{
"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": {
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" filename fastdup_id error_code is_valid\n",
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},
"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": {
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" 1.0 | \n",
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" 1.0 | \n",
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" From | \n",
" english_cocker_spaniel_176.jpg | \n",
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" To | \n",
" english_cocker_spaniel_179.jpg | \n",
"
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" \n",
" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fd.vis.duplicates_gallery()"
]
},
{
"cell_type": "markdown",
"id": "530988f2-a98e-4516-90e1-0d94bcac9951",
"metadata": {},
"source": [
"## Outliers\n",
"\n",
"Outliers are computed based on the distance of the image compared to other images in the dataset. View the docs [here](https://visual-layer.readme.io/docs/v1-api#outliers_gallery)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "7d83835b-0223-445f-9700-052fc4ca58a1",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 24679.64it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Stored outliers visual view in fastdup_work_dir/galleries/outliers.html\n"
]
},
{
"data": {
"text/html": [
" \n",
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" Outliers Report\n",
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Outliers Report
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" Info | \n",
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" Distance | \n",
" 0.59692 | \n",
"
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" Path | \n",
" Bengal_105.jpg | \n",
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" Distance | \n",
" 0.611524 | \n",
"
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" 0.617132 | \n",
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" 0.621796 | \n",
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" 0.646996 | \n",
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" Distance | \n",
" 0.653168 | \n",
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" Path | \n",
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" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fd.vis.outliers_gallery() "
]
},
{
"cell_type": "markdown",
"id": "789da241-e9cd-4568-9d19-aa5c80567415",
"metadata": {},
"source": [
"## Dark, Bright and Blurry Images\n",
"\n",
"You can also visualize the dataset sorted by a specific metric. View the docs [here](https://visual-layer.readme.io/docs/v1-api#duplicates_gallery)."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "292bdd75-5df0-4617-bd1e-8bcdd147e215",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 25/25 [00:00<00:00, 262.41it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Stored mean visual view in fastdup_work_dir/galleries/mean.html\n"
]
},
{
"data": {
"text/html": [
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" 19.567 | \n",
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" filename | \n",
" images/Abyssinian_4.jpg | \n",
"
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" mean | \n",
" 22.0709 | \n",
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" images/Bombay_33.jpg | \n",
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" 25.2039 | \n",
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" filename | \n",
" images/Bombay_108.jpg | \n",
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" 26.5806 | \n",
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" images/Abyssinian_114.jpg | \n",
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" 28.2537 | \n",
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" filename | \n",
" images/Maine_Coon_8.jpg | \n",
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" 28.6222 | \n",
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" filename | \n",
" images/scottish_terrier_171.jpg | \n",
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" 30.6038 | \n",
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" filename | \n",
" images/boxer_189.jpg | \n",
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" 31.0021 | \n",
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" filename | \n",
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" 31.8424 | \n",
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" images/shiba_inu_33.jpg | \n",
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"\n",
" mean | \n",
" 32.1091 | \n",
"
\n",
"\n",
" filename | \n",
" images/Egyptian_Mau_46.jpg | \n",
"
\n",
" \n",
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 32.1753 | \n",
"
\n",
"\n",
" filename | \n",
" images/Russian_Blue_13.jpg | \n",
"
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" \n",
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"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 33.3259 | \n",
"
\n",
"\n",
" filename | \n",
" images/Sphynx_93.jpg | \n",
"
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" \n",
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 33.7525 | \n",
"
\n",
"\n",
" filename | \n",
" images/japanese_chin_175.jpg | \n",
"
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" \n",
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"
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"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 33.889 | \n",
"
\n",
"\n",
" filename | \n",
" images/Egyptian_Mau_186.jpg | \n",
"
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" \n",
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"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 34.379 | \n",
"
\n",
"\n",
" filename | \n",
" images/shiba_inu_137.jpg | \n",
"
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" \n",
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"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 34.5139 | \n",
"
\n",
"\n",
" filename | \n",
" images/Egyptian_Mau_6.jpg | \n",
"
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" \n",
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\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 35.7243 | \n",
"
\n",
"\n",
" filename | \n",
" images/chihuahua_78.jpg | \n",
"
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" \n",
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" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 36.3198 | \n",
"
\n",
"\n",
" filename | \n",
" images/Egyptian_Mau_59.jpg | \n",
"
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"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 36.6248 | \n",
"
\n",
"\n",
" filename | \n",
" images/Sphynx_46.jpg | \n",
"
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" \n",
