├── _config.yml ├── README.md ├── custom_datasets.py ├── metrics_on_folder.py ├── metrics_on_video.py ├── vocToCoco_unity_synthetic.py ├── vocToCoco_simulacro.py ├── index.md ├── batch_experiments.py └── LICENSE.md /_config.yml: -------------------------------------------------------------------------------- 1 | theme: jekyll-theme-cayman 2 | title: "Real-time gun detection in CCTV : An open problem" 3 | description: " " 4 | google_analytics: UA-178393192-1 5 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Real-time gun detection in CCTV : An open problem 2 | [![License: CC BY-NC 4.0](https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc/4.0/) 3 | 4 | All the information can be found in the following link: [https://deepknowledge-us.github.io/US-Real-time-gun-detection-in-CCTV-An-open-problem-dataset/](https://deepknowledge-us.github.io/US-Real-time-gun-detection-in-CCTV-An-open-problem-dataset/). 5 | 6 |

7 | 8 | Image 9 | 10 |

11 | 12 | 13 | ## Terms of use 14 | This dataset can be used for academic research free of charge, citing the paper as we explain below. If you seek to use the data for commercial purposes please [contact us](mailto:jaalvarez@us.es). 15 | 16 | 17 | ## Citation 18 | If you use our dataset, please kindly cite the following paper: Real-time gun detection in CCTV: An open problem. Neural Networks (2020), doi: [https://doi.org/10.1016/j.neunet.2020.09.013](https://doi.org/10.1016/j.neunet.2020.09.013). 19 | 20 | ``` 21 | @article{SalazarGonzalez2020, 22 | title = "Real-time gun detection in CCTV: An open problem", 23 | journal = "Neural Networks", 24 | year = "2020", 25 | issn = "0893-6080", 26 | doi = "https://doi.org/10.1016/j.neunet.2020.09.013", 27 | url = "http://www.sciencedirect.com/science/article/pii/S0893608020303361", 28 | author = "Salazar Gonz{\'{a}}lez, Jose L. and Zaccaro, Carlos and {\'{A}}lvarez-Garc{\'{i}}a, Juan A. and Soria-Morillo, Luis M. and Sancho Caparrini, Fernando", 29 | } 30 | ``` 31 | 32 | ## License 33 | 34 | This work is licensed under a 35 | [Creative Commons Attribution-NonCommercial 4.0 International License][cc-by-nc]. Contact the authors of this work for commercial use. 36 | 37 | [![CC BY NC 4.0][cc-by-nc-image]][cc-by-nc] 38 | 39 | [cc-by-nc]: http://creativecommons.org/licenses/by-nc/4.0/ 40 | [cc-by-nc-image]: https://i.creativecommons.org/l/by-nc/4.0/88x31.png 41 | -------------------------------------------------------------------------------- /custom_datasets.py: -------------------------------------------------------------------------------- 1 | 2 | from detectron2.data import MetadataCatalog, DatasetCatalog 3 | from detectron2.data.datasets import load_coco_json, register_coco_instances 4 | 5 | def loadDatasets(): 6 | classRemapping = {1: 0} 7 | meta = {'thing_classes': ['gun'], 'thing_dataset_id_to_contiguous_id': classRemapping} 8 | 9 | def register_coco_instances(name, metadata, classRemapping, json_file, image_root): 10 | DatasetCatalog.register(name, lambda: load_coco_json(json_file, image_root, None, classRemapping=classRemapping)) 11 | MetadataCatalog.get(name).set( 12 | json_file=json_file, image_root=image_root, evaluator_type="coco", **metadata 13 | ) 14 | 15 | ROOT_PATH = '/media/datos/shared_datasets/simulacro_guns' 16 | register_coco_instances("guns_simulacro_1", meta, classRemapping, json_file=f'{ROOT_PATH}/cam1.json', image_root=f'{ROOT_PATH}/images') 17 | register_coco_instances("guns_simulacro_5", meta, classRemapping, json_file=f'{ROOT_PATH}/cam5.json', image_root=f'{ROOT_PATH}/images') 18 | register_coco_instances("guns_simulacro_7", meta, classRemapping, json_file=f'{ROOT_PATH}/cam7.json', image_root=f'{ROOT_PATH}/images') 19 | register_coco_instances("guns_simulacro_all", meta, classRemapping, json_file=f'{ROOT_PATH}/all_cams.json', image_root=f'{ROOT_PATH}/images') 20 | 21 | ROOT_PATH = '/media/datos/shared_datasets/guns_edgecase' 22 | register_coco_instances("guns_edgecase", meta, classRemapping, json_file=f'{ROOT_PATH}/synthetic_train.json', image_root=f'{ROOT_PATH}/images') 23 | 24 | ROOT_PATH = '/media/datos/shared_datasets/guns_granada' 25 | register_coco_instances("guns_granada_train", meta, classRemapping, json_file=f'{ROOT_PATH}/real_train.json', image_root=f'{ROOT_PATH}/images') 26 | register_coco_instances("guns_granada_test", meta, classRemapping, json_file=f'{ROOT_PATH}/real_val.json', image_root=f'{ROOT_PATH}/images') 27 | 28 | ROOT_PATH = '/media/datos/shared_datasets/unity_syntectic_victory/split-500' 29 | register_coco_instances("unity_synthetic_500", meta, classRemapping, json_file=f'{ROOT_PATH}/split_coco.json', image_root=f'{ROOT_PATH}') 30 | ROOT_PATH = '/media/datos/shared_datasets/unity_syntectic_victory/split-1000' 31 | register_coco_instances("unity_synthetic_1000", meta, classRemapping, json_file=f'{ROOT_PATH}/split_coco.json', image_root=f'{ROOT_PATH}') 32 | ROOT_PATH = '/media/datos/shared_datasets/unity_syntectic_victory/split-2500' 33 | register_coco_instances("unity_synthetic_2500", meta, classRemapping, json_file=f'{ROOT_PATH}/split_coco.json', image_root=f'{ROOT_PATH}') 34 | ROOT_PATH = '/media/datos/shared_datasets/unity_syntectic_victory/split-5000' 35 | register_coco_instances("unity_synthetic_5000", meta, classRemapping, json_file=f'{ROOT_PATH}/split_coco.json', image_root=f'{ROOT_PATH}') -------------------------------------------------------------------------------- /metrics_on_folder.py: -------------------------------------------------------------------------------- 1 | import random 2 | import os 3 | import json 4 | import glob 5 | 6 | CREATE_ANNOS = True 7 | CALCULATE_METRICS = True 8 | THS = [0.99, 0.98, 0.95] 9 | 10 | dstDir = '/mnt/datos/custom_datasets/simulacro/no_guns/Simulacro_guns_annos' 11 | uris = glob.glob('/media/datos/shared_datasets/guns_granada/Test/*') 12 | 13 | 14 | # def create_annos(cfg, dstPath, uris): 15 | # 16 | if CREATE_ANNOS: 17 | os.environ["CUDA_VISIBLE_DEVICES"] = "2" 18 | 19 | from detectron2.utils.logger import setup_logger 20 | 21 | setup_logger() 22 | 23 | import cv2 as cv 24 | from detectron2.engine import DefaultPredictor 25 | from detectron2.config import get_cfg 26 | cfg = get_cfg() 27 | if True: 28 | cfg.merge_from_file("../../build/configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml") 29 | cfg.MODEL.WEIGHTS = "detectron2://COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl" # initialize from model zoo 30 | else: 31 | cfg.merge_from_file("../../build/configs/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml") 32 | cfg.MODEL.WEIGHTS = "detectron2://COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x/139173657/model_final_68b088.pkl" # initialize from model zoo 33 | 34 | cfg.INPUT.MAX_SIZE_TEST = 2500 35 | cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1 36 | cfg.DATALOADER.NUM_WORKERS = 2 37 | 38 | cfg.MODEL.WEIGHTS = "/mnt/datos/experiments/guns_detection/faster_rcnn_R_50_FPN_1x[LR=0.002][('guns_granada_train')]FROM[faster_rcnn_R_50_FPN_1x[LR=0.002][('guns_edgecase',)]]/model_final.pth" 39 | 40 | predictor = DefaultPredictor(cfg) 