├── demo ├── voc │ ├── ImageSets │ │ └── Main │ │ │ ├── test.txt │ │ │ ├── val.txt │ │ │ ├── train.txt │ │ │ └── trainval.txt │ ├── worktxt │ │ ├── train_30178.txt │ │ ├── train_30131.txt │ │ ├── train_30180.txt │ │ ├── train_30185.txt │ │ ├── train_30101.txt │ │ ├── train_30147.txt │ │ ├── train_30126.txt │ │ ├── train_30209.txt │ │ ├── train_29635.txt │ │ ├── train_30169.txt │ │ ├── train_30092.txt │ │ ├── train_30202.txt │ │ ├── train_30123.txt │ │ ├── train_30138.txt │ │ ├── train_30190.txt │ │ ├── train_30156.txt │ │ ├── train_30183.txt │ │ ├── train_30116.txt │ │ ├── train_29641.txt │ │ └── train_30090.txt │ ├── JPEGImages │ │ ├── train_29635.jpg │ │ ├── train_29641.jpg │ │ ├── train_30090.jpg │ │ ├── train_30092.jpg │ │ ├── train_30101.jpg │ │ ├── train_30116.jpg │ │ ├── train_30123.jpg │ │ ├── train_30126.jpg │ │ ├── train_30131.jpg │ │ ├── train_30138.jpg │ │ ├── train_30147.jpg │ │ ├── train_30156.jpg │ │ ├── train_30169.jpg │ │ ├── train_30178.jpg │ │ ├── train_30180.jpg │ │ ├── train_30183.jpg │ │ ├── train_30185.jpg │ │ ├── train_30190.jpg │ │ ├── train_30202.jpg │ │ └── train_30209.jpg │ └── Annotations │ │ ├── train_30131.xml │ │ ├── train_30178.xml │ │ ├── train_30180.xml │ │ ├── train_30185.xml │ │ ├── train_29635.xml │ │ ├── train_30092.xml │ │ ├── train_30101.xml │ │ ├── train_30126.xml │ │ ├── train_30147.xml │ │ ├── train_30169.xml │ │ ├── train_30202.xml │ │ ├── train_30209.xml │ │ ├── train_30123.xml │ │ ├── train_30138.xml │ │ ├── train_30183.xml │ │ ├── train_30190.xml │ │ ├── train_30156.xml │ │ ├── train_29641.xml │ │ ├── train_30090.xml │ │ └── train_30116.xml ├── yolov3 │ └── custom │ │ ├── labels │ │ ├── train_30178.txt │ │ ├── train_30131.txt │ │ ├── train_30180.txt │ │ ├── train_30185.txt │ │ ├── train_30202.txt │ │ ├── train_30092.txt │ │ ├── train_30147.txt │ │ ├── train_30169.txt │ │ ├── train_29635.txt │ │ ├── train_30209.txt │ │ ├── train_30101.txt │ │ ├── train_30126.txt │ │ ├── train_30123.txt │ │ ├── train_30190.txt │ │ ├── train_30138.txt │ │ ├── train_30156.txt │ │ ├── train_30183.txt │ │ ├── train_29641.txt │ │ ├── train_30090.txt │ │ └── train_30116.txt │ │ └── trainval.txt └── coco │ ├── images │ ├── train_29635.jpg │ ├── train_29641.jpg │ ├── train_30090.jpg │ ├── train_30092.jpg │ ├── train_30101.jpg │ ├── train_30116.jpg │ ├── train_30123.jpg │ ├── train_30126.jpg │ ├── train_30131.jpg │ ├── train_30138.jpg │ ├── train_30147.jpg │ ├── train_30156.jpg │ ├── train_30169.jpg │ ├── train_30178.jpg │ ├── train_30180.jpg │ ├── train_30183.jpg │ ├── train_30185.jpg │ ├── train_30190.jpg │ ├── train_30202.jpg │ └── train_30209.jpg │ ├── val2017 │ ├── train_30092.jpg │ └── train_30202.jpg │ ├── train2017 │ ├── train_29635.jpg │ ├── train_29641.jpg │ ├── train_30090.jpg │ ├── train_30101.jpg │ ├── train_30116.jpg │ ├── train_30123.jpg │ ├── train_30126.jpg │ ├── train_30131.jpg │ ├── train_30138.jpg │ ├── train_30147.jpg │ ├── train_30156.jpg │ ├── train_30169.jpg │ ├── train_30178.jpg │ ├── train_30180.jpg │ ├── train_30183.jpg │ ├── train_30185.jpg │ ├── train_30190.jpg │ └── train_30209.jpg │ └── annotations │ ├── val2017.json │ ├── train2017.json │ ├── annotations.json │ └── annotations_washed.json ├── .idea ├── .gitignore ├── vcs.xml ├── misc.xml ├── inspectionProfiles │ ├── profiles_settings.xml │ └── Project_Default.xml ├── modules.xml └── objectDetectionDatasets.iml ├── coco_visulize.py ├── voc_split_trainVal.py ├── voc_to_yoloV3.py ├── README.md ├── voc_to_yoloV5.py ├── make_voc.py ├── voc_to_coco_v2.py ├── generate_persudo_json.py ├── coco_split_trainVal.py └── voc_to_coco_v1.py /demo/voc/ImageSets/Main/test.txt: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /demo/voc/ImageSets/Main/val.txt: -------------------------------------------------------------------------------- 1 | train_30190 2 | train_30180 3 | -------------------------------------------------------------------------------- /.idea/.gitignore: -------------------------------------------------------------------------------- 1 | # Default ignored files 2 | /shelf/ 3 | /workspace.xml 4 | -------------------------------------------------------------------------------- /demo/yolov3/custom/labels/train_30178.txt: -------------------------------------------------------------------------------- 1 | 0 0.6072916666666667 0.24296875 0.78125 0.1796875 -------------------------------------------------------------------------------- /demo/voc/worktxt/train_30178.txt: -------------------------------------------------------------------------------- 1 | 0 0.6052083333333333 0.24140625000000002 0.78125 0.1796875 2 | -------------------------------------------------------------------------------- /demo/voc/worktxt/train_30131.txt: -------------------------------------------------------------------------------- 1 | 0 0.7760416666666666 0.6414062500000001 0.39791666666666664 0.1421875 2 | -------------------------------------------------------------------------------- /demo/voc/worktxt/train_30180.txt: -------------------------------------------------------------------------------- 1 | 2 0.5645833333333333 0.39453125 0.2916666666666667 0.38593750000000004 2 | -------------------------------------------------------------------------------- /demo/yolov3/custom/labels/train_30131.txt: -------------------------------------------------------------------------------- 1 | 0 0.778125 0.6429687500000001 0.3979166666666667 0.14218750000000002 -------------------------------------------------------------------------------- /demo/yolov3/custom/labels/train_30180.txt: -------------------------------------------------------------------------------- 1 | 2 0.5666666666666667 0.39609375 0.2916666666666667 0.38593750000000004 -------------------------------------------------------------------------------- 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0.2916666666666667 0.421875 3 | -------------------------------------------------------------------------------- /demo/voc/worktxt/train_30147.txt: -------------------------------------------------------------------------------- 1 | 1 0.4041666666666667 0.2609375 0.275 0.134375 2 | 1 0.196875 0.26640625 0.15208333333333332 0.13593750000000002 3 | -------------------------------------------------------------------------------- /demo/voc/worktxt/train_30126.txt: -------------------------------------------------------------------------------- 1 | 1 0.81640625 0.184375 0.0890625 0.03958333333333333 2 | 1 0.79609375 0.29375 0.29531250000000003 0.09166666666666666 3 | -------------------------------------------------------------------------------- /demo/voc/worktxt/train_30209.txt: -------------------------------------------------------------------------------- 1 | 1 0.3364583333333333 0.30078125 0.13125 0.3984375 2 | 1 0.7229166666666667 0.7796875000000001 0.15 0.053125000000000006 3 | -------------------------------------------------------------------------------- /demo/voc/worktxt/train_29635.txt: -------------------------------------------------------------------------------- 1 | 1 0.6520833333333333 0.21875 0.6666666666666666 0.19375 2 | 1 0.24270833333333333 0.26484375 0.15208333333333332 0.2265625 3 | -------------------------------------------------------------------------------- /demo/yolov3/custom/labels/train_30202.txt: -------------------------------------------------------------------------------- 1 | 1 0.31354166666666666 0.17109375000000002 0.6229166666666667 0.3171875 2 | 1 0.53125 0.44140625 0.1875 0.03281249999999997 -------------------------------------------------------------------------------- /demo/voc/worktxt/train_30169.txt: -------------------------------------------------------------------------------- 