{
"cells": [
{
"cell_type": "markdown",
"id": "79e47a0f-eb0a-43f1-81f1-26ef84a3ab2f",
"metadata": {},
"source": [
"# Object Detection from Videos with YOLOv5\n",
"\n",
"In this tutorial, we will use fastdup with a pretrained yolov5 object detection model to detect and crop from videos. Following that we analyze the cropped objects for issues such as duplicates, near-duplicates, outliers, bright/dark/blurry objects.\n",
"\n",
"> This is an advanced functionality of fastdup. Sign up for free to be an beta tester and get early access at info@visual-layer.com ."
]
},
{
"cell_type": "markdown",
"id": "cef8afd5-45d1-400b-b5a2-8b56095ae66c",
"metadata": {},
"source": [
"## Installation & Setting Up"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1fb73f7e-1bd9-4e8e-b113-94a714abca73",
"metadata": {},
"outputs": [],
"source": [
"!pip install pip -U\n",
"!pip install fastdup"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "90d6aea7-f03e-4a9c-ba2c-dee490579304",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"'0.910'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import fastdup\n",
"fastdup.__version__"
]
},
{
"cell_type": "markdown",
"id": "372a2c62-82aa-4b78-828c-95edf6b74c91",
"metadata": {},
"source": [
"## Download & Extract Dataset"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "a9525826-b454-43e4-a359-969e44335861",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading...\n",
"From: https://drive.google.com/uc?id=1fzmOgmRu557aU4lEbzL7XCf78KntFCeQ\n",
"To: /media/dnth/Active-Projects/fastdup/examples/data.zip\n",
"100%|██████████████████████████████████████| 56.9M/56.9M [00:05<00:00, 10.4MB/s]\n"
]
}
],
"source": [
"!gdown --fuzzy https://drive.google.com/file/d/1fzmOgmRu557aU4lEbzL7XCf78KntFCeQ/view"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "02650635-fb41-47f5-bd5c-ea78e8c80b15",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Archive: data.zip\n",
" creating: data/\n",
" inflating: data/video_14.mp4 \n",
" inflating: data/video_13.mp4 \n",
" inflating: data/video_12.mp4 \n",
" inflating: data/video_9.mp4 \n",
" inflating: data/video_15.mp4 \n",
" inflating: data/video_10.mp4 \n",
" inflating: data/video_11.mp4 \n",
" inflating: data/video_8.mp4 \n",
" inflating: data/video_1.mp4 \n",
" inflating: data/video_2.mp4 \n",
" inflating: data/video_3.mp4 \n",
" inflating: data/video_4.mp4 \n",
" inflating: data/video_5.mp4 \n",
" inflating: data/video_6.mp4 \n",
" inflating: data/video_7.mp4 \n"
]
}
],
"source": [
"!unzip data.zip"
]
},
{
"cell_type": "markdown",
"id": "fecc72b9-17fd-4f6c-9d2a-a68345461443",
"metadata": {},
"source": [
"## Video to Images\n",
"\n",
"fastdup works on images. We must first turn the videos into frames of images.\n",
"\n",
"We can use a one-liner fastdup utility function to turn all the videos in a folder into frames:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "9258e5af-0802-476b-85ba-2423240a2771",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"FastDup Software, (C) copyright 2022 Dr. Amir Alush and Dr. Danny Bickson.\n",
"2023-03-29 17:50:39 [INFO] Going to loop over dir data\n",
"2023-03-29 17:50:39 [INFO] Found total 15 videos to run on\n"
]
},
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fastdup.extract_video_frames(input_dir=\"data\", work_dir=\"frames\")"
]
},
{
"cell_type": "markdown",
"id": "ce318e6e-ba76-4e07-bd91-1b56da39c952",
"metadata": {},
"source": [
"## Run fastdup\n",
"\n",
"Now that we have the frames of images, let's run fastdup and analyze the frames."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "bd3bf19f-1322-46cc-bfc3-d8db50689367",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"fd = fastdup.create(input_dir='frames', work_dir='yolov5_detection_work_dir')"
]
},
{
"cell_type": "markdown",
"id": "8f872fa0-35a3-4eb1-80b8-20a415bbd4d5",
"metadata": {},
"source": [
"As this is an advance functionality of fastdup, you'd need a license key to use this function, sign up and get your license key for free at info@visual-layer.com ."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "29fde366-db7d-4fff-a537-2ff589cddffd",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"FastDup Software, (C) copyright 2022 Dr. Amir Alush and Dr. Danny Bickson.\n",
"2023-03-29 17:50:56 [INFO] Going to loop over dir frames\n",