41 | 42 | for uri in uris[:]: 43 | print(uri) 44 | cap = cv.VideoCapture(uri) 45 | frameIdx = 0 46 | imagesAnnos = [] 47 | while True: 48 | for i in range(1): 49 | ret, img = cap.read() 50 | if not ret or not cap.isOpened(): 51 | break 52 | 53 | preds = predictor(img)["instances"].to("cpu") 54 | 55 | bboxes = [b.numpy().tolist() for b in preds.pred_boxes] if preds.pred_boxes else [] 56 | scores = preds.scores.numpy().tolist() 57 | for i in range(len(bboxes)): 58 | bboxes[i].append(scores[i]) 59 | imagesAnnos.append({'frameIdx': frameIdx, 'bboxes': bboxes}) 60 | 61 | frameIdx += 1 62 | if frameIdx % 10 == 0: 63 | print(frameIdx) 64 | 65 | with open(os.path.join(dstDir, os.path.splitext(os.path.basename(uri))[0]) + '_annos.json', 'w') as f: 66 | print(imagesAnnos) 67 | json.dump(imagesAnnos, f) 68 | 69 | if CALCULATE_METRICS: 70 | for th in THS: 71 | metrics = {} 72 | for uri in uris[:]: 73 | basename = os.path.splitext(os.path.basename(uri))[0] 74 | with open(os.path.join(dstDir, basename) + '_annos.json') as f: 75 | imagesAnnos = json.load(f) 76 | numDetections = len([bbox for anno in imagesAnnos for bbox in anno['bboxes'] if bbox[4] >= th]) 77 | metrics[basename] = {'FP': numDetections, 'NUM_FRAMES': len(imagesAnnos)} 78 | 79 | with open(os.path.join(dstDir, f'metrics_summary_th-{th}.json'), 'w') as f: 80 | json.dump(metrics, f) 81 | 82 | detailList = ', '.join([f"{v['FP']}" for k, v in metrics.items()]) 83 | numFrames = sum([v['NUM_FRAMES'] for k, v in metrics.items()]) 84 | print(f"FP{th}: {sum([m['FP'] for m in metrics.values()])}/{numFrames} ({detailList})") 85 | -------------------------------------------------------------------------------- /metrics_on_video.py: -------------------------------------------------------------------------------- 1 | import random 2 | import os 3 | import json 4 | import glob 5 | import cv2 as cv 6 | 7 | CREATE_ANNOS = True 8 | CALCULATE_METRICS = True 9 | THS = [0.99, 0.98, 0.95] 10 | 11 | dstDir = '/mnt/datos/custom_datasets/simulacro/no_guns/Simulacro_guns_annos' 12 | uris = glob.glob('/mnt/datos/custom_datasets/simulacro/no_guns/Simulacro/*.mp4') 13 | 14 | VIS_RESULT = True 15 | confidence = 0.99 16 | def drawAnnos(annoGuns, img, fname): 17 | for gun in annoGuns: 18 | (x0, y0, x1, y1, c) = gun 19 | x1, y1 = int(x1), int(y1) 20 | x0, y0 = int(x0), int(y0) 21 | if c >= confidence: 22 | cv.rectangle(img, (x0, y0), (x1, y1), (0, 255, 0), thickness=2) 23 | cv.putText(img, str(int(c*100)), (x0, y0-20), cv.FONT_HERSHEY_TRIPLEX, 0.5, (0, 0, 255), 1) 24 | 25 | # def create_annos(cfg, dstPath, uris): 26 | # 27 | if CREATE_ANNOS: 28 | os.environ["CUDA_VISIBLE_DEVICES"] = "2" 29 | 30 | from detectron2.utils.logger import setup_logger 31 | 32 | setup_logger() 33 | 34 | import cv2 as cv 35 | from detectron2.engine import DefaultPredictor 36 | from detectron2.config import get_cfg 37 | cfg = get_cfg() 38 | if True: 39 | cfg.merge_from_file("../../build/configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml") 40 | cfg.MODEL.WEIGHTS = "detectron2://COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl" # initialize from model zoo 41 | else: 42 | cfg.merge_from_file("../../build/configs/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml") 43 | cfg.MODEL.WEIGHTS = "detectron2://COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x/139173657/model_final_68b088.pkl" # initialize from model zoo 44 | 45 | cfg.INPUT.MAX_SIZE_TEST = 2500 46 | cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1 47 | cfg.DATALOADER.NUM_WORKERS = 2 48 | 49 | cfg.MODEL.WEIGHTS = "/mnt/datos/experiments/guns_detection/faster_rcnn_R_50_FPN_1x[LR=0.002][('guns_granada_train', 'guns_simulacro_1', 'guns_simulacro_7')]FROM[faster_rcnn_R_50_FPN_1x[LR=0.002][('unity_synthetic_2500',)]]/model_final.pth" 50 | 51 | predictor = DefaultPredictor(cfg) 52 | 53 | testImgIdx = 0 54 | for uri in uris[:]: 55 | print(uri) 56 | cap = cv.VideoCapture(uri) 57 | frameIdx = 0 58 | imagesAnnos = [] 59 | while True: 60 | for i in range(1): 61 | ret, img = cap.read() 62 | if not ret or not cap.isOpened(): 63 | break 64 | 65 | preds = predictor(img)["instances"].to("cpu") 66 | 67 | bboxes = [b.numpy().tolist() for b in preds.pred_boxes] if preds.pred_boxes else [] 68 | scores = preds.scores.numpy().tolist() 69 | for i in range(len(bboxes)): 70 | bboxes[i].append(scores[i]) 71 | if VIS_RESULT: 72 | if any([bbox[4] >= confidence for bbox in bboxes]): 73 | drawAnnos(bboxes, img, '') 74 | cv.imwrite(os.path.join('fp_no_guns_detections', f'img{testImgIdx}.jpg'), img) 75 | testImgIdx+=1 76 | # factor = .5 77 | # img = cv.resize(img, (0, 0), fx=factor, fy=factor) 78 | # cv.imshow('win', img) 79 | # k = cv.waitKey(0) 80 | imagesAnnos.append({'frameIdx': frameIdx, 'bboxes': bboxes}) 81 | 82 | frameIdx += 1 83 | if frameIdx % 10 == 0: 84 | print(f"frameIdx: {frameIdx}, boxes: {len(bboxes)}") 85 | 86 | # with open(os.path.join(dstDir, os.path.splitext(os.path.basename(uri))[0]) + '_annos.json', 'w') as f: 87 | # print(imagesAnnos) 88 | # json.dump(imagesAnnos, f) 89 | 90 | if CALCULATE_METRICS: 91 | for th in THS: 92 | metrics = {} 93 | for uri in uris[:]: 94 | basename = os.path.splitext(os.path.basename(uri))[0] 95 | with open(os.path.join(dstDir, basename) + '_annos.json') as f: 96 | imagesAnnos = json.load(f) 97 | numDetections = len([bbox for anno in imagesAnnos for bbox in anno['bboxes'] if bbox[4] >= th]) 98 | metrics[basename] = {'FP': numDetections, 'NUM_FRAMES': len(imagesAnnos)} 99 | 100 | with open(os.path.join(dstDir, f'metrics_summary_th-{th}.json'), 'w') as f: 101 | json.dump(metrics, f) 102 | 103 | detailList = ', '.join([f"{v['FP']}" for k, v in metrics.items()]) 104 | numFrames = sum([v['NUM_FRAMES'] for k, v in metrics.items()]) 105 | print(f"FP{th}: {sum([m['FP'] for m in metrics.values()])}/{numFrames} ({detailList})") 106 | -------------------------------------------------------------------------------- /vocToCoco_unity_synthetic.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | 3 | # pip install lxml 4 | 5 | import sys 6 | import os 7 | import json 8 | import xml.etree.ElementTree as ET 9 | import glob 10 | import pickle 11 | 12 | START_BOUNDING_BOX_ID = 1 13 | PRE_DEFINE_CATEGORIES = {'Pistol': 1} 14 | 15 | def get(root, name): 16 | vars = root.findall(name) 17 | return vars 18 | 19 | 20 | def get_and_check(root, name, length): 21 | vars = root.findall(name) 22 | if len(vars) == 0: 23 | raise ValueError("Can not find %s in %s." % (name, root.tag)) 24 | if length > 0 and len(vars) != length: 25 | raise ValueError( 26 | "The size of %s is supposed to be %d, but is %d." 27 | % (name, length, len(vars)) 28 | ) 29 | if length == 1: 30 | vars = vars[0] 31 | return vars 32 | 33 | 34 | def get_categories(xml_files): 35 | classes_names = [] 36 | for xml_file in xml_files: 37 | tree = ET.parse(xml_file) 38 | root = tree.getroot() 39 | for member in root.findall("object"): 40 | classes_names.append(member[0].text) 41 | classes_names = list(set(classes_names)) 42 | classes_names.sort() 43 | return {name: i for i, name in enumerate(classes_names)} 