{
"cells": [
{
"cell_type": "markdown",
"id": "dc9e798f",
"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": "a4b4c6a4",
"metadata": {},
"source": [
"# Optical Character Recognition\n",
"\n",
"This notebook shows how you can perform OCR with fastdup."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eb7bae6e",
"metadata": {},
"outputs": [],
"source": [
"!pip install -Uq fastdup paddleocr paddlepaddle kaggle"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d32320e4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/usr/bin/dpkg\n"
]
},
{
"data": {
"text/plain": [
"'1.26'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import fastdup\n",
"fastdup.__version__"
]
},
{
"cell_type": "markdown",
"id": "7869e373",
"metadata": {},
"source": [
"## Download Video Dataset\n",
"\n",
"Let's download a Tiktok [trending video dataset](https://www.kaggle.com/datasets/erikvdven/tiktok-trending-december-2020) from Kaggle. The dataset consists of the first 1000 trending videos scraped from TikTok on December 2020.\n",
"\n",
"You can download the dataset by manually by heading to the dataset [homepage](https://www.kaggle.com/datasets/erikvdven/tiktok-trending-december-2020) 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": "ad3ac6b1",
"metadata": {},
"outputs": [],
"source": [
"!kaggle datasets download -d erikvdven/tiktok-trending-december-2020"
]
},
{
"cell_type": "markdown",
"id": "384a7f3a",
"metadata": {},
"source": [
"Now, unzip the dataset into our local directory. You'll find a folder name `videos` that has all the trending clips."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "272abe6a",
"metadata": {},
"outputs": [],
"source": [
"!unzip tiktok-trending-december-2020.zip"
]
},
{
"cell_type": "markdown",
"id": "f455b752",
"metadata": {},
"source": [
"## Extract frames\n",
"To run fastdup, we will need to extract the clips in the `videos` folder into frames and store them in another folder, let's name the folder `frames`. fastdup provides a convenience function for that."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "86a21118",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"FastDup Software, (C) copyright 2022 Dr. Amir Alush and Dr. Danny Bickson.\n",
"2023-07-13 17:48:56 [INFO] Going to loop over dir videos\n",
"2023-07-13 17:48:56 [INFO] Found total 1000 videos to run on, 1000 train, 0 test, name list 1000, counter 1000 \n"
]
},
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fastdup.extract_video_frames('videos', 'frames')"
]
},
{
"cell_type": "markdown",
"id": "fdd6e307",
"metadata": {},
"source": [
"## Run fastdup\n",
"With the extracted frames, we can run fastdup to analyze them. \n",
"\n",
"To use the optical character recognition feature, specify `bounding_box='ocr'` in the `run` method.\n",
"\n",
"For demonstration, we'll specify `num_images=1000` in the `run` method which limits the run to only 1000 images. Feel free to specify a different value or omitting this parameter altogether to run on the entire dataset."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "3a303d5d",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"FastDup Software, (C) copyright 2022 Dr. Amir Alush and Dr. Danny Bickson.\n",
"2023-07-13 18:01:48 [INFO] Going to loop over dir frames\n",
"2023-07-13 18:01:48 [INFO] Found total 1000 images to run on, 1000 train, 0 test, name list 1000, counter 1000 \n",
"2023-07-13 18:02:02 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:02:02 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:02:15 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:02:16 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:02:16 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:02:28 [WARNING] Failed to find bounding box in wow..',0.9485152959823608\n",
"2023-07-13 18:02:28 [WARNING] Failed to find bounding box in hijis\n",
"2023-07-13 18:02:29 [WARNING] Failed to find bounding box in mijnexisdaar”',0.8912146091461182\n",
"2023-07-13 18:02:29 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:02:30 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:02:30 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:02:30 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:02:31 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:02:31 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:02:43 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:02:56 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:02:56 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:02:56 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:03:10 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:03:10 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:03:10 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:03:10 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:03:10 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:03:11 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:03:11 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:03:23 [WARNING] Failed to find bounding box in cheatonme',\n",
"2023-07-13 18:03:23 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:03:23 [WARNING] Failed to find bounding box in cheatonme',0.952932596206665\n",
"2023-07-13 18:03:23 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:04:01 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:04:02 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:04:03 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:04:03 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:04:03 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:04:03 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:04:03 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:04:16 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:04:17 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:04:17 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:04:17 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:04:17 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:04:24 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:04:24 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:04:26 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:05:04 [WARNING] Failed to find bounding box in just\n",
"2023-07-13 18:05:05 [WARNING] Failed to find bounding box in justhad\n",
"2023-07-13 18:05:06 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:05:07 [WARNING] Failed to find bounding box in just\n",
"2023-07-13 18:05:12 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:05:19 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:05:20 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:05:20 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:05:21 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:05:22 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:05:22 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:05:23 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:05:24 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:05:27 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:05:28 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:05:32 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:05:45 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:05:58 [WARNING] Failed to find bounding box in sayIlikeit',\n",
"2023-07-13 18:05:58 [WARNING] Failed to find bounding box in likeit\n",
"2023-07-13 18:05:59 [WARNING] Failed to find bounding box in sayilike\n",
"2023-07-13 18:05:59 [WARNING] Failed to find bounding box in likeit\n",
"2023-07-13 18:06:28 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:06:44 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:06:53 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:06:55 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:06:56 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:06:59 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:07:15 [WARNING] Failed to find bounding box in ',0.97483229637146\n",
