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AlexeyAB / darknet

YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
http://pjreddie.com/darknet/
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OpenCV exception: load_image_mat_cv #5469

Open Sunmingyang1987 opened 4 years ago

Sunmingyang1987 commented 4 years ago

Hello@AlexeyAB I started training by using the command line: ./darknet detector train data/obj.data yolo-obj.cfg yolov4.conv.137 -dont_show, and one error occured, as shown below, "OpenCV exception: load_image_mat_cv OpenCV exception: load_image_mat_cv try to allocate additional workspace_size = 52.43 MB CUDA allocate done! Loaded: 0.000032 seconds Segmentation fault (core dumped)". I used a very small datasets, 35 images in total. Could you please give me some advise? Thanks a lot.

Wenting-Xu commented 4 years ago

Hello, I have the same problem.

Have you solved your problem?Can you tell me the reason if it has been solved?Thank you very much!

AlexeyAB commented 4 years ago

Or incorrect dataset/train.txt/image-path/image format Or incorrect OpenCV installation

Wenting-Xu commented 4 years ago

Thank you for your prompt reply! I checked my trian.txt/image-path/image format.I didn't find anything wrong.

This is my train.txt file.

/home/xuwt/xu/yolov4/darknet/data/images/RARP2_frame_5199.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP2_frame_395.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_30486.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP2_frame_3189.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_319.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_11957.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_26795.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_4582.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_11644.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_17632.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_2335.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP2_frame_7118.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_4656.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_13292.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_9769.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_23736.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_19618.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_26359.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_29523.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_9648.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP2_frame_8656.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_29220.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_4486.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_6066.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_24192.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_12176.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_17765.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP2_frame_5288.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_30316.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_20015.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP2_frame_5812.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_12674.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_14445.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_10542.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_1001.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP2_frame_392.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_25343.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_1092.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_23885.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP2_frame_1640.jpg /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_9082.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_29503.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_3488.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_19933.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_14709.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP4_frame_11663.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP2_frame_236.txt /home/xuwt/xu/yolov4/darknet/data/images/RARP2_frame_503.txt

Here is my obj.data file.

classes= 21 train = /home/xuwt/xu/yolov4/darknet/data/train.txt valid = /home/xuwt/xu/yolov4/darknet/data/test.txt names = data/obj.names backup = data/backup

Here is my Makefile file.

GPU=1 CUDNN=1 CUDNN_HALF=1 OPENCV=1 AVX=0 OPENMP=0 LIBSO=0

I run the training command"./darknet detector train data/obj.data cfg/yolo-obj.cfg yolov4.conv.137". The following information appears.