1 | 1 0.6229166666666667 0.2484375 0.7375 0.42812500000000003 2 | 1 0.4583333333333333 0.540625 0.9249999999999999 0.20625000000000002 3 | 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0.096875 3 | -------------------------------------------------------------------------------- /demo/voc/worktxt/train_30202.txt: -------------------------------------------------------------------------------- 1 | 1 0.31145833333333334 0.16953125000000002 0.6229166666666667 0.3171875 2 | 1 0.5291666666666667 0.43984375000000003 0.1875 0.0328125 3 | -------------------------------------------------------------------------------- /demo/yolov3/custom/labels/train_30169.txt: -------------------------------------------------------------------------------- 1 | 1 0.6270833333333333 0.2515625 0.7375 0.428125 2 | 1 0.46249999999999997 0.5437500000000001 0.9249999999999999 0.20625000000000004 -------------------------------------------------------------------------------- /demo/yolov3/custom/labels/train_29635.txt: -------------------------------------------------------------------------------- 1 | 1 0.6541666666666666 0.22031250000000002 0.6666666666666666 0.19375 2 | 1 0.24479166666666666 0.26640625 0.1520833333333333 0.2265625 -------------------------------------------------------------------------------- /demo/yolov3/custom/labels/train_30209.txt: -------------------------------------------------------------------------------- 1 | 1 0.33854166666666663 0.30234375 0.13125000000000003 0.3984375 2 | 1 0.7250000000000001 0.78125 0.15000000000000002 0.05312499999999998 -------------------------------------------------------------------------------- /demo/voc/worktxt/train_30123.txt: -------------------------------------------------------------------------------- 1 | 1 0.5875 0.109375 0.5375 0.10312500000000001 2 | 1 0.0375 0.16875 0.07916666666666666 0.0625 3 | 0 0.584375 0.11015625000000001 0.53125 0.1015625 4 | -------------------------------------------------------------------------------- /demo/yolov3/custom/labels/train_30101.txt: -------------------------------------------------------------------------------- 1 | 1 0.5770833333333334 0.359375 0.2791666666666667 0.40625 2 | 1 0.7541666666666667 0.36406250000000007 0.29166666666666674 0.42187500000000006 -------------------------------------------------------------------------------- /demo/yolov3/custom/labels/train_30126.txt: -------------------------------------------------------------------------------- 1 | 1 0.81796875 0.18645833333333334 0.08906250000000004 0.03958333333333333 2 | 1 0.79765625 0.29583333333333334 0.2953125 0.09166666666666667 -------------------------------------------------------------------------------- /.idea/vcs.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | -------------------------------------------------------------------------------- /demo/yolov3/custom/labels/train_30123.txt: -------------------------------------------------------------------------------- 1 | 1 0.5895833333333333 0.11093750000000001 0.5375 0.103125 2 | 1 0.03958333333333333 0.1703125 0.07916666666666666 0.0625 3 | 0 0.5864583333333333 0.11171875 0.53125 0.1015625 -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | -------------------------------------------------------------------------------- /demo/voc/worktxt/train_30138.txt: -------------------------------------------------------------------------------- 1 | 1 0.7354166666666666 0.3375 0.5166666666666666 0.178125 2 | 1 0.73125 0.48750000000000004 0.5291666666666667 0.09062500000000001 3 | 1 0.21666666666666667 0.484375 0.43333333333333335 0.08125 4 | -------------------------------------------------------------------------------- /demo/yolov3/custom/labels/train_30190.txt: -------------------------------------------------------------------------------- 1 | 1 0.640625 0.265625 0.7156250000000001 0.41874999999999996 2 | 1 0.328125 0.75 0.08437500000000003 0.3041666666666667 3 | 1 0.08203125 0.7427083333333333 0.09843750000000001 0.19791666666666663 -------------------------------------------------------------------------------- /.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /demo/voc/worktxt/train_30190.txt: -------------------------------------------------------------------------------- 1 | 1 0.6390625000000001 0.2635416666666667 0.7156250000000001 0.41875 2 | 1 0.32656250000000003 0.7479166666666667 0.084375 0.30416666666666664 3 | 1 0.08046875 0.740625 0.09843750000000001 0.19791666666666666 4 | -------------------------------------------------------------------------------- /demo/voc/worktxt/train_30156.txt: -------------------------------------------------------------------------------- 1 | 0 0.74609375 0.1451388888888889 0.171875 0.04583333333333334 2 | 0 0.38007812500000004 0.11041666666666668 0.057031250000000006 0.08194444444444444 3 | 0 0.337890625 0.13402777777777777 0.03359375 0.05694444444444444 4 | -------------------------------------------------------------------------------- /demo/yolov3/custom/labels/train_30138.txt: -------------------------------------------------------------------------------- 1 | 1 0.7375 0.33906250000000004 0.5166666666666666 0.17812500000000003 2 | 1 0.7333333333333334 0.48906250000000007 0.5291666666666667 0.09062500000000001 3 | 1 0.21875 0.4859375 0.43333333333333335 0.08125000000000004 -------------------------------------------------------------------------------- /demo/yolov3/custom/labels/train_30156.txt: -------------------------------------------------------------------------------- 1 | 0 0.7468750000000001 0.14652777777777778 0.171875 0.04583333333333334 2 | 0 0.380859375 0.11180555555555556 0.05703125000000003 0.08194444444444446 3 | 0 0.338671875 0.13541666666666669 0.03359374999999998 0.056944444444444436 -------------------------------------------------------------------------------- /demo/yolov3/custom/labels/train_30183.txt: -------------------------------------------------------------------------------- 1 | 1 0.4197916666666667 0.340625 0.22291666666666665 0.16562500000000002 2 | 1 0.25104166666666666 0.45390625000000007 0.05208333333333334 0.08281250000000001 3 | 1 0.26458333333333334 0.3046875 0.06666666666666668 0.07500000000000001 -------------------------------------------------------------------------------- /demo/voc/worktxt/train_30183.txt: -------------------------------------------------------------------------------- 1 | 1 0.41770833333333335 0.33906250000000004 0.22291666666666665 0.16562500000000002 2 | 1 0.24895833333333334 0.45234375000000004 0.052083333333333336 0.08281250000000001 3 | 1 0.2625 0.30312500000000003 0.06666666666666667 0.07500000000000001 4 | -------------------------------------------------------------------------------- /demo/voc/worktxt/train_30116.txt: -------------------------------------------------------------------------------- 1 | 0 0.18645833333333334 0.178125 0.2604166666666667 0.225 2 | 0 0.3885416666666667 0.1296875 0.06875 0.084375 3 | 0 0.38125 0.23203125000000002 0.14583333333333334 0.1921875 4 | 0 0.4708333333333333 0.24375000000000002 0.041666666666666664 0.1875 5 | -------------------------------------------------------------------------------- /demo/voc/worktxt/train_29641.txt: -------------------------------------------------------------------------------- 1 | 0 0.8520833333333333 0.32421875 0.2625 0.035937500000000004 2 | 0 0.22083333333333333 0.38906250000000003 0.225 0.03125 3 | 1 0.64375 0.51796875 0.11666666666666667 0.1328125 4 | 1 0.5302083333333333 0.5140625 0.08541666666666667 0.09062500000000001 5 | -------------------------------------------------------------------------------- /demo/voc/worktxt/train_30090.txt: -------------------------------------------------------------------------------- 1 | 1 0.471875 0.40468750000000003 0.18541666666666667 0.33437500000000003 2 | 1 0.3614583333333333 0.40859375000000003 0.07291666666666667 0.3328125 3 | 2 0.4739583333333333 0.40390625 0.18125 0.33906250000000004 4 | 2 0.36041666666666666 0.40703125 0.075 0.33906250000000004 5 | -------------------------------------------------------------------------------- /demo/yolov3/custom/labels/train_29641.txt: -------------------------------------------------------------------------------- 1 | 0 0.8541666666666666 0.32578125 0.26249999999999996 0.035937499999999956 2 | 0 0.22291666666666665 0.390625 0.22499999999999998 0.03125 3 | 1 0.6458333333333333 0.51953125 0.11666666666666659 0.1328125 4 | 1 0.5322916666666666 0.515625 0.08541666666666664 0.09062499999999996 -------------------------------------------------------------------------------- /demo/yolov3/custom/labels/train_30090.txt: -------------------------------------------------------------------------------- 1 | 1 0.4739583333333333 0.40625 0.18541666666666667 0.33437500000000003 2 | 1 0.36354166666666665 0.41015625 0.07291666666666669 0.33281249999999996 3 | 2 0.4760416666666667 0.40546875000000004 0.18124999999999997 0.33906250000000004 4 | 2 0.36250000000000004 0.40859375 0.07500000000000001 0.3390625 -------------------------------------------------------------------------------- /demo/yolov3/custom/labels/train_30116.txt: -------------------------------------------------------------------------------- 1 | 0 0.18854166666666666 0.1796875 0.26041666666666663 0.22499999999999998 2 | 0 0.390625 0.13125 0.06874999999999998 0.08437500000000002 3 | 0 0.3833333333333333 0.23359375000000002 0.14583333333333331 0.1921875 4 | 0 0.47291666666666665 0.24531250000000004 0.04166666666666663 0.18750000000000003 -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /demo/voc/ImageSets/Main/train.txt: -------------------------------------------------------------------------------- 1 | train_29641 2 | train_30183 3 | train_30092 4 | train_30116 5 | train_30202 6 | train_30156 7 | train_30090 8 | train_30178 9 | train_30131 10 | train_30147 11 | train_30209 12 | train_30169 13 | train_30185 14 | train_30101 15 | train_30123 16 | train_30126 17 | train_30138 18 | train_29635 19 | -------------------------------------------------------------------------------- /demo/voc/ImageSets/Main/trainval.txt: -------------------------------------------------------------------------------- 1 | train_29641 2 | train_30190 3 | train_30183 4 | train_30092 5 | train_30116 6 | train_30202 7 | train_30156 8 | train_30090 9 | train_30178 10 | train_30131 11 | train_30147 12 | train_30209 13 | train_30169 14 | train_30185 15 | train_30180 16 | train_30101 17 | train_30123 18 | train_30126 19 | train_30138 20 | train_29635 21 | -------------------------------------------------------------------------------- /.idea/objectDetectionDatasets.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 11 | -------------------------------------------------------------------------------- /demo/voc/Annotations/train_30131.xml: -------------------------------------------------------------------------------- 1 | 2 | down 3 | train_30131.jpg 4 | ./savePicture/train_30131.jpg 5 | 6 | Unknown 7 | 8 | 9 | 480 10 | 640 11 | 3 12 | 13 | 0 14 | 15 | window_shielding 16 | 17 | Unspecified 18 | 0 19 | 0 20 | 21 | 278 22 | 366 23 | 469 24 | 457 25 | 26 | 27 | -------------------------------------------------------------------------------- /demo/voc/Annotations/train_30178.xml: -------------------------------------------------------------------------------- 1 | 2 | down 3 | train_30178.jpg 4 | ./savePicture/train_30178.jpg 5 | 6 | Unknown 7 | 8 | 9 | 480 10 | 640 11 | 3 12 | 13 | 0 14 | 15 | window_shielding 16 | 17 | Unspecified 18 | 0 19 | 0 20 | 21 | 104 22 | 98 23 | 479 24 | 213 25 | 26 | 27 | -------------------------------------------------------------------------------- /demo/voc/Annotations/train_30180.xml: -------------------------------------------------------------------------------- 1 | 2 | down 3 | train_30180.jpg 4 | ./savePicture/train_30180.jpg 5 | 6 | Unknown 7 | 8 | 9 | 480 10 | 640 11 | 3 12 | 13 | 0 14 | 15 | non_traffic_sign 16 | 17 | Unspecified 18 | 0 19 | 0 20 | 21 | 202 22 | 130 23 | 342 24 | 377 25 | 26 | 27 | -------------------------------------------------------------------------------- /demo/voc/Annotations/train_30185.xml: -------------------------------------------------------------------------------- 1 | 2 | down 3 | train_30185.jpg 4 | ./savePicture/train_30185.jpg 5 | 6 | Unknown 7 | 8 | 9 | 480 10 | 640 11 | 3 12 | 13 | 0 14 | 15 | window_shielding 16 | 17 | Unspecified 18 | 0 19 | 0 20 | 21 | 367 22 | 55 23 | 479 24 | 254 25 | 26 | 27 | -------------------------------------------------------------------------------- /coco_visulize.py: -------------------------------------------------------------------------------- 1 | import os 2 | import cv2 3 | 4 | from pycocotools.coco import COCO 5 | 6 | json_file = '/home/trojanjet/baidu_qyl/tianma/detect/mmdetection/data/coco/annotations/instances_val2017.json' 7 | dataset_dir = '/home/trojanjet/baidu_qyl/tianma/detect/mmdetection/data/coco/val2017/' 8 | coco = COCO(json_file) 9 | imgIds = coco.getImgIds() # 10 | for i in range(len(imgIds)): 11 | img = coco.loadImgs(imgIds[i])[0] 12 | image = cv2.imread(dataset_dir + img['file_name']) 13 | annIds = coco.getAnnIds(imgIds=img['id']) 14 | annos = coco.loadAnns(annIds) 15 | for ann in annos: 16 | bbox = ann['bbox'] 17 | x, y, w, h = bbox 18 | anno_image = cv2.rectangle(image, (int(x), int(y)), (int(x + w), int(y + h)), (0, 255, 255), 2) 19 | cv2.imwrite('demo.jpg', anno_image) 20 | break 21 | 22 | -------------------------------------------------------------------------------- /demo/coco/annotations/val2017.json: -------------------------------------------------------------------------------- 1 | {"images": [{"file_name": "train_30092.jpg", "height": 640, "width": 360, "id": 5}, {"file_name": "train_30202.jpg", "height": 640, "width": 480, "id": 7}], "annotations": [{"segmentation": [181, 26, 279, 26, 279, 186, 181, 186], "area": 15680, "iscrowd": 0, "image_id": 5, "bbox": [181, 26, 98, 160], "category_id": 2, "id": 0, "ignore": 0}, {"segmentation": [272, 180, 355, 180, 355, 243, 272, 243], "area": 5229, "iscrowd": 0, "image_id": 5, "bbox": [272, 180, 83, 63], "category_id": 2, "id": 1, "ignore": 0}, {"segmentation": [1, 8, 301, 8, 301, 212, 1, 212], "area": 61200, "iscrowd": 0, "image_id": 7, "bbox": [1, 8, 300, 204], "category_id": 2, "id": 2, "ignore": 0}, {"segmentation": [210, 272, 301, 272, 301, 294, 210, 294], "area": 2002, "iscrowd": 0, "image_id": 7, "bbox": [210, 272, 91, 22], "category_id": 2, "id": 3, "ignore": 0}], "categories": [{"name": "window_shielding", "id": 1}, {"name": "multi_signs", "id": 2}, {"name": "non_traffic_sign", "id": 3}]} -------------------------------------------------------------------------------- /demo/voc/Annotations/train_29635.xml: -------------------------------------------------------------------------------- 1 | 2 | down 3 | train_29635.jpg 4 | ./savePicture/train_29635.jpg 5 | 6 | Unknown 7 | 8 | 9 | 480 10 | 640 11 | 3 12 | 13 | 0 14 | 15 | multi_signs 16 | 17 | Unspecified 18 | 0 19 | 0 20 | 21 | 154 22 | 79 23 | 474 24 | 203 25 | 26 | 27 | 28 | multi_signs 29 | 30 | Unspecified 31 | 0 32 | 0 33 | 34 | 81 35 | 98 36 | 154 37 | 243 38 | 39 | 40 | -------------------------------------------------------------------------------- /demo/voc/Annotations/train_30092.xml: -------------------------------------------------------------------------------- 1 | 2 | down 3 | train_30092.jpg 4 | ./savePicture/train_30092.jpg 5 | 6 | Unknown 7 | 8 | 9 | 360 10 | 640 11 | 3 12 | 13 | 0 14 | 15 | multi_signs 16 | 17 | Unspecified 18 | 0 19 | 0 20 | 21 | 181 22 | 26 23 | 278 24 | 185 25 | 26 | 27 | 28 | multi_signs 29 | 30 | Unspecified 31 | 0 32 | 0 33 | 34 | 272 35 | 180 36 | 354 37 | 242 38 | 39 | 40 | -------------------------------------------------------------------------------- /demo/voc/Annotations/train_30101.xml: -------------------------------------------------------------------------------- 1 | 2 | down 3 | train_30101.jpg 4 | ./savePicture/train_30101.jpg 5 | 6 | Unknown 7 | 8 | 9 | 240 10 | 320 11 | 3 12 | 13 | 0 14 | 15 | multi_signs 16 | 17 | Unspecified 18 | 0 19 | 0 20 | 21 | 105 22 | 50 23 | 172 24 | 180 25 | 26 | 27 | 28 | multi_signs 29 | 30 | Unspecified 31 | 0 32 | 0 33 | 34 | 146 35 | 49 36 | 216 37 | 184 38 | 39 | 40 | -------------------------------------------------------------------------------- /demo/voc/Annotations/train_30126.xml: -------------------------------------------------------------------------------- 1 | 2 | down 3 | train_30126.jpg 4 | ./savePicture/train_30126.jpg 5 | 6 | Unknown 7 | 8 | 9 | 640 10 | 480 11 | 3 12 | 13 | 0 14 | 15 | multi_signs 16 | 17 | Unspecified 18 | 0 19 | 0 20 | 21 | 495 22 | 80 23 | 552 24 | 99 25 | 26 | 27 | 28 | multi_signs 29 | 30 | Unspecified 31 | 0 32 | 0 33 | 34 | 416 35 | 120 36 | 605 37 | 164 38 | 39 | 40 | -------------------------------------------------------------------------------- /demo/voc/Annotations/train_30147.xml: -------------------------------------------------------------------------------- 1 | 2 | down 3 | train_30147.jpg 4 | ./savePicture/train_30147.jpg 5 | 6 | Unknown 7 | 8 | 9 | 480 10 | 640 11 | 3 12 | 13 | 0 14 | 15 | multi_signs 16 | 17 | Unspecified 18 | 0 19 | 0 20 | 21 | 129 22 | 125 23 | 261 24 | 211 25 | 26 | 27 | 28 | multi_signs 29 | 30 | Unspecified 31 | 0 32 | 0 33 | 34 | 59 35 | 128 36 | 132 37 | 215 38 | 39 | 40 | -------------------------------------------------------------------------------- /demo/voc/Annotations/train_30169.xml: -------------------------------------------------------------------------------- 1 | 2 | down 3 | train_30169.jpg 4 | ./savePicture/train_30169.jpg 5 | 6 | Unknown 7 | 8 | 9 | 240 10 | 320 11 | 3 12 | 13 | 0 14 | 15 | multi_signs 16 | 17 | Unspecified 18 | 0 19 | 0 20 | 21 | 62 22 | 12 23 | 239 24 | 149 25 | 26 | 27 | 28 | multi_signs 29 | 30 | Unspecified 31 | 0 32 | 0 33 | 34 | 0 35 | 141 36 | 222 37 | 207 38 | 39 | 40 | -------------------------------------------------------------------------------- /demo/voc/Annotations/train_30202.xml: -------------------------------------------------------------------------------- 1 | 2 | down 3 | train_30202.jpg 4 | ./savePicture/train_30202.jpg 5 | 6 | Unknown 7 | 8 | 9 | 480 10 | 640 11 | 3 12 | 13 | 0 14 | 15 | multi_signs 16 | 17 | Unspecified 18 | 0 19 | 0 20 | 21 | 1 22 | 8 23 | 300 24 | 211 25 | 26 | 27 | 28 | multi_signs 29 | 30 | Unspecified 31 | 0 32 | 0 33 | 34 | 210 35 | 272 36 | 300 37 | 293 38 | 39 | 40 | -------------------------------------------------------------------------------- /demo/voc/Annotations/train_30209.xml: -------------------------------------------------------------------------------- 1 | 2 | down 3 | train_30209.jpg 4 | ./savePicture/train_30209.jpg 5 | 6 | Unknown 7 | 8 | 9 | 480 10 | 640 11 | 3 12 | 13 | 0 14 | 15 | multi_signs 16 | 17 | Unspecified 18 | 0 19 | 0 20 | 21 | 131 22 | 66 23 | 194 24 | 321 25 | 26 | 27 | 28 | multi_signs 29 | 30 | Unspecified 31 | 0 32 | 0 33 | 34 | 312 35 | 483 36 | 384 37 | 517 38 | 39 | 40 | -------------------------------------------------------------------------------- /voc_split_trainVal.py: -------------------------------------------------------------------------------- 1 | import os 2 | import random 3 | import sys 4 | 5 | 6 | root_path = 'demo/voc' 7 | 8 | xmlfilepath = root_path + '/Annotations' 9 | 10 | txtsavepath = root_path + '/ImageSets/Main' 11 | 12 | 13 | if not os.path.exists(txtsavepath): 14 | os.makedirs(txtsavepath) 15 | 16 | trainval_percent = 1 17 | train_percent = 0.9 18 | total_xml = os.listdir(xmlfilepath) 19 | num = len(total_xml) 20 | list = range(num) 21 | tv = int(num * trainval_percent) 22 | tr = int(tv * train_percent) 23 | trainval = random.sample(list, tv) 24 | train = random.sample(trainval, tr) 25 | 26 | print("train and val size:", tv) 27 | print("train size:", tr) 28 | 29 | ftrainval = open(txtsavepath + '/trainval.txt', 'w') 30 | ftest = open(txtsavepath + '/test.txt', 'w') 31 | ftrain = open(txtsavepath + '/train.txt', 'w') 32 | fval = open(txtsavepath + '/val.txt', 'w') 33 | 34 | for i in list: 35 | name = total_xml[i][:-4] + '\n' 36 | if i in trainval: 37 | ftrainval.write(name) 38 | if i in train: 39 | ftrain.write(name) 40 | else: 41 | fval.write(name) 42 | else: 43 | ftest.write(name) 44 | 45 | ftrainval.close() 46 | ftrain.close() 47 | fval.close() 48 | ftest.close() 49 | -------------------------------------------------------------------------------- /demo/voc/Annotations/train_30123.xml: -------------------------------------------------------------------------------- 1 | 2 | down 3 | train_30123.jpg 4 | ./savePicture/train_30123.jpg 5 | 6 | Unknown 7 | 8 | 9 | 480 10 | 640 11 | 3 12 | 13 | 0 14 | 15 | multi_signs 16 | 17 | Unspecified 18 | 0 19 | 0 20 | 21 | 154 22 | 38 23 | 412 24 | 104 25 | 26 | 27 | 28 | multi_signs 29 | 30 | Unspecified 31 | 0 32 | 0 33 | 34 | 0 35 | 89 36 | 38 37 | 129 38 | 39 | 40 | 41 | window_shielding 42 | 43 | Unspecified 44 | 0 45 | 0 46 | 47 | 154 48 | 39 49 | 409 50 | 104 51 | 52 | 53 | -------------------------------------------------------------------------------- /demo/voc/Annotations/train_30138.xml: -------------------------------------------------------------------------------- 1 | 2 | down 3 | train_30138.jpg 4 | ./savePicture/train_30138.jpg 5 | 6 | Unknown 7 | 8 | 9 | 480 10 | 640 11 | 3 12 | 13 | 0 14 | 15 | multi_signs 16 | 17 | Unspecified 18 | 0 19 | 0 20 | 21 | 230 22 | 160 23 | 478 24 | 274 25 | 26 | 27 | 28 | multi_signs 29 | 30 | Unspecified 31 | 0 32 | 0 33 | 34 | 225 35 | 284 36 | 479 37 | 342 38 | 39 | 40 | 41 | multi_signs 42 | 43 | Unspecified 44 | 0 45 | 0 46 | 47 | 1 48 | 285 49 | 209 50 | 337 51 | 52 | 53 | -------------------------------------------------------------------------------- /demo/voc/Annotations/train_30183.xml: -------------------------------------------------------------------------------- 1 | 2 | down 3 | train_30183.jpg 4 | ./savePicture/train_30183.jpg 5 | 6 | Unknown 7 | 8 | 9 | 480 10 | 640 11 | 3 12 | 13 | 0 14 | 15 | multi_signs 16 | 17 | Unspecified 18 | 0 19 | 0 20 | 21 | 148 22 | 165 23 | 255 24 | 271 25 | 26 | 27 | 28 | multi_signs 29 | 30 | Unspecified 31 | 0 32 | 0 33 | 34 | 108 35 | 264 36 | 133 37 | 317 38 | 39 | 40 | 41 | multi_signs 42 | 43 | Unspecified 44 | 0 45 | 0 46 | 47 | 111 48 | 171 49 | 143 50 | 219 51 | 52 | 53 | -------------------------------------------------------------------------------- /demo/voc/Annotations/train_30190.xml: -------------------------------------------------------------------------------- 1 | 2 | down 3 | train_30190.jpg 4 | ./savePicture/train_30190.jpg 5 | 6 | Unknown 7 | 8 | 9 | 640 10 | 480 11 | 3 12 | 13 | 0 14 | 15 | multi_signs 16 | 17 | Unspecified 18 | 0 19 | 0 20 | 21 | 181 22 | 27 23 | 639 24 | 228 25 | 26 | 27 | 28 | multi_signs 29 | 30 | Unspecified 31 | 0 32 | 0 33 | 34 | 183 35 | 287 36 | 237 37 | 433 38 | 39 | 40 | 41 | multi_signs 42 | 43 | Unspecified 44 | 0 