"2023-07-13 18:07:15 [WARNING] Failed to find bounding box in ',0.8968293070793152\n",
"2023-07-13 18:07:15 [WARNING] Failed to find bounding box in ',0.9721850752830505\n",
"2023-07-13 18:07:15 [WARNING] Failed to find bounding box in ',0.9775885343551636\n",
"2023-07-13 18:07:17 [WARNING] Failed to find bounding box in ',0.9966372847557068\n",
"2023-07-13 18:07:17 [WARNING] Failed to find bounding box in ',0.9780963063240051\n",
"2023-07-13 18:07:17 [WARNING] Failed to find bounding box in ',0.9760942459106445\n",
"2023-07-13 18:07:17 [WARNING] Failed to find bounding box in ',0.8360178470611572\n",
"2023-07-13 18:07:17 [WARNING] Failed to find bounding box in \",0.8635482788085938\n",
"2023-07-13 18:07:35 [WARNING] Failed to find bounding box in ',0.9401166439056396\n",
"2023-07-13 18:07:39 [WARNING] Failed to find bounding box in ',0.940094530582428\n",
"2023-07-13 18:07:47 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:07:48 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:07:58 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:08:35 [WARNING] Failed to find bounding box in necesitandome',0.9334614276885986\n",
"2023-07-13 18:08:35 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:08:35 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:08:36 [WARNING] Failed to find bounding box in iporque',\n",
"2023-07-13 18:08:41 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:07 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:15 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:39 [WARNING] Failed to find bounding box in Kane',0.9282829761505127\n",
"2023-07-13 18:09:39 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:39 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:43 [WARNING] Failed to find bounding box in Kane',0.9648606181144714\n",
"2023-07-13 18:09:43 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:43 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:43 [WARNING] Failed to find bounding box in Kane',0.9045147895812988\n",
"2023-07-13 18:09:43 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:43 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:43 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:44 [WARNING] Failed to find bounding box in Kane',0.9655975103378296\n",
"2023-07-13 18:09:44 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:44 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:44 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:44 [WARNING] Failed to find bounding box in Kane',0.9391220808029175\n",
"2023-07-13 18:09:44 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:44 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:45 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:45 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:45 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:45 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:46 [WARNING] Failed to find bounding box in Kane',0.9477143287658691\n",
"2023-07-13 18:09:46 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:46 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:46 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:46 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:46 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:46 [WARNING] Failed to find bounding box in \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2023-07-13 18:09:47 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:47 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:47 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:47 [WARNING] Failed to find bounding box in jebent\n",
"2023-07-13 18:09:47 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:09:47 [WARNING] Failed to find bounding box in jebent\n",
"2023-07-13 18:09:47 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:10:27 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:10:43 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:10:56 [WARNING] Failed to find bounding box in hand',0.9338752627372742\n",
"2023-07-13 18:10:56 [WARNING] Failed to find bounding box in cursivewriting,etc.ohmy',0.8433731198310852\n",
"2023-07-13 18:10:57 [WARNING] Failed to find bounding box in cursivewriting,etc.ohmy,thelist',0.9520007967948914\n",
"2023-07-13 18:11:15 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:11:18 [WARNING] Failed to find bounding box in could\n",
"2023-07-13 18:11:28 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:11:58 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:12:10 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:12:10 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:12:11 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:12:11 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:12:12 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:12:14 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:12:14 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:12:16 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:12:16 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:12:29 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:12:30 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:12:30 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:12:43 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:13:20 [WARNING] Failed to find bounding box in KOTOpbIeBaM',0.8349074125289917\n",
"2023-07-13 18:13:20 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:13:30 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:13:44 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:13:44 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:13:45 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:14:01 [WARNING] Failed to find bounding box in ',0.8406596183776855\n",
"2023-07-13 18:14:13 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:14:40 [WARNING] Failed to find bounding box in ikben100%Nederlands',0.8602634072303772\n",
"2023-07-13 18:14:53 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:14:53 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:14:54 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:14:55 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:15:00 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:15:30 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:15:32 [WARNING] Failed to find bounding box in ',0.8815599679946899\n",
"2023-07-13 18:15:32 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:15:33 [WARNING] Failed to find bounding box in \n",
"2023-07-13 18:15:44 [WARNING] Failed to find bounding box in \n",
"FastDup Software, (C) copyright 2022 Dr. Amir Alush and Dr. Danny Bickson.\n",
"2023-07-13 18:15:46 [INFO] Going to loop over dir /tmp/crops_input.csv\n",
"2023-07-13 18:15:46 [INFO] Found total 1000 images to run on, 1000 train, 0 test, name list 1000, counter 1000 \n",
"2023-07-13 18:15:49 [INFO] Found total 1000 images to run ontimated: 0 Minutes975030598917.mp4output_000005.jpg_166_533_195_533_195_541_166_541.jpg - file does not exist[■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ ] 83% Estimated: 0 Minutes\n",
"Finished histogram 0.211\n",
"Finished bucket sort 0.219\n",
"2023-07-13 18:15:49 [INFO] 70) Finished write_index() NN model\n",
"2023-07-13 18:15:49 [INFO] Stored nn model index file work_dir/nnf.index\n",
"2023-07-13 18:15:49 [INFO] Total time took 3118 ms\n",
"2023-07-13 18:15:49 [INFO] Found a total of 219 fully identical images (d>0.990), which are 10.95 %\n",
"2023-07-13 18:15:49 [INFO] Found a total of 148 nearly identical images(d>0.980), which are 7.40 %\n",
