{ "cells": [ { "cell_type": "markdown", "id": "1e6cfefc", "metadata": {}, "source": [ "[![image](https://raw.githubusercontent.com/visual-layer/visuallayer/main/imgs/vl_horizontal_logo.png)](https://www.visual-layer.com)" ] }, { "cell_type": "markdown", "id": "79e47a0f-eb0a-43f1-81f1-26ef84a3ab2f", "metadata": {}, "source": [ "# Object Detection from Videos with YOLOv5\n", "\n", "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/visual-layer/fastdup/blob/main/examples/video-yolov5-detection.ipynb)\n", "[![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/visual-layer/fastdup/blob/main/examples/video-yolov5-detection.ipynb)\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." ] }, { "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 fastdup kaggle -Uq" ] }, { "cell_type": "code", "execution_count": 1, "id": "90d6aea7-f03e-4a9c-ba2c-dee490579304", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/usr/bin/dpkg\n" ] }, { "data": { "text/plain": [ "'1.30'" ] }, "execution_count": 1, "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\n", "\n", "\n", "Let's download the [Video Fight Detection Dataset](https://www.kaggle.com/datasets/naveenk903/movies-fight-detection-dataset) from Kaggle. The dataset consists of real life videos of fight and non-fight scenes meant for video classification task.\n", "\n", "You can download the dataset by manually by heading to the dataset [homepage](https://www.kaggle.com/datasets/naveenk903/movies-fight-detection-dataset) or using the [Kaggle API](https://github.com/Kaggle/kaggle-api). \n", "\n", "Let's use the Kaggle API to download the dataset:" ] }, { "cell_type": "code", "execution_count": null, "id": "6022c79f", "metadata": {}, "outputs": [], "source": [ "!kaggle datasets download -d naveenk903/movies-fight-detection-dataset" ] }, { "cell_type": "markdown", "id": "8233b1b7", "metadata": {}, "source": [ "Next, extract the .zip file into a folder named `data`." ] }, { "cell_type": "code", "execution_count": null, "id": "2e0674a5", "metadata": { "scrolled": true }, "outputs": [], "source": [ "!unzip -q movies-fight-detection-dataset.zip -d data" ] }, { "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. This should create a new folder called `frames` that has all the frames from the videos." ] }, { "cell_type": "code", "execution_count": null, "id": "9258e5af-0802-476b-85ba-2423240a2771", "metadata": { "tags": [] }, "outputs": [], "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": 2, "id": "bd3bf19f-1322-46cc-bfc3-d8db50689367", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Warning: fastdup create() without work_dir argument, output is stored in a folder named work_dir in your current working path.\n" ] } ], "source": [ "fd = fastdup.create(input_dir='frames/')" ] }, { "cell_type": "markdown", "id": "3d1dfd32", "metadata": {}, "source": [ "For demonstration we'll just run on `num_images=2000`, feel free to change its value or omitting it altogether to run it on the entire dataset." ] }, { "cell_type": "code", "execution_count": 3, "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", "2023-07-24 15:37:16 [INFO] Going to loop over dir frames\n", "2023-07-24 15:37:16 [INFO] Found total 4791 images to run on, 4791 train, 0 test, name list 4791, counter 4791 \n", "FastDup Software, (C) copyright 2022 Dr. Amir Alush and Dr. Danny Bickson.utes\n", "2023-07-24 15:46:41 [INFO] Going to loop over dir /tmp/crops_input.csv\n", "2023-07-24 15:46:41 [INFO] Found total 4908 images to run on, 4908 train, 0 test, name list 4908, counter 4908 \n", "2023-07-24 15:46:54 [INFO] Found total 4908 images to run ontimated: 0 Minutes\n", "Finished histogram 1.056\n", "Finished bucket sort 1.071\n", "2023-07-24 15:46:54 [INFO] 74) Finished write_index() NN model\n", "2023-07-24 15:46:54 [INFO] Stored nn model index file work_dir/nnf.index\n", "2023-07-24 15:46:54 [INFO] Total time took 13135 ms\n", "2023-07-24 15:46:54 [INFO] Found a total of 154 fully identical images (d>0.990), which are 1.57 %\n", "2023-07-24 15:46:54 [INFO] Found a total of 553 nearly identical images(d>0.980), which are 5.63 %\n", "2023-07-24 15:46:54 [INFO] Found a total of 7904 above threshold images (d>0.900), which are 80.52 %\n", "2023-07-24 15:46:54 [INFO] Found a total of 492 outlier images (d<0.050), which are 5.01 %\n", "2023-07-24 15:46:54 [INFO] Min distance found 0.581 max distance 1.000\n", "2023-07-24 15:46:54 [INFO] Running connected components for ccthreshold 0.960000 \n", ".0\n", " ########################################################################################\n", "\n", "Dataset Analysis Summary: \n", "\n", " Dataset contains 4908 images\n", " Valid images are 100.00% (4,908) of the data, invalid are 0.00% (0) of the data\n", " Similarity: 19.01% (933) belong to 28 similarity clusters (components).\n", " 80.99% (3,975) images do not belong to any similarity cluster.\n", " Largest cluster has 128 (2.61%) 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: 6.58% (323) 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", "\n" ] }, { "data": { "text/plain": [ "0" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fd.run(bounding_box='yolov5s')" ] }, { "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": 4, "id": "c13076de-3cf2-428f-866f-24278b082c02", "metadata": { "scrolled": false, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|████████████| 20/20 [00:01<00:00, 17.59it/s]\n", "/home/dnth/anaconda3/envs/fastdup/lib/python3.10/site-packages/fastdup/galleries.py:933: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " top_components['label'] = top_components['label'].apply(lambda x: x[:MAX_IMAGES_IN_GRID])\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Finished OK. Components are stored as image files work_dir/galleries/components_[index].jpg\n", "Stored components visual view in work_dir/galleries/components.html\n", "Execution time in seconds 1.6\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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Components Report