"2023-03-29 17:50:56 [INFO] Found total 99 images to run on\n",
"FastDup Software, (C) copyright 2022 Dr. Amir Alush and Dr. Danny Bickson.utes 0 Features\n",
"2023-03-29 17:51:07 [INFO] Going to loop over dir /tmp/crops_input.csv\n",
"2023-03-29 17:51:07 [INFO] Found total 130 images to run on\n",
"2023-03-29 17:51:08 [INFO] Found total 130 images to run onstimated: 0 Minutes 0 Features\n",
"Finished histogram 0.174\n",
"Finished bucket sort 0.189\n",
"2023-03-29 17:51:08 [INFO] 13) Finished write_index() NN model\n",
"2023-03-29 17:51:08 [INFO] Stored nn model index file yolov5_detection_work_dir/nnf.index\n",
"2023-03-29 17:51:08 [INFO] Total time took 1021 ms\n",
"2023-03-29 17:51:08 [INFO] Found a total of 0 fully identical images (d>0.990), which are 0.00 %\n",
"2023-03-29 17:51:08 [INFO] Found a total of 6 nearly identical images(d>0.980), which are 1.54 %\n",
"2023-03-29 17:51:08 [INFO] Found a total of 65 above threshold images (d>0.900), which are 16.67 %\n",
"2023-03-29 17:51:08 [INFO] Found a total of 13 outlier images (d<0.050), which are 3.33 %\n",
"2023-03-29 17:51:08 [INFO] Min distance found 0.555 max distance 0.988\n",
"2023-03-29 17:51:08 [INFO] Running connected components for ccthreshold 0.960000 \n",
".0\n",
" ########################################################################################\n",
"\n",
"Dataset Analysis Summary: \n",
"\n",
" Dataset contains 130 images\n",
" Valid images are 100.00% (130) of the data, invalid are 0.00% (0) of the data\n",
" Similarity: 4.62% (6) belong to 3 similarity clusters (components).\n",
" 95.38% (124) images do not belong to any similarity cluster.\n",
" Largest cluster has 12 (9.23%) images.\n",
" For a detailed analysis, use `.connected_components()`\n",
"(similarity threshold used is 0.9, connected component threshold used is 0.96).\n",
"\n",
" Outliers: 8.46% (11) of images are possible outliers, and fall in the bottom 5.00% of similarity values.\n",
" For a detailed list of outliers, use `.outliers()`.\n"
]
}
],
"source": [
"fd.run(bounding_box='yolov5s', license='your_license_key', overwrite=True)"
]
},
{
"cell_type": "markdown",
"id": "d6098b39-9ac7-4766-a0eb-e5ff255ce05d",
"metadata": {},
"source": [
"## Components Gallery\n",
"\n",
"We can visualize the cluster of similar detections using the components gallery view. Specify `draw_bbox=True` to see the detection bounding box on the original image."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c13076de-3cf2-428f-866f-24278b082c02",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████| 4/4 [00:00<00:00, 43.32it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Finished OK. Components are stored as image files yolov5_detection_work_dir/galleries/components_[index].jpg\n",
"Stored components visual view in yolov5_detection_work_dir/galleries/components.html\n",
"Execution time in seconds 0.2\n"
]
},
{
"data": {
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},
"metadata": {},
"output_type": "display_data"
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],
"source": [
"fd.vis.component_gallery(draw_bbox=True)"
]
},
{
"cell_type": "markdown",
"id": "057288e5-3787-448b-b218-2cb0c3bd2926",
"metadata": {},
"source": [
"If you'd like to view just the cropped bounding box images, specify `draw_bbox=False`"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "3edfd955-da16-455d-9089-87ee06161d9c",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|████████████| 4/4 [00:00<00:00, 110.03it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Finished OK. Components are stored as image files yolov5_detection_work_dir/galleries/components_[index].jpg\n",
"Stored components visual view in yolov5_detection_work_dir/galleries/components.html\n",
"Execution time in seconds 0.1\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
"text/html": [
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fd.vis.component_gallery(draw_bbox=False)"
]
},
{
"cell_type": "markdown",
"id": "9f81f218-2b43-4eaf-9305-91e6f4ce2890",
"metadata": {},
"source": [
"## Find Similar Objects Across Videos\n",
"\n",