"2023-07-13 18:15:49 [INFO] Found a total of 1569 above threshold images (d>0.900), which are 78.45 %\n",
"2023-07-13 18:15:49 [INFO] Found a total of 99 outlier images (d<0.050), which are 4.95 %\n",
"2023-07-13 18:15:49 [INFO] Min distance found 0.621 max distance 0.999\n",
"2023-07-13 18:15:49 [INFO] Running connected components for ccthreshold 0.960000 \n",
".0\n",
" ########################################################################################\n",
"\n",
"Dataset Analysis Summary: \n",
"\n",
" Dataset contains 997 images\n",
" Valid images are 100.00% (997) of the data, invalid are 0.00% (0) of the data\n",
" Similarity: 20.96% (209) belong to 17 similarity clusters (components).\n",
" 79.04% (788) images do not belong to any similarity cluster.\n",
" Largest cluster has 134 (13.44%) 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: 6.22% (62) 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": [
"0"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fd = fastdup.create(input_dir='./frames', work_dir='work_dir')\n",
"fd.run(bounding_box='ocr', num_images=1000)"
]
},
{
"cell_type": "markdown",
"id": "58f09dd8",
"metadata": {},
"source": [
"## Duplicate/Near-duplicate Detections"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "526030e4",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 459.87it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Stored similarity visual view in work_dir/galleries/duplicates.html\n"
]
},
{
"data": {
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" /crops/tmpvideos6875872124968439046.mp4output_000004.jpg_78_604_308_605_308_623_78_622.jpg | \n",
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"\n",
" To | \n",
" /crops/tmpvideos6875872124968439046.mp4output_000003.jpg_78_604_308_605_308_623_78_622.jpg | \n",
"
\n",
"\n",
" From_Label | \n",
" N/A | \n",
"
\n",
"\n",
" To_Label | \n",
" N/A | \n",
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" \n",
"
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"
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" Distance | \n",
" 0.999164 | \n",
"
\n",
"\n",
" From | \n",
" /crops/tmpvideos6875872124968439046.mp4output_000003.jpg_74_554_333_555_332_576_74_575.jpg | \n",
"
\n",
"\n",
" To | \n",
" /crops/tmpvideos6875872124968439046.mp4output_000004.jpg_74_554_333_555_332_576_74_575.jpg | \n",
"
\n",
"\n",
" From_Label | \n",
" N/A | \n",
"
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"\n",
" To_Label | \n",
" N/A | \n",
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" \n",
"
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"
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"
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"
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" Distance | \n",
" 0.99886 | \n",
"
\n",
"\n",
" From | \n",
" /crops/tmpvideos6877747486451043590.mp4output_000002.jpg_72_122_320_122_320_135_72_135.jpg | \n",
"
\n",
"\n",
" To | \n",
" /crops/tmpvideos6877747486451043590.mp4output_000005.jpg_72_122_320_122_320_135_72_135.jpg | \n",
"
\n",
"\n",
" From_Label | \n",
" N/A | \n",
"
\n",
"\n",
" To_Label | \n",
" N/A | \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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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" Distance | \n",
" 0.998814 | \n",
"
\n",
"\n",
" From | \n",
" /crops/tmpvideos6875405441472498949.mp4output_000002.jpg_76_161_320_161_320_179_76_179.jpg | \n",
"
\n",
"\n",
" To | \n",
" /crops/tmpvideos6875405441472498949.mp4output_000005.jpg_76_161_320_161_320_179_76_179.jpg | \n",
"
\n",
"\n",
" From_Label | \n",
" N/A | \n",
"
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"\n",
" To_Label | \n",
" N/A | \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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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" Distance | \n",
" 0.998212 | \n",
"
\n",
"\n",
" From | \n",
" /crops/tmpvideos6875436892226178305.mp4output_000002.jpg_108_286_468_286_468_311_108_311.jpg | \n",
"
\n",
"\n",
" To | \n",
" /crops/tmpvideos6875436892226178305.mp4output_000001.jpg_108_286_468_286_468_311_108_311.jpg | \n",
"
\n",
"\n",
" From_Label | \n",
" N/A | \n",
"
\n",
"\n",
" To_Label | \n",
" N/A | \n",
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" \n",
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"
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"
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"
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" Distance | \n",
" 0.998153 | \n",
"
\n",
"\n",
" From | \n",
" /crops/tmpvideos6878165800902085890.mp4output_000001.jpg_125_581_381_473_400_517_144_625.jpg | \n",
"
\n",
"\n",
" To | \n",
" /crops/tmpvideos6878165800902085890.mp4output_000002.jpg_125_581_381_473_400_517_144_625.jpg | \n",
"
\n",
"\n",
" From_Label | \n",
" N/A | \n",
"
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"\n",
" To_Label | \n",
" N/A | \n",
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" \n",
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" Distance | \n",
" 0.998042 | \n",
"
\n",
"\n",
" From | \n",
" /crops/tmpvideos6876156631651077381.mp4output_000002.jpg_78_206_311_206_311_221_78_221.jpg | \n",
"
\n",
"\n",
" To | \n",
" /crops/tmpvideos6876156631651077381.mp4output_000003.jpg_78_206_311_206_311_221_78_221.jpg | \n",
"
\n",
"\n",
" From_Label | \n",
" N/A | \n",
"
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"\n",
" To_Label | \n",
" N/A | \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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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" Distance | \n",
" 0.997734 | \n",
"
\n",
"\n",
" From | \n",
" /crops/tmpvideos6875405441472498949.mp4output_000004.jpg_76_161_320_161_320_179_76_179.jpg | \n",
"
\n",
"\n",
" To | \n",
" /crops/tmpvideos6875405441472498949.mp4output_000002.jpg_76_161_320_161_320_179_76_179.jpg | \n",
"
\n",
"\n",
" From_Label | \n",
" N/A | \n",
"
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"\n",
" To_Label | \n",
" N/A | \n",
"
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" \n",
"
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" Distance | \n",
" 0.997723 | \n",
"
\n",
"\n",
" From | \n",
" /crops/tmpvideos6877796349459336450.mp4output_000004.jpg_435_988_562_988_562_1006_435_1006.jpg | \n",
"
\n",
"\n",
" To | \n",
" /crops/tmpvideos6877796349459336450.mp4output_000002.jpg_435_988_562_988_562_1006_435_1006.jpg | \n",
"
\n",
"\n",
" From_Label | \n",
" N/A | \n",
"
\n",
"\n",
" To_Label | \n",
" N/A | \n",
"
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" \n",
"
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"
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" Distance | \n",
" 0.997721 | \n",
"
\n",
"\n",
" From | \n",
" /crops/tmpvideos6875872124968439046.mp4output_000003.jpg_78_604_308_605_308_623_78_622.jpg | \n",
"
\n",
"\n",
" To | \n",
" /crops/tmpvideos6875872124968439046.mp4output_000005.jpg_78_604_308_605_308_623_78_622.jpg | \n",
"
\n",
"\n",
" From_Label | \n",
" N/A | \n",
"
\n",
"\n",
" To_Label | \n",
" N/A | \n",
"
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" \n",
"
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"
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"
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"
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" \n",
"
\n",
" \n",
" \n",
" \n",
" \n",
" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fd.vis.duplicates_gallery()"
]
},
{
"cell_type": "markdown",
"id": "322949c4",
"metadata": {},
"source": [
"## Outliers\n",
"\n",
"Let's visualize the outliers in the OCR detections."