45 | 0 46 | 47 | 21 48 | 309 49 | 84 50 | 404 51 | 52 | 53 | -------------------------------------------------------------------------------- /demo/voc/Annotations/train_30156.xml: -------------------------------------------------------------------------------- 1 | 2 | down 3 | train_30156.jpg 4 | ./savePicture/train_30156.jpg 5 | 6 | Unknown 7 | 8 | 9 | 1280 10 | 720 11 | 3 12 | 13 | 0 14 | 15 | window_shielding 16 | 17 | Unspecified 18 | 0 19 | 0 20 | 21 | 846 22 | 89 23 | 1066 24 | 122 25 | 26 | 27 | 28 | window_shielding 29 | 30 | Unspecified 31 | 0 32 | 0 33 | 34 | 451 35 | 51 36 | 524 37 | 110 38 | 39 | 40 | 41 | window_shielding 42 | 43 | Unspecified 44 | 0 45 | 0 46 | 47 | 412 48 | 77 49 | 455 50 | 118 51 | 52 | 53 | -------------------------------------------------------------------------------- /demo/yolov3/custom/trainval.txt: -------------------------------------------------------------------------------- 1 | demo/voc/JPEGImages/train_29641.jpg 2 | demo/voc/JPEGImages/train_30190.jpg 3 | demo/voc/JPEGImages/train_30183.jpg 4 | demo/voc/JPEGImages/train_30092.jpg 5 | demo/voc/JPEGImages/train_30116.jpg 6 | demo/voc/JPEGImages/train_30202.jpg 7 | demo/voc/JPEGImages/train_30156.jpg 8 | demo/voc/JPEGImages/train_30090.jpg 9 | demo/voc/JPEGImages/train_30178.jpg 10 | demo/voc/JPEGImages/train_30131.jpg 11 | demo/voc/JPEGImages/train_30147.jpg 12 | demo/voc/JPEGImages/train_30209.jpg 13 | demo/voc/JPEGImages/train_30169.jpg 14 | demo/voc/JPEGImages/train_30185.jpg 15 | demo/voc/JPEGImages/train_30180.jpg 16 | demo/voc/JPEGImages/train_30101.jpg 17 | demo/voc/JPEGImages/train_30123.jpg 18 | demo/voc/JPEGImages/train_30126.jpg 19 | demo/voc/JPEGImages/train_30138.jpg 20 | demo/voc/JPEGImages/train_29635.jpg 21 | demo/voc/JPEGImages/train_29641.jpg 22 | demo/voc/JPEGImages/train_30190.jpg 23 | demo/voc/JPEGImages/train_30183.jpg 24 | demo/voc/JPEGImages/train_30092.jpg 25 | demo/voc/JPEGImages/train_30116.jpg 26 | demo/voc/JPEGImages/train_30202.jpg 27 | demo/voc/JPEGImages/train_30156.jpg 28 | demo/voc/JPEGImages/train_30090.jpg 29 | demo/voc/JPEGImages/train_30178.jpg 30 | demo/voc/JPEGImages/train_30131.jpg 31 | demo/voc/JPEGImages/train_30147.jpg 32 | demo/voc/JPEGImages/train_30209.jpg 33 | demo/voc/JPEGImages/train_30169.jpg 34 | demo/voc/JPEGImages/train_30185.jpg 35 | demo/voc/JPEGImages/train_30180.jpg 36 | demo/voc/JPEGImages/train_30101.jpg 37 | demo/voc/JPEGImages/train_30123.jpg 38 | demo/voc/JPEGImages/train_30126.jpg 39 | demo/voc/JPEGImages/train_30138.jpg 40 | demo/voc/JPEGImages/train_29635.jpg 41 | -------------------------------------------------------------------------------- /demo/voc/Annotations/train_29641.xml: -------------------------------------------------------------------------------- 1 | 2 | down 3 | train_29641.jpg 4 | ./savePicture/train_29641.jpg 5 | 6 | Unknown 7 | 8 | 9 | 480 10 | 640 11 | 3 12 | 13 | 0 14 | 15 | window_shielding 16 | 17 | Unspecified 18 | 0 19 | 0 20 | 21 | 347 22 | 197 23 | 473 24 | 220 25 | 26 | 27 | 28 | window_shielding 29 | 30 | Unspecified 31 | 0 32 | 0 33 | 34 | 53 35 | 240 36 | 161 37 | 260 38 | 39 | 40 | 41 | multi_signs 42 | 43 | Unspecified 44 | 0 45 | 0 46 | 47 | 282 48 | 290 49 | 338 50 | 375 51 | 52 | 53 | 54 | multi_signs 55 | 56 | Unspecified 57 | 0 58 | 0 59 | 60 | 235 61 | 301 62 | 276 63 | 359 64 | 65 | 66 | -------------------------------------------------------------------------------- /demo/voc/Annotations/train_30090.xml: -------------------------------------------------------------------------------- 1 | 2 | down 3 | train_30090.jpg 4 | ./savePicture/train_30090.jpg 5 | 6 | Unknown 7 | 8 | 9 | 480 10 | 640 11 | 3 12 | 13 | 0 14 | 15 | multi_signs 16 | 17 | Unspecified 18 | 0 19 | 0 20 | 21 | 183 22 | 153 23 | 272 24 | 367 25 | 26 | 27 | 28 | multi_signs 29 | 30 | Unspecified 31 | 0 32 | 0 33 | 34 | 157 35 | 156 36 | 192 37 | 369 38 | 39 | 40 | 41 | non_traffic_sign 42 | 43 | Unspecified 44 | 0 45 | 0 46 | 47 | 185 48 | 151 49 | 272 50 | 368 51 | 52 | 53 | 54 | non_traffic_sign 55 | 56 | Unspecified 57 | 0 58 | 0 59 | 60 | 156 61 | 153 62 | 192 63 | 370 64 | 65 | 66 | -------------------------------------------------------------------------------- /demo/voc/Annotations/train_30116.xml: -------------------------------------------------------------------------------- 1 | 2 | down 3 | train_30116.jpg 4 | ./savePicture/train_30116.jpg 5 | 6 | Unknown 7 | 8 | 9 | 480 10 | 640 11 | 3 12 | 13 | 0 14 | 15 | window_shielding 16 | 17 | Unspecified 18 | 0 19 | 0 20 | 21 | 28 22 | 43 23 | 153 24 | 187 25 | 26 | 27 | 28 | window_shielding 29 | 30 | Unspecified 31 | 0 32 | 0 33 | 34 | 171 35 | 57 36 | 204 37 | 111 38 | 39 | 40 | 41 | window_shielding 42 | 43 | Unspecified 44 | 0 45 | 0 46 | 47 | 149 48 | 88 49 | 219 50 | 211 51 | 52 | 53 | 54 | window_shielding 55 | 56 | Unspecified 57 | 0 58 | 0 59 | 60 | 217 61 | 97 62 | 237 63 | 217 64 | 65 | 66 | -------------------------------------------------------------------------------- /voc_to_yoloV3.py: -------------------------------------------------------------------------------- 1 | import xml.etree.ElementTree as ET 2 | import os 3 | import cv2 4 | classes = ['window_shielding', 'multi_signs', 'non_traffic_sign'] 5 | 6 | def convert_annotation(image_id): 7 | in_file = open('demo/voc/Annotations/%s.xml' % image_id) 8 | 9 | if not os.path.exists('demo/yolov3/custom/labels/'): 10 | os.makedirs('demo/yolov3/custom/labels/') 11 | out_file_img = open('demo/yolov3/custom/trainval.txt', 'a') # 生成txt格式文件 12 | 13 | out_file_label = open('demo/yolov3/custom/labels/%s.txt' % image_id,'a') # 生成txt格式文件 14 | 15 | tree = ET.parse(in_file) 16 | root = tree.getroot() 17 | size = root.find('size') 18 | voc_img_dir='demo/voc/JPEGImages/{}.jpg'.format(image_id) 19 | out_file_img.write(voc_img_dir) 20 | out_file_img.write("\n") 21 | img=cv2.imread(voc_img_dir) 22 | dh = 1. / img.shape[0] 23 | dw = 1. / img.shape[1] 24 | cnt=len(root.findall('object')) 25 | if cnt==0: 26 | print('nulll null null.....') 27 | print(image_id) 28 | cc=0 29 | for obj in root.iter('object'): 30 | cc+=1 31 | cls = obj.find('name').text 32 | if cls not in classes: 33 | continue 34 | cls_id = classes.index(cls) 35 | xmlbox = obj.find('bndbox') 36 | if dw*float(xmlbox.find('xmin').text)<0. or dw*float(xmlbox.find('xmax').text)<0. or dh*float(xmlbox.find('ymin').text)<0. or dh*float(xmlbox.find('ymax').text)<0.: 37 | print(image_id) 38 | 39 | b = (dw*float(xmlbox.find('xmin').text), dw*float(xmlbox.find('xmax').text), dh*float(xmlbox.find('ymin').text), 40 | dh*float(xmlbox.find('ymax').text)) 41 | out_file_label.write(str(cls_id)+ " " + str((b[0]+b[1])/2) + " " + str((b[2]+b[3])/2) + " " + str(b[1]-b[0]) + " " + str(b[3]-b[2])) 42 | if cc - v1版本实现了转换的同时进行训练／验证的分割 9 | > - v2版本包含了segemetation字段(当训练htc等需要分割的任务时候网络需要用到) 10 | ## convert_voc_to_yoloV5.py　和 convert_voc_to_yoloV3.py 11 | 两个脚本实现的功能几乎相同,灵活取用 12 | > - V5脚本实现将voc格式的数据转化为yoloV5需要的.txt标注文件,运行该脚本，会在voc/目录下生成 13 | worktxt/目录(yolo需要的格式). 14 | > - V3这个脚本除了生成.txt的标注(同上)，还会生成一个trianval.txt的索引,以前的yolov3系列用的多一点 15 | 16 | ## coco_split_trainVal.py 17 | 该脚本实现coco格式的数据分割出训练集和验证集,同时里面还实现了一个去除背景图的方法(没有标注框的图)，可以结合上面的 18 | voc_to_coco_v2.py使用. 19 | 20 | ## make_voc.py(其余各种格式转voc) 21 | 前面没有写coco转voc格式的脚本,make_voc.py就提供了一个制作voc格式数据的通用套路（核心代码）. 