CUDA-version: 10000 (10020), cuDNN: 7.6.5, CUDNN_HALF=1, GPU count: 2
CUDNN_HALF=1 OpenCV version: 3.2.0 yolo-obj 0 : compute_capability = 750, cudnn_half = 1, GPU: TITAN RTX net.optimized_memory = 0 mini_batch = 4, batch = 64, time_steps = 1, train = 1 layer filters size/strd(dil) input output 0 conv 32 3 x 3/ 1 608 x 608 x 3 -> 608 x 608 x 32 0.639 BF 1 conv 64 3 x 3/ 2 608 x 608 x 32 -> 304 x 304 x 64 3.407 BF 2 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF 3 route 1 -> 304 x 304 x 64 4 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF 5 conv 32 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 32 0.379 BF 6 conv 64 3 x 3/ 1 304 x 304 x 32 -> 304 x 304 x 64 3.407 BF 7 Shortcut Layer: 4, wt = 0, wn = 0, outputs: 304 x 304 x 64 0.006 BF 8 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF 9 route 8 2 -> 304 x 304 x 128 10 conv 64 1 x 1/ 1 304 x 304 x 128 -> 304 x 304 x 64 1.514 BF 11 conv 128 3 x 3/ 2 304 x 304 x 64 -> 152 x 152 x 128 3.407 BF 12 conv 64 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BF 13 route 11 -> 152 x 152 x 128 14 conv 64 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BF 15 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF 16 conv 64 3 x 3/ 1 152 x 152 x 64 -> 152 x 152 x 64 1.703 BF 17 Shortcut Layer: 14, wt = 0, wn = 0, outputs: 152 x 152 x 64 0.001 BF 18 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF 19 conv 64 3 x 3/ 1 152 x 152 x 64 -> 152 x 152 x 64 1.703 BF 20 Shortcut Layer: 17, wt = 0, wn = 0, outputs: 152 x 152 x 64 0.001 BF 21 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF 22 route 21 12 -> 152 x 152 x 128 23 conv 128 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 128 0.757 BF 24 conv 256 3 x 3/ 2 152 x 152 x 128 -> 76 x 76 x 256 3.407 BF 25 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF 26 route 24 -> 76 x 76 x 256 27 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF 28 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF 29 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF 30 Shortcut Layer: 27, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF 31 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF 32 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF 33 Shortcut Layer: 30, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF 34 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF 35 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF 36 Shortcut Layer: 33, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF 37 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF 38 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF 39 Shortcut Layer: 36, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF 40 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF 41 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF 42 Shortcut Layer: 39, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF 43 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF 44 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF 45 Shortcut Layer: 42, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF 46 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF 47 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF 48 Shortcut Layer: 45, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF 49 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF 50 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF 51 Shortcut Layer: 48, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF 52 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF 53 route 52 25 -> 76 x 76 x 256 54 conv 256 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 256 0.757 BF 55 conv 512 3 x 3/ 2 76 x 76 x 256 -> 38 x 38 x 512 3.407 BF 56 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF 57 route 55 -> 38 x 38 x 512 58 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF 59 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF 60 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF 61 Shortcut Layer: 58, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF 62 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF 63 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF 64 Shortcut Layer: 61, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF 65 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF 66 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF 67 Shortcut Layer: 64, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF 68 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF 69 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF 70 Shortcut Layer: 67, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF 71 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF 72 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF 73 Shortcut Layer: 70, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF 74 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF 75 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF 76 Shortcut Layer: 73, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF 77 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF 78 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF 79 Shortcut Layer: 76, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF 80 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF 81 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF 82 Shortcut Layer: 79, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF 83 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF 84 route 83 56 -> 38 x 38 x 512 85 conv 512 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 512 0.757 BF 86 conv 1024 3 x 3/ 2 38 x 38 x 512 -> 19 x 19 x1024 3.407 BF 87 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF 88 route 86 -> 19 x 19 x1024 89 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF 90 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF 91 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF 92 Shortcut Layer: 89, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF 93 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF 94 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF 95 Shortcut Layer: 92, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF 96 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF 97 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF 98 Shortcut Layer: 95, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF 99 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF 100 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF 101 Shortcut Layer: 98, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF 102 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF 103 route 102 87 -> 19 x 19 x1024 104 conv 1024 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x1024 0.757 BF 105 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF 106 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF 107 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF 108 max 5x 5/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.005 BF 109 route 107 -> 19 x 19 x 512 110 max 9x 9/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.015 BF 111 route 107 -> 19 x 19 x 512 112 max 13x13/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.031 BF 113 route 112 110 108 107 -> 19 x 19 x2048 114 conv 512 1 x 1/ 1 19 x 19 x2048 -> 19 x 19 x 512 0.757 BF 115 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF 116 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF 117 conv 256 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 256 0.095 BF 118 upsample 2x 19 x 19 x 256 -> 38 x 38 x 256 119 route 85 -> 38 x 38 x 512 120 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF 121 route 120 118 -> 38 x 38 x 512 122 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF 123 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF 124 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF 125 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF 126 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF 127 conv 128 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 128 0.095 BF 128 upsample 2x 38 x 38 x 128 -> 76 x 76 x 128 129 route 54 -> 76 x 76 x 256 130 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF 131 route 130 128 -> 76 x 76 x 256 132 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF 133 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF 134 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF 135 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF 136 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF 137 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF 138 conv 78 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 78 0.231 BF 139 yolo [yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.20 nms_kind: greedynms (1), beta = 0.600000 140 route 136 -> 76 x 76 x 128 141 conv 256 3 x 3/ 2 76 x 76 x 128 -> 38 x 38 x 256 0.852 BF 142 route 141 126 -> 38 x 38 x 512 143 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF 144 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF 145 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF 146 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF 147 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF 148 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF 149 conv 78 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 78 0.115 BF 150 yolo [yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.10 nms_kind: greedynms (1), beta = 0.600000 151 route 147 -> 38 x 38 x 256 152 conv 512 3 x 3/ 2 38 x 38 x 256 -> 19 x 19 x 512 0.852 BF 153 route 152 116 -> 19 x 19 x1024 154 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF 155 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF 156 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF 157 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF 158 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF 159 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF 160 conv 78 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 78 0.058 BF 161 yolo [yolo] params: iou loss: ciou (4), iou_norm: 0.07, cls_norm: 1.00, scale_x_y: 1.05 nms_kind: greedynms (1), beta = 0.600000 Total BFLOPS 127.543 avg_outputs = 1051829 Allocate additional workspace_size = 237.17 MB Loading weights from yolov4.conv.137... seen 64, trained: 0 K-images (0 Kilo-batches_64) Done! Loaded 137 layers from weights-file Learning Rate: 0.001, Momentum: 0.949, Decay: 0.0005 If error occurs - run training with flag: -dont_show Resizing, random_coef = 1.40

