opencv实现图像校正_python_

import cv2 import numpy as np import imutils import time def Img_Outline(img_path):     original_img = cv2.imread(img_path)     gray_img = cv2.cvtColor(original_img, cv2.COLOR_BGR2GRAY)     blurred = cv2.GaussianBlur(gray_img, (9, 9), 0)                     # 高斯模糊去噪（设定卷积核大小影响效果）     _, RedThresh = cv2.threshold(blurred, 165, 255, cv2.THRESH_BINARY)  # 设定阈值165（阈值影响开闭运算效果）     kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))          # 定义矩形结构元素     closed = cv2.morphologyEx(RedThresh, cv2.MORPH_CLOSE, kernel)       # 闭运算（链接块）     opened = cv2.morphologyEx(closed, cv2.MORPH_OPEN, kernel)           # 开运算（去噪点）     return original_img, opened def findContours_img(original_img, opened):     contours = cv2.findContours(opened, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)     cnts = imutils.grab_contours(contours)     # print(cnts)     # c = sorted(cnts, key=cv2.contourArea, reverse=True)[0]          # 计算最大轮廓的旋转包围盒     c = max(cnts, key=cv2.contourArea)     rect = cv2.minAreaRect(c)     # print(rect)     angle = rect[2]  # rect[2] 返回的是矩形的旋转角度     print("angle", angle)     if angle == 90.0:         return original_img, original_img     else:         box = np.int0(cv2.boxPoints(rect))         draw_img = cv2.drawContours(original_img.copy(), [box], -1, (0, 0, 255), 3)         rows, cols = original_img.shape[:2]         M = cv2.getRotationMatrix2D((cols / 2, rows / 2), angle, 1)         result_img = cv2.warpAffine(original_img, M, (cols, rows))         return result_img,draw_img if __name__ == "__main__":     img_path = './result.jpg'     start_time = time.time()     original_img, opened = Img_Outline(img_path)     result_img,draw_img = findContours_img(original_img,opened)     print('消耗的时间为：',(time.time() - start_time))     cv2.imshow("original_img", original_img)     cv2.imshow("draw_img", draw_img)     cv2.imshow("result_img", result_img)     cv2.waitKey(0)     cv2.destroyAllWindows()