YOLO数据集制作（二）|json文件转txt验证

 以下教程用于验证转成YOLO使用的txt格式，适用场景：矩形框，配合json格式文件转成YOLO使用的txt格式脚本使用。

https://blog.csdn.net/StopAndGoyyy/article/details/138681454

使用方式：将img_path和label_path分别填入对应的图片（文件夹）及标签（文件夹）路径，运行。show_num参数控制最大展示数量，按空格切换。

import os
import numpy as np
import cv2img_format = ['.jpg', '.png', '.jpeg']
colors = [(0, 0, 0), (128, 0, 0), (0, 128, 0), (128, 128, 0), (0, 0, 128), (128, 0, 128), (0, 128, 128),(128, 128, 128), (64, 0, 0), (192, 0, 0), (64, 128, 0), (192, 128, 0), (64, 0, 128), (192, 0, 128),(64, 128, 128), (192, 128, 128), (0, 64, 0), (128, 64, 0), (0, 192, 0), (128, 192, 0), (0, 64, 128),(128, 64, 12)]def get_files(img_path, label_path):img_list = []label_list = []datast_img_format = Noneassert os.path.exists(img_path) and os.path.exists(label_path), print("⭐⭐文件夹不存在⭐⭐")if os.path.isdir(img_path):for i in os.listdir(img_path):if os.path.splitext(i)[-1] in img_format:# i = i.replace(os.path.splitext(i)[-1], '')img_list.append(i)datast_img_format=img_list[-1].split('.')[-1]if os.path.isdir(label_path):for i in os.listdir(label_path):if os.path.splitext(i)[-1] == '.txt':i = i.replace(os.path.splitext(i)[-1], '')label_list.append(i)print("路径下无jpg,png,jpeg格式的图片,当前图像路径:" + str(img_path) if len(img_list) == 0 else "图像总数为：" + str(len(img_list)))print("路径下无标签文件,当前标签路径" + str(label_path) if len(label_list) == 0 else "标签总数为：" + str(len(label_list)))img_without_label = []for i in img_list:if i.replace(os.path.splitext(i)[-1], '') not in label_list:img_without_label.append(i)if len(img_without_label) != 0:print("标签丢失的图像有：" + str(img_without_label))ok_img = list(set(img_list) - set(img_without_label))return [i.replace('.jpg', '') for i in ok_img], '.'+datast_img_format# 坐标转换
def xywh2xyxy(x, w1, h1, img):label, x, y, w, h = xx_t = x * w1y_t = y * h1w_t = w * w1h_t = h * h1top_left_x = x_t - w_t / 2top_left_y = y_t - h_t / 2bottom_right_x = x_t + w_t / 2bottom_right_y = y_t + h_t / 2cv2.rectangle(img, (int(top_left_x), int(top_left_y)), (int(bottom_right_x), int(bottom_right_y)),colors[int(label)] if int(label) < len(colors) else colors[0], 2)cv2.putText(img, text=str(int(label)), org=(int(top_left_x), int(top_left_y)), fontFace=cv2.FONT_HERSHEY_COMPLEX,fontScale=1, color=colors[int(label)] if int(label) < len(colors) else colors[0], thickness=3)return imgif __name__ == '__main__':# 修改输入图片文件夹img_path = r"O:\DeepLearningTool\01_handle_dataset\dataset\object\image"# img_path = r"O:\DeepLearningTool\dataset\image"# 修改输入标签文件夹label_path = r"O:\DeepLearningTool\01_handle_dataset\dataset\object\label"# label_path = r"O:\DeepLearningTool\dataset\label"# 输出文件夹outfile = './'# 是否展示绘制的图片if_show = True# 最大展示图片的数量(按空格切换)show_num = 3# 是否保存绘制的图片if_save = Falseif os.path.isdir(img_path):ok_img, datast_img_format = get_files(img_path, label_path)haveShow = 0for i in ok_img:assert os.path.exists(str(img_path) + '\\' + i + datast_img_format)img = cv2.imread(str(img_path) + '\\' + i + datast_img_format)h, w = img.shape[:2]with open(label_path + '\\' + i + '.txt', 'r') as f:lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32)for x in lb:img = xywh2xyxy(x, w, h, img)if if_show:cv2.namedWindow('Image with label', 0)cv2.resizeWindow('Image with label', 600, 500)cv2.imshow('Image with label', img)cv2.waitKey(0)if if_save:outfile = outfile if len(outfile) > 0 else './output/'if not os.path.exists('./output/'):os.mkdir('./output/')print(outfile + i)cv2.imwrite(outfile+i+datast_img_format, img)haveShow += 1if haveShow == show_num:breakelse:img = cv2.imread(str(img_path))h, w = img.shape[:2]assert os.path.isfile(label_path), '标签路径错误'with open(label_path) as f:lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32)for x in lb:img = xywh2xyxy(x, w, h, img)if if_show:cv2.namedWindow('Image with label', 0)cv2.resizeWindow('Image with label', 600, 500)cv2.imshow('Image with label', img)cv2.waitKey(0)