"Using the `similarity_gallery` view, we can find similar looking detections across all the extracted frames."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "ae3e6a8d-340d-4dfb-a91a-32fde7522325",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Warning: you are running create_similarity_gallery() without providing get_label_func so similarities are not computed between different classes. It is recommended to run this report with labels. Without labels this report output is similar to create_duplicate_gallery()\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 20/20 [00:00<00:00, 109.04it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Stored similar images visual view in yolov5_detection_work_dir/galleries/similarity.html\n"
]
},
{
"data": {
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" /crops/datavideo_3.mp4output_000021.jpg_259_462_80_117.jpg | \n",
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" /crops/datavideo_1.mp4output_000003.jpg_12_148_716_1134.jpg | \n",
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"\n",
" to \\\n",
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"16 [yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000009.jpg_90_294_418_466.jpg] \n",
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"1 [yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000003.jpg_12_148_716_1134.jpg] \n",
"0 [yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000004.jpg_20_192_696_1090.jpg] \n",
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"15 [yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000009.jpg_4_240_724_1044.jpg] \n",
"17 [yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000008.jpg_10_302_704_744.jpg] \n",
"11 [yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000006.jpg_444_410_182_178.jpg, yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000007.jpg_448_284_184_174.jpg] \n",
"5 [yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000007.jpg_260_282_164_172.jpg] \n",
"37 [yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000016.jpg_259_461_80_120.jpg, yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000018.jpg_261_462_78_117.jpg] \n",
"33 [yolov5_detection_work_dir/crops/framesdatavideo_2.mp4output_000001.jpg_86_253_366_338.jpg] \n",
"32 [yolov5_detection_work_dir/crops/framesdatavideo_2.mp4output_000002.jpg_102_251_330_339.jpg] \n",
"13 [yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000006.jpg_468_872_108_88.jpg] \n",
"7 [yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000007.jpg_312_726_90_80.jpg] \n",
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"12 [yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000006.jpg_444_650_148_174.jpg] \n",
"6 [yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000007.jpg_280_518_132_156.jpg] \n",
"39 [yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000017.jpg_262_461_75_118.jpg, yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000018.jpg_261_462_78_117.jpg] \n",
"29 [yolov5_detection_work_dir/crops/framesdatavideo_14.mp4output_000002.jpg_14_523_144_355.jpg] \n",
"30 [yolov5_detection_work_dir/crops/framesdatavideo_14.mp4output_000001.jpg_42_509_141_357.jpg] \n",
"4 [yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000007.jpg_144_726_76_68.jpg] \n",
"10 [yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000006.jpg_288_856_100_74.jpg] \n",
"23 [yolov5_detection_work_dir/crops/framesdatavideo_10.mp4output_000004.jpg_106_146_275_462.jpg] \n",
"22 [yolov5_detection_work_dir/crops/framesdatavideo_10.mp4output_000004.jpg_46_-3_526_603.jpg] \n",
"9 [yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000006.jpg_270_408_124_168.jpg] \n",
"3 [yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000007.jpg_106_286_110_164.jpg] \n",
"20 [yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000010.jpg_10_134_714_1128.jpg] \n",
"19 [yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000011.jpg_20_70_702_1192.jpg] \n",
"48 [yolov5_detection_work_dir/crops/framesdatavideo_6.mp4output_000007.jpg_2_22_549_957.jpg, yolov5_detection_work_dir/crops/framesdatavideo_6.mp4output_000003.jpg_2_13_560_995.jpg] \n",
"49 [yolov5_detection_work_dir/crops/framesdatavideo_6.mp4output_000005.jpg_21_32_557_1000.jpg] \n",
"25 [yolov5_detection_work_dir/crops/framesdatavideo_10.mp4output_000005.jpg_-2_202_558_824.jpg, yolov5_detection_work_dir/crops/framesdatavideo_10.mp4output_000001.jpg_8_214_542_808.jpg] \n",
"47 [yolov5_detection_work_dir/crops/framesdatavideo_6.mp4output_000005.jpg_21_32_557_1000.jpg] \n",
"31 [yolov5_detection_work_dir/crops/framesdatavideo_14.mp4output_000001.jpg_368_378_186_478.jpg] \n",