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "4abd3d6c",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22976.19it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Stored outliers visual view in work_dir/galleries/outliers.html\n"
]
},
{
"data": {
"text/html": [
" \n",
" \n",
" \n",
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" \n",
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" Outliers Report\n",
" \n",
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"\n",
"\n",
"\n",
" \n",
" \n",
" \n",
" \n",
" \n",
"
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"
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"
Outliers Report
Showing image outliers, one per row
\n",
"
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" \n",
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"
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"\n",
" Distance | \n",
" 0.644383 | \n",
"
\n",
"\n",
" Path | \n",
" /crops/tmpvideos6875749962681044230.mp4output_000001.jpg_329_267_431_253_437_299_335_313.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.745696 | \n",
"
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" \n",
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"
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"\n",
" Distance | \n",
" 0.654496 | \n",
"
\n",
"\n",
" Path | \n",
" /crops/tmpvideos6876369534018669825.mp4output_000005.jpg_220_419_445_470_424_563_199_511.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.672772 | \n",
"
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" \n",
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" \n",
" Info | \n",
"
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"\n",
" Distance | \n",
" 0.675129 | \n",
"
\n",
"\n",
" Path | \n",
" /crops/tmpvideos6877789463221947650.mp4output_000001.jpg_45_72_503_79_499_321_41_314.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.947268 | \n",
"
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" \n",
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" \n",
" Info | \n",
"
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"\n",
" Distance | \n",
" 0.695931 | \n",
"
\n",
"\n",
" Path | \n",
" /crops/tmpvideos6876857501397159170.mp4output_000001.jpg_218_537_326_543_324_588_216_582.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.732296 | \n",
"
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" \n",
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" \n",
" Info | \n",
"
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"\n",
" Distance | \n",
" 0.697093 | \n",
"
\n",
"\n",
" Path | \n",
" /crops/tmpvideos6876369534018669825.mp4output_000003.jpg_128_630_279_582_309_680_158_728.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.992506 | \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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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" Distance | \n",
" 0.700451 | \n",
"
\n",
"\n",
" Path | \n",
" /crops/tmpvideos6875317312082201857.mp4output_000001.jpg_307_399_352_399_352_446_307_446.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.536331 | \n",
"
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" \n",
"
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"
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"
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" Distance | \n",
" 0.70144 | \n",
"
\n",
"\n",
" Path | \n",
" /crops/tmpvideos6875872124968439046.mp4output_000006.jpg_38_914_91_958_72_981_19_938.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.58447 | \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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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" Distance | \n",
" 0.711805 | \n",
"
\n",
"\n",
" Path | \n",
" /crops/tmpvideos6877179236386376961.mp4output_000002.jpg_10_139_86_83_120_129_43_185.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.992461 | \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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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" Distance | \n",
" 0.714143 | \n",
"
\n",
"\n",
" Path | \n",
" /crops/tmpvideos6876787355181665537.mp4output_000004.jpg_289_391_476_391_476_508_289_508.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.651683 | \n",
"
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" \n",
"
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"
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"
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" Distance | \n",
" 0.720347 | \n",
"
\n",
"\n",
" Path | \n",
" /crops/tmpvideos6876245387871587590.mp4output_000001.jpg_510_252_565_252_565_298_510_298.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.556968 | \n",
"
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" \n",
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" Distance | \n",
" 0.722568 | \n",
"
\n",
"\n",
" Path | \n",
" /crops/tmpvideos6875323773755657474.mp4output_000002.jpg_410_216_485_209_489_249_414_256.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.665932 | \n",
"
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" \n",
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" Distance | \n",
" 0.722777 | \n",
"
\n",
"\n",
" Path | \n",
" /crops/tmpvideos6875323773755657474.mp4output_000003.jpg_176_383_419_390_417_438_174_431.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.802938 | \n",
"
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" \n",
"
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"
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"
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" Distance | \n",
" 0.726905 | \n",
"
\n",
"\n",
" Path | \n",
" /crops/tmpvideos6877191692341054721.mp4output_000002.jpg_206_521_239_535_210_602_177_587.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.676398 | \n",
"
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" \n",
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" Distance | \n",
" 0.728571 | \n",
"
\n",
"\n",
" Path | \n",
" /crops/tmpvideos6875739742340762885.mp4output_000002.jpg_206_580_278_586_276_613_204_606.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.62836 | \n",
"
\n",
" \n",
"
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"
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"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" Distance | \n",
" 0.732963 | \n",
"
\n",
"\n",
" Path | \n",
" /crops/tmpvideos6875323773755657474.mp4output_000002.jpg_161_217_238_217_238_261_161_261.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.977016 | \n",
"
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" \n",
"
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" Distance | \n",
" 0.733121 | \n",
"
\n",
"\n",
" Path | \n",
" /crops/tmpvideos6876603307708665093.mp4output_000007.jpg_301_476_439_434_453_482_315_523.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.517169 | \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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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" Distance | \n",
" 0.734482 | \n",
"
\n",
"\n",
" Path | \n",
" /crops/tmpvideos6876369534018669825.mp4output_000002.jpg_144_448_173_444_177_474_148_478.jpg | \n",
"
\n",
"\n",
" label | \n",
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"
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" Distance | \n",
" 0.735593 | \n",
"
\n",
"\n",
" Path | \n",
" /crops/tmpvideos6875872124968439046.mp4output_000005.jpg_36_917_107_959_91_986_20_945.jpg | \n",
"
\n",
"\n",
" label | \n",
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" Distance | \n",
" 0.736088 | \n",
"
\n",
"\n",
" Path | \n",
" /crops/tmpvideos6877178763474423041.mp4output_000002.jpg_177_808_543_829_533_1000_168_979.jpg | \n",
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\n",
"\n",
" label | \n",
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\n",
"
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" Distance | \n",
" 0.740881 | \n",
"
\n",
"\n",
" Path | \n",
" /crops/tmpvideos6877301356101782785.mp4output_000003.jpg_308_509_335_509_335_559_308_559.jpg | \n",
"
\n",
"\n",
" label | \n",
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"
\n",
" \n",
"
\n",
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\n",
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\n",
" \n",
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\n",
" \n",
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" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 25,
"metadata": {},
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}
],
"source": [
"fd.vis.outliers_gallery(load_crops=True)"
]
},
{
"cell_type": "markdown",
"id": "9b2d472b",
"metadata": {},
"source": [
"## Dark Detections"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "4a660315",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 1555.75it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Stored mean visual view in work_dir/galleries/mean.html\n"
]
},
{
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" Dark Image Report\n",
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Dark Image Report
Showing example images, sort by ascending order
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\n",
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"\n",
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" 41.8634 | \n",
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\n",
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\n",
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\n",
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\n",
"\n",
" mean | \n",
" 45.9549 | \n",
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\n",
"\n",
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\n",
"\n",
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"\n",
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"\n",
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" label | \n",
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" \n",
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 48.379 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6877473712652799234.mp4output_000001.jpg_8_46_123_47_123_61_8_60.jpg | \n",
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"\n",
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"\n",
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"\n",
" mean | \n",
" 49.5633 | \n",
"
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"\n",
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"
\n",
"\n",
" mean | \n",
" 49.7569 | \n",
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\n",
"\n",
" filename | \n",
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"
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 49.8006 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6877301356101782785.mp4output_000006.jpg_387_926_521_927_521_944_387_943.jpg | \n",
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"
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"
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 49.9399 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6877301356101782785.mp4output_000003.jpg_387_926_520_927_520_944_387_943.jpg | \n",
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"\n",
" label | \n",
