自学教程：python opencv 找出图像中的最大轮廓并填充(生成mask)

import cv2import numpy as npfrom PIL import Image from joblib import Parallelfrom joblib import delayed# Parallel 和 delayed是为了使用多线程处理# 使用前需要安装joblib：pip install joblib # img_stack的shape为：num, h, w# 是三维的图像，可以理解为是num张二维的图像组成# mask是用来保存最后的结果的mask = np.ones_like(img_stack)for i in range(num):  # 阈值化  _, binaryzation = cv2.threshold(img_stack[i], 5, 255, cv2.THRESH_BINARY_INV)  # 找到所有的轮廓  contours, _ = cv2.findContours(binaryzation, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)  area = []  # 找到最大的轮廓  for k in range(len(contours)):    area.append(cv2.contourArea(contours[k]))  max_idx = np.argmax(np.array(area))  # cv2.fillContexPoly(mask[i], contours[max_idx], 0)  # 填充最大的轮廓  cv2.drawContours(mask[i], contours, max_idx, 0, cv2.FILLED)  del area   # 保存def _write_mask(mask, i):  Image.fromarray(mask.astype(np.uint8)*255).save(os.path.join(path, 'm%d.png' % i)) # 使用多线程进行保存num_cores = 10parallel = Parallel(n_jobs=num_cores, backend='threading')parallel(delayed(_write_mask)(mask[i, :, :], i) for i in range(0, num))

import cv2import numpy as np # 以灰度方式读取图像img = cv2.imread('img.png', cv2.IMREAD_GRAYSCALE)mask = img.copy() # 二值化，100为阈值，小于100的变为255，大于100的变为0# 也可以根据自己的要求，改变参数：# cv2.THRESH_BINARY# cv2.THRESH_BINARY_INV# cv2.THRESH_TRUNC# cv2.THRESH_TOZERO_INV# cv2.THRESH_TOZERO_, binaryzation = cv2.threshold(img, 100, 255, cv2.THRESH_BINARY_INV) # 找到所有的轮廓contours, _ = cv2.findContours(binaryzation, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) area = [] # 找到最大的轮廓for k in range(len(contours)):	area.append(cv2.contourArea(contours[k]))max_idx = np.argmax(np.array(area)) # 填充最大的轮廓mask = cv2.drawContours(mask, contours, max_idx, 0, cv2.FILLED) # 保存填充后的图像cv2.imwrite('masked.png', mask)