896 x 896 Create 6 permanent cpu-threads OpenCV exception: load_image_mat_cv

Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv

Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv

Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv

Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv

Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv

Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv

Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv

Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv

Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv

Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv

Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv

Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv

Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv

Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv

Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv

Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv

Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv

Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv

Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv

Error in load_data_detection() - OpenCV OpenCV exception: load_image_mat_cv

Error in load_data_detection() - OpenCV

I can't find anything wrong.Can you give me a hand @AlexeyAB .Thank you very much for your kind help and your excellent work!!

AlexeyAB commented 4 years ago
Wenting-Xu commented 4 years ago

I have 40152 training pictures. But the bad. list only contains 28804 lines .txt.

My image is in. JPG format. There are only 896 lines of JPG paths in the bad.

The remaining 2280 lines are the path of the PNG image.This struck me as odd.The PNG image's name and path format looks like this: data/labels/100_3.png.

The bad_label.list file was not found.

I'm sorry to bother you @AlexeyAB again.Your work is of great help to my study. Thank you very much!!

Wenting-Xu commented 4 years ago

Hello @AlexeyAB

The data set I used is SARAS_ESAD. It only contains.jpg files and.txt files.The names of the two files correspond. Each class has five Numbers, the first represents the class, the last four Numbers represent the coordinates.

Here is my TXT .The above file has only one class, and the following file has two classes.

8 0.455208 0.897685 0.193750 0.202778

14 0.517708 0.442130 0.411458 0.7250008 0.641406 0.853704 0.228646 0.292593

AlexeyAB commented 4 years ago

Try to run detection by using default yolov4.cfg/weights model, do you see this error? I try to run detection by using default yolov4.cfg/weights model as you say.I got the same error as above

I don't get any error when try to detect on your image.

So the issue is in your JPG-library or OpenCV installation.

Wenting-Xu commented 4 years ago

The problem is that the python version of OpenCV is 4.2, and the OpenCV version of the system is 3.2. Is that the problem?

Thank you for your patience in answering my question @AlexeyAB .Have a good day!

AlexeyAB commented 4 years ago

The problem is that the python version of OpenCV is 4.2, and the OpenCV version of the system is 3.2. Is that the problem?

I don't know. May be yes.

Wenting-Xu commented 4 years ago

Hello @AlexeyAB . I have trained my data set with your yolov3 code, and the experiment can proceed normally.I also set the OpenCV in the makefile to 1, which means that my OpenCV is ok, right?