利用python进行图片的位置矫正

利用python进行图片的位置矫正，为什么会出现这样的错误

img



```python
import cv2
import numpy as np

def Img_Outline(input_dir):
    original_img = cv2.imread(input_dir)
    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, gray_img, RedThresh, closed, opened


def findContours_img(original_img, opened):
    image, contours, hierarchy = cv2.findContours(opened, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
    c = sorted(contours, key=cv2.contourArea, reverse=True)[1]          # 计算最大轮廓的旋转包围盒
    rect = cv2.minAreaRect(c)
    angle = rect[2]
    print("angle",angle)
    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__":
    input_dir = "qingxie.PNG"
    original_img, gray_img, RedThresh, closed, opened = Img_Outline(input_dir)
    result_img,draw_img = findContours_img(original_img,opened)

    cv2.imshow("original_img", original_img)
    cv2.imshow("gray_img", gray_img)
    cv2.imshow("RedThresh", RedThresh)
    cv2.imshow("Close", closed)
    cv2.imshow("Open", opened)
    cv2.imshow("draw_img", draw_img)
    cv2.imshow("result_img", result_img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

```


import cv2
import numpy as np
 
def Img_Outline(input_dir):
    original_img = cv2.imread(input_dir)
    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, gray_img, RedThresh, closed, opened
 
 
def findContours_img(original_img, opened):
    contours, hierarchy = cv2.findContours(opened, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[-2:]  # 处理返回值，获取轮廓信息
    c = sorted(contours, key=cv2.contourArea, reverse=True)[1]          # 计算最大轮廓的旋转包围盒
    rect = cv2.minAreaRect(c)
    angle = rect[2]
    print("angle",angle)
    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__":
    input_dir = "qingxie.PNG"
    original_img, gray_img, RedThresh, closed, opened = Img_Outline(input_dir)
    result_img,draw_img = findContours_img(original_img,opened)
 
    cv2.imshow("original_img", original_img)
    cv2.imshow("gray_img", gray_img)
    cv2.imshow("RedThresh", RedThresh)
    cv2.imshow("Close", closed)
    cv2.imshow("Open", opened)
    cv2.imshow("draw_img", draw_img)
    cv2.imshow("result_img", result_img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

试试这个行不行，可以的话给个采纳👀👀