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"
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"
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 49.9419 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6877301356101782785.mp4output_000004.jpg_387_926_520_927_520_944_387_943.jpg | \n",
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\n",
"\n",
" label | \n",
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" \n",
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"
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"
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 50.1834 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6877301356101782785.mp4output_000007.jpg_387_926_521_927_521_944_387_943.jpg | \n",
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 50.8746 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6875528457388903681.mp4output_000005.jpg_894_539_1010_537_1010_558_894_560.jpg | \n",
"
\n",
"\n",
" label | \n",
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"
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" \n",
"
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" \n",
" \n",
" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fd.vis.stats_gallery(load_crops=True)"
]
},
{
"cell_type": "markdown",
"id": "84f0082f",
"metadata": {},
"source": [
"## Blurry Detections"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "e13ccbaa",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 4670.46it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Stored blur visual view in work_dir/galleries/blur.html\n"
]
},
{
"data": {
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Blurry Image Report
Showing example images, sort by ascending order
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" blur | \n",
" 11.368 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876787355181665537.mp4output_000005.jpg_197_611_218_608_220_620_199_623.jpg | \n",
"
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"\n",
" label | \n",
" 0.763627 | \n",
"
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" \n",
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" blur | \n",
" 12.6647 | \n",
"
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"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6877179236386376961.mp4output_000002.jpg_103_849_341_829_348_914_110_934.jpg | \n",
"
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"\n",
" label | \n",
" 0.958016 | \n",
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" \n",
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" blur | \n",
" 14.282 | \n",
"
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"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6877179236386376961.mp4output_000003.jpg_48_470_108_470_108_493_48_493.jpg | \n",
"
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"\n",
" label | \n",
" 0.932451 | \n",
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" blur | \n",
" 17.6397 | \n",
"
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"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876787355181665537.mp4output_000005.jpg_160_596_210_583_213_597_163_610.jpg | \n",
"
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"\n",
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" 0.709151 | \n",
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" \n",
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" blur | \n",
" 20.3299 | \n",
"
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"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876787355181665537.mp4output_000005.jpg_166_644_220_629_225_645_171_660.jpg | \n",
"
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"\n",
" label | \n",
" 0.887496 | \n",
"
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" \n",
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" blur | \n",
" 21.5456 | \n",
"
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"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876787355181665537.mp4output_000003.jpg_171_135_206_139_204_159_169_154.jpg | \n",
"
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"\n",
" label | \n",
" 0.717846 | \n",
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" \n",
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" Info | \n",
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" blur | \n",
" 23.9951 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876787355181665537.mp4output_000005.jpg_217_656_235_656_235_669_217_669.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.677347 | \n",
"
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" \n",
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" Info | \n",
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"\n",
" blur | \n",
" 27.2666 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876787355181665537.mp4output_000002.jpg_184_91_221_94_219_115_183_112.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.790454 | \n",
"
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" \n",
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"\n",
" blur | \n",
" 33.5997 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6877606750900538626.mp4output_000004.jpg_2_990_53_984_54_996_3_1002.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.51632 | \n",
"
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" \n",
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" \n",
" \n",
" Info | \n",
"
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"\n",
" blur | \n",
" 38.4142 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876787355181665537.mp4output_000003.jpg_311_573_343_573_343_594_311_594.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.99319 | \n",
"
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" \n",
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" \n",
" \n",
" Info | \n",
"
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"\n",
" blur | \n",
" 39.8873 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876787355181665537.mp4output_000003.jpg_311_517_342_517_342_533_311_533.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.506171 | \n",
"
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" \n",
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" \n",
" \n",
" Info | \n",
"
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"\n",
" blur | \n",
" 41.6052 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876787355181665537.mp4output_000003.jpg_271_609_337_601_339_620_274_628.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.844861 | \n",
"
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" \n",
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" \n",
" \n",
" Info | \n",
"
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"\n",
" blur | \n",
" 43.3049 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876787355181665537.mp4output_000002.jpg_330_503_363_503_363_521_330_521.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.660055 | \n",
"
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" \n",
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" \n",
" \n",
" Info | \n",
"
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"\n",
" blur | \n",
" 45.5358 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876787355181665537.mp4output_000003.jpg_196_301_240_301_240_311_196_311.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.771191 | \n",
"
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" \n",
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"
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"
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" \n",
" \n",
" Info | \n",
"
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"\n",
" blur | \n",
" 46.0039 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876369534018669825.mp4output_000003.jpg_128_630_279_582_309_680_158_728.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.992506 | \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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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" blur | \n",
" 48.2854 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876787355181665537.mp4output_000003.jpg_312_635_341_635_341_654_312_654.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.972614 | \n",
"
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" \n",
"
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" blur | \n",
" 49.4907 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876787355181665537.mp4output_000003.jpg_377_287_438_285_439_300_377_302.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.845535 | \n",
"
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" \n",
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" blur | \n",
" 52.3385 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876787355181665537.mp4output_000003.jpg_200_315_256_315_256_325_200_325.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.530487 | \n",
"
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" \n",
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" blur | \n",
" 56.4384 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876787355181665537.mp4output_000003.jpg_227_272_300_277_299_292_226_287.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.697303 | \n",
"
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" \n",
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" blur | \n",
" 57.5365 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876369534018669825.mp4output_000005.jpg_89_932_107_932_107_953_89_953.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.960756 | \n",
"
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" \n",
"
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"
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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"
},
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fd.vis.stats_gallery(metric='blur', load_crops=True)"
]
},
{
"cell_type": "markdown",
"id": "51e1c939",
"metadata": {},
"source": [
"## Bright Detections"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "20b721e7",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 3067.02it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Stored mean visual view in work_dir/galleries/mean.html\n"
]
},
{
"data": {
"text/html": [
" \n",
" \n",
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" Bright Image Report\n",
" \n",
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"\n",
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" \n",
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" \n",
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"
Bright Image Report
Showing example images, sort by descending order
\n",
"
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"
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"
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" \n",
" \n",
" \n",
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"
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"
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 241.9651 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876805952117558529.mp4output_000003.jpg_182_641_350_641_350_702_182_702.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.885485 | \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",
