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Consistency_Annotation/test.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "5bdfe87c",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import json\n",
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"from itertools import combinations\n",
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"IMAGE_DIR = \"/root/siton-tmp/images_divided\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fb8c9036",
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"metadata": {},
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"outputs": [],
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"source": [
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"folders = sorted([\n",
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" f for f in os.listdir(IMAGE_DIR)\n",
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" if os.path.isdir(os.path.join(IMAGE_DIR, f)) and not f.startswith('.')\n",
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"])\n",
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"data = []\n",
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"for cnt, idx in enumerate(folders):\n",
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" folder_path = os.path.join(IMAGE_DIR, idx)\n",
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" images = sorted([img for img in os.listdir(folder_path) if img.endswith('.png')])\n",
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" first_indices, second_indices = zip(*[\n",
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" list(map(int, img.split('.')[0].split('_')))\n",
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" for img in images\n",
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" ])\n",
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" first_indices = sorted(set(first_indices))\n",
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" second_indices = sorted(set(second_indices))\n",
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" \n",
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" for i in first_indices:\n",
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" for j in second_indices:\n",
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" ref_img = f\"{i}_{j}.png\"\n",
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" candidates = [\n",
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" [\n",
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" f\"{x}_{y}.png\"\n",
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" for x in first_indices\n",
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" ]\n",
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" for y in second_indices if y != j\n",
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" ]\n",
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" items = [\n",
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" {\n",
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" 'ref_image': ref_img,\n",
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" 'rank_images': cand,\n",
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" 'idx': idx,\n",
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" } for cand in candidates\n",
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" ]\n",
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" data.extend(items)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "36e0603b",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[{'ref_image': '0_0.png',\n",
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" 'rank_images': ['0_1.png', '1_1.png', '2_1.png', '3_1.png'],\n",
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" 'idx': '0000'},\n",
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| 70 |
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" {'ref_image': '0_0.png',\n",
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| 71 |
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" 'rank_images': ['0_2.png', '1_2.png', '2_2.png', '3_2.png'],\n",
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| 72 |
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" 'idx': '0000'},\n",
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| 73 |
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" {'ref_image': '0_1.png',\n",
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| 74 |
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" 'rank_images': ['0_0.png', '1_0.png', '2_0.png', '3_0.png'],\n",
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" 'idx': '0000'},\n",
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| 76 |
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" {'ref_image': '0_1.png',\n",
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| 77 |
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" 'rank_images': ['0_2.png', '1_2.png', '2_2.png', '3_2.png'],\n",
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| 78 |
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" 'idx': '0000'},\n",
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| 79 |
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" {'ref_image': '0_2.png',\n",
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| 80 |
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" 'rank_images': ['0_0.png', '1_0.png', '2_0.png', '3_0.png'],\n",
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| 81 |
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" 'idx': '0000'},\n",
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| 82 |
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" {'ref_image': '0_2.png',\n",
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| 83 |
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" 'rank_images': ['0_1.png', '1_1.png', '2_1.png', '3_1.png'],\n",
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| 84 |
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" 'idx': '0000'},\n",
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| 85 |
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" {'ref_image': '1_0.png',\n",
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| 86 |
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" 'rank_images': ['0_1.png', '1_1.png', '2_1.png', '3_1.png'],\n",
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| 87 |
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" 'idx': '0000'},\n",
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| 88 |
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" {'ref_image': '1_0.png',\n",
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| 89 |
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" 'rank_images': ['0_2.png', '1_2.png', '2_2.png', '3_2.png'],\n",
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| 90 |
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" 'idx': '0000'},\n",
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| 91 |
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" {'ref_image': '1_1.png',\n",
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| 92 |
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" 'rank_images': ['0_0.png', '1_0.png', '2_0.png', '3_0.png'],\n",
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| 93 |
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" 'idx': '0000'},\n",
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| 94 |
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" {'ref_image': '1_1.png',\n",
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| 95 |
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" 'rank_images': ['0_2.png', '1_2.png', '2_2.png', '3_2.png'],\n",
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" 'idx': '0000'}]"
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| 97 |
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]
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| 98 |
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},
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| 99 |
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"execution_count": 19,
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| 100 |
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"metadata": {},
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| 101 |
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"output_type": "execute_result"
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}
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],
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"source": [
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"data[:10]"
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]
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}
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],
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"metadata": {
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| 110 |
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"kernelspec": {
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| 111 |
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"display_name": "pytorch-env",
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| 112 |
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"language": "python",
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| 113 |
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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| 117 |
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"name": "ipython",
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| 118 |
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"version": 3
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| 119 |
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},
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"file_extension": ".py",
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| 121 |
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"mimetype": "text/x-python",
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| 122 |
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"name": "python",
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| 123 |
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"nbconvert_exporter": "python",
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| 124 |
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"pygments_lexer": "ipython3",
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| 125 |
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"version": "3.12.11"
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| 126 |
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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