22 | ```python 23 | `img = cv2.imread(image_path) 24 | height, width, depth = img.shape 25 | with codecs.open(anno_dir + imgId_frame_name[:-4] + '.xml', 'w', 'utf-8') as xml: 26 | xml.write('\n') 27 | xml.write('\t' + imgId_frame_name + '\n') 28 | xml.write('\t\n') 29 | xml.write('\t\t' + str(width) + '\n') 30 | xml.write('\t\t' + str(height) + '\n') 31 | xml.write('\t\t' + str(depth) + '\n') 32 | xml.write('\t\n') 33 | cnt = 0 34 | for bbox in bboxs: 35 | xmin, ymin, xmax, ymax = bbox 36 | class_name = 'obstacles' 37 | # 38 | xml.write('\t\n') 39 | xml.write('\t\t' + class_name + '\n') 40 | xml.write('\t\t\n') 41 | xml.write('\t\t\t' + str(xmin) + '\n') 42 | xml.write('\t\t\t' + str(ymin) + '\n') 43 | xml.write('\t\t\t' + str(xmax) + '\n') 44 | xml.write('\t\t\t' + str(ymax) + '\n') 45 | xml.write('\t\t\n') 46 | xml.write('\t\n') 47 | cnt += 1 48 | assert cnt > 0 49 | xml.write('')` 50 | ``` 51 | -------------------------------------------------------------------------------- /voc_to_yoloV5.py: -------------------------------------------------------------------------------- 1 | ''' 2 | 2020/6/15,标注文件转换xml转txt（vol to yolo）转完后需添加labels文件，即数字序号对应的标签名。 3 | 4 | ''' 5 | 6 | import xml.etree.ElementTree as ET 7 | import pickle 8 | import os 9 | from os import listdir, getcwd 10 | from os.path import join 11 | 12 | classes = ['window_shielding', 'multi_signs', 'non_traffic_sign'] 13 | 14 | 15 | def convert(size, box): 16 | dw = 1./(size[0]) 17 | dh = 1./(size[1]) 18 | x = (box[0] + box[1])/2.0 - 1 19 | y = (box[2] + box[3])/2.0 - 1 20 | w = box[1] - box[0] 21 | h = box[3] - box[2] 22 | x = x*dw 23 | w = w*dw 24 | y = y*dh 25 | h = h*dh 26 | if w>=1: 27 | w=0.99 28 | if h>=1: 29 | h=0.99 30 | return (x,y,w,h) 31 | 32 | def convert_annotation(rootpath,xmlname): 33 | xmlpath = rootpath + '/Annotations' 34 | xmlfile = os.path.join(xmlpath,xmlname) 35 | with open(xmlfile, "r", encoding='UTF-8') as in_file: 36 | txtname = xmlname[:-4]+'.txt' 37 | print(txtname) 38 | txtpath = rootpath + '/worktxt'#生成的.txt文件会被保存在worktxt目录下 39 | if not os.path.exists(txtpath): 40 | os.makedirs(txtpath) 41 | txtfile = os.path.join(txtpath,txtname) 42 | with open(txtfile, "w+" ,encoding='UTF-8') as out_file: 43 | tree=ET.parse(in_file) 44 | root = tree.getroot() 45 | size = root.find('size') 46 | w = int(size.find('width').text) 47 | h = int(size.find('height').text) 48 | out_file.truncate() 49 | for obj in root.iter('object'): 50 | difficult = obj.find('difficult').text 51 | cls = obj.find('name').text 52 | if cls not in classes or int(difficult)==1: 53 | continue 54 | cls_id = classes.index(cls) 55 | xmlbox = obj.find('bndbox') 56 | b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text)) 57 | bb = convert((w,h), b) 58 | out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n') 59 | 60 | 61 | if __name__ == "__main__": 62 | rootpath='demo/voc/' 63 | xmlpath=rootpath+'/Annotations' 64 | list=os.listdir(xmlpath) 65 | for i in range(0,len(list)) : 66 | path = os.path.join(xmlpath,list[i]) 67 | if ('.xml' in path)or('.XML' in path): 68 | convert_annotation(rootpath,list[i]) 69 | print('done', i) 70 | else: 71 | print('not xml file',i) 72 | -------------------------------------------------------------------------------- /make_voc.py: -------------------------------------------------------------------------------- 1 | import os 2 | import xml 3 | import json 4 | import codecs 5 | import cv2 6 | import shutil 7 | from config import Config 8 | 9 | obstacles_classes = ['施工围挡', '路障', '锥桶', '告示牌1','告示牌2','施工痕迹','施工机械','工地正门'] 10 | opt=Config() 11 | rawImgDir=opt.raw_data_dir 12 | rawLabelDir=opt.raw_json 13 | anno_dir='../demo/voc/annotations/' 14 | image_dir='../demo/voc/JPEGImages' 15 | if not os.path.exists(anno_dir): 16 | os.makedirs(anno_dir) 17 | if not os.path.exists(image_dir): 18 | os.makedirs(image_dir) 19 | with open(rawLabelDir) as f: 20 | d=json.load(f) 21 | # 22 | annos=d['annotations'] 23 | for anno in annos: 24 | status=anno['status'] 25 | frames=anno['frames'] 26 | imgId = anno['id'] 27 | if status==3: 28 | for frame in frames: 29 | if 'obstacles' not in frame: 30 | continue 31 | obstacles=frame['obstacles'] 32 | bboxs=[item['bbox'] for item in obstacles] 33 | frame_name=frame['frame_name'] 34 | imgId_frame_name=imgId+'_'+frame_name 35 | image_path=os.path.join(rawImgDir, imgId, frame_name) 36 | shutil.copy(os.path.join(rawImgDir, imgId, frame_name), os.path.join(image_dir, imgId_frame_name)) 37 | img = cv2.imread(image_path) 38 | height, width, depth = img.shape 39 | with codecs.open(anno_dir + imgId_frame_name[:-4] + '.xml', 'w', 'utf-8') as xml: 40 | xml.write('\n') 41 | xml.write('\t' + imgId_frame_name + '\n') 42 | xml.write('\t\n') 43 | xml.write('\t\t' + str(width) + '\n') 44 | xml.write('\t\t' + str(height) + '\n') 45 | xml.write('\t\t' + str(depth) + '\n') 46 | xml.write('\t\n') 47 | cnt = 0 48 | for bbox in bboxs: 49 | xmin, ymin, xmax, ymax = bbox 50 | class_name = 'obstacles' 51 | # 52 | xml.write('\t\n') 53 | xml.write('\t\t' + class_name + '\n') 54 | xml.write('\t\t\n') 55 | xml.write('\t\t\t' + str(xmin) + '\n') 56 | xml.write('\t\t\t' + str(ymin) + '\n') 57 | xml.write('\t\t\t' + str(xmax) + '\n') 58 | xml.write('\t\t\t' + str(ymax) + '\n') 59 | xml.write('\t\t\n') 60 | xml.write('\t\n') 61 | cnt += 1 62 | assert cnt > 0 63 | xml.write('') 64 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/Project_Default.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 47 | -------------------------------------------------------------------------------- /voc_to_coco_v2.py: -------------------------------------------------------------------------------- 1 | 2 | import os.path as osp 3 | import xml.etree.ElementTree as ET 4 | 5 | import mmcv 6 | import os 7 | 8 | from glob import glob 9 | from tqdm import tqdm 10 | from PIL import Image 11 | def object_classes():#这里定义了自己的数据集的目标类别 12 | return ['window_shielding', 'multi_signs', 'non_traffic_sign'] 13 | label_ids = {name: i + 1 for i, name in enumerate(object_classes())} 14 | print(label_ids) 15 | 16 | def get_segmentation(points): 17 | 18 | return [points[0], points[1], points[2] + points[0], points[1], 19 | points[2] + points[0], points[3] + points[1], points[0], points[3] + points[1]] 20 | 21 | 22 | def parse_xml(xml_path, img_id, anno_id): 23 | tree = ET.parse(xml_path) 24 | root = tree.getroot() 25 | annotation = [] 26 | for obj in root.findall('object'): 27 | name = obj.find('name').text 28 | if name == 'xxx':#当要忽略某一个类别时 29 | continue 30 | category_id = label_ids[name] 31 | bnd_box = obj.find('bndbox') 32 | xmin = int(bnd_box.find('xmin').text) 33 | ymin = int(bnd_box.find('ymin').text) 34 | xmax = int(bnd_box.find('xmax').text) 35 | ymax = int(bnd_box.find('ymax').text) 36 | w = xmax - xmin + 1 37 | h = ymax - ymin + 1 38 | area = w*h 39 | segmentation = get_segmentation([xmin, ymin, w, h]) 40 | annotation.append({ 41 | "segmentation": segmentation, 42 | "area": area, 43 | "iscrowd": 0, 44 | "image_id": img_id, 45 | "bbox": [xmin, ymin, w, h], 46 | "category_id": category_id, 47 | "id": anno_id, 48 | "ignore": 0}) 49 | anno_id += 1 50 | return annotation, anno_id 51 | 52 | 53 | def cvt_annotations(img_path, xml_path, out_file): 54 | images = [] 55 | annotations = [] 56 | 57 | # xml_paths = glob(xml_path + '/*.xml') 58 | img_id = 1 59 | anno_id = 1 60 | for img_path in tqdm(glob(img_path + '/*.jpg')): 61 | w, h = Image.open(img_path).size 62 | img_name = osp.basename(img_path) 63 | img = {"file_name": img_name, "height": int(h), "width": int(w), "id": img_id} 64 | images.append(img) 65 | 66 | xml_file_name = img_name.split('.')[0] + '.xml' 67 | xml_file_path = osp.join(xml_path, xml_file_name) 68 | annos, anno_id = parse_xml(xml_file_path, img_id, anno_id) 69 | annotations.extend(annos) 70 | img_id += 1 71 | 72 | categories = [] 73 | for k,v in label_ids.items(): 74 | categories.append({"name": k, "id": v}) 75 | final_result = {"images": images, "annotations": annotations, "categories": categories} 76 | mmcv.dump(final_result, out_file) 77 | return annotations 78 | 79 | 80 | def main(): 81 | 82 | xml_path = 'demo/voc/Annotations' 83 | img_path = 'demo/voc/JPEGImages' 84 | print('processing {} ...'