本文实例为大家分享了opencv实现图像校正的具体代码，供大家参考，具体内容如下
1.引言：python实现倾斜图像校正操作
2.思路流程：
（1）读入，灰度化；
（2）高斯模糊；
（3）二值化图像；
（4）闭开操作；
（5）获取图像顶点；
（6）旋转校正
3.实现代码：
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.webp'
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()
4.效果展示：
原图
标框出图
旋转后的图