44 | 45 | 46 | def convert(xml_files, json_file): 47 | json_dict = {"images": [], 'categories': [], "annotations": [], "type": "instances"} 48 | 49 | if PRE_DEFINE_CATEGORIES is not None: 50 | categories = PRE_DEFINE_CATEGORIES 51 | else: 52 | categories = get_categories(xml_files) 53 | bnd_id = START_BOUNDING_BOX_ID 54 | for xml_file in sorted(xml_files): 55 | tree = ET.parse(xml_file) 56 | root = tree.getroot() 57 | path = get(root, "path") 58 | if len(path) == 1: 59 | filename = os.path.basename(path[0].text) 60 | elif len(path) == 0: 61 | filename = get_and_check(root, "filename", 1).text 62 | else: 63 | raise ValueError("%d paths found in %s" % (len(path), xml_file)) 64 | 65 | fname = filename.split('\\')[-1] 66 | 67 | objects = [] 68 | for obj in get(root, "object"): 69 | category = get_and_check(obj, "name", 1).text 70 | # TODO: Change to match edgecase dataset 71 | if category == 'Knife': 72 | continue 73 | else: 74 | category = 'Pistol' 75 | 76 | if category not in categories: 77 | new_id = len(categories) 78 | categories[category] = new_id 79 | category_id = categories[category] 80 | bndbox = get_and_check(obj, "bndbox", 1) 81 | xmin = int(float(get_and_check(bndbox, "xmin", 1).text)) - 1 82 | ymin = int(float(get_and_check(bndbox, "ymin", 1).text)) - 1 83 | xmax = int(float(get_and_check(bndbox, "xmax", 1).text)) 84 | ymax = int(float(get_and_check(bndbox, "ymax", 1).text)) 85 | assert xmax > xmin 86 | assert ymax > ymin 87 | o_width = abs(xmax - xmin) 88 | o_height = abs(ymax - ymin) 89 | ann = { 90 | "area": int(o_width * o_height), 91 | "iscrowd": 0, 92 | "image_id": fname, 93 | "bbox": [xmin, ymin, o_width, o_height], 94 | "category_id": category_id, 95 | "ignore": 0, 96 | "segmentation": [], 97 | "id": bnd_id, 98 | } 99 | json_dict["annotations"].append(ann) 100 | objects.append(ann) 101 | bnd_id = bnd_id + 1 102 | 103 | size = get_and_check(root, "size", 1) 104 | width = int(float(get_and_check(size, "width", 1).text)) 105 | height = int(float(get_and_check(size, "height", 1).text)) 106 | image = { 107 | "file_name": fname, 108 | "height": height, 109 | "width": width, 110 | #"objects": objects, 111 | "id": fname, 112 | } 113 | json_dict["images"].append(image) 114 | 115 | for cate, cid in categories.items(): 116 | cat = {"supercategory": "none", "id": cid, "name": cate} 117 | json_dict["categories"].append(cat) 118 | 119 | os.makedirs(os.path.dirname(json_file), exist_ok=True) 120 | with open(json_file, "w") as f: 121 | json.dump(json_dict, f) 122 | 123 | 124 | if __name__ == "__main__": 125 | from glob import glob 126 | 127 | for folder in [ 128 | '/media/datos/shared_datasets/unity_syntectic_victory/split-500', 129 | '/media/datos/shared_datasets/unity_syntectic_victory/split-1000', 130 | '/media/datos/shared_datasets/unity_syntectic_victory/split-2500', 131 | '/media/datos/shared_datasets/unity_syntectic_victory/split-5000', 132 | ]: 133 | 134 | name = os.path.basename(folder) 135 | 136 | allFiles = glob(os.path.join(folder, '*.xml')) 137 | fname = os.path.join(folder, f'split_coco.json') 138 | print("Number of xml files: {}".format(len(allFiles))) 139 | convert(allFiles, fname) 140 | print("Success: {}".format(fname)) -------------------------------------------------------------------------------- /vocToCoco_simulacro.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | 3 | # pip install lxml 4 | 5 | import sys 6 | import os 7 | import json 8 | import xml.etree.ElementTree as ET 9 | import glob 10 | import pickle 11 | 12 | START_BOUNDING_BOX_ID = 1 13 | PRE_DEFINE_CATEGORIES = {'Pistol': 1} 14 | 15 | def get(root, name): 16 | vars = root.findall(name) 17 | return vars 18 | 19 | 20 | def get_and_check(root, name, length): 21 | vars = root.findall(name) 22 | if len(vars) == 0: 23 | raise ValueError("Can not find %s in %s." % (name, root.tag)) 24 | if length > 0 and len(vars) != length: 25 | raise ValueError( 26 | "The size of %s is supposed to be %d, but is %d." 27 | % (name, length, len(vars)) 28 | ) 29 | if length == 1: 30 | vars = vars[0] 31 | return vars 32 | 33 | 34 | def get_categories(xml_files): 35 | classes_names = [] 36 | for xml_file in xml_files: 37 | tree = ET.parse(xml_file) 38 | root = tree.getroot() 39 | for member in root.findall("object"): 40 | classes_names.append(member[0].text) 41 | classes_names = list(set(classes_names)) 42 | classes_names.sort() 43 | return {name: i for i, name in enumerate(classes_names)} 44 | 45 | 46 | def convert(xml_files, json_file): 47 | json_dict = {"images": [], 'categories': [], "annotations": [], "type": "instances"} 48 | 49 | if PRE_DEFINE_CATEGORIES is not None: 50 | categories = PRE_DEFINE_CATEGORIES 51 | else: 52 | categories = get_categories(xml_files) 53 | bnd_id = START_BOUNDING_BOX_ID 54 | for xml_file in sorted(xml_files): 55 | tree = ET.parse(xml_file) 56 | root = tree.getroot() 57 | path = get(root, "path") 58 | if len(path) == 1: 59 | filename = os.path.basename(path[0].text) 60 | elif len(path) == 0: 61 | filename = get_and_check(root, "filename", 1).text 62 | else: 63 | raise ValueError("%d paths found in %s" % (len(path), xml_file)) 64 | 65 | fname = filename.split('\\')[-1] 66 | 67 | objects = [] 68 | for obj in get(root, "object"): 69 | category = get_and_check(obj, "name", 1).text 70 | # TODO: Change to match edgecase dataset 71 | if category == 'Knife': 72 | continue 73 | else: 74 | category = 'Pistol' 75 | 76 | if category not in categories: 77 | new_id = len(categories) 78 | categories[category] = new_id 79 | category_id = categories[category] 80 | bndbox = get_and_check(obj, "bndbox", 1) 81 | xmin = int(float(get_and_check(bndbox, "xmin", 1).text)) - 1 82 | ymin = int(float(get_and_check(bndbox, "ymin", 1).text)) - 1 83 | xmax = int(float(get_and_check(bndbox, "xmax", 1).text)) 84 | ymax = int(float(get_and_check(bndbox, "ymax", 1).text)) 85 | assert xmax > xmin 86 | assert ymax > ymin 87 | o_width = abs(xmax - xmin) 88 | o_height = abs(ymax - ymin) 89 | ann = { 90 | "area": int(o_width * o_height), 91 | "iscrowd": 0, 92 | "image_id": fname, 93 | "bbox": [xmin, ymin, o_width, o_height], 94 | "category_id": category_id, 95 | "ignore": 0, 96 | "segmentation": [], 97 | "id": bnd_id, 98 | } 99 | json_dict["annotations"].append(ann) 100 | objects.append(ann) 101 | bnd_id = bnd_id + 1 102 | 103 | size = get_and_check(root, "size", 1) 104 | width = int(float(get_and_check(size, "width", 1).text)) 105 | height = int(float(get_and_check(size, "height", 1).text)) 106 | image = { 107 | "file_name": fname, 108 | "height": height, 109 | "width": width, 110 | #"objects": objects, 111 | "id": fname, 112 | } 113 | json_dict["images"].append(image) 114 | 115 | for cate, cid in categories.items(): 116 | cat = {"supercategory": "none", "id": cid, "name": cate} 117 | json_dict["categories"].append(cat) 118 | 119 | os.makedirs(os.path.dirname(json_file), exist_ok=True) 120 | with open(json_file, "w") as f: 121 | json.dump(json_dict, f) 122 | 123 | 124 | if __name__ == "__main__": 125 | from glob import glob 126 | import shutil 127 | dirs = [ 128 | '/home/datos/Downloads/Images', 129 | ] 130 | 131 | RAW_DATASET = '/home/datos/Downloads/Images' 132 | ROOT_DATASET_FOLDER = '/media/datos/shared_datasets/simulacro_guns' 133 | 134 | allFiles = glob(os.path.join(RAW_DATASET, '*.jpg')) 135 | jpgDir = os.path.join(ROOT_DATASET_FOLDER, 'allCams') 136 | os.makedirs(jpgDir, exist_ok=True) 137 | for f in allFiles: 138 | if not os.path.exists(os.path.join(jpgDir, f)): 139 | shutil.copy(f, jpgDir) 140 | 141 | matchTuples = [('*.xml', 'all_cams') , ('Cam1*.xml', 'cam1'), ('Cam5*.xml', 'cam5'), ('Cam7*.xml', 'cam7')] 142 | 143 | for matchStr, datasetName in matchTuples: 144 | allFiles = glob(os.path.join(RAW_DATASET, matchStr)) 145 | fname = os.path.join(ROOT_DATASET_FOLDER, f'{datasetName}.json') 146 | print("Number of xml files: {}".format(len(allFiles))) 147 | convert(allFiles, fname) 148 | print("Success: {}".format(fname)) -------------------------------------------------------------------------------- /index.md: -------------------------------------------------------------------------------- 1 | ## Abstract 2 | Object detectors have improved in recent years, obtaining better results and faster inference time. However, small object detection is still a problem that has not yet a definitive solution. The autonomous weapons detection on Closed-circuit television (CCTV) has been studied recently, being extremely useful in the field of security, counter-terrorism, and risk mitigation. This article presents a new dataset obtained from a real CCTV installed in a university and the generation of synthetic images, to which Faster R-CNN was applied using Feature Pyramid Network with ResNet-50 resulting in a weapon detection model able to be used in quasi real-time CCTV (90 ms of inference time with an NVIDIA GeForce GTX-1080Ti card) improving the state of the art on weapon detection in a two stages training. In this work, an exhaustive experimental study of the detector with these datasets was performed, showing the impact of synthetic datasets on the training of weapons detection systems, as well as the main limitations that these systems present nowadays. The generated synthetic dataset and the real CCTV dataset are available to the whole research community. 3 | 4 | ## Datasets 5 | This study presents two new datasets, being these: "Mock attack dataset" and "Unity synthetic dataset". 6 | 7 | ### Mock attack dataset 8 | This dataset has been manually annotated and collected during a mock attack, after obtaining all the permissions by our University and the security personnel. Details are presented below, indicating each of the cameras used during the mock attack and the scenarios they present. 9 | 10 | Infrastructure for data acquisition is composed of three surveillance cameras located at different places in the same area covering two different corridors and one entrance, forming different scenarios. The description of each camera is as follows: 11 | 12 | - Cam1: located in one of the two corridors, it presents some conflicting objects, such as doors or bins, and the lighting is uniform. The time of the video sequence for this camera is 40 min and 25 s. Selecting the five segments with movement, the total duration is 5 min and 4 s. These segments were manually annotated at 2 frames per second (FPS), resulting in a total of 607 frames. 13 | 14 | - Cam7: located in the other corridor, this camera presents similarities with Cam1, both in scenery and lighting. However, Cam7 presents more conflicting objects, such as a fire extinguisher or objects on the walls. The duration of the sequence for this camera is 1 h, 3 min and 34 s. Selecting the segments with movement, the total duration is 29 min and 16 s. These segments were annotated at 2 FPS, resulting in a total of 3511 frames. 15 | 16 | - Cam5: located at the entrance of a university module, presents some conflicting objects, such as a black carpet on the floor, and also irregular lighting with rays covering part of the scene. The duration of the video sequence for this camera is 39 min and 7 s. Choosing segments with movement, the time decreases to 8 min and 36 s. This sequence was annotated at 2 FPS, resulting in a total of 1031 frames. 17 | 18 |

19 | Image 20 |

21 | 22 | ### Unity synthetic dataset 23 | This dataset was generated by modeling in Unity Game Engine a scenario that emulates a part of a city and an educational center within it. Several cameras capture the movements of multiple characters, made up of 11 different models and 7 animations. These images enhance the generated datasets with 11 different objects: 4 types of handguns, 5 types of rifles, a knife, and a smartphone. This dataset consists of three splits with 500 (U0.5), 1000 (U1) and 2500 (U2.5) images. 24 | 25 |

26 | Image 27 |

28 | 29 | ## Download Datasets 30 | - Full dataset: [Hugging Face link](https://huggingface.co/datasets/jsalazar/US-Real-time-gun-detection-in-CCTV-An-open-problem-dataset){:target="_blank" rel="noopener"}. 31 | - Mock attack dataset: [US - Mock Attack Cam 1, 5, 7](https://huggingface.co/datasets/jsalazar/US-Real-time-gun-detection-in-CCTV-An-open-problem-dataset/resolve/main/weapons_images_2fps.zip){:target="_blank" rel="noopener"}. 32 | - Unity synthetic dataset: US - Unity Synthetic Dataset [U0.5](https://huggingface.co/datasets/jsalazar/US-Real-time-gun-detection-in-CCTV-An-open-problem-dataset/resolve/main/Unity/split-500.zip){:target="_blank" rel="noopener"}, [U1](https://huggingface.co/datasets/jsalazar/US-Real-time-gun-detection-in-CCTV-An-open-problem-dataset/resolve/main/Unity/split-1000.zip){:target="_blank" rel="noopener"} and [U2.5](https://huggingface.co/datasets/jsalazar/US-Real-time-gun-detection-in-CCTV-An-open-problem-dataset/resolve/main/Unity/split-2500.zip){:target="_blank" rel="noopener"}. 33 | 34 | ## Terms of use 35 | This dataset can be used for academic research free of charge, citing the paper as we explain below. If you seek to use the data for commercial purposes please [contact us](mailto:jaalvarez@us.es). 36 | 37 | 38 | ## Citation 39 | If you use our dataset, please kindly cite the following paper: Real-time gun detection in CCTV: An open problem. Neural Networks (2020), doi: [https://doi.org/10.1016/j.neunet.2020.09.013](https://doi.org/10.1016/j.neunet.2020.09.013){:target="_blank" rel="noopener"}. 40 | 41 | ``` 42 | @article{SalazarGonzalez2020, 43 | title = "Real-time gun detection in CCTV: An open problem", 44 | journal = "Neural Networks", 45 | year = "2020", 46 | issn = "0893-6080", 47 | doi = "https://doi.org/10.1016/j.neunet.2020.09.013", 48 | url = "http://www.sciencedirect.com/science/article/pii/S0893608020303361", 49 | author = "Salazar Gonz{\'{a}}lez, Jose L. and Zaccaro, Carlos and {\'{A}}lvarez-Garc{\'{i}}a, Juan A. and Soria-Morillo, Luis M. and Sancho Caparrini, Fernando", 50 | } 51 | ``` 52 | 53 | 54 | ## License 55 | 56 | This work is licensed under a 57 | [Creative Commons Attribution-NonCommercial 4.0 International License][cc-by-nc]. Contact the authors of this work for commercial use. 58 | 59 | [![CC BY NC 4.0][cc-by-nc-image]][cc-by-nc] 60 | 61 | [cc-by-nc]: http://creativecommons.org/licenses/by-nc/4.0/ 62 | [cc-by-nc-image]: https://i.creativecommons.org/l/by-nc/4.0/88x31.png 63 | -------------------------------------------------------------------------------- /batch_experiments.py: -------------------------------------------------------------------------------- 1 | #import torch; from torch.utils.cpp_extension import CUDA_HOME; print(torch.cuda.is_available(), CUDA_HOME) 2 | import random 3 | import os 4 | import json 5 | import argparse 6 | import shutil 7 | import re 8 | import openpyxl 9 | import glob 10 | 11 | parser = argparse.ArgumentParser(description='Process some integers.') 