.format("xml format annotations")) 85 | cvt_annotations(img_path, xml_path, 'demo/coco/annotations/annotations.json') 86 | print('Done!') 87 | 88 | 89 | if __name__ == '__main__': 90 | root_path='./demo' 91 | if not os.path.exists(os.path.join(root_path,'coco/annotations')): 92 | os.makedirs(os.path.join(root_path,'coco/annotations')) 93 | main() 94 | -------------------------------------------------------------------------------- /generate_persudo_json.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding:utf-8 -*- 3 | import sys 4 | import os 5 | import codecs 6 | import cv2 7 | import json 8 | underwater_classes = ['holothurian', 'echinus', 'scallop', 'starfish'] 9 | #!/usr/bin/env python 10 | # -*- coding: utf-8 -*- 11 | import os 12 | # 批量重命名文件 13 | 14 | 15 | def interpr_json(): 16 | test_json_raw = json.load(open("../../data/train/annotations/testA.json", "r")) 17 | test_json = json.load(open("../../results/cas_r50.bbox.json" , "r")) 18 | img_dir='../../data/test-A-image' 19 | root = '../../data/persudo/' 20 | img = test_json_raw['images'] 21 | images = [] 22 | imgid2anno = {} 23 | imgid2name = {} 24 | for imageinfo in test_json_raw['images']: 25 | imgid = imageinfo['id'] 26 | imgid2name[imgid] = imageinfo['file_name'] 27 | for anno in test_json: 28 | img_id = anno['image_id'] 29 | if img_id not in imgid2anno: 30 | imgid2anno[img_id] = [] 31 | imgid2anno[img_id].append(anno) 32 | for imgid, annos in imgid2anno.items(): 33 | image_name = imgid2name[imgid] 34 | image_id = image_name.split('.')[0] 35 | image_path = os.path.join(img_dir, image_id + '.jpg') 36 | img = cv2.imread(image_path) 37 | height, width ,depth= img.shape 38 | with codecs.open(root+ image_id + '_test.xml', 'w', 'utf-8') as xml: 39 | xml.write('\n') 40 | xml.write('\t' + image_id + '_test' + '\n') 41 | xml.write('\t\n') 42 | xml.write('\t\t' + str(width) + '\n') 43 | xml.write('\t\t' + str(height) + '\n') 44 | xml.write('\t\t' + str(depth) + '\n') 45 | xml.write('\t\n') 46 | cnt=0 47 | for anno in annos: 48 | xmin, ymin, w, h = anno['bbox'] 49 | xmax = xmin + w 50 | ymax = ymin + h 51 | xmin = int(xmin) 52 | ymin = int(ymin) 53 | xmax = int(xmax) 54 | ymax = int(ymax) 55 | confidence = anno['score'] 56 | class_id = int(anno['category_id']) 57 | class_name = underwater_classes[class_id - 1] 58 | image_name = imgid2name[imgid] 59 | image_id = image_name.split('.')[0] 60 | # 61 | if cnt==0: 62 | xml.write('\t\n') 63 | xml.write('\t\t' + class_name + '\n') 64 | xml.write('\t\t\n') 65 | xml.write('\t\t\t' + str(xmin) + '\n') 66 | xml.write('\t\t\t' + str(ymin) + '\n') 67 | xml.write('\t\t\t' + str(xmax) + '\n') 68 | xml.write('\t\t\t' + str(ymax) + '\n') 69 | xml.write('\t\t\n') 70 | xml.write('\t\n') 71 | cnt+=1 72 | if confidence>0.4: 73 | cnt+=1 74 | xml.write('\t\n') 75 | xml.write('\t\t'+class_name+'\n') 76 | xml.write('\t\t\n') 77 | xml.write('\t\t\t' + str(xmin) + '\n') 78 | xml.write('\t\t\t' + str(ymin) + '\n') 79 | xml.write('\t\t\t' + str(xmax) + '\n') 80 | xml.write('\t\t\t' + str(ymax) + '\n') 81 | xml.write('\t\t\n') 82 | xml.write('\t\n') 83 | assert cnt>0 84 | xml.write('') 85 | 86 | interpr_json() 87 | -------------------------------------------------------------------------------- /coco_split_trainVal.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | import shutil 4 | json_dir="demo/coco/annotations/annotations.json" 5 | with open(json_dir) as f: 6 | json_file = json.load(f) 7 | print('所有图片的数量：', len(json_file['images'])) 8 | print('所有标注的数量：', len(json_file['annotations'])) 9 | 10 | 11 | def get_key(images, image_id): 12 | for image in images: 13 | if image["id"] == image_id: # 根据anno的id反推图像的名称 14 | return image["file_name"] 15 | 16 | background=[] 17 | obj=[] 18 | # read box info for csv format 19 | annotations = json_file['annotations'] 20 | images = json_file['images'] 21 | 22 | all_images=[] 23 | for image in images: 24 | all_images.append(image["file_name"]) 25 | 26 | for annotation in annotations: 27 | key = annotation["image_id"] # 图像的名字 28 | im_id=get_key(images,key) 29 | if im_id not in obj: 30 | obj.append(im_id) 31 | 32 | #value = annotation["bbox"] + annotation["category_id"] 33 | 34 | #删除背景图像 35 | print('原始图像数量:', len(images)) 36 | 37 | print('有标注的图像数量:', len(obj)) 38 | 39 | for img in images: 40 | if img["file_name"] not in obj: 41 | background.append(img) 42 | 43 | for i in background: 44 | images.remove(i) 45 | print('删除背景后的图像数量',len(images))# 46 | #根据obj筛选图片 47 | image_dir='demo/coco/images' 48 | #dst_dir='/home/limzero/clear_images' 49 | #for name in obj: 50 | #shutil.copy(os.path.join(image_dir,name),os.path.join(dst_dir,name)) 51 | 52 | json_file['images']=images 53 | with open('demo/coco/annotations/annotations_washed.json', 'w') as f: 54 | json.dump(json_file, f) 55 | 56 | #分割训练集和验证集 57 | import random 58 | val = random.sample(obj, int(len(images)*0.1)) 59 | train=[] 60 | for o in obj: 61 | if o not in val: 62 | train.append(o) 63 | 64 | # 65 | train_dir='demo/coco/train2017' 66 | val_dir='demo/coco/val2017' 67 | if not os.path.exists(train_dir): 68 | os.makedirs(train_dir) 69 | if not os.path.exists(val_dir): 70 | os.makedirs(val_dir) 71 | for v in val: 72 | shutil.copy(os.path.join(image_dir,v),os.path.join(val_dir,v)) 73 | for t in train: 74 | shutil.copy(os.path.join(image_dir,t),os.path.join(train_dir,t)) 75 | 76 | 77 | #annotations 78 | 79 | val_images=images[:] 80 | train_images=images[:] 81 | val_annotations=annotations[:] 82 | train_annotations=annotations[:] 83 | 84 | print('images:',len(images),'val:',len(val),'train',len(train)) 85 | c=0 86 | for img in images: 87 | if img['file_name'] in train: 88 | c=c+1 89 | val_images.remove(img) 90 | else: 91 | train_images.remove(img) 92 | print('len(images):',len(images)) 93 | print("c:",c) 94 | print('val_images:',len(val_images),'train_images:',len(train_images)) 95 | 96 | def get_id(images,name): 97 | for image in images: 98 | if image['file_name']==name: 99 | return image['id'] 100 | for t in train: 101 | id=get_id(images,t) 102 | for ann in annotations: 103 | if ann['image_id']==id: 104 | val_annotations.remove(ann) 105 | for v in val: 106 | id=get_id(images,v) 107 | for ann in annotations: 108 | if ann['image_id']==id: 109 | train_annotations.remove(ann) 110 | print('train_ann:',len(train_annotations),'val_ann:',len(val_annotations)) 111 | 112 | json_train=json_file.copy() 113 | json_val=json_file.copy() 114 | json_train['images']=train_images 115 | json_train['annotations']=train_annotations 116 | json_val['images']=val_images 117 | json_val['annotations']=val_annotations 118 | 119 | #reindex 120 | for idx in range(len(json_train['annotations'])): 121 | json_train['annotations'][idx]['id'] = idx 122 | 123 | for idx in range(len(json_val['annotations'])): 124 | json_val['annotations'][idx]['id'] = idx 125 | 126 | #write in json file 127 | with open('demo/coco/annotations/train2017.json', 'w') as f: 128 | json.dump(json_train, f) 129 | 130 | with open('demo/coco/annotations/val2017.json', 'w') as f: 131 | json.dump(json_val, f) 132 | 133 | 134 | -------------------------------------------------------------------------------- /voc_to_coco_v1.py: -------------------------------------------------------------------------------- 1 | # -*- coding=utf-8 -*- 2 | #!