{ "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": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Components Report\n", " \n", " \n", "\n", "\n", "\n", "
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component85
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component46
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mean_distance0.9672
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component47
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mean_distance0.988
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component86
num_images2
mean_distance0.961
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\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "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": [ " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Components Report\n", " \n", " \n", "\n", "\n", "\n", "
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component85
num_images6
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component46
num_images2
mean_distance0.9672
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component47
num_images2
mean_distance0.988
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component86
num_images2
mean_distance0.961
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\n", " \n", " " ], "text/plain": [ "" ] }, "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": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Similarity Report\n", " \n", " \n", "\n", "\n", "\n", "
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Info From
from/crops/datavideo_3.mp4output_000021.jpg_259_462_80_117.jpg
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Info To
0.981744/crops/datavideo_3.mp4output_000016.jpg_259_461_80_120.jpg
0.970134/crops/datavideo_3.mp4output_000015.jpg_258_462_80_117.jpg
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Query Image
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Info From
from/crops/datavideo_3.mp4output_000016.jpg_259_461_80_120.jpg
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Info To
0.981744/crops/datavideo_3.mp4output_000021.jpg_259_462_80_117.jpg
0.96619/crops/datavideo_3.mp4output_000018.jpg_261_462_78_117.jpg
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Query Image
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Info From
from/crops/datavideo_3.mp4output_000018.jpg_261_462_78_117.jpg
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Info To
0.973552/crops/datavideo_3.mp4output_000019.jpg_261_461_77_118.jpg
0.972028/crops/datavideo_3.mp4output_000020.jpg_261_461_77_118.jpg
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Query Image
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Info From
from/crops/datavideo_3.mp4output_000017.jpg_262_461_75_118.jpg
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Info To
0.972658/crops/datavideo_3.mp4output_000019.jpg_261_461_77_118.jpg
0.967754/crops/datavideo_3.mp4output_000018.jpg_261_462_78_117.jpg
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Query Image
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Info From
from/crops/datavideo_3.mp4output_000015.jpg_258_462_80_117.jpg
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Info To
0.970134/crops/datavideo_3.mp4output_000021.jpg_259_462_80_117.jpg
0.961261/crops/datavideo_3.mp4output_000016.jpg_259_461_80_120.jpg
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Query Image
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Info From
from/crops/datavideo_1.mp4output_000004.jpg_20_192_696_1090.jpg
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Info To
0.967231/crops/datavideo_1.mp4output_000003.jpg_12_148_716_1134.jpg
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Info From
from/crops/datavideo_1.mp4output_000003.jpg_12_148_716_1134.jpg
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Info To
0.967231/crops/datavideo_1.mp4output_000004.jpg_20_192_696_1090.jpg
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Query Image
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Info From
from/crops/datavideo_3.mp4output_000011.jpg_262_462_75_117.jpg
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Info To
0.961022/crops/datavideo_3.mp4output_000017.jpg_262_461_75_118.jpg
0.956333/crops/datavideo_3.mp4output_000018.jpg_261_462_78_117.jpg
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Query Image
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Info From
from/crops/datavideo_1.mp4output_000008.jpg_10_302_704_744.jpg
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Info To
0.955364/crops/datavideo_1.mp4output_000009.jpg_4_240_724_1044.jpg
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Query Image
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Similar
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Info From
from/crops/datavideo_1.mp4output_000009.jpg_4_240_724_1044.jpg
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Info To
0.955364/crops/datavideo_1.mp4output_000008.jpg_10_302_704_744.jpg
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Query Image
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Info From
from/crops/datavideo_1.mp4output_000007.jpg_260_282_164_172.jpg
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fromtodistance
18yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000009.jpg_90_294_418_466.jpg[yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000008.jpg_124_386_316_366.jpg][0.987968]
16yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000008.jpg_124_386_316_366.jpg[yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000009.jpg_90_294_418_466.jpg][0.987968]
44yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000019.jpg_261_461_77_118.jpg[yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000020.jpg_261_461_77_118.jpg, yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000018.jpg_261_462_78_117.jpg][0.987846, 0.973552]
45yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000020.jpg_261_461_77_118.jpg[yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000019.jpg_261_461_77_118.jpg, yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000018.jpg_261_462_78_117.jpg][0.987846, 0.972028]
46yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000021.jpg_259_462_80_117.jpg[yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000016.jpg_259_461_80_120.jpg, yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000015.jpg_258_462_80_117.jpg][0.981744, 0.970134]
41yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000016.jpg_259_461_80_120.jpg[yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000021.jpg_259_462_80_117.jpg, yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000018.jpg_261_462_78_117.jpg][0.981744, 0.96619]
43yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000018.jpg_261_462_78_117.jpg[yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000019.jpg_261_461_77_118.jpg, yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000020.jpg_261_461_77_118.jpg][0.973552, 0.972028]
42yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000017.jpg_262_461_75_118.jpg[yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000019.jpg_261_461_77_118.jpg, yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000018.jpg_261_462_78_117.jpg][0.972658, 0.967754]
40yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000015.jpg_258_462_80_117.jpg[yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000021.jpg_259_462_80_117.jpg, yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000016.jpg_259_461_80_120.jpg][0.970134, 0.961261]
1yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000004.jpg_20_192_696_1090.jpg[yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000003.jpg_12_148_716_1134.jpg][0.967231]
0yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000003.jpg_12_148_716_1134.jpg[yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000004.jpg_20_192_696_1090.jpg][0.967231]
38yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000011.jpg_262_462_75_117.jpg[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][0.961022, 0.956333]
15yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000008.jpg_10_302_704_744.jpg[yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000009.jpg_4_240_724_1044.jpg][0.955364]
17yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000009.jpg_4_240_724_1044.jpg[yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000008.jpg_10_302_704_744.jpg][0.955364]
11yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000007.jpg_260_282_164_172.jpg[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][0.951178, 0.908653]
5yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000006.jpg_444_410_182_178.jpg[yolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000007.jpg_260_282_164_172.jpg][0.951178]
37yolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000010.jpg_259_461_77_118.jpg[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][0.950091, 0.945763]
33yolov5_detection_work_dir/crops/framesdatavideo_2.mp4output_000002.jpg_102_251_330_339.jpg[yolov5_detection_work_dir/crops/framesdatavideo_2.mp4output_000001.jpg_86_253_366_338.jpg][0.947915]
32yolov5_detection_work_dir/crops/framesdatavideo_2.mp4output_000001.jpg_86_253_366_338.jpg[yolov5_detection_work_dir/crops/framesdatavideo_2.mp4output_000002.jpg_102_251_330_339.jpg][0.947915]
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"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" ] }, { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Outliers Report\n", " \n", " \n", "\n", "\n", "\n", "
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Outliers Report