" 239.1625 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876805952117558529.mp4output_000004.jpg_147_654_356_656_355_721_146_719.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.735413 | \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",
" 238.5339 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876805952117558529.mp4output_000002.jpg_150_635_381_640_379_708_149_702.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.841424 | \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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\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 238.1793 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6875739742340762885.mp4output_000001.jpg_60_271_111_271_111_282_60_282.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.866655 | \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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"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 237.8891 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876805952117558529.mp4output_000006.jpg_229_637_396_650_391_712_224_698.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.804841 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 234.394 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876787355181665537.mp4output_000005.jpg_166_644_220_629_225_645_171_660.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.887496 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
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"
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"
\n",
"
\n",
"
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"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 233.5309 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876156631651077381.mp4output_000004.jpg_78_206_312_206_312_221_78_221.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.882049 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
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"
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"
\n",
"
\n",
"
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"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 233.4604 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876787355181665537.mp4output_000002.jpg_330_503_363_503_363_521_330_521.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.660055 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
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"
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"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 233.2266 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876156631651077381.mp4output_000003.jpg_78_206_311_206_311_221_78_221.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.888761 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 233.2228 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876787355181665537.mp4output_000005.jpg_160_596_210_583_213_597_163_610.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.709151 | \n",
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\n",
" \n",
"
\n",
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\n",
"
\n",
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"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 233.1428 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876156631651077381.mp4output_000002.jpg_78_206_311_206_311_221_78_221.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.914209 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 233.1201 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876156631651077381.mp4output_000005.jpg_78_206_311_206_311_221_78_221.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.911282 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 233.0334 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876156631651077381.mp4output_000001.jpg_78_206_311_206_311_221_78_221.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.937742 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 232.7812 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876643374548438274.mp4output_000002.jpg_15_64_174_65_174_79_15_78.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.829832 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 232.7233 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876787355181665537.mp4output_000002.jpg_281_537_359_532_360_550_282_555.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.873083 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 232.0589 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876787355181665537.mp4output_000005.jpg_217_656_235_656_235_669_217_669.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.677347 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 231.8961 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876787355181665537.mp4output_000005.jpg_197_611_218_608_220_620_199_623.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.763627 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 230.7012 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876857501397159170.mp4output_000001.jpg_67_175_238_175_238_188_67_188.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.90828 | \n",
"
\n",
" \n",
"
\n",
"
\n",
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\n",
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\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 229.6535 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6877774951361875201.mp4output_000001.jpg_61_25_148_25_148_54_61_54.jpg | \n",
"
\n",
"\n",
" label | \n",
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\n",
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\n",
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\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" mean | \n",
" 229.6462 | \n",
"
\n",
"\n",
" filename | \n",
" work_dir/crops/framestmpvideos6876369534018669825.mp4output_000001.jpg_14_67_146_71_146_89_14_85.jpg | \n",
"
\n",
"\n",
" label | \n",
" 0.804711 | \n",
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\n",
" \n",
"
\n",
"
\n",
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],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fd.vis.stats_gallery(metric='bright', load_crops=True)"
]
},
{
"cell_type": "markdown",
"id": "1ba3047a",
"metadata": {},
"source": [
"## Detection Clusters"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "f507710d",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.912512\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/20 [00:00, ?it/s]Traceback (most recent call last):\n",
" File \"/home/dnth/anaconda3/envs/fastdup/lib/python3.10/site-packages/fastdup/galleries.py\", line 699, in load_one_image\n",
" assert img is not None, f\"Failed to read image {f} {input_dir} {kwargs}\"\n",
"AssertionError: Failed to read image work_dir/crops/framestmpvideos6877337975030598917.mp4output_000005.jpg_166_533_195_533_195_541_166_541.jpg work_dir {'save_artifacts': False, 'id_to_filename_func': .get_filename_func at 0x7fe316dd1b40>, 'jupyter_html': True, 'load_crops': True, 'draw_bbox': False, 'sort_by': 'comp_size', 'lazy_load': False, 'run_hierarchical': False, 'selection_strategy': 0, 'input_dir': 'work_dir'}\n",
"Traceback (most recent call last):\n",
" File \"/home/dnth/anaconda3/envs/fastdup/lib/python3.10/site-packages/fastdup/galleries.py\", line 699, in load_one_image\n",
" assert img is not None, f\"Failed to read image {f} {input_dir} {kwargs}\"\n",
"AssertionError: Failed to read image work_dir/crops/framestmpvideos6877036154982616321.mp4output_000002.jpg_197_420_231_420_231_427_197_427.jpg work_dir {'save_artifacts': False, 'id_to_filename_func': .get_filename_func at 0x7fe316dd1b40>, 'jupyter_html': True, 'load_crops': True, 'draw_bbox': False, 'sort_by': 'comp_size', 'lazy_load': False, 'run_hierarchical': False, 'selection_strategy': 0, 'input_dir': 'work_dir'}\n",
"100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 332.76it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Failed to read image from img_path work_dir/crops/framestmpvideos6877337975030598917.mp4output_000005.jpg_166_533_195_533_195_541_166_541.jpg\n",
"Warning: Failed to load image work_dir/crops/framestmpvideos6877337975030598917.mp4output_000005.jpg_166_533_195_533_195_541_166_541.jpg skipping image due to error Failed to read image work_dir/crops/framestmpvideos6877337975030598917.mp4output_000005.jpg_166_533_195_533_195_541_166_541.jpg work_dir {'save_artifacts': False, 'id_to_filename_func': .get_filename_func at 0x7fe316dd1b40>, 'jupyter_html': True, 'load_crops': True, 'draw_bbox': False, 'sort_by': 'comp_size', 'lazy_load': False, 'run_hierarchical': False, 'selection_strategy': 0, 'input_dir': 'work_dir'}\n",
"Failed to read image from img_path work_dir/crops/framestmpvideos6877036154982616321.mp4output_000002.jpg_197_420_231_420_231_427_197_427.jpg\n",
"Warning: Failed to load image work_dir/crops/framestmpvideos6877036154982616321.mp4output_000002.jpg_197_420_231_420_231_427_197_427.jpg skipping image due to error Failed to read image work_dir/crops/framestmpvideos6877036154982616321.mp4output_000002.jpg_197_420_231_420_231_427_197_427.jpg work_dir {'save_artifacts': False, 'id_to_filename_func': .get_filename_func at 0x7fe316dd1b40>, 'jupyter_html': True, 'load_crops': True, 'draw_bbox': False, 'sort_by': 'comp_size', 'lazy_load': False, 'run_hierarchical': False, 'selection_strategy': 0, 'input_dir': 'work_dir'}\n",
"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 0.2\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
},
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"data": {
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Components Report
Showing groups of similar images
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"\n",
" 0.972217 | \n",
" 1 | \n",
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"\n",
" 0.97659 | \n",
" 1 | \n",
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"\n",
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" 1 | \n",
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"\n",
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" 1 | \n",
"
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" 0.980341 | \n",
" 1 | \n",
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" 0.981573 | \n",
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" 0.98168 | \n",
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" 0.982998 | \n",
" 1 | \n",
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"\n",
" 0.988336 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.960789 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.958286 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.940294 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.880495 | \n",
" 1 | \n",
"
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"\n",
" 0.735413 | \n",
" 1 | \n",
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"\n",
" 0.832029 | \n",
" 1 | \n",
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" 0.8549 | \n",
" 1 | \n",
"
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"\n",
" 0.869606 | \n",
" 1 | \n",
"
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" 0.873701 | \n",
" 1 | \n",
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" 0.875007 | \n",
" 1 | \n",
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"\n",