"28 [yolov5_detection_work_dir/crops/framesdatavideo_14.mp4output_000002.jpg_331_403_226_472.jpg] \n",
"36 [yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000011.jpg_262_462_75_117.jpg] \n",
"14 [yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000007.jpg_260_282_164_172.jpg] \n",
"35 [yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000003.jpg_35_14_530_1005.jpg] \n",
"34 [yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000006.jpg_14_43_531_907.jpg] \n",
"8 [yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000006.jpg_136_832_72_74.jpg] \n",
"2 [yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000007.jpg_0_710_72_74.jpg] \n",
"27 [yolov5_detection_work_dir/crops/framesdatavideo_11.mp4output_000001.jpg_39_25_515_703.jpg] \n",
"26 [yolov5_detection_work_dir/crops/framesdatavideo_11.mp4output_000002.jpg_15_11_529_692.jpg] \n",
"\n",
" distance \n",
"18 [0.987968] \n",
"16 [0.987968] \n",
"44 [0.987846, 0.973552] \n",
"45 [0.987846, 0.972028] \n",
"46 [0.981744, 0.970134] \n",
"41 [0.981744, 0.96619] \n",
"43 [0.973552, 0.972028] \n",
"42 [0.972658, 0.967754] \n",
"40 [0.970134, 0.961261] \n",
"1 [0.967231] \n",
"0 [0.967231] \n",
"38 [0.961022, 0.956333] \n",
"15 [0.955364] \n",
"17 [0.955364] \n",
"11 [0.951178, 0.908653] \n",
"5 [0.951178] \n",
"37 [0.950091, 0.945763] \n",
"33 [0.947915] \n",
"32 [0.947915] \n",
"13 [0.945265] \n",
"7 [0.945265] \n",
"24 [0.943799, 0.919327] \n",
"21 [0.943799, 0.910453] \n",
"12 [0.942117] \n",
"6 [0.942117] \n",
"39 [0.938259, 0.933798] \n",
"29 [0.938023] \n",
"30 [0.938023] \n",
"4 [0.930739] \n",
"10 [0.930739] \n",
"23 [0.929371] \n",
"22 [0.929371] \n",
"9 [0.929298] \n",
"3 [0.929298] \n",
"20 [0.921314] \n",
"19 [0.921314] \n",
"48 [0.921029, 0.919047] \n",
"49 [0.921029] \n",
"25 [0.919327, 0.910453] \n",
"47 [0.919047] \n",
"31 [0.91401] \n",
"28 [0.91401] \n",
"36 [0.910065] \n",
"14 [0.908653] \n",
"35 [0.908426] \n",
"34 [0.908426] \n",
"8 [0.903201] \n",
"2 [0.903201] \n",
"27 [0.901143] \n",
"26 [0.901143] "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fd.vis.similarity_gallery(draw_bbox=False)"
]
},
{
"cell_type": "markdown",
"id": "7cf59dc4-9fd9-4e5a-a502-c8b715b772a3",
"metadata": {},
"source": [
"## Find Outliers\n",
"\n",
"Useing the `outliers_gallery` we can also visualize detections that looks visually different from others."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "5e774ad3-3817-417f-915c-31efbb544fbc",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|████████| 11/11 [00:00<00:00, 33924.52it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Stored outliers visual view in yolov5_detection_work_dir/galleries/outliers.html\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
},
{
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"text/plain": [
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fd.vis.outliers_gallery()"
]
},
{
"cell_type": "markdown",
"id": "720944df-3bce-44ba-876f-8b383a84445b",
"metadata": {},
"source": [
"## Duplicate Detections\n",
"\n",
"With the `duplicates_gallery` view, visualize duplicate image pairs across videos."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "00bf2f0c-ac22-4fd4-b245-9fbed745a128",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 20/20 [00:00<00:00, 272.91it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Stored similarity visual view in yolov5_detection_work_dir/galleries/duplicates.html\n"
]
},
{
"data": {
"text/html": [
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"text/plain": [
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fd.vis.duplicates_gallery()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "574eef9d-e860-4e0f-9aa1-4a4e8f7f0f52",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "49078755-13d3-4420-85bb-7c772bf203a9",
"metadata": {},
"source": [
"## Dark Detections\n",
"\n",
"Using the `stats_gallery` view, we can sort the detections following a desired `metric` such as 'dark', 'bright' and 'blur'. "
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "5977b2db-6dd9-404e-9af5-53dd6292f87f",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 20/20 [00:00<00:00, 800.01it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Stored mean visual view in yolov5_detection_work_dir/galleries/mean.html\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
