PNG │ ├── q (25).PNG │ ├── q (26).PNG │ ├── q (27).PNG │ ├── q (28).PNG │ ├── q (29).PNG │ ├── q (3).PNG │ ├── q (30).PNG │ ├── q (31).PNG │ ├── q (32).PNG │ ├── q (33).PNG │ ├── q (34).PNG │ ├── q (35).PNG │ ├── q (36).PNG │ ├── q (37).PNG │ ├── q (38).PNG │ ├── q (39).PNG │ ├── q (4).PNG │ ├── q (40).PNG │ ├── q (41).PNG │ ├── q (42).PNG │ ├── q (43).PNG │ ├── q (44).PNG │ ├── q (45).PNG │ ├── q (46).PNG │ ├── q (47).PNG │ ├── q (48).PNG │ ├── q (49).PNG │ ├── q (5).PNG │ ├── q (50).PNG │ ├── q (51).PNG │ ├── q (52).PNG │ ├── q (53).PNG │ ├── q (54).PNG │ ├── q (55).PNG │ ├── q (56).PNG │ ├── q (57).PNG │ ├── q (58).PNG │ ├── q (59).PNG │ ├── q (6).PNG │ ├── q (60).PNG │ ├── q (61).PNG │ ├── q (62).PNG │ ├── q (63).PNG │ ├── q (64).PNG │ ├── q (65).PNG │ ├── q (66).PNG │ ├── q (67).PNG │ ├── q (68).PNG │ ├── q (69).PNG │ ├── q (7).PNG │ ├── q (70).PNG │ ├── q (71).PNG │ ├── q (72).PNG │ ├── q (73).PNG │ ├── q (74).PNG │ ├── q (75).PNG │ ├── q (76).PNG │ ├── q (77).PNG │ ├── q (78).PNG │ ├── q (79).PNG │ ├── q (8).PNG │ ├── q (80).PNG │ ├── q (81).PNG │ ├── q (82).PNG │ ├── q (83).PNG │ ├── q (84).PNG │ ├── q (85).PNG │ ├── q (86).PNG │ ├── q (87).PNG │ ├── q (88).PNG │ ├── q (89).PNG │ ├── q (9).PNG │ ├── q (90).PNG │ ├── q (91).PNG │ ├── q (92).PNG │ ├── q (93).PNG │ ├── q (94).PNG │ ├── q (95).PNG │ ├── w (1).PNG │ ├── w (10).PNG │ ├── w (11).PNG │ ├── w (12).PNG │ ├── w (13).PNG │ ├── w (14).PNG │ ├── w (15).PNG │ ├── w (2).PNG │ ├── w (3).PNG │ ├── w (4).PNG │ ├── w (5).PNG │ ├── w (6).PNG │ ├── w (7).PNG │ ├── w (8).PNG │ └── w (9).PNG ├── __pycache__ └── main.cpython-38.pyc ├── coco_eval └── instances_predictions.pth ├── inferenceContent ├── input │ ├── 1605093291894X9lSAxKRQ.png │ ├── 1605093311288yTJoC8gCO.png │ ├── 1605093321272qkEPt5N4q.png │ ├── 1605093382698GhQrcl4vJ.png │ └── 1605093391941wTIBFvsO7.png ├── output │ ├── 1605093291894X9lSAxKRQ.png │ ├── 1605093311288yTJoC8gCO.png │ ├── 1605093321272qkEPt5N4q.png │ ├── 1605093382698GhQrcl4vJ.png │ └── 1605093391941wTIBFvsO7.png └── outputData │ └── outputData.json ├── views ├── footer.ejs ├── head.ejs ├── scripts.ejs ├── header.ejs └── index.ejs ├── package.json ├── README.md ├── main.py ├── inferenceSingle.py ├── conda-spec-file.txt └── index.js 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"node index.js", 9 | "test": "echo \"Error: no test specified\" && exit 1" 10 | }, 11 | "keywords": [], 12 | "author": "", 13 | "license": "ISC", 14 | "dependencies": { 15 | "express": "^4.17.1", 16 | "mime-types": "^2.1.27", 17 | "multer": "^1.4.2", 18 | "shortid": "^2.2.15", 19 | "ejs": "^3.1.2", 20 | "shelljs": "0.8.4" 21 | }, 22 | "devDependencies": { 23 | "nodemon": "^2.0.4" 24 | } 25 | } 26 | -------------------------------------------------------------------------------- /views/scripts.ejs: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | -------------------------------------------------------------------------------- /views/header.ejs: -------------------------------------------------------------------------------- 1 |
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4 | Web Design Prototype Segmentation:
5 | Using Fast-RCNN to find design elements 6 |

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14 | Page - Contact Us 15 | Page - About 16 | Page - Products 17 | Page - Homepage 18 | Page - Blog 19 |
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-------------------------------------------------------------------------------- /inferenceContent/outputData/outputData.json: -------------------------------------------------------------------------------- 1 | {"AllClasses": ["contactsPhone", "contactsSubtitle", "contactsSendformButton", "contactsEmail", "contactsTitle", "contactsAddress", "contactsEmailFormInput", "contactsMessageFormInput", "contactsSocialButtons", "contactsNameFormInput", "contactsSubjectFormInput", "contactsDescription"], "imageData": {"1605093291894X9lSAxKRQ.png": ["contactsTitle 95%", "contactsNameFormInput 95%", "contactsSendformButton 91%", "contactsNameFormInput 89%", "contactsEmailFormInput 89%", "contactsMessageFormInput 87%", "contactsDescription 79%", "contactsNameFormInput 78%", "contactsEmailFormInput 76%", "contactsNameFormInput 