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"
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 36.9849 | \n",
"
\n",
"\n",
" filename | \n",
" images/american_bulldog_150.jpg | \n",
"
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\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 37.3306 | \n",
"
\n",
"\n",
" filename | \n",
" images/japanese_chin_40.jpg | \n",
"
\n",
" \n",
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"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 37.5096 | \n",
"
\n",
"\n",
" filename | \n",
" images/Abyssinian_62.jpg | \n",
"
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" \n",
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"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 37.5354 | \n",
"
\n",
"\n",
" filename | \n",
" images/Sphynx_119.jpg | \n",
"
\n",
" \n",
"
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" \n",
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" \n",
" \n",
" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fd.vis.stats_gallery(metric='dark')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "6e4cd628-ee7b-4eb9-b2d0-bde4f0beb22d",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 25/25 [00:00<00:00, 315.85it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Stored mean visual view in fastdup_work_dir/galleries/mean.html\n"
]
},
{
"data": {
"text/html": [
" \n",
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mean Image Report
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" mean | \n",
" 242.6047 | \n",
"
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"\n",
" filename | \n",
" images/saint_bernard_183.jpg | \n",
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" mean | \n",
" 239.4395 | \n",
"
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"\n",
" filename | \n",
" images/saint_bernard_188.jpg | \n",
"
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" mean | \n",
" 238.5204 | \n",
"
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"\n",
" filename | \n",
" images/saint_bernard_186.jpg | \n",
"
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" \n",
" Info | \n",
"
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"\n",
" mean | \n",
" 237.767 | \n",
"
\n",
"\n",
" filename | \n",
" images/boxer_162.jpg | \n",
"
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" \n",
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" Info | \n",
"
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"\n",
" mean | \n",
" 235.5402 | \n",
"
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"\n",
" filename | \n",
" images/Egyptian_Mau_99.jpg | \n",
"
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" mean | \n",
" 234.968 | \n",
"
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"\n",
" filename | \n",
" images/Abyssinian_127.jpg | \n",
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" \n",
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" Info | \n",
"
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"\n",
" mean | \n",
" 232.9795 | \n",
"
\n",
"\n",
" filename | \n",
" images/saint_bernard_187.jpg | \n",
"
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" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 231.1052 | \n",
"
\n",
"\n",
" filename | \n",
" images/British_Shorthair_274.jpg | \n",
"
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" Info | \n",
"
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"\n",
" mean | \n",
" 230.8341 | \n",
"
\n",
"\n",
" filename | \n",
" images/staffordshire_bull_terrier_25.jpg | \n",
"
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" \n",
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" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 228.7601 | \n",
"
\n",
"\n",
" filename | \n",
" images/great_pyrenees_88.jpg | \n",
"
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" \n",
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" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 228.4892 | \n",
"
\n",
"\n",
" filename | \n",
" images/Egyptian_Mau_110.jpg | \n",
"
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" \n",
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"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 225.4876 | \n",
"
\n",
"\n",
" filename | \n",
" images/Egyptian_Mau_1.jpg | \n",
"
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" \n",
"
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"
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"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 224.8601 | \n",
"
\n",
"\n",
" filename | \n",
" images/Maine_Coon_267.jpg | \n",
"
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" \n",
"
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"
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"
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"
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"
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"
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"
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"
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 224.1675 | \n",
"
\n",
"\n",
" filename | \n",
" images/Bombay_182.jpg | \n",
"
\n",
" \n",
"
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"
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"
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"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 220.5146 | \n",
"
\n",
"\n",
" filename | \n",
" images/Egyptian_Mau_39.jpg | \n",
"
\n",
" \n",
"
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"
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"
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"
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"
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"
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"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 219.7123 | \n",
"
\n",
"\n",
" filename | \n",
" images/pug_76.jpg | \n",
"
\n",
" \n",
"
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"
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"
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\n",
"
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"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 219.2608 | \n",
"
\n",
"\n",
" filename | \n",
" images/Abyssinian_66.jpg | \n",
"
\n",
" \n",
"
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"
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"
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"
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"
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"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 218.1195 | \n",
"
\n",
"\n",
" filename | \n",