Showing image outliers, one per row

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Info
Distance0.5551
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Distance0.561762
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Distance0.573938
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Distance0.591067
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Distance0.605987
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Distance0.606454
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Distance0.609484
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Distance0.612329
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Distance0.612496
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Distance0.613818
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Distance0.617775
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Duplicates Report

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Info
Distance0.987968
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Info
Distance0.987846
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Distance0.981744
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Info
Distance0.973552
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Info
Distance0.972658
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Distance0.972028
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Distance0.970134
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Distance0.967754
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Distance0.967231
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Distance0.96619
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To/crops/datavideo_3.mp4output_000018.jpg_261_462_78_117.jpg
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Distance0.961261
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Distance0.961022
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Distance0.956333
From/crops/datavideo_3.mp4output_000011.jpg_262_462_75_117.jpg
To/crops/datavideo_3.mp4output_000018.jpg_261_462_78_117.jpg
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Dark Image Report

Showing example images, sort by ascending order

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mean24.2666
filenameyolov5_detection_work_dir/crops/framesdatavideo_3.mp4output_000002.jpg_32_48_544_947.jpg
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mean24.7811
filenameyolov5_detection_work_dir/crops/framesdatavideo_10.mp4output_000002.jpg_11_563_50_64.jpg
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mean38.3933
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mean39.1952
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mean39.4475
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mean39.4819
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mean39.5481
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mean39.6248
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mean39.7982
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mean40.0266
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mean40.1606
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mean40.9729
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mean46.1288
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mean46.9854
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mean47.9432
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mean52.7754
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Bright Image Report

Showing example images, sort by descending order

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mean184.1021
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Info
mean183.7514
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mean182.4801
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mean175.5681
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mean166.794
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mean163.784
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mean161.2058
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mean160.5472
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mean158.8031
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mean155.5531
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mean151.7028
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mean151.5927
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mean150.3423
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mean149.706
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mean149.1573
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mean147.9895
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mean147.3343
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mean144.6158
filenameyolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000006.jpg_624_684_96_154.jpg
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mean142.6441
filenameyolov5_detection_work_dir/crops/framesdatavideo_12.mp4output_000001.jpg_10_-5_557_1027.jpg
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mean141.2871
filenameyolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000008.jpg_234_0_224_68.jpg
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Blurry Image Report

Showing example images, sort by ascending order

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blur26.9125
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Info
blur80.0756
filenameyolov5_detection_work_dir/crops/framesdatavideo_10.mp4output_000002.jpg_11_563_50_64.jpg
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blur107.5881
filenameyolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000006.jpg_444_650_148_174.jpg
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blur111.7562
filenameyolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000006.jpg_136_832_72_74.jpg
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blur113.4236
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blur117.3839
filenameyolov5_detection_work_dir/crops/framesdatavideo_1.mp4output_000006.jpg_288_856_100_74.jpg
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