opencv实现图像校正

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()