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" 39.5481 | \n",
"
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" filename | \n",
" yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000017.jpg_262_461_75_118.jpg | \n",
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" filename | \n",
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" 40.1606 | \n",
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" filename | \n",
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" 46.1288 | \n",
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" filename | \n",
" yolov5_detection_work_dir/crops/framesdatavideo_2.mp4output_000003.jpg_-8_152_581_798.jpg | \n",
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" 47.9432 | \n",
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" filename | \n",
" yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000004.jpg_302_50_262_645.jpg | \n",
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" mean | \n",
" 52.7754 | \n",
"
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" filename | \n",
" yolov5_detection_work_dir/crops/framesdatavideo_5.mp4output_000005.jpg_221_166_352_698.jpg | \n",
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" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fd.vis.stats_gallery(metric='dark')"
]
},
{
"cell_type": "markdown",
"id": "8ff9bdf8-89d9-4851-8911-30ca555210b6",
"metadata": {},
"source": [
"## Bright Detections"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "2d27902d-cd84-4f23-b86b-4eb9cfd7f863",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 20/20 [00:00<00:00, 415.64it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Stored mean visual view in yolov5_detection_work_dir/galleries/mean.html\n"
]
},
{
"data": {
"text/html": [
" \n",
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Bright Image Report
Showing example images, sort by descending order
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" 184.1021 | \n",
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" 182.4801 | \n",
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" filename | \n",
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" 175.5681 | \n",
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" yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000007.jpg_280_518_132_156.jpg | \n",
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" filename | \n",
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" filename | \n",
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" mean | \n",
" 149.706 | \n",
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" filename | \n",
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" 149.1573 | \n",
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" filename | \n",
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" 147.3343 | \n",
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" yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000010.jpg_10_134_714_1128.jpg | \n",
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" mean | \n",
" 142.6441 | \n",
"
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"\n",
" filename | \n",
" yolov5_detection_work_dir/crops/framesdatavideo_12.mp4output_000001.jpg_10_-5_557_1027.jpg | \n",
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" mean | \n",
" 141.2871 | \n",
"
\n",
"\n",
" filename | \n",
" yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000008.jpg_234_0_224_68.jpg | \n",
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" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fd.vis.stats_gallery(metric='bright')"
]
},
{
"cell_type": "markdown",
"id": "d35bd1bd-a008-417e-9015-e7149e91fb7f",
"metadata": {},
"source": [
"## Blurry Detections"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "3d9dd6ff-165a-48e3-a498-6e719f253b9a",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 20/20 [00:00<00:00, 989.84it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Stored blur visual view in yolov5_detection_work_dir/galleries/blur.html\n"
]
},
{
"data": {
"text/html": [
" \n",
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Blurry Image Report
Showing example images, sort by ascending order
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" yolov5_detection_work_dir/crops/framesdatavideo_2.mp4output_000004.jpg_397_477_54_82.jpg | \n",