71%"], "1605093311288yTJoC8gCO.png": ["contactsNameFormInput 89%", "contactsTitle 88%", "contactsNameFormInput 88%", "contactsEmailFormInput 79%", "contactsSendformButton 78%", "contactsNameFormInput 71%"], "1605093321272qkEPt5N4q.png": ["contactsSendformButton 98%", "contactsEmailFormInput 77%", "contactsNameFormInput 76%"], "1605093382698GhQrcl4vJ.png": ["contactsSendformButton 99%", "contactsMessageFormInput 99%", "contactsTitle 93%", "contactsNameFormInput 91%", "contactsEmailFormInput 84%", "contactsTitle 83%", "contactsAddress 73%", "contactsNameFormInput 69%", "contactsDescription 65%"], "1605093391941wTIBFvsO7.png": ["contactsSendformButton 100%", "contactsMessageFormInput 96%", "contactsSocialButtons 95%", "contactsNameFormInput 88%", "contactsNameFormInput 81%", "contactsEmailFormInput 76%", "contactsDescription 67%"]}} -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Readme 2 | 3 | ## Overview 4 | 5 | This is a custom implementation of Detectron2 Fast-RCNN, which can find elements - buttons, titles, different input fields and much more - in any web design prototype or web UI image. 6 | 7 | The implementation comes with a client, which can be used locally, to quickly and easily upload images for inference and get visualised results. The client creates directories and manages images, runs shell scripts and helps to visualise output data. It also serves as a proof-of-concept implementation of complex machine learning technologies in a user-friendly and highly accessible format for all users. 8 | 9 | This project aims to show that it is possible to visually analyse UI design prototype structure automatically using RCNN technology. Furthermore, it shows that this functionality and its value can be served online (if hosted on a webserver) and can be used by interested users (designers) in the browser. 10 | 11 | # Installation 12 | 13 | ## Install Cuda and cuDNN 14 | 15 | Below is the setup process. Assuming you have Ubuntu 20.04 installed with an Nvidia GPU available. 16 | 17 | To make pytorch work with GPU, it is necessary to install CUDA and cuDNN. If working from Ubuntu 20.04 the [following guide](https://askubuntu.com/questions/1230645/when-is-cuda-gonna-be-released-for-ubuntu-20-04) is suitable. 18 | 19 | 20 | **After installing both CUDA and cuDNN check CUDA and cuDNN are version 10.1 and 7.6.5 respectively:** 21 | 22 | **CUDA:** 23 | 24 | $ nvcc -V 25 | 26 | **cuDNN:** 27 | 28 | get where cuda installed: 29 | 30 | $ whereis cuda 31 | then navigate to that folder's /include folder and: 32 | 33 | $ cat cudnn.h | grep CUDNN_MAJOR -A 2 34 | 35 | 36 | 37 | 38 | ## Activate conda venv 39 | 40 | ensure that conda is initialised with Python 3.8.3 (set it within conda environment creation). 41 | 42 | See [cheatsheet](https://docs.conda.io/projects/conda/en/4.6.0/_downloads/52a95608c49671267e40c689e0bc00ca/conda-cheatsheet.pdf) ! 43 | 44 | With conda environment activated: 45 | 46 | $ pip install tensorflow-gpu==2.2.0 47 | 48 | To install correct pytorch and torchvision do this with conda activated environment: 49 | 50 | $ conda install -c pytorch torchvision cudatoolkit=10.1 pytorch 51 | 52 | Check that GPU is available and working. In virtual environment `$ python`: 53 | 54 | import torch 55 | torch.cuda.current_device() 56 | torch.cuda.get_device_name(0) 57 | 58 | Then install some Detectron2 dependencies: 59 | 60 | $ pip install cython pyyaml==5.1 61 | $ pip install pycocotools 62 | $ pip install opencv-python 63 | 64 | Then install Detectron2: 65 | 66 | $ python -m pip install detectron2==0.2.1 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.6/index.html 67 | 68 | For more information see also Detectron2 [installation guide](https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md). 69 | 70 | 71 | 72 | **Note:** 73 | If using a different dataset than the one provided in this repository, ensure that the eval set contains at least 1 instance of all classes that can be encountered in the training dataset: 74 | 75 | cfg.DATASETS.TEST = ("my_dataset_val", 76 | 77 | 78 | Otherwise the validation step will fail at EVAL_PERIOD. 