12 | parser.add_argument('id', type=int, action="store") 13 | 14 | args = parser.parse_args() 15 | import os 16 | os.environ["CUDA_VISIBLE_DEVICES"]=f"{args.id}" 17 | 18 | from detectron2.engine import launch 19 | import detectron2 20 | from detectron2.utils.logger import setup_logger 21 | setup_logger() 22 | 23 | # import some common libraries 24 | import matplotlib.pyplot as plt 25 | import numpy as np 26 | import cv2 as cv 27 | import importlib.util 28 | 29 | from detectron2.checkpoint import DetectionCheckpointer, PeriodicCheckpointer 30 | from detectron2.engine import DefaultTrainer 31 | from detectron2.config import get_cfg 32 | 33 | # import some common detectron2 utilities 34 | from detectron2.engine import DefaultPredictor 35 | from detectron2.config import get_cfg 36 | from detectron2.utils.visualizer import Visualizer 37 | from detectron2.data import MetadataCatalog, DatasetCatalog 38 | from detectron2.data.datasets import load_coco_json, register_coco_instances 39 | from detectron2.evaluation import COCOEvaluator, inference_on_dataset 40 | from detectron2.evaluation.coco_evaluation import _evaluate_predictions_on_coco 41 | 42 | from detectron2.data import build_detection_test_loader 43 | from detectron2.modeling import build_model 44 | 45 | spec = importlib.util.spec_from_file_location("custom_datasets", os.path.join(os.path.dirname(__file__), 'custom_datasets.py')) 46 | custom_datasets = importlib.util.module_from_spec(spec) 47 | spec.loader.exec_module(custom_datasets) 48 | 49 | DATASETS_REAL = ['guns_granada_train', 'guns_simulacro_1', 'guns_simulacro_7'] 50 | DATASETS_SYTH = ['guns_edgecase', 'unity_synthetic_500', 'unity_synthetic_1000', 'unity_synthetic_2500', 'unity_synthetic_5000'] 51 | 52 | 53 | SHOW_DATASET = False 54 | TRAIN = False 55 | SAVE_DATASET_PREDICTIONS = False 56 | SHOW_PREVIEW = False 57 | PREVIEW_ON_DIR = False 58 | MAKE_ANNOS = False 59 | SHOW_EVALUATION = True 60 | 61 | class JsonCOCOEvaluator(COCOEvaluator): 62 | def __init__(self, *arg, **kwargs): 63 | super().__init__(*arg, **kwargs) 64 | with open(os.path.join(self._output_dir, 'coco_instances_results.json')) as f: 65 | self._coco_results = json.load(f) 66 | 67 | def evaluate(self, theshold): 68 | task ='bbox' 69 | coco_eval = ( 70 | _evaluate_predictions_on_coco( 71 | self._coco_api, self._coco_results, task, kpt_oks_sigmas=self._kpt_oks_sigmas 72 | ) 73 | if len(self._coco_results) > 0 74 | else None # cocoapi does not handle empty results very well 75 | ) 76 | res = self._derive_coco_results( 77 | coco_eval, task, class_names=self._metadata.get("thing_classes") 78 | ) 79 | 80 | FP = FN = TP = 0. 81 | prob_preds = [] 82 | for cocoRes in coco_eval.evalImgs[:int(len(coco_eval.evalImgs)/4)]: 83 | if cocoRes is None: 84 | continue 85 | for det, score in zip(cocoRes['dtMatches'][0], cocoRes['dtScores']): 86 | # No gt match 87 | if det == 0.: 88 | prob_preds.append([0, score]) 89 | if score >= theshold: 90 | FP += 1 91 | else: 92 | prob_preds.append([1, score]) 93 | if score >= theshold: 94 | TP += 1 95 | else: 96 | FN += 1 97 | 98 | for gt in cocoRes['gtMatches'][0]: 99 | if gt == 0.: 100 | prob_preds.append([1, 0.]) 101 | FN += 1 102 | 103 | res.update({'TP': TP, 'FP': FP, 'FN': FN, 'ProbPreds': prob_preds}) 104 | return {task: res} 105 | 106 | custom_datasets.loadDatasets() 107 | 108 | EXPERIMENTS_OUTPUT_PATH = '/mnt/datos/experiments/guns_detection' 109 | 110 | networks = { 111 | 'faster_rcnn_R_50_FPN_1x': { 112 | 'cfg': '../../build/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml', 113 | 'weights': 'detectron2://COCO-Detection/faster_rcnn_R_50_FPN_1x/137257794/model_final_b275ba.pkl' 114 | }, 115 | 'faster101': { 116 | 'cfg': '../../build/configs/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml', 117 | 'weights': 'detectron2://COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x/139173657/model_final_68b088.pkl' 118 | }, 119 | } 120 | 121 | experiments = [ 122 | {'steps': 40000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_edgecase',), 'test': ("guns_granada_test",)}, 123 | {'steps': 40000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('unity_synthetic_500',), 'test': ("guns_granada_test",),}, 124 | {'steps': 40000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('unity_synthetic_1000',), 'test': ("guns_granada_test",),}, 125 | {'steps': 40000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('unity_synthetic_2500',), 'test': ("guns_granada_test",),}, 126 | 127 | {'steps': 80000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_granada_train',), 'test': ('guns_granada_test',), 'from': f"{EXPERIMENTS_OUTPUT_PATH}/faster_rcnn_R_50_FPN_1x[LR=0.002][('guns_edgecase',)]/model_0039999.pth"}, 128 | {'steps': 80000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_granada_train',), 'test': ('guns_granada_test',), 'from': f"{EXPERIMENTS_OUTPUT_PATH}/faster_rcnn_R_50_FPN_1x[LR=0.002][('unity_synthetic_500',)]/model_0039999.pth"}, 129 | {'steps': 80000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_granada_train',), 'test': ('guns_granada_test',), 'from': f"{EXPERIMENTS_OUTPUT_PATH}/faster_rcnn_R_50_FPN_1x[LR=0.002][('unity_synthetic_1000',)]/model_0039999.pth"}, 130 | {'steps': 80000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_granada_train',), 'test': ('guns_granada_test',), 'from': f"{EXPERIMENTS_OUTPUT_PATH}/faster_rcnn_R_50_FPN_1x[LR=0.002][('unity_synthetic_2500',)]/model_0039999.pth"}, 131 | {'steps': 80000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_granada_train', 'guns_simulacro_1', 'guns_simulacro_7',), 'test': ("guns_granada_test",), 'from': f"{EXPERIMENTS_OUTPUT_PATH}/faster_rcnn_R_50_FPN_1x[LR=0.002][('guns_edgecase',)]/model_0039999.pth"}, 132 | {'steps': 80000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_granada_train', 'guns_simulacro_1', 'guns_simulacro_7',), 'test': ("guns_granada_test",), 'from': f"{EXPERIMENTS_OUTPUT_PATH}/faster_rcnn_R_50_FPN_1x[LR=0.002][('unity_synthetic_500',)]/model_0039999.pth"}, 133 | {'steps': 80000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_granada_train', 'guns_simulacro_1', 'guns_simulacro_7',), 'test': ("guns_granada_test",), 'from': f"{EXPERIMENTS_OUTPUT_PATH}/faster_rcnn_R_50_FPN_1x[LR=0.002][('unity_synthetic_2500',)]/model_0039999.pth"}, 134 | {'steps': 40000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_granada_train',), 