Showing groups of similar images

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Info
component692
num_images32
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Label
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Info
component637
num_images30
mean_distance0.9601
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Label
person28
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Info
component678
num_images27
mean_distance0.9708
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Label
person27
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Info
component2233
num_images24
mean_distance0.9604
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Label
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Info
component448
num_images18
mean_distance0.9627
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Label
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component2132
num_images14
mean_distance0.9601
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Label
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Info
component1644
num_images14
mean_distance0.9698
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Info
component2168
num_images14
mean_distance0.9604
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Info
component1060
num_images14
mean_distance0.9601
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Info
component449
num_images13
mean_distance0.9626
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Label
person13
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Info
component2448
num_images12
mean_distance0.9636
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Label
person12
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component2530
num_images12
mean_distance0.9618
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person12
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component2772
num_images12
mean_distance0.9621
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person12
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Info
component211
num_images12
mean_distance0.9669
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Info
component1012
num_images11
mean_distance0.9654
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person11
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Info
component1645
num_images10
mean_distance0.9626
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person10
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Info
component1668
num_images10
mean_distance0.9603
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person10
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Info
component1563
num_images10
mean_distance0.9722
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person10
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Info
component627
num_images10
mean_distance0.9721
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Info
component2201
num_images9
mean_distance0.9609
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Label
person9
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\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "0" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "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": 5, "id": "3edfd955-da16-455d-9089-87ee06161d9c", "metadata": { "scrolled": false, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|████████████| 20/20 [00:00<00:00, 39.85it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Finished OK. Components are stored as image files work_dir/galleries/components_[index].jpg\n", "Stored components visual view in work_dir/galleries/components.html\n", "Execution time in seconds 1.1\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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Components Report