/usr/bin/python 3 | 4 | import sys 5 | import os 6 | import shutil 7 | import numpy as np 8 | import json 9 | import xml.etree.ElementTree as ET 10 | import mmcv 11 | # 检测框的ID起始值 12 | START_BOUNDING_BOX_ID = 1 13 | # 类别列表无必要预先创建，程序中会根据所有图像中包含的ID来创建并更新 14 | PRE_DEFINE_CATEGORIES = {} 15 | # If necessary, pre-define category and its id 16 | # PRE_DEFINE_CATEGORIES = {"aeroplane": 1, "bicycle": 2, "bird": 3, "boat": 4, 17 | # "bottle":5, "bus": 6, "car": 7, "cat": 8, "chair": 9, 18 | # "cow": 10, "diningtable": 11, "dog": 12, "horse": 13, 19 | # "motorbike": 14, "person": 15, "pottedplant": 16, 20 | # "sheep": 17, "sofa": 18, "train": 19, "tvmonitor": 20} 21 | 22 | 23 | def get(root, name): 24 | vars = root.findall(name) 25 | return vars 26 | 27 | 28 | def get_and_check(root, name, length): 29 | vars = root.findall(name) 30 | if len(vars) == 0: 31 | raise NotImplementedError('Can not find %s in %s.'%(name, root.tag)) 32 | if length > 0 and len(vars) != length: 33 | raise NotImplementedError('The size of %s is supposed to be %d, but is %d.'%(name, length, len(vars))) 34 | if length == 1: 35 | vars = vars[0] 36 | return vars 37 | 38 | 39 | 40 | def convert(xml_list, xml_dir, json_file): 41 | ''' 42 | :param xml_list: 需要转换的XML文件列表 43 | :param xml_dir: XML的存储文件夹 44 | :param json_file: 导出json文件的路径 45 | :return: None 46 | ''' 47 | list_fp = xml_list 48 | image_id=1 49 | # 标注基本结构 50 | json_dict = {"images":[], 51 | "type": "instances", 52 | "annotations": [], 53 | "categories": []} 54 | categories = PRE_DEFINE_CATEGORIES 55 | bnd_id = START_BOUNDING_BOX_ID 56 | for line in list_fp: 57 | line = line.strip() 58 | print(" Processing {}".format(line)) 59 | # 解析XML 60 | xml_f = os.path.join(xml_dir, line) 61 | tree = ET.parse(xml_f) 62 | root = tree.getroot() 63 | filename = root.find('filename').text 64 | # 取出图片名字 65 | image_id+=1 66 | size = get_and_check(root, 'size', 1) 67 | # 图片的基本信息 68 | width = int(get_and_check(size, 'width', 1).text) 69 | height = int(get_and_check(size, 'height', 1).text) 70 | image = {'file_name': filename, 71 | 'height': height, 72 | 'width': width, 73 | 'id':image_id} 74 | json_dict['images'].append(image) 75 | # 处理每个标注的检测框 76 | for obj in get(root, 'object'): 77 | # 取出检测框类别名称 78 | category = get_and_check(obj, 'name', 1).text 79 | # 更新类别ID字典 80 | if category not in categories: 81 | new_id = len(categories) 82 | categories[category] = new_id 83 | category_id = categories[category] 84 | bndbox = get_and_check(obj, 'bndbox', 1) 85 | xmin = int(get_and_check(bndbox, 'xmin', 1).text) - 1 86 | ymin = int(get_and_check(bndbox, 'ymin', 1).text) - 1 87 | xmax = int(get_and_check(bndbox, 'xmax', 1).text) 88 | ymax = int(get_and_check(bndbox, 'ymax', 1).text) 89 | assert(xmax > xmin) 90 | assert(ymax > ymin) 91 | o_width = abs(xmax - xmin) 92 | o_height = abs(ymax - ymin) 93 | annotation = dict() 94 | annotation['area'] = o_width*o_height 95 | annotation['iscrowd'] = 0 96 | annotation['image_id'] = image_id 97 | annotation['bbox'] = [xmin, ymin, o_width, o_height] 98 | annotation['category_id'] = category_id 99 | annotation['id'] = bnd_id 100 | annotation['ignore'] = 0 101 | # 设置分割数据，点的顺序为逆时针方向 102 | annotation['segmentation'] = [[xmin,ymin,xmin,ymax,xmax,ymax,xmax,ymin]] 103 | 104 | json_dict['annotations'].append(annotation) 105 | bnd_id = bnd_id + 1 106 | 107 | # 写入类别ID字典 108 | for cate, cid in categories.items(): 109 | cat = {'supercategory': 'none', 'id': cid, 'name': cate} 110 | json_dict['categories'].append(cat) 111 | # 导出到json 112 | #mmcv.dump(json_dict, json_file) 113 | print(type(json_dict)) 114 | json_data = json.dumps(json_dict) 115 | with open(json_file, 'w') as w: 116 | w.write(json_data) 117 | 118 | 119 | if __name__ == '__main__': 120 | root_path = './demo' 121 | 122 | if not os.path.exists(os.path.join(root_path,'coco/annotations')): 123 | os.makedirs(os.path.join(root_path,'coco/annotations')) 124 | if not os.path.exists(os.path.join(root_path, 'coco/train2014')): 125 | os.makedirs(os.path.join(root_path, 'coco/train2014')) 126 | if not os.path.exists(os.path.join(root_path, 'coco/val2014')): 127 | os.makedirs(os.path.join(root_path, 'coco/val2014')) 128 | xml_dir = os.path.join(root_path,'voc/Annotations') #已知的voc的标注 129 | 130 | xml_labels = os.listdir(xml_dir) 131 | np.random.shuffle(xml_labels) 132 | split_point = int(len(xml_labels)/10) 133 | 134 | # validation data 135 | xml_list = xml_labels[0:split_point] 136 | json_file = os.path.join(root_path,'coco/annotations/instances_val2014.json') 137 | convert(xml_list, xml_dir, json_file) 138 | for xml_file in xml_list: 139 | img_name = xml_file[:-4] + '.jpg' 140 | shutil.copy(os.path.join(root_path, 'voc/JPEGImages', img_name), 141 | os.path.join(root_path, 'coco/val2014', img_name)) 142 | # train data 143 | xml_list = xml_labels[split_point:] 144 | json_file = os.path.join(root_path,'coco/annotations/instances_train2014.json') 145 | convert(xml_list, xml_dir, json_file) 146 | for xml_file in xml_list: 147 | img_name = xml_file[:-4] + '.jpg' 148 | shutil.copy(os.path.join(root_path, 'voc/JPEGImages', img_name), 149 | os.path.join(root_path, 'coco/train2014', img_name)) 150 | -------------------------------------------------------------------------------- /demo/coco/annotations/train2017.json: -------------------------------------------------------------------------------- 1 | {"images": [{"file_name": "train_30116.jpg", "height": 640, "width": 480, "id": 1}, {"file_name": "train_30147.jpg", "height": 640, "width": 480, "id": 2}, {"file_name": "train_30183.jpg", "height": 640, "width": 480, "id": 3}, {"file_name": "train_30156.jpg", "height": 720, "width": 1280, "id": 4}, {"file_name": "train_29641.jpg", "height": 640, "width": 480, "id": 6}, {"file_name": "train_30185.jpg", "height": 640, "width": 480, "id": 8}, {"file_name": "train_30190.jpg", "height": 480, "width": 640, "id": 9}, {"file_name": "train_30101.jpg", "height": 320, "width": 240, "id": 10}, {"file_name": "train_30090.jpg", "height": 640, "width": 480, "id": 11}, {"file_name": "train_30169.jpg", "height": 320, "width": 240, "id": 12}, {"file_name": "train_30209.jpg", "height": 640, "width": 480, "id": 13}, {"file_name": "train_29635.jpg", "height": 640, "width": 480, "id": 14}, {"file_name": "train_30138.jpg", "height": 640, "width": 480, "id": 15}, {"file_name": "train_30123.jpg", "height": 640, "width": 480, "id": 16}, {"file_name": "train_30131.jpg", "height": 640, "width": 480, "id": 17}, {"file_name": "train_30180.jpg", "height": 640, "width": 480, "id": 18}, {"file_name": "train_30126.jpg", "height": 480, "width": 640, "id": 19}, {"file_name": "train_30178.jpg", "height": 640, "width": 480, "id": 20}], "annotations": [{"segmentation": [28, 43, 154, 43, 154, 188, 28, 188], "area": 18270, "iscrowd": 0, "image_id": 1, "bbox": [28, 43, 126, 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"area": 8550, "iscrowd": 0, "image_id": 19, "bbox": [416, 120, 190, 45], "category_id": 2, "id": 41, "ignore": 0}, {"segmentation": [104, 98, 480, 98, 480, 214, 104, 214], "area": 43616, "iscrowd": 0, "image_id": 20, "bbox": [104, 98, 376, 116], "category_id": 1, "id": 42, "ignore": 0}], "categories": [{"name": "window_shielding", "id": 1}, {"name": "multi_signs", "id": 2}, {"name": "non_traffic_sign", "id": 3}]} -------------------------------------------------------------------------------- /demo/coco/annotations/annotations.json: -------------------------------------------------------------------------------- 1 | {"images": [{"file_name": "train_30116.jpg", "height": 640, "width": 480, "id": 1}, {"file_name": "train_30147.jpg", "height": 640, "width": 480, "id": 2}, {"file_name": "train_30183.jpg", "height": 640, "width": 480, "id": 3}, {"file_name": "train_30156.jpg", "height": 720, "width": 1280, "id": 4}, {"file_name": "train_30092.jpg", "height": 640, "width": 360, "id": 5}, 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