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" 80.0756 | \n",
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" filename | \n",
" yolov5_detection_work_dir/crops/framesdatavideo_10.mp4output_000002.jpg_11_563_50_64.jpg | \n",
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" yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000006.jpg_444_650_148_174.jpg | \n",
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" filename | \n",
" yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000006.jpg_136_832_72_74.jpg | \n",
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" filename | \n",
" yolov5_detection_work_dir/crops/framesdatavideo_15.mp4output_000002.jpg_130_85_285_286.jpg | \n",
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" filename | \n",
" yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000006.jpg_288_856_100_74.jpg | \n",
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" filename | \n",
" yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000008.jpg_234_0_224_68.jpg | \n",
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" filename | \n",
" yolov5_detection_work_dir/crops/framesdatavideo_10.mp4output_000004.jpg_499_219_74_141.jpg | \n",
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" filename | \n",
" yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000006.jpg_468_872_108_88.jpg | \n",
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" blur | \n",
" 151.2386 | \n",
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" filename | \n",
" yolov5_detection_work_dir/crops/framesdatavideo_2.mp4output_000004.jpg_384_490_176_219.jpg | \n",
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" blur | \n",
" 172.3633 | \n",
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" filename | \n",
" yolov5_detection_work_dir/crops/framesdatavideo_9.mp4output_000004.jpg_13_10_498_1026.jpg | \n",
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" blur | \n",
" 184.5228 | \n",
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" filename | \n",
" yolov5_detection_work_dir/crops/framesdatavideo_6.mp4output_000006.jpg_142_702_237_208.jpg | \n",
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" blur | \n",
" 190.6148 | \n",
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" filename | \n",
" yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000007.jpg_616_542_104_156.jpg | \n",
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" 191.1069 | \n",
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" filename | \n",
" yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000006.jpg_106_626_128_152.jpg | \n",
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" 191.4133 | \n",
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" filename | \n",
" yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000006.jpg_254_632_166_168.jpg | \n",
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" 194.46 | \n",
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" filename | \n",
" yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000007.jpg_280_518_132_156.jpg | \n",
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" filename | \n",
" yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000007.jpg_144_726_76_68.jpg | \n",
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" 198.0649 | \n",
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" filename | \n",
" yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000014.jpg_221_667_152_69.jpg | \n",
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" 202.5941 | \n",
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" filename | \n",
" yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000007.jpg_0_710_72_74.jpg | \n",
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" blur | \n",
" 213.758 | \n",
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" filename | \n",
" yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000006.jpg_624_684_96_154.jpg | \n",
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" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fd.vis.stats_gallery(metric='blur')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "689f05a4-37c3-452a-a085-2f0d05155ae5",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}