Showing groups of similar images

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0.913802/crops/tmpdataPeliculasfightsnewfi66.avioutput_000006.jpg_468_134_132_330.jpg
0.900177/crops/tmpdataPeliculasfightsnewfi66.avioutput_000005.jpg_473_141_115_327.jpg
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from/crops/tmpdataPeliculasfightsnewfi100.avioutput_000021.jpg_186_267_149_124.jpg
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0.900201/crops/tmpdataPeliculasfightsnewfi100.avioutput_000020.jpg_181_266_170_155.jpg
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from/crops/tmpdataPeliculasfightsnewfi90.avioutput_000003.jpg_1_69_262_351.jpg
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0.917534/crops/tmpdataPeliculasfightsnewfi90.avioutput_000002.jpg_1_69_262_348.jpg
0.90022/crops/tmpdataPeliculasfightsnewfi90.avioutput_000001.jpg_2_70_282_347.jpg
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from/crops/tmpdataPeliculasfightsnewfi90.avioutput_000001.jpg_2_70_282_347.jpg
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0.951013/crops/tmpdataPeliculasfightsnewfi90.avioutput_000002.jpg_1_69_262_348.jpg
0.90022/crops/tmpdataPeliculasfightsnewfi90.avioutput_000003.jpg_1_69_262_351.jpg
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from/crops/tmpdataPeliculasfightsnewfi74avi.avioutput_000009.jpg_176_192_74_200.jpg
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0.900355/crops/tmpdataPeliculasfightsnewfi74avi.avioutput_000007.jpg_146_186_91_237.jpg
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from/crops/tmpdataPeliculasfightsnewfi4.avioutput_000005.jpg_230_110_226_408.jpg
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0.900361/crops/tmpdataPeliculasfightsnewfi4.avioutput_000007.jpg_221_131_261_387.jpg
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from/crops/tmpdataPeliculasfightsnewfi3.avioutput_000020.jpg_453_290_83_233.jpg
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0.907882/crops/tmpdataPeliculasfightsnewfi19.avioutput_000003.jpg_399_287_83_210.jpg
0.900385/crops/tmpdataPeliculasfightsnewfi19.avioutput_000002.jpg_406_289_81_212.jpg
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from/crops/tmpdataPeliculasfightsnewfi19.avioutput_000002.jpg_406_289_81_212.jpg
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0.966457/crops/tmpdataPeliculasfightsnewfi19.avioutput_000003.jpg_399_287_83_210.jpg
0.900385/crops/tmpdataPeliculasfightsnewfi3.avioutput_000020.jpg_453_290_83_233.jpg
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from/crops/tmpdataPeliculasfightsnewfi97.avioutput_000050.jpg_168_235_36_48.jpg
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0.950719/crops/tmpdataPeliculasfightsnewfi97.avioutput_000049.jpg_171_233_33_52.jpg
0.900387/crops/tmpdataPeliculasfightsnewfi97.avioutput_000024.jpg_200_232_24_46.jpg
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from/crops/tmpdataPeliculasfightsnewfi39.avioutput_000001.jpg_176_69_188_476.jpg
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0.964918/crops/tmpdataPeliculasfightsnewfi39.avioutput_000002.jpg_177_70_186_476.jpg
0.900394/crops/tmpdataPeliculasfightsnewfi39.avioutput_000004.jpg_180_70_187_477.jpg
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from/crops/tmpdataPeliculasfightsnewfi77.avioutput_000050.jpg_339_143_178_278.jpg
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fromtodistance
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............
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Outliers Report

Showing image outliers, one per row

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Distance0.683235
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Distance0.685564
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Distance0.68916
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Distance0.695353
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Distance0.699362
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Distance0.701543
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Distance0.70211
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Distance0.704878
Path/crops/tmpdataPeliculasfightsnewfi81.avioutput_000007.jpg_145_21_163_450.jpg
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Distance0.705163
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Distance0.710253
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Distance0.711675
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Distance0.71494
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Dark Image Report

Showing example images, sort by ascending order

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Bright Image Report

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mean193.9089
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mean180.5055
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mean156.7711
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Blurry Image Report

Showing example images, sort by ascending order

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blur5.1635
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blur6.2514
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blur6.7307
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blur6.8313
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blur7.7214
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blur8.0188
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