深度学习之图像分割数据集：大型遥感道路图像分割数据集（测试集）

import cv2 import numpy as np import os import random from PIL import Image import matplotlib.pyplot as plt # 将mask绘制在原图 def draw_image(im,ms,brg,opacity): image_mask = im.copy() contours, _ = cv2.findContours(ms, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) # 查找轮廓 image_mask = cv2.drawContours(image_mask, contours, -1, (0, 255, 0), 2) # 绘制边界 image_mask = cv2.fillPoly(image_mask, contours, color=brg) # 填充 img_bgr = cv2.addWeighted(im, opacity, image_mask, 1 - opacity, 0) return im,ms,img_bgr def main(imagePath,labelPath,bgr,opacity): image = np.array(Image.open(imagePath).convert('RGB')) label = np.array(Image.open(labelPath).convert('L')) a,b,c = draw_image(image,label,bgr,opacity) plt.figure(figsize=(12,8)) for index,i in enumerate((a,b,c)): plt.subplot(1,3,index+1) plt.imshow(i) plt.savefig('./result.png') # plt.show() if __name__ == '__main__': root = './data/test/images' images_path = [os.path.join(root,i) for i in os.listdir(root)] r = random.randint(0,len(images_path)-1) img_path = images_path[r] # 随机取出一张图片 mask_path = img_path.replace('images','masks') mask_path = mask_path.replace('.jpg','.png') # opacity 越小，掩膜效果越深 main(imagePath=img_path, labelPath=mask_path,bgr=(0,0,255),opacity=0.5)