'test': ("guns_granada_test",)}, 135 | {'steps': 40000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_granada_train', 'guns_simulacro_1', 'guns_simulacro_7'), 'test': ("guns_granada_test",)}, 136 | 137 | {'steps': 40000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_simulacro_1', 'guns_simulacro_7'), 'test': ("guns_granada_test",)}, 138 | {'steps': 80000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_granada_train',), 'test': ("guns_granada_test",)}, 139 | 140 | {'steps': 40000, 'lr': 0.002, 'net': 'faster101', 'train': ('guns_edgecase',), 'test': ('guns_granada_test',),}, 141 | {'steps': 40000, 'lr': 0.002, 'net': 'faster101', 'train': ('unity_synthetic_500',), 'test': ('guns_granada_test',),}, 142 | {'steps': 40000, 'lr': 0.002, 'net': 'faster101', 'train': ('unity_synthetic_1000',), 'test': ('guns_granada_test',),}, 143 | {'steps': 40000, 'lr': 0.002, 'net': 'faster101', 'train': ('unity_synthetic_2500',), 'test': ('guns_granada_test',),}, 144 | 145 | {'steps': 80000, 'lr': 0.002, 'net': 'faster101', 'train': ('guns_granada_train',), 'test': ('guns_granada_test',), 'from': f"{EXPERIMENTS_OUTPUT_PATH}/faster101[LR=0.002][('guns_edgecase',)]/model_0039999.pth"}, 146 | {'steps': 80000, 'lr': 0.002, 'net': 'faster101', 'train': ('guns_granada_train',), 'test': ('guns_granada_test',), 'from': f"{EXPERIMENTS_OUTPUT_PATH}/faster101[LR=0.002][('unity_synthetic_500',)]/model_0039999.pth"}, 147 | {'steps': 80000, 'lr': 0.002, 'net': 'faster101', 'train': ('guns_granada_train',), 'test': ('guns_granada_test',), 'from': f"{EXPERIMENTS_OUTPUT_PATH}/faster101[LR=0.002][('unity_synthetic_1000',)]/model_0039999.pth"}, 148 | {'steps': 80000, 'lr': 0.002, 'net': 'faster101', 'train': ('guns_granada_train',), 'test': ('guns_granada_test',), 'from': f"{EXPERIMENTS_OUTPUT_PATH}/faster101[LR=0.002][('unity_synthetic_2500',)]/model_0039999.pth"}, 149 | 150 | {'steps': 20000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_edgecase',),}, 151 | {'steps': 20000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_edgecase', 'guns_granada_train', 'guns_simulacro_1', 'guns_simulacro_7'), 'test': ("guns_granada_test",),}, 152 | {'steps': 20000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('guns_edgecase', 'guns_granada_train', 'guns_simulacro_1', 'guns_simulacro_7', 'alamy'), 'test': ("guns_granada_test",), 'test': ("guns_granada_test",),}, 153 | {'steps': 20000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('unity_synthetic_1000',), 'test': ("guns_granada_test",),}, 154 | {'steps': 20000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('unity_synthetic_2500',), 'test': ("guns_granada_test",),}, 155 | {'steps': 20000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('unity_synthetic_1000', 'guns_granada_train', 'guns_simulacro_1', 'guns_simulacro_7'), 'test': ("guns_granada_test",),}, 156 | {'steps': 20000, 'lr': 0.002, 'net': 'faster_rcnn_R_50_FPN_1x', 'train': ('unity_synthetic_2500', 'guns_granada_train', 'guns_simulacro_1', 'guns_simulacro_7'), 'test': ("guns_granada_test",),}, 157 | 158 | 159 | ] 160 | 161 | def train(exp): 162 | print(f'Executing experiment: \n{exp}\n') 163 | cfg = get_cfg() 164 | 165 | cfg.merge_from_file(networks[exp['net']]['cfg']) 166 | 167 | cfg.MODEL.WEIGHTS = exp.get('from', networks[exp['net']]['weights']) 168 | 169 | cfg.INPUT.MAX_SIZE_TEST = 2500 170 | cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1 171 | cfg.DATALOADER.NUM_WORKERS = 2 172 | 173 | cfg.DATASETS.TRAIN = exp['train'] 174 | cfg.DATASETS.TEST = exp['test'] 175 | cfg.SOLVER.IMS_PER_BATCH = 2 176 | cfg.SOLVER.BASE_LR = exp['lr'] 177 | cfg.SOLVER.MAX_ITER = exp['steps'] # 300 iterations seems good enough, but you can certainly train longer 178 | cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512 179 | cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS = True 180 | 181 | cfg.SOLVER.CHECKPOINT_PERIOD = 5000 182 | cfg.TEST.EVAL_PERIOD = 0 183 | #cfg.INPUT.MIN_SIZE_TRAIN = (480, 512, 640, 672, 704, 736, 768, 800) 184 | 185 | if 'from' in exp: 186 | trainFromStr = exp['from'].split('/')[-2] 187 | cfg.OUTPUT_DIR = f"{EXPERIMENTS_OUTPUT_PATH}/{exp['net']}[LR={exp['lr']}][{str(exp['train'])}]FROM[{trainFromStr}]" 188 | #if not os.path.exists(cfg.OUTPUT_DIR): 189 | # print(f"copying {trainFromFolder}\nto {cfg.OUTPUT_DIR}") 190 | # shutil.copytree(trainFromFolder, cfg.OUTPUT_DIR) 191 | else: 192 | cfg.OUTPUT_DIR = f"{EXPERIMENTS_OUTPUT_PATH}/{exp['net']}[LR={exp['lr']}][{str(exp['train'])}]" 193 | 194 | cfg.freeze() 195 | 196 | os.makedirs(cfg.OUTPUT_DIR, exist_ok=True) 197 | 198 | class Trainer(DefaultTrainer): 199 | @classmethod 200 | def build_evaluator(cls, cfg_, dataset_name): 201 | return COCOEvaluator(dataset_name, cfg_, distributed=False) 202 | 203 | trainer = Trainer(cfg) 204 | trainer.resume_or_load(resume=True) 205 | trainer.train() 206 | 207 | def test(exp, selected_dataset, theshold): 208 | print(f'Testing experiment: \n{exp}\n') 209 | cfg = get_cfg() 210 | cfg.merge_from_file(networks[exp['net']]['cfg']) 211 | cfg.DATASETS.TEST = exp['test'] 212 | cfg.INPUT.MAX_SIZE_TEST = 2500 213 | cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1 214 | cfg.DATALOADER.NUM_WORKERS = 2 215 | 216 | if 'from' in exp: 217 | trainFromStr = exp['from'].split('/')[-2] 218 | 219 | cfg.OUTPUT_DIR = f"{EXPERIMENTS_OUTPUT_PATH}/{exp['net']}[LR={exp['lr']}][{str(exp['train'])}]FROM[{trainFromStr}]" 220 | else: 221 | cfg.OUTPUT_DIR = f"{EXPERIMENTS_OUTPUT_PATH}/{exp['net']}[LR={exp['lr']}][{str(exp['train'])}]" 222 | 223 | cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, 'model_final.pth') 224 | 225 | if all([ds in DATASETS_REAL or ds in DATASETS_SYTH for ds in exp['train']]): 226 | output_dir = f"{cfg.OUTPUT_DIR}/{selected_dataset}" 227 | model = build_model(cfg) 228 | DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load( 229 | cfg.MODEL.WEIGHTS, resume=False 230 | ) 231 | evaluator = COCOEvaluator(selected_dataset, cfg, False, output_dir=output_dir) 232 | val_loader = build_detection_test_loader(cfg, selected_dataset) 233 | inference_on_dataset(model, val_loader, evaluator) 234 | 235 | if __name__ == '__main__': 236 | module = args.id - 1 237 | size = 2 238 | 239 | num_gpus = 2 240 | port = 2 ** 15 + 2 ** 14 + hash(os.getuid()) % 2 ** 14 241 | 242 | for i, exp in enumerate(experiments): 243 | if i % size != module: 244 | continue 245 | train(exp) 246 | # launch( 247 | # train, 248 | # num_gpus, 249 | # dist_url="tcp://127.0.0.1:{}".format(port), 250 | # args=(exp,) 251 | # ) 252 | 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More considerations for the public : 48 | wiki.creativecommons.org/Considerations\_for\_licensees 49 | 50 | Creative Commons Attribution-NonCommercial 4.0 International Public 51 | License 52 | 53 | By exercising the Licensed Rights (defined below), You accept and agree 54 | to be bound by the terms and conditions of this Creative Commons 55 | Attribution-NonCommercial 4.0 International Public License ("Public 56 | License"). To the extent this Public License may be interpreted as a 57 | contract, You are granted the Licensed Rights in consideration of Your 58 | acceptance of these terms and conditions, and the Licensor grants You 59 | such rights in consideration of benefits the Licensor receives from 60 | making the Licensed Material available under these terms and conditions. 