79 | 80 | Also, if you want to use a different dataset than the one provided, make sure that your data is marked according to COCO standard. A good tool to use is the [VGG annotator](http://www.robots.ox.ac.uk/~vgg/software/via/via_demo.html) (export annotations to COCO json). 81 | 82 | **Run from conda virtual env:** 83 | The model weights are not included in this repository, since the trained weights are too large. Hence, it is necessary to train the model before launching the web client and doing inference: 84 | 85 | $ python main.py 86 | 87 | Alternatively the weights and checkpoint folder can be downloaded here: ['link'](https://drive.google.com/drive/folders/1uPjHWBYHyAh6wSqbnlC-8xg8S3DUi7b3?usp=sharing) - all contents to be placed within 'output/' directory. 88 | **Output:** 89 | to graphically visualise and get very detailed data about model performance, after training completes, run: 90 | 91 | $ tensorboard --logdir output 92 | 93 | ## To Launch Web Client (NodeJS,ExpressJS) 94 | 95 | $ npm install 96 | then: 97 | 98 | $ npm run dev 99 | 100 | The app will then be ready and serve on port 3000. 101 | Important note: if the webserver is launched from a virtual environment like conda, you must activate the environment. Otherwise, the python scripts called by the webserver will not work. 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | # This file is meant to train the model on the sets provided in the /content folder 2 | # This file will also test on the resulting weights file and run inference for /content/valid images. 3 | # After the training finishes and it starts inferencing you can just escape the process ^C in shell 4 | # After the training finishes you can launch the webserver and it will be fully functional. See Readme for more details. 5 | 6 | # Basic setup 7 | # Setup detectron2 logger 8 | import detectron2 9 | from detectron2.utils.logger import setup_logger 10 | setup_logger() 11 | 12 | # import common libraries 13 | import numpy as np 14 | from cv2 import cv2 15 | import random 16 | import os 17 | 18 | # import common detectron2 utilities 19 | from detectron2 import model_zoo 20 | from detectron2.engine import DefaultTrainer 21 | from detectron2.engine import DefaultPredictor 22 | from detectron2.config import get_cfg 23 | from detectron2.utils.visualizer import Visualizer 24 | from detectron2.data import MetadataCatalog 25 | from detectron2.data.catalog import DatasetCatalog 26 | from detectron2.data.datasets import register_coco_instances 27 | from detectron2.evaluation import COCOEvaluator 28 | from detectron2.data import build_detection_test_loader 29 | from detectron2.evaluation import inference_on_dataset 30 | 31 | register_coco_instances("my_dataset_train", {}, "content/train/_annotations.coco.json", "content/train") 32 | register_coco_instances("my_dataset_val", {}, "content/valid/_annotations.coco.json", "content/valid") 33 | 34 | #visualize training data 35 | my_dataset_train_metadata = MetadataCatalog.get("my_dataset_train") 36 | dataset_dicts = DatasetCatalog.get("my_dataset_train") 37 | 38 | for d in random.sample(dataset_dicts, 3): 39 | img = cv2.imread(d["file_name"]) 40 | visualizer = Visualizer(img[:, :, ::-1], metadata=my_dataset_train_metadata, scale=0.5) 41 | vis = visualizer.draw_dataset_dict(d) 42 | cv2.imshow('preview',vis.get_image()[:, :, ::-1]) 43 | cv2.waitKey(100) 44 | 45 | #Importing our own Trainer Module to use the COCO validation evaluation 46 | #during training. Otherwise no validation eval occurs. 47 | 48 | class CocoTrainer(DefaultTrainer): 49 | 50 | @classmethod 51 | def build_evaluator(cls, cfg, dataset_name, output_folder=None): 52 | 53 | if output_folder is None: 54 | os.makedirs("coco_eval", exist_ok=True) 55 | output_folder = "coco_eval" 56 | 57 | return COCOEvaluator(dataset_name, cfg, False, output_folder) 58 | 59 | # An overview of the various available models is available: https://github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md 60 | # Selecting the one with least VRAM requirement 61 | cfg = get_cfg() 62 | cfg.merge_from_file(model_zoo.get_config_file("COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml")) 63 | cfg.DATASETS.TRAIN = ("my_dataset_train",) 64 | cfg.DATASETS.TEST = ("my_dataset_val",) 65 | 66 | #Number of workers and 'images per batch' are effectively multipliers for training speed and VRAM requirement. 