一种基于OpenCV的图片倾斜矫正方法

需求描述：

对倾斜的图片进行矫正，返回倾斜角度和矫正后的图片。

解决方法：

1、各种角度点被投影到一个累加器阵列中，其中倾斜角度可以定义为在最大化对齐的搜索间隔内的投影角度。

2、以不同的角度旋转图像，并为每次迭代生成像素的直方图。

3、为了确定倾斜角度，比较峰值之间的最大差异，并使用这个倾斜角度，旋转图像来纠正倾斜。

#coding=utf-8
import cv2
import numpy as np

def rotate_image(image, angle):
    (h, w) = image.shape[: 2]
    center = (w // 2, h // 2)
    M = cv2.getRotationMatrix2D(center, angle, 1.0)
    corrected = cv2.warpAffine(image, M, (w, h), flags = cv2.INTER_CUBIC, \
        borderMode = cv2.BORDER_REPLICATE)
    return corrected

def determine_score(arr):
     histogram = np.sum(arr, axis = 2, dtype = float)
     score = np.sum((histogram[..., 1 :