" images/Birman_136.jpg | \n",
"
\n",
" \n",
"
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"
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"
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"
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"
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"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 217.4757 | \n",
"
\n",
"\n",
" filename | \n",
" images/chihuahua_97.jpg | \n",
"
\n",
" \n",
"
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"
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"
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"
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"
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"
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"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 217.39 | \n",
"
\n",
"\n",
" filename | \n",
" images/Maine_Coon_239.jpg | \n",
"
\n",
" \n",
"
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"
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"
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"
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"
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"
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"
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"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 217.1701 | \n",
"
\n",
"\n",
" filename | \n",
" images/Birman_61.jpg | \n",
"
\n",
" \n",
"
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"
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"
\n",
"
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"
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"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 217.0271 | \n",
"
\n",
"\n",
" filename | \n",
" images/pug_96.jpg | \n",
"
\n",
" \n",
"
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"
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"
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"
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"
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"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 216.7833 | \n",
"
\n",
"\n",
" filename | \n",
" images/saint_bernard_14.jpg | \n",
"
\n",
" \n",
"
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\n",
"
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"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 216.6674 | \n",
"
\n",
"\n",
" filename | \n",
" images/saint_bernard_189.jpg | \n",
"
\n",
" \n",
"
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"
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"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 216.2317 | \n",
"
\n",
"\n",
" filename | \n",
" images/basset_hound_24.jpg | \n",
"
\n",
" \n",
"
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"
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"
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" \n",
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" \n",
" \n",
" \n",
" \n",
" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fd.vis.stats_gallery(metric='bright')"
]
},
{
"cell_type": "code",
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"id": "aeb3a18e-1c2e-4ce4-94c0-61cdddae0619",
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"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 25/25 [00:00<00:00, 660.86it/s]"
]
},
{
"name": "stdout",
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"text": [
"Stored blur visual view in fastdup_work_dir/galleries/blur.html\n"
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fd.vis.stats_gallery(metric='blur')"
]
},
{
"cell_type": "markdown",
"id": "a6808750-d5d7-44bc-a6b0-aa985255407b",
"metadata": {
"tags": []
},
"source": [
"## Image Clusters\n",
"\n",
"Visualize similar looking images as clusters. View the docs [here](https://visual-layer.readme.io/docs/v1-api#component_gallery)."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "2cc2b317-e92e-4e40-9655-0a6b7c569dfa",
"metadata": {
"tags": []
},
"outputs": [
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"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 88.66it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Finished OK. Components are stored as image files fastdup_work_dir/galleries/components_[index].jpg\n",
"Stored components visual view in fastdup_work_dir/galleries/components.html\n",
"Execution time in seconds 0.8\n"
]
},
{
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"\n",
" num_images | \n",
" 2 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 1.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 3398 | \n",
"
\n",
"\n",
" num_images | \n",
" 2 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 1.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 3417 | \n",
"
\n",
"\n",
" num_images | \n",
" 2 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 1.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 3592 | \n",
"
\n",
"\n",
" num_images | \n",
" 2 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 1.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 3394 | \n",
"
\n",
"\n",
" num_images | \n",
" 2 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 1.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 3036 | \n",
"
\n",
"\n",
" num_images | \n",
" 2 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 0.9999 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 2959 | \n",
"
\n",
"\n",
" num_images | \n",
" 2 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 0.9604 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 2847 | \n",
"
\n",
"\n",
" num_images | \n",
" 2 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 1.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
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"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 2845 | \n",
"
\n",
"\n",
" num_images | \n",
" 2 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 1.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 3591 | \n",
"
\n",
"\n",
" num_images | \n",
" 2 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 0.9997 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
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"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 3626 | \n",
"
\n",
"\n",
" num_images | \n",
" 2 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 1.0 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
"
\n",
" \n",
" \n",
" \n",
" \n",
" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fd.vis.component_gallery()"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"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"
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},
"nbformat": 4,
"nbformat_minor": 5
}