67 | # With this setup the model requires approximately 5GB VRAM during training 68 | cfg.DATALOADER.NUM_WORKERS = 2 69 | cfg.SOLVER.IMS_PER_BATCH = 2 70 | cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml") # Let training initialize from model zoo 71 | cfg.SOLVER.BASE_LR = 0.001 72 | cfg.SOLVER.WARMUP_ITERS = 1000 73 | cfg.SOLVER.MAX_ITER = 1500 #Suitable for proof-of-concept and quick training. Can raise if mAP is still rising at final epochs. If not rising, can overfit. 74 | cfg.SOLVER.STEPS = (1000, 1500) 75 | cfg.SOLVER.GAMMA = 0.05 76 | 77 | 78 | cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 62 79 | cfg.MODEL.ROI_HEADS.NUM_CLASSES = 12 #your number of classes + 1 80 | 81 | cfg.TEST.EVAL_PERIOD = 100 #Adjust this to set at what interval the model performance should be 82 | #evaluated. Currently adjusted for eval debug. 83 | 84 | 85 | os.makedirs(cfg.OUTPUT_DIR, exist_ok=True) 86 | trainer = CocoTrainer(cfg) 87 | 88 | 89 | # Set (resume=False) if running for the first time 90 | 91 | trainer.resume_or_load(resume=True) 92 | 93 | trainer.train() 94 | 95 | #Should now have "model_final.pth" weights file. 96 | 97 | 98 | 99 | # Run evaluation on the test set. Specify the sets appropriately if using multiple. 100 | """ 101 | cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, "model_final.pth") 102 | cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.85 103 | predictor = DefaultPredictor(cfg) 104 | evaluator = COCOEvaluator("my_dataset_val", cfg, False, output_dir="./output/") 105 | val_loader = build_detection_test_loader(cfg, "my_dataset_val") 106 | inference_on_dataset(trainer.model, val_loader, evaluator) 107 | 108 | print("Evaluation on test set finished") 109 | """ 110 | # Inference with Detectron2 Saved Weights on a previously unknown image 111 | 112 | cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, "model_final.pth") 113 | cfg.DATASETS.TEST = ("my_dataset_val", ) 114 | # set the testing threshold for this model 115 | threshold = 0.65 116 | cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = threshold 117 | predictor = DefaultPredictor(cfg) 118 | test_metadata = MetadataCatalog.get("my_dataset_val") 119 | 120 | from detectron2.utils.visualizer import ColorMode 121 | import glob 122 | 123 | print("Starting to output inference results. Predictions below confidence score of {} score ignored.".format(threshold)) 124 | #Set the correct format. If using JPG files set *.JPG instead of *.PNG ! Also it's case sensitive! 125 | for imageName in glob.glob('content/valid/*.PNG'): 126 | 127 | print("{}".format(imageName)) 128 | 129 | im = cv2.imread(imageName) 130 | outputs = predictor(im) 131 | v = Visualizer(im[:, :, ::-1], 132 | #Get class names from dataset train metadata to put on visualization. 133 | metadata=my_dataset_train_metadata, 134 | scale=1 135 | ) 136 | out = v.draw_instance_predictions(outputs["instances"].to("cpu")) 137 | cv2.imshow('Inference Preview',out.get_image()[:, :, ::-1]) 138 | cv2.waitKey(10000) 139 | 140 | #For single image inference see inferenceSingle.py file. -------------------------------------------------------------------------------- /inferenceSingle.py: -------------------------------------------------------------------------------- 1 | # You may need to restart your runtime prior to this, to let your installation take effect 2 | # Some basic setup: 3 | # Setup detectron2 and logger 4 | import detectron2 5 | from detectron2.utils.logger import setup_logger 6 | setup_logger() 7 | 8 | # import some common libraries 9 | import numpy as np 10 | from cv2 import cv2 11 | import random 12 | import os 13 | import argparse 14 | 15 | # import some common detectron2 utilities 16 | from detectron2 import model_zoo 17 | from detectron2.engine import DefaultPredictor 18 | from detectron2.config import get_cfg 19 | from detectron2.utils.visualizer import Visualizer 20 | from detectron2.utils.visualizer import _create_text_labels 21 | from detectron2.data import MetadataCatalog 22 | from detectron2.data.catalog import DatasetCatalog 23 | from detectron2.data.datasets import register_coco_instances 24 | from detectron2.utils.visualizer import ColorMode 25 | 26 | #Setup arguments 27 | parser = argparse.ArgumentParser(usage="run inference on one specified image") 28 | parser.add_argument('--image', required=True, help="relative path to image to run inference on") 29 | args = parser.parse_args() 30 | 31 | #Register dataset annotations in coco format. This is important for metadata used by Detectron, for example in inference. 