" 0.881332 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.936252 | \n",
" 1 | \n",
"
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"\n",
" 0.891715 | \n",
" 1 | \n",
"
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" 0.898937 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.904909 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.918436 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.92461 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.925156 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.995082 | \n",
" 1 | \n",
"
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" \n",
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 3 | \n",
"
\n",
"\n",
" num_images | \n",
" 18 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 0.9601 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Label | \n",
"
\n",
"\n",
" 0.582775 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.776851 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.968139 | \n",
" 1 | \n",
"
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"\n",
" 0.967538 | \n",
" 1 | \n",
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"\n",
" 0.9673 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.963748 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.955813 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.952471 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.943044 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.940816 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.938854 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.935622 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.913456 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.912512 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.901923 | \n",
" 1 | \n",
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" 1 | \n",
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"\n",
" 0.898119 | \n",
" 1 | \n",
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"\n",
" 0.980959 | \n",
" 1 | \n",
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" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 557 | \n",
"
\n",
"\n",
" num_images | \n",
" 12 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 0.9615 | \n",
"
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" \n",
"
\n",
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" Label | \n",
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"\n",
" 0.51632 | \n",
" 1 | \n",
"
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"\n",
" 0.568036 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.576053 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.658896 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.872467 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.905367 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.910863 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.932909 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.934804 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.938696 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.98103 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.983885 | \n",
" 1 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 491 | \n",
"
\n",
"\n",
" num_images | \n",
" 10 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 0.9865 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Label | \n",
"
\n",
"\n",
" 0.694068 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.90516 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.921454 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.943429 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.944783 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.944897 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.958016 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.969519 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.974236 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.976503 | \n",
" 1 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 185 | \n",
"
\n",
"\n",
" num_images | \n",
" 10 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 0.9604 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Label | \n",
"
\n",
"\n",
" 0.898507 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.899538 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.905345 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.905881 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.917017 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.921527 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.940975 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.984398 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.986932 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.987669 | \n",
" 1 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 29 | \n",
"
\n",
"\n",
" num_images | \n",
" 9 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 0.9614 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Label | \n",
"
\n",
"\n",
" 0.897021 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.915021 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.917719 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.918148 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.935715 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.940089 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.951066 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.980969 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.982242 | \n",
" 1 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 106 | \n",
"
\n",
"\n",
" num_images | \n",
" 8 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 0.9633 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Label | \n",
"
\n",
"\n",
" 0.89313 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.922129 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.930321 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.951415 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.953237 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.957237 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.993251 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.996365 | \n",
" 1 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 62 | \n",
"
\n",
"\n",
" num_images | \n",
" 8 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 0.9635 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Label | \n",
"
\n",
"\n",
" 0.639288 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.910821 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.915858 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.918426 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.93316 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.934579 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.949467 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.968144 | \n",
" 1 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 490 | \n",
"
\n",
"\n",
" num_images | \n",
" 8 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 0.9808 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Label | \n",
"
\n",
"\n",
" 0.872586 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.905988 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.929356 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.930516 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.946273 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.950314 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.950973 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.957764 | \n",
" 1 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 455 | \n",
"
\n",
"\n",
" num_images | \n",
" 8 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 0.9603 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Label | \n",
"
\n",
"\n",
" 0.7064 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.90828 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.927472 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.931385 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.937934 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.953506 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.982314 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.993107 | \n",
" 1 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 27 | \n",
"
\n",
"\n",
" num_images | \n",
" 7 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 0.9668 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Label | \n",
"
\n",
"\n",
" 0.903862 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.910805 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.912512 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.912723 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.916848 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.930749 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.960439 | \n",
" 1 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 620 | \n",
"
\n",
"\n",
" num_images | \n",
" 7 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 0.9652 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Label | \n",
"
\n",
"\n",
" 0.89443 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.911825 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.913297 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.917963 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.955546 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.960902 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.97509 | \n",
" 1 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 119 | \n",
"
\n",
"\n",
" num_images | \n",
" 7 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 0.9606 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Label | \n",
"
\n",
"\n",
" 0.882049 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.888761 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.911282 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.914209 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.937742 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.950713 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.965892 | \n",
" 1 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 26 | \n",
"
\n",
"\n",
" num_images | \n",
" 7 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 0.9764 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Label | \n",
"
\n",
"\n",
" 0.897349 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.901716 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.930532 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.93272 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.932828 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.941728 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.94206 | \n",
" 1 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 492 | \n",
"
\n",
"\n",
" num_images | \n",
" 6 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 0.9629 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Label | \n",
"
\n",
"\n",
" 0.876738 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.906598 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.936391 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.949162 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.951972 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.959641 | \n",