61 | 62 | - Section 1 – Definitions. 63 | 64 | - a. Adapted Material means material subject to Copyright and 65 | Similar Rights that is derived from or based upon the Licensed 66 | Material and in which the Licensed Material is translated, 67 | altered, arranged, transformed, or otherwise modified in a 68 | manner requiring permission under the Copyright and Similar 69 | Rights held by the Licensor. For purposes of this Public 70 | License, where the Licensed Material is a musical work, 71 | performance, or sound recording, Adapted Material is always 72 | produced where the Licensed Material is synched in timed 73 | relation with a moving image. 74 | - b. Adapter's License means the license You apply to Your 75 | Copyright and Similar Rights in Your contributions to Adapted 76 | Material in accordance with the terms and conditions of this 77 | Public License. 78 | - c. 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Licensed Material means the artistic or literary work, 95 | database, or other material to which the Licensor applied this 96 | Public License. 97 | - g. Licensed Rights means the rights granted to You subject to 98 | the terms and conditions of this Public License, which are 99 | limited to all Copyright and Similar Rights that apply to Your 100 | use of the Licensed Material and that the Licensor has authority 101 | to license. 102 | - h. Licensor means the individual(s) or entity(ies) granting 103 | rights under this Public License. 104 | - i. NonCommercial means not primarily intended for or directed 105 | towards commercial advantage or monetary compensation. For 106 | purposes of this Public License, the exchange of the Licensed 107 | Material for other material subject to Copyright and Similar 108 | Rights by digital file-sharing or similar means is NonCommercial 109 | provided there is no payment of monetary compensation in 110 | connection with the exchange. 111 | - j. Share means to provide material to the public by any means or 112 | process that requires permission under the Licensed Rights, such 113 | as reproduction, public display, public performance, 114 | distribution, dissemination, communication, or importation, and 115 | to make material available to the public including in ways that 116 | members of the public may access the material from a place and 117 | at a time individually chosen by them. 118 | - k. Sui Generis Database Rights means rights other than copyright 119 | resulting from Directive 96/9/EC of the European Parliament and 120 | of the Council of 11 March 1996 on the legal protection of 121 | databases, as amended and/or succeeded, as well as other 122 | essentially equivalent rights anywhere in the world. 123 | - l. You means the individual or entity exercising the Licensed 124 | Rights under this Public License. Your has a corresponding 125 | meaning. 126 | 127 | - Section 2 – Scope. 128 | 129 | - a. License grant. 130 | - 1. Subject to the terms and conditions of this Public 131 | License, the Licensor hereby grants You a worldwide, 132 | royalty-free, non-sublicensable, non-exclusive, irrevocable 133 | license to exercise the Licensed Rights in the Licensed 134 | Material to: 135 | - A. reproduce and Share the Licensed Material, in whole 136 | or in part, for NonCommercial purposes only; and 137 | - B. produce, reproduce, and Share Adapted Material for 138 | NonCommercial purposes only. 139 | - 2. Exceptions and Limitations. For the avoidance of doubt, 140 | where Exceptions and Limitations apply to Your use, this 141 | Public License does not apply, and You do not need to comply 142 | with its terms and conditions. 143 | - 3. Term. The term of this Public License is specified in 144 | Section 6(a). 145 | - 4. Media and formats; technical modifications allowed. The 146 | Licensor authorizes You to exercise the Licensed Rights in 147 | all media and formats whether now known or hereafter 148 | created, and to make technical modifications necessary to do 149 | so. The Licensor waives and/or agrees not to assert any 150 | right or authority to forbid You from making technical 151 | modifications necessary to exercise the Licensed Rights, 152 | including technical modifications necessary to circumvent 153 | Effective Technological Measures. For purposes of this 154 | Public License, simply making modifications authorized by 155 | this Section 2(a)(4) never produces Adapted Material. 156 | - 5. Downstream recipients. 157 | - A. Offer from the Licensor – Licensed Material. Every 158 | recipient of the Licensed Material automatically 159 | receives an offer from the Licensor to exercise the 160 | Licensed Rights under the terms and conditions of this 161 | Public License. 162 | - B. No downstream restrictions. You may not offer or 163 | impose any additional or different terms or conditions 164 | on, or apply any Effective Technological Measures to, 165 | the Licensed Material if doing so restricts exercise of 166 | the Licensed Rights by any recipient of the Licensed 167 | Material. 168 | - 6. No endorsement. Nothing in this Public License 169 | constitutes or may be construed as permission to assert or 170 | imply that You are, or that Your use of the Licensed 171 | Material is, connected with, or sponsored, endorsed, or 172 | granted official status by, the Licensor or others 173 | designated to receive attribution as provided in Section 174 | 3(a)(1)(A)(i). 175 | - b. Other rights. 176 | - 1. Moral rights, such as the right of integrity, are not 177 | licensed under this Public License, nor are publicity, 178 | privacy, and/or other similar personality rights; however, 179 | to the extent possible, the Licensor waives and/or agrees 180 | not to assert any such rights held by the Licensor to the 181 | limited extent necessary to allow You to exercise the 182 | Licensed Rights, but not otherwise. 183 | - 2. Patent and trademark rights are not licensed under this 184 | Public License. 185 | - 3. To the extent possible, the Licensor waives any right to 186 | collect royalties from You for the exercise of the Licensed 187 | Rights, whether directly or through a collecting society 188 | under any voluntary or waivable statutory or compulsory 189 | licensing scheme. In all other cases the Licensor expressly 190 | reserves any right to collect such royalties, including when 191 | the Licensed Material is used other than for NonCommercial 192 | purposes. 193 | 194 | - Section 3 – License Conditions. 195 | 196 | Your exercise of the Licensed Rights is expressly made subject to 197 | the following conditions. 198 | 199 | - a. Attribution. 