32 | register_coco_instances("my_dataset_train", {}, "content/train/_annotations.coco.json", "content/train") 33 | register_coco_instances("my_dataset_val", {}, "content/valid/_annotations.coco.json", "content/valid") 34 | 35 | #Get metadata from training set, to provide metadata during inference. 36 | #This metadata allows the predictor to assign predictions to human-readable classes for output. 37 | my_dataset_train_metadata = MetadataCatalog.get("my_dataset_train") 38 | dataset_dicts = DatasetCatalog.get("my_dataset_train") 39 | 40 | #setup config for inference. 41 | cfg = get_cfg() 42 | cfg.merge_from_file(model_zoo.get_config_file("COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml")) 43 | cfg.DATASETS.TRAIN = ("my_dataset_train",) 44 | cfg.DATASETS.TEST = ("my_dataset_val",) 45 | cfg.DATALOADER.NUM_WORKERS = 2 46 | cfg.SOLVER.IMS_PER_BATCH = 2 47 | cfg.SOLVER.BASE_LR = 0.001 48 | cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 64 49 | cfg.MODEL.ROI_HEADS.NUM_CLASSES = 12 #your number of classes + 1 50 | 51 | # Inference with Detectron2 Saved Weights on a previously unknown image 52 | 53 | # specify trained weights file 54 | cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, "model_final.pth") 55 | cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.65 # set the class confidence threshold for this model! 56 | predictor = DefaultPredictor(cfg) 57 | 58 | 59 | print("Starting to output inference results. Predictions below confidence score of 0.65 score ignored.") 60 | 61 | #Initialise file for inference output data: 62 | import json 63 | 64 | #check if file has been written previously, and load it to be later merged with output data from new file 65 | if os.path.exists('inferenceContent/outputData/outputData.json'): 66 | with open('inferenceContent/outputData/outputData.json', 'r') as jsonFile: 67 | dataCollection = json.load(jsonFile) 68 | 69 | #if the file has not been yet initialised, or does not exist, write it with metadata to alter merge with output data from new file 70 | else: 71 | dataCollection = {} 72 | AllClasses = my_dataset_train_metadata.thing_classes 73 | #add this to json object, if not there already. 74 | dataCollection['AllClasses'] = AllClasses 75 | #initialize empty dict to store image inference ouput results in the future 76 | dataCollection['imageData'] = {} 77 | #Get all available classes for this dataset from training metadata. Needed to make counts for each class in inference results. 78 | 79 | 80 | #Get name of uploaded image from argument. Nodejs launches shell script with filled argument. argument stored in imageName, similarly to standard batch inference. 81 | imageName = args.image 82 | if imageName: 83 | #Get uploaded image name without path so we can save to JSON conveniently. 84 | print("Running inference on image name with path: {}".format(imageName)) 85 | imageBaseName = format(os.path.basename(imageName)) 86 | print("Image base name: {}".format(imageBaseName)) 87 | 88 | im = cv2.imread(imageName) 89 | outputs = predictor(im) 90 | v = Visualizer(im[:, :, ::-1], 91 | #Get class names from dataset train metadata to put on visualization. 92 | metadata=my_dataset_train_metadata, 93 | scale=1 94 | ) 95 | out = v.draw_instance_predictions(outputs["instances"].to("cpu")) 96 | #As described in draw_instance_predictions function of Visualizer, we can get data about each image. 97 | #We might need these, but currently only interested in labels as they contain predicted classes for each image and prediction score. 98 | imagePredClasses = outputs["instances"].pred_classes 99 | imagePredBoxes = outputs["instances"].pred_boxes 100 | imagePredScores = outputs["instances"].scores 101 | 102 | #previously initialised dict for storing image output. Inside it make another key:value pair with key as image name, and value as inference output values array. 