131 lines
3.2 KiB
Plaintext
131 lines
3.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "82e802bd",
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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 cv2 as cv\n",
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"import json"
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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": 2,
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"id": "5be9c4de",
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"metadata": {},
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"outputs": [],
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"source": [
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"img = cv.imread(r\"D:\\lzy\\yolov5\\yolov5\\data_org\\yolo_dataset\\test\\images\\9_jpg.rf.eb8e232922688878a850763f2127e8fd.jpg\")\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": 3,
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"id": "9ea190dd",
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"metadata": {},
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"outputs": [],
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"source": [
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"cv.imshow(\"img\", img)\n",
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"if cv.waitKey(0) == ord(\"q\"):\n",
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" cv.destroyAllWindows()"
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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": 4,
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"id": "335bf1ae",
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"metadata": {},
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"outputs": [],
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"source": [
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"img_folder = r\"D:\\lzy\\yolov5\\yolov5\\data_org\\yolo_dataset\\train\\images\"\n",
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"label_path = r\"D:\\lzy\\yolov5\\yolov5\\data_org\\dataset\\train\\_annotations.coco.json\""
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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": 5,
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"id": "b8fe5313",
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"metadata": {},
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"outputs": [],
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"source": [
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"img_path_list = os.listdir(img_folder)\n",
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"\n",
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"for img_name in img_path_list:\n",
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" with open(os.path.join(img_folder.replace(\"images\", \"labels\"), img_name.replace(\".jpg\", \".txt\")), \"w\") as f:\n",
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" pass"
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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": 6,
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"id": "101e48e9",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 读取文件\n",
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"with open(label_path, 'r') as f:\n",
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" content = json.load(f)\n",
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"pass"
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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": 7,
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"id": "6edd8ee3",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 创建一个id到图片名称的映射，以字典形式存储\n",
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"dict_id2img = dict()\n",
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"for img_info in content['images']:\n",
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" dict_id2img[img_info['id']] = img_info['file_name']\n",
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"pass"
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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": 8,
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"id": "a9923894",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 开始读取标签信息，处理后写入txt文件\n",
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"for label_info in content['annotations']:\n",
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" img_name = dict_id2img[label_info['image_id']]\n",
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" class_name = label_info['category_id']\n",
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" x_center = (label_info['bbox'][0] + label_info['bbox'][2] / 2) / 640\n",
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" y_center = (label_info['bbox'][1] + label_info['bbox'][3] / 2) / 640\n",
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" w = label_info['bbox'][1] / 640\n",
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" h = label_info['bbox'][3] / 640\n",
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" with open(os.path.join(img_folder.replace(\"images\", \"labels\"), img_name.replace(\".jpg\", \".txt\")), 'a') as f:\n",
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" f.write(\"{} {} {} {} {}\\n\".format(class_name, x_center, y_center, w, h))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "yolov5",
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"language": "python",
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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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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.9"
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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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