200 | - 1. If You Share the Licensed Material (including in modified 201 | form), You must: 202 | - A. retain the following if it is supplied by the 203 | Licensor with the Licensed Material: 204 | - i. identification of the creator(s) of the Licensed 205 | Material and any others designated to receive 206 | attribution, in any reasonable manner requested by 207 | the Licensor (including by pseudonym if designated); 208 | - ii. a copyright notice; 209 | - iii. a notice that refers to this Public License; 210 | - iv. a notice that refers to the disclaimer of 211 | warranties; 212 | - v. a URI or hyperlink to the Licensed Material to 213 | the extent reasonably practicable; 214 | - B. indicate if You modified the Licensed Material and 215 | retain an indication of any previous modifications; and 216 | - C. indicate the Licensed Material is licensed under this 217 | Public License, and include the text of, or the URI or 218 | hyperlink to, this Public License. 219 | - 2. You may satisfy the conditions in Section 3(a)(1) in any 220 | reasonable manner based on the medium, means, and context in 221 | which You Share the Licensed Material. For example, it may 222 | be reasonable to satisfy the conditions by providing a URI 223 | or hyperlink to a resource that includes the required 224 | information. 225 | - 3. If requested by the Licensor, You must remove any of the 226 | information required by Section 3(a)(1)(A) to the extent 227 | reasonably practicable. 228 | - 4. If You Share Adapted Material You produce, the Adapter's 229 | License You apply must not prevent recipients of the Adapted 230 | Material from complying with this Public License. 231 | 232 | - Section 4 – Sui Generis Database Rights. 233 | 234 | Where the Licensed Rights include Sui Generis Database Rights that 235 | apply to Your use of the Licensed Material: 236 | 237 | - a. for the avoidance of doubt, Section 2(a)(1) grants You the 238 | right to extract, reuse, reproduce, and Share all or a 239 | substantial portion of the contents of the database for 240 | NonCommercial purposes only; 241 | - b. if You include all or a substantial portion of the database 242 | contents in a database in which You have Sui Generis Database 243 | Rights, then the database in which You have Sui Generis Database 244 | Rights (but not its individual contents) is Adapted Material; 245 | and 246 | - c. You must comply with the conditions in Section 3(a) if You 247 | Share all or a substantial portion of the contents of the 248 | database. 249 | 250 | For the avoidance of doubt, this Section 4 supplements and does not 251 | replace Your obligations under this Public License where the 252 | Licensed Rights include other Copyright and Similar Rights. 253 | 254 | - Section 5 – Disclaimer of Warranties and Limitation of Liability. 255 | 256 | - a. Unless otherwise separately undertaken by the Licensor, to 257 | the extent possible, the Licensor offers the Licensed Material 258 | as-is and as-available, and makes no representations or 259 | warranties of any kind concerning the Licensed Material, whether 260 | express, implied, statutory, or other. This includes, without 261 | limitation, warranties of title, merchantability, fitness for a 262 | particular purpose, non-infringement, absence of latent or other 263 | defects, accuracy, or the presence or absence of errors, whether 264 | or not known or discoverable. Where disclaimers of warranties 265 | are not allowed in full or in part, this disclaimer may not 266 | apply to You. 267 | - b. To the extent possible, in no event will the Licensor be 268 | liable to You on any legal theory (including, without 269 | limitation, negligence) or otherwise for any direct, special, 270 | indirect, incidental, consequential, punitive, exemplary, or 271 | other losses, costs, expenses, or damages arising out of this 272 | Public License or use of the Licensed Material, even if the 273 | Licensor has been advised of the possibility of such losses, 274 | costs, expenses, or damages. Where a limitation of liability is 275 | not allowed in full or in part, this limitation may not apply to 276 | You. 277 | - c. The disclaimer of warranties and limitation of liability 278 | provided above shall be interpreted in a manner that, to the 279 | extent possible, most closely approximates an absolute 280 | disclaimer and waiver of all liability. 281 | 282 | - Section 6 – Term and Termination. 283 | 284 | - a. This Public License applies for the term of the Copyright and 285 | Similar Rights licensed here. However, if You fail to comply 286 | with this Public License, then Your rights under this Public 287 | License terminate automatically. 288 | - b. Where Your right to use the Licensed Material has terminated 289 | under Section 6(a), it reinstates: 290 | 291 | - 1. automatically as of the date the violation is cured, 292 | provided it is cured within 30 days of Your discovery of the 293 | violation; or 294 | - 2. upon express reinstatement by the Licensor. 295 | 296 | For the avoidance of doubt, this Section 6(b) does not affect 297 | any right the Licensor may have to seek remedies for Your 298 | violations of this Public License. 299 | 300 | - c. For the avoidance of doubt, the Licensor may also offer the 301 | Licensed Material under separate terms or conditions or stop 302 | distributing the Licensed Material at any time; however, doing 303 | so will not terminate this Public License. 304 | - d. Sections 1, 5, 6, 7, and 8 survive termination of this Public 305 | License. 306 | 307 | - Section 7 – Other Terms and Conditions. 308 | 309 | - a. The Licensor shall not be bound by any additional or 310 | different terms or conditions communicated by You unless 311 | expressly agreed. 312 | - b. Any arrangements, understandings, or agreements regarding the 313 | Licensed Material not stated herein are separate from and 314 | independent of the terms and conditions of this Public License. 315 | 316 | - Section 8 – Interpretation. 317 | 318 | - a. For the avoidance of doubt, this Public License does not, and 319 | shall not be interpreted to, reduce, limit, restrict, or impose 320 | conditions on any use of the Licensed Material that could 321 | lawfully be made without permission under this Public License. 322 | - b. To the extent possible, if any provision of this Public 323 | License is deemed unenforceable, it shall be automatically 324 | reformed to the minimum extent necessary to make it enforceable. 325 | If the provision cannot be reformed, it shall be severed from 326 | this Public License without affecting the enforceability of the 327 | remaining terms and conditions. 328 | - c. No term or condition of this Public License will be waived 329 | and no failure to comply consented to unless expressly agreed to 330 | by the Licensor. 331 | - d. Nothing in this Public License constitutes or may be 332 | interpreted as a limitation upon, or waiver of, any privileges 333 | and immunities that apply to the Licensor or You, including from 334 | the legal processes of any jurisdiction or authority. 335 | 336 | Creative Commons is not a party to its public licenses. 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