103 | dataCollection['imageData'][imageBaseName] = _create_text_labels(outputs["instances"].pred_classes, outputs["instances"].scores, my_dataset_train_metadata.get("thing_classes", None)) 104 | 105 | 106 | if not os.path.exists('inferenceContent/outputData'): 107 | os.mkdir('inferenceContent/outputData') 108 | 109 | with open('inferenceContent/outputData/outputData.json', 'w') as jsonFile: 110 | json.dump(dataCollection, jsonFile) 111 | #data in it. As it loops through all images it will fill the JSON with needed data. 112 | 113 | # Show images with predictions in system window. If not using host, then: 114 | #cv2.imshow('Inference Preview',out.get_image()[:, :, ::-1]) 115 | # If running inference locally, to view result with timer in system window, uncomment: 116 | #cv2.waitKey(10000) 117 | # Save images with predictions to savePath folder and imageName with path to image removed from name. 118 | savePath = './inferenceContent/output' 119 | cv2.imwrite(os.path.join(savePath , imageBaseName), out.get_image()[:, :, ::-1]) -------------------------------------------------------------------------------- /views/index.ejs: -------------------------------------------------------------------------------- 1 | 2 | 3 | <%- include('head.ejs') %> 4 | 5 | 6 | <%- include('header.ejs') %> 7 |
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Run model inference with an image:

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Summary of results:

34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | <% for (i in results) { %> 46 | 47 | 48 | 49 | <% for (j in results[i]) { %> 50 | 51 | <% } %> 52 | 53 | <% } %> 54 | 55 |
Class NameClass CountPrecision Values Average Class Precision
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Inference Results:

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Inference found classes with precision:

67 | 68 | <% for(var i=0; i 69 | <%= i+1+') '+item.classesFound[i] %>
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72 | 73 | Inference result image 75 | 76 |
77 | 78 | <% }); %> 79 |
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83 | <%- include('footer.ejs') %> 84 | <%- include('scripts.ejs') %> 85 | 135 | 136 | 137 | -------------------------------------------------------------------------------- /conda-spec-file.txt: -------------------------------------------------------------------------------- 1 | # This file may be used to create an environment using: 2 | # $ conda create --name --file 3 | # platform: linux-64 4 | @EXPLICIT 5 | https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda 6 | https://repo.anaconda.com/pkgs/main/linux-64/_tflow_select-2.1.0-gpu.conda 7 | https://repo.anaconda.com/pkgs/main/linux-64/blas-1.0-mkl.conda 8 | https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2020.6.24-0.conda 9 | https://repo.anaconda.com/pkgs/main/linux-64/cudatoolkit-10.1.243-h6bb024c_0.conda 10 | https://repo.anaconda.com/pkgs/main/linux-64/intel-openmp-2020.1-217.conda 11 | https://repo.anaconda.com/pkgs/main/linux-64/ld_impl_linux-64-2.33.1-h53a641e_7.conda 12 | 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https://repo.anaconda.com/pkgs/main/linux-64/pyjwt-1.7.1-py38_0.conda 74 | https://repo.anaconda.com/pkgs/main/noarch/pyopenssl-19.1.0-py_1.conda 75 | https://repo.anaconda.com/pkgs/main/noarch/oauthlib-3.1.0-py_0.conda 76 | https://repo.anaconda.com/pkgs/main/noarch/urllib3-1.25.9-py_0.conda 77 | https://repo.anaconda.com/pkgs/main/noarch/requests-2.24.0-py_0.conda 78 | https://repo.anaconda.com/pkgs/main/noarch/requests-oauthlib-1.3.0-py_0.conda 79 | https://repo.anaconda.com/pkgs/main/noarch/google-auth-oauthlib-0.4.1-py_2.conda 80 | https://repo.anaconda.com/pkgs/main/linux-64/h5py-2.10.0-py38hd6299e0_1.conda 81 | https://repo.anaconda.com/pkgs/main/linux-64/mkl_fft-1.1.0-py38h23d657b_0.conda 82 | https://repo.anaconda.com/pkgs/main/linux-64/mkl_random-1.1.1-py38h0573a6f_0.conda 83 | https://repo.anaconda.com/pkgs/main/linux-64/numpy-1.18.5-py38ha1c710e_0.conda 84 | https://repo.anaconda.com/pkgs/main/noarch/opt_einsum-3.1.0-py_0.conda 85 | https://conda.anaconda.org/pytorch/linux-64/pytorch-1.5.1-py3.8_cuda10.1.243_cudnn7.6.3_0.tar.bz2 86 | https://repo.anaconda.com/pkgs/main/linux-64/scipy-1.5.0-py38h0b6359f_0.conda 87 | https://repo.anaconda.com/pkgs/main/noarch/tensorboard-2.2.1-pyh532a8cf_0.conda 88 | https://repo.anaconda.com/pkgs/main/noarch/keras-preprocessing-1.1.0-py_1.conda 89 | https://conda.anaconda.org/pytorch/linux-64/torchvision-0.6.1-py38_cu101.tar.bz2 90 | https://repo.anaconda.com/pkgs/main/linux-64/tensorflow-base-2.2.0-gpu_py38h83e3d50_0.conda 91 | https://repo.anaconda.com/pkgs/main/noarch/tensorflow-estimator-2.2.0-pyh208ff02_0.conda 92 | https://repo.anaconda.com/pkgs/main/linux-64/tensorflow-2.2.0-gpu_py38hb782248_0.conda 93 | https://repo.anaconda.com/pkgs/main/linux-64/tensorflow-gpu-2.2.0-h0d30ee6_0.conda 94 | -------------------------------------------------------------------------------- /index.js: -------------------------------------------------------------------------------- 1 | const express = require('express') 2 | const multer = require('multer') 3 | const mime = require('mime-types') 4 | const shortid = require('shortid') 5 | const { exec } = require('child_process') 6 | const fs = require('fs') 7 | const path = require('path') 8 | var shell = require('shelljs') 9 | 10 | // CREATE APPLICATION 11 | const app = express() 12 | app.set("view engine", "ejs") 13 | 14 | // FILE STORAGE 15 | // This is a middleware general file storage. Images for inference will be copied out of here. 16 | const storage = multer.diskStorage({ 17 | //Handle default uploaded image destination. 18 | destination: function (req, file, cb) { 19 | cb(null, 'public') 20 | }, 21 | //Handle uploaded image names by assigning a short id and extension. 22 | //todo: make items sortable. 23 | filename: function (req, file, cb) { 24 | let id = Date.now() + shortid.generate() 25 | let ext = mime.extension(file.mimetype) 26 | cb(null, `${id}.${ext}`) 27 | } 28 | }) 29 | const upload = multer({ storage }) 30 | 31 | // SERVE STATIC FILES from ./public and ./inferenceContent 32 | app.use(express.static('public')) 33 | app.use(express.static('inferenceContent')) 34 | 35 | //Function for async read JSON used later 36 | function readFileAsync(path) { 37 | return new Promise((resolve, reject) => { 38 | fs.readFile(path, (err, data) => { 39 | if (err) reject(err) 40 | resolve(JSON.parse(data)) 41 | }) 42 | }) 43 | } 44 | 45 | // ROUTES 46 | app.get('/', (req, res) => { 47 | 48 | //joining path of directory 49 | 50 | const directoryPath = './inferenceContent/output/' 51 | const directoryShortPath = '/output/' 52 | 53 | //Passing directoryPath and callback function 54 | fs.readdir(directoryPath, async function (err, files) { 55 | 56 | //handling error 57 | if (err) { 58 | console.log('Unable to find directory: ' + err + '. Output directory must exist in inferenceContent directory.') 59 | } 60 | 61 | //set up array to store links to images, to show on main screen. 62 | var imageData = [] 63 | //outputData will store output from inference for each file and all classes. This will allow to make counts. 64 | var outputData = '' 65 | //results contains all encountered classes in total for all inferences and shows statistics. See below. 66 | var results = [] 67 | //push all files as dictionaries into list to easily parse them into .ejs template. Using forEach. : 68 | if (files.length) { 69 | //This JSON is used to transport data from Detectron2 to Nodejs 70 | var outputDataFilePath = 'inferenceContent/outputData/outputData.json' 71 | if (!fs.existsSync(outputDataFilePath)) { 72 | console.log("No inference output to display yet.") 73 | } else { 74 | try { 75 | outputData = await readFileAsync(outputDataFilePath) 76 | // Map objects together, for each image: 77 | Object.keys(outputData.imageData).forEach(key => { 78 | let items = outputData.imageData[key] 79 | items.forEach(item => { 80 | //split because Detectron2 lables contain predicted classname and confidence as string with values separated by space 81 | let itemClassName = item.split(' ')[0] 82 | let itemClassValue = item.split(' ')[1] 83 | if (outputData.AllClasses.includes(itemClassName)) { 84 | if (itemClassName in results) { 85 | //increment counter for existing class initialised below 86 | results[itemClassName].count++ 87 | results[itemClassName].values.push(itemClassValue) 88 | } else { 89 | //initialise if there is a class which has been predicted 90 | let value = { count: 1, values: [itemClassValue], average: 0 } 91 | results[itemClassName] = value 92 | } 93 | } 94 | }) 95 | }) 96 | 97 | // Count average percision percentage for each found class 98 | Object.keys(results).forEach(key => { 99 | let item = results[key] 100 | if (item.count > 1) { 101 | let total = item.values.reduce((acc, value) => { 102 | return acc + parseInt(value.split('%')[0]) 103 | }, 0) 104 | let average = total / item.values.length 105 | results[key].average = average 106 | } else { 107 | results[key].average = parseInt(item.values[0].split('%')[0]) 108 | } 109 | }) 110 | 111 | } catch (error) { 112 | throw error 113 | } 114 | } 115 | files.forEach(function (file) { 116 | //imageData is composed here to transfer data to our index.ejs template. 117 | //Data from here will be used to render our html file. 118 | //unshift newer images to appear on top, to avoid having to sort on client. 119 | //add image statistical data from inference to each image, parsed above, from outputData. 120 | imageData.unshift({ 121 | key: 'image', 122 | name: file, 123 | classesFound: outputData.imageData[file], 124 | url: directoryShortPath + file 125 | 126 | }) 127 | }) 128 | 129 | //if there are no files in directory: 130 | } else { 131 | console.log('No images to fetch from output directory. Upload an image for inference.') 132 | } 133 | 134 | console.log('results:', results) 135 | console.log('outputdata:', outputData) 136 | console.log('imageData:', imageData) 137 | 138 | //output list of images present at home directory load. Pass object as itself to index.ejs. 139 | res.render('index', { 140 | //results for overall averages and class information. 141 | //Takes into account all inferenced images since project has been wiped. 142 | results, 143 | imageData 144 | }) 145 | 146 | }) 147 | 148 | }) 149 | 150 | 151 | app.post('/form', upload.single('file'), async (req, res) => { 152 | const image = req.file 153 | //Set path to directory where images will be stored for and used for Protectron2 operations. 154 | const inputContentPath = './inferenceContent/input/' 155 | const imageNamePath = inputContentPath + image.filename 156 | console.log(imageNamePath) 157 | if (fs.existsSync(inputContentPath)) { 158 | //If directory is already present: 159 | shell.exec(`cp ${image.path} ${inputContentPath} && python inferenceSingle.py --image ${imageNamePath}`, function (code, stdout, stderr) { 160 | if (stderr) { 161 | console.log('Program stderr:', stderr) 162 | return res.json({ 163 | success: false 164 | }) 165 | } else { 166 | console.log('Success!') 167 | return res.json({ 168 | //Output filename is the same. The directory is different, so: 169 | image: `http://localhost:3000/output/${image.filename}` 170 | }) 171 | } 172 | }) 173 | } else { 174 | //If directory is not present: 175 | shell.exec(`mkdir ${inputContentPath} && cp ${image.path} ${inputContentPath} && python inferenceSingle.py --image ${imageNamePath}`, function (code, stdout, stderr) { 176 | if (stderr) { 177 | console.log('Program stderr:', stderr) 178 | return res.json({ 179 | success: false 180 | }) 181 | } else { 182 | console.log('Success!') 183 | return res.json({ 184 | //Output filename is the same. The directory is different, so: 185 | image: `http://localhost:3000/output/${image.filename}` 186 | }) 187 | } 188 | }) 189 | } 190 | 191 | 192 | //console.log('stdout:', stdout) 193 | //console.log('stderr:', stderr) 194 | }) 195 | 196 | // SERVE APPLICATION 197 | app.listen(3000, () => { 198 | console.log('App is working on port 3000') 199 | }) -------------------------------------------------------------------------------- /content/valid/_annotations.coco.json: -------------------------------------------------------------------------------- 1 | {"info":{"year":2020,"version":"1.0","description":"VIA project exported to COCO format using VGG Image Annotator (http://www.robots.ox.ac.uk/~vgg/software/via/)","contributor":"","url":"http://www.robots.ox.ac.uk/~vgg/software/via/","date_created":"Sun Aug 09 2020 10:16:35 GMT+0400 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