" 1 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 619 | \n",
"
\n",
"\n",
" num_images | \n",
" 6 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 0.9776 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Label | \n",
"
\n",
"\n",
" 0.889022 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.893933 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.90888 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.928806 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.937852 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.950581 | \n",
" 1 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 148 | \n",
"
\n",
"\n",
" num_images | \n",
" 6 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 0.9956 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Label | \n",
"
\n",
"\n",
" 0.940358 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.942999 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.943252 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.945848 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.945968 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.967571 | \n",
" 1 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Info | \n",
"
\n",
"\n",
" component | \n",
" 621 | \n",
"
\n",
"\n",
" num_images | \n",
" 6 | \n",
"
\n",
"\n",
" mean_distance | \n",
" 0.9836 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" Label | \n",
"
\n",
"\n",
" 0.921039 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.95587 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.966351 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.97009 | \n",
" 1 | \n",
"
\n",
"\n",
" 0.970162 | \n",
" 1 | \n",
"
\n",
"\n",
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" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fd.vis.component_gallery()"
]
},
{
"cell_type": "markdown",
"id": "8038c705",
"metadata": {},
"source": [
"## Similar Detections"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "333568b4",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 371.96it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Stored similar images visual view in work_dir/galleries/similarity.html\n"
]
},
{
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"
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" 0.917096 | \n",
" /crops/tmpvideos6877178763474423041.mp4output_000002.jpg_467_945_560_949_559_975_466_971.jpg | \n",
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"
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" /crops/tmpvideos6877411003160694017.mp4output_000002.jpg_429_944_561_942_561_975_429_977.jpg | \n",
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" /crops/tmpvideos6877179236386376961.mp4output_000010.jpg_388_985_562_989_562_1010_387_1007.jpg | \n",
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" /crops/tmpvideos6875317312082201857.mp4output_000001.jpg_13_67_202_69_202_88_13_85.jpg | \n",
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" 0.959249 | \n",
"
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" /crops/tmpvideos6878259877353966850.mp4output_000001.jpg_12_62_100_66_99_83_12_79.jpg | \n",
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" /crops/tmpvideos6876262384093236485.mp4output_000001.jpg_11_62_133_65_133_83_11_80.jpg | \n",
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"
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" 0.901708 | \n",
" /crops/tmpvideos6876674985746795778.mp4output_000007.jpg_456_987_564_986_564_1005_456_1006.jpg | \n",
" 0.981889 | \n",
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" 0.839425 | \n",
"
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" /crops/tmpvideos6875405441472498949.mp4output_000006.jpg_428_944_561_942_561_975_428_977.jpg | \n",
"
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" 0.916229 | \n",
" /crops/tmpvideos6875405441472498949.mp4output_000007.jpg_458_944_560_946_560_976_457_974.jpg | \n",
" 0.824197 | \n",
"
\n",
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" 0.902018 | \n",
" /crops/tmpvideos6876369534018669825.mp4output_000006.jpg_434_943_561_943_561_976_434_976.jpg | \n",
" 0.804691 | \n",
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"\t\t\t\t\t\t\t\t\t\tSimilar | \n",
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"\t\t\t\t\t\t\t\t\t\t | \n",
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" label | \n",
" 0.647072 | \n",
"
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"\n",
" from | \n",
" /crops/tmpvideos6877251331145370881.mp4output_000002.jpg_38_16_60_38_34_65_12_43.jpg | \n",
"
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" 0.902097 | \n",
" /crops/tmpvideos6876145412105899265.mp4output_000001.jpg_31_16_57_29_41_62_15_48.jpg | \n",
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"\t\t\t\t\t\t\t\t\t\t | \n",
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"\t\t\t\t\t\t\t\t\t\tSimilar | \n",
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"\t\t\t\t\t\t\t\t\t\t | \n",
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" label | \n",
" 0.536829 | \n",
"
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"\n",
" from | \n",
" /crops/tmpvideos6876145412105899265.mp4output_000001.jpg_31_16_57_29_41_62_15_48.jpg | \n",
"
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" 0.90959 | \n",
" /crops/tmpvideos6876369534018669825.mp4output_000005.jpg_438_941_461_949_453_977_429_969.jpg | \n",
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\n",
"\n",
" 0.902097 | \n",
" /crops/tmpvideos6877251331145370881.mp4output_000002.jpg_38_16_60_38_34_65_12_43.jpg | \n",
" 0.647072 | \n",
"
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"\t\t\t\t\t\t\t\t\t\tQuery Image | \n",
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\n",
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"\t\t\t\t\t\t\t\t\t\t | \n",
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"\t\t\t\t\t\t\t\t\t\tSimilar | \n",
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\n",
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"\t\t\t\t\t\t\t\t\t\t | \n",
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" label | \n",
" 0.972871 | \n",
"
\n",
"\n",
" from | \n",
" /crops/tmpvideos6876369534018669825.mp4output_000002.jpg_342_752_442_719_447_732_346_766.jpg | \n",
"
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"
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"\n",
" 0.90236 | \n",
" /crops/tmpvideos6876369534018669825.mp4output_000002.jpg_130_703_319_656_322_670_133_717.jpg | \n",
" 0.931903 | \n",
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"\t\t\t\t\t\t\t\t\t\tQuery Image | \n",
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\n",
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"\t\t\t\t\t\t\t\t\t\t | \n",
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"\t\t\t\t\t\t\t\t\t\tSimilar | \n",
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\n",
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"\t\t\t\t\t\t\t\t\t\t | \n",
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" label | \n",
" 0.931903 | \n",
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\n",
"\n",
" from | \n",
" /crops/tmpvideos6876369534018669825.mp4output_000002.jpg_130_703_319_656_322_670_133_717.jpg | \n",
"
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"
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"\n",
" 0.90236 | \n",
" /crops/tmpvideos6876369534018669825.mp4output_000002.jpg_342_752_442_719_447_732_346_766.jpg | \n",
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"\t\t\t\t\t\t\t\t\t\tQuery Image | \n",
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\n",
"\t\t\t\t\t\t\t\t\t\n",
"\t\t\t\t\t\t\t\t\t\t | \n",
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"\t\t\t\t\t\t\t\t\t\tSimilar | \n",
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"\t\t\t\t\t\t\t\t\t\t | \n",
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" label | \n",
" 0.908666 | \n",
"
\n",
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\n",
" \n",
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\n",
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822 rows × 5 columns
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"
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" from to label label2 distance\n",
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".. ... ... ... ... ...\n",
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"\n",
"[822 rows x 5 columns]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fd.vis.similarity_gallery()"
]
},
{
"cell_type": "markdown",
"id": "f7134521",
"metadata": {},
"source": [
"## Wrap Up\n",
"\n",
"In this notebook we show how you can run OCR models with fastdup and analyze the bounding boxes for issues.\n",
"\n",
"Next, feel free to check out other tutorials -\n",
"\n",
"+ ⚡ [**Quickstart**](https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/quick-dataset-analysis.ipynb): Learn how to install fastdup, load a dataset and analyze it for potential issues such as duplicates/near-duplicates, broken images, outliers, dark/bright/blurry images, and view visually similar image clusters. If you're new, start here!\n",
"+ 🧹 [**Clean Image Folder**](https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/cleaning-image-dataset.ipynb): Learn how to analyze and clean a folder of images from potential issues and export a list of problematic files for further action. If you have an unorganized folder of images, this is a good place to start.\n",
"+ 🖼 [**Analyze Image Classification Dataset**](https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/analyzing-image-classification-dataset.ipynb): Learn how to load a labeled image classification dataset and analyze for potential issues. If you have labeled ImageNet-style folder structure, have a go!\n",
"+ 🎁 [**Analyze Object Detection Dataset**](https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/analyzing-object-detection-dataset.ipynb): Learn how to load bounding box annotations for object detection and analyze for potential issues. If you have a COCO-style labeled object detection dataset, give this example a try. "
]
},
{
"cell_type": "markdown",
"id": "2c8db74e",
"metadata": {},
"source": [
"\n",
"# VL Profiler\n",
"If you prefer a no-code platform to inspect and visualize your dataset, [**try our free cloud product VL Profiler**](https://app.visual-layer.com) - VL Profiler is our first no-code commercial product that lets you visualize and inspect your dataset in your browser. \n",
"\n",
"[Sign up](https://app.visual-layer.com) now, it's free.\n",
"\n",
"[![image](https://raw.githubusercontent.com/visual-layer/fastdup/main/gallery/vl_profiler_promo.svg)](https://app.visual-layer.com)\n",
"\n",
"As usual, feedback is welcome! \n",
"\n",
"Questions? Drop by our [Slack channel](https://visualdatabase.slack.com/join/shared_invite/zt-19jaydbjn-lNDEDkgvSI1QwbTXSY6dlA#/shared-invite/email) or open an issue on [GitHub](https://github.com/visual-layer/fastdup/issues)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b862206d",
"metadata": {},
"outputs": [],
"source": []
}
],
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"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": "3.10.11"
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