{ "cells": [ { "cell_type": "markdown", "id": "pN6wiKBax7Pa", "metadata": { "id": "pN6wiKBax7Pa", "tags": [] }, "source": [ "# Quick Dataset Analysis\n", "This notebook shows how to quickly analyze an image dataset for potential issues using fastdup. We'll take you on a high level tour showcasing the core functions of fastdup in the shortest time." ] }, { "cell_type": "markdown", "id": "c0727302-dbe5-46b3-a5ff-b039811a7e7e", "metadata": { "tags": [] }, "source": [ "## Installation & Setting Up\n", "\n", "This notebook is written to be run on [Google Colab](https://colab.research.google.com/github/visual-layer/fastdup/blob/main/examples/quick-dataset-analysis.ipynb). If you're running fastdup locally, view the installation instructions for your operating system [here](https://visual-layer.readme.io/docs/installation)." ] }, { "cell_type": "code", "execution_count": null, "id": "8e6dd3e6-0f72-456b-9b16-2e53d5d5c099", "metadata": {}, "outputs": [], "source": [ "!pip install pip -U\n", "!pip install fastdup matplotlib" ] }, { "cell_type": "markdown", "id": "2d30a901-4ba8-48cf-9a2f-37e0f70fa1ae", "metadata": { "tags": [] }, "source": [ "## Download Oxford Pets Dataset\n", "\n", "For demonstration, we will use a widely available and well curated dataset. For that reason we might not find a lot of issues here. Feel free to swap this dataset with your own." ] }, { "cell_type": "code", "execution_count": null, "id": "00276083-cb7f-4867-b9c5-4e3ed8db255c", "metadata": {}, "outputs": [], "source": [ "!wget https://thor.robots.ox.ac.uk/~vgg/data/pets/images.tar.gz -O images.tar.gz\n", "!tar xf images.tar.gz" ] }, { "cell_type": "markdown", "id": "8cd8a7da-2e05-4c38-aa37-33fd466a61e2", "metadata": { "tags": [] }, "source": [ "## Import and Run fastdup" ] }, { "cell_type": "code", "execution_count": 1, "id": "e301485f", "metadata": { "id": "e301485f", "tags": [] }, "outputs": [ { "data": { "text/plain": [ "'0.903'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import fastdup\n", "fastdup.__version__" ] }, { "cell_type": "markdown", "id": "4acb64a1-ab06-4fa2-8111-65b5d4f2a335", "metadata": {}, "source": [ "Let's start by creating a `Fastdup` object.\n", "\n", "+ `work_dir` - path to store artifacts from the run. \n", "\n", "+ `input_dir` - path to your images folder." ] }, { "cell_type": "code", "execution_count": null, "id": "fe4d8211-89b2-4a2f-91f4-8074d2314aef", "metadata": {}, "outputs": [], "source": [ "fd = fastdup.create(work_dir=\"fastdup_work_dir/\", input_dir=\"images/\")" ] }, { "cell_type": "code", "execution_count": 2, "id": "beac4c50-3084-47fe-9b22-b14c3d3cb139", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "FastDup Software, (C) copyright 2022 Dr. Amir Alush and Dr. Danny Bickson.\n", "2023-03-15 18:49:07 [INFO] Going to loop over dir images\n", "2023-03-15 18:49:07 [INFO] Found total 7390 images to run on\n", "2023-03-15 18:49:07 [ERROR] Failed to read image images/Abyssinian_34.jpgtes 0 Features\n", "2023-03-15 18:49:13 [ERROR] Failed to read image images/Egyptian_Mau_139.jpgs 0 Features\n", "2023-03-15 18:49:13 [ERROR] Failed to read image images/Egyptian_Mau_145.jpg\n", "2023-03-15 18:49:13 [ERROR] Failed to read image images/Egyptian_Mau_167.jpgs 0 Features\n", "2023-03-15 18:49:13 [ERROR] Failed to read image images/Egyptian_Mau_177.jpg\n", "2023-03-15 18:49:13 [ERROR] Failed to read image images/Egyptian_Mau_191.jpgs 0 Features\n", "2023-03-15 18:49:27 [INFO] Found total 7390 images to run ontimated: 0 Minutes 0 Features\n", "2023-03-15 18:49:28 [INFO] 1039) Finished write_index() NN model\n", "2023-03-15 18:49:28 [INFO] Stored nn model index file fastdup_work_dir/nnf.index\n", "2023-03-15 18:49:29 [INFO] Total time took 21607 ms\n", "2023-03-15 18:49:29 [INFO] Found a total of 90 fully identical images (d>0.990), which are 0.41 %\n", "2023-03-15 18:49:29 [INFO] Found a total of 8 nearly identical images(d>0.980), which are 0.04 %\n", "2023-03-15 18:49:29 [INFO] Found a total of 976 above threshold images (d>0.900), which are 4.40 %\n", "2023-03-15 18:49:29 [INFO] Found a total of 738 outlier images (d<0.050), which are 3.33 %\n", "2023-03-15 18:49:29 [INFO] Min distance found 0.597 max distance 1.000\n", "2023-03-15 18:49:29 [INFO] Running connected components for ccthreshold 0.960000 \n", ".0\n", " ########################################################################################\n", "\n", "Dataset Analysis Summary: \n", "\n", " Dataset contains 7390 images\n", " Valid images are 99.92% (7,384) of the data, invalid are 0.08% (6) of the data\n", " For a detailed analysis, use `.invalid_instances()`.\n", "\n", " Similarity: 1.00% (74) belong to 3 similarity clusters (components).\n", " 99.00% (7,316) images do not belong to any similarity cluster.\n", " Largest cluster has 6 (0.08%) 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: 6.13% (453) 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()" ] }, { "cell_type": "markdown", "id": "24b9d94d-7458-42f0-bf77-1b33491279f2", "metadata": {}, "source": [ "## View Run Summary" ] }, { "cell_type": "code", "execution_count": 3, "id": "b546398f-e555-42b7-83ad-fd9ba9286d41", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " ########################################################################################\n", "\n", "Dataset Analysis Summary: \n", "\n", " Dataset contains 7390 images\n", " Valid images are 99.92% (7,384) of the data, invalid are 0.08% (6) of the data\n", " For a detailed analysis, use `.invalid_instances()`.\n", "\n", " Similarity: 1.00% (74) belong to 3 similarity clusters (components).\n", " 99.00% (7,316) images do not belong to any similarity cluster.\n", " Largest cluster has 6 (0.08%) 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: 6.13% (453) 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" ] }, { "data": { "text/plain": [ "['Dataset contains 7390 images',\n", " 'Valid images are 99.92% (7,384) of the data, invalid are 0.08% (6) of the data',\n", " 'For a detailed analysis, use `.invalid_instances()`.\\n',\n", " 'Similarity: 1.00% (74) belong to 3 similarity clusters (components).',\n", " '99.00% (7,316) images do not belong to any similarity cluster.',\n", " 'Largest cluster has 6 (0.08%) 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: 6.13% (453) of images are possible outliers, and fall in the bottom 5.00% of similarity values.',\n", " 'For a detailed list of outliers, use `.outliers()`.']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fd.summary()" ] }, { "cell_type": "markdown", "id": "9cde5da4-960b-469e-bba2-32736c5131f8", "metadata": { "id": "67205fab", "tags": [] }, "source": [ "## Invalid Images\n", "\n", "Get a list of broken images." ] }, { "cell_type": "code", "execution_count": 4, "id": "883435db-3097-4449-ab1a-c522d48edbd9", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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2Egyptian_Mau_145.jpg2247ERROR_ZERO_SIZE_FILEFalse
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" ], "text/plain": [ " img_filename fastdup_id error_code is_valid\n", "0 Abyssinian_34.jpg 135 ERROR_ZERO_SIZE_FILE False\n", "1 Egyptian_Mau_139.jpg 2240 ERROR_ZERO_SIZE_FILE False\n", "2 Egyptian_Mau_145.jpg 2247 ERROR_ZERO_SIZE_FILE False\n", "3 Egyptian_Mau_167.jpg 2268 ERROR_ZERO_SIZE_FILE False\n", "4 Egyptian_Mau_177.jpg 2278 ERROR_ZERO_SIZE_FILE False\n", "5 Egyptian_Mau_191.jpg 2293 ERROR_ZERO_SIZE_FILE False" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fd.invalid_instances()" ] }, { "cell_type": "markdown", "id": "22e04b25-0fe7-409d-8bd9-3b92c2ec8c5b", "metadata": {}, "source": [ "## Duplicate Image Pairs\n", "\n", "Duplicate image pairs are computed based on the cosine distance of an image pair. View the docs [here](https://visual-layer.readme.io/docs/v1-api#duplicates_gallery)." ] }, { "cell_type": "code", "execution_count": 5, "id": "27b091e6-fffa-4701-8a9a-19b7b087314a", "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 112.30it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Stored similarity visual view in fastdup_work_dir/galleries/duplicates.html\n" ] }, { "data": { "text/html": [ " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " Duplicates Report\n", " \n", " \n", "\n", "\n", "\n", "
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mean19.567
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mean22.0709
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mean25.2039
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mean25.5381
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mean26.5806
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mean28.0547
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mean28.2537
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mean28.6222
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mean30.6038
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mean31.0021
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mean31.8424
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mean32.1091
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mean32.1753
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mean33.3259
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mean33.7525
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mean33.889
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mean34.379
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mean34.5139
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mean35.7243
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mean36.3198
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mean36.6248
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mean36.9849
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mean37.3306
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mean37.5096
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mean37.5354
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mean Image Report

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mean242.6047
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mean239.4395
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mean238.5204
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mean237.767
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mean235.5402
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mean234.968
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mean232.9795
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mean231.1052
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mean230.8341
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mean228.7601
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mean228.4892
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mean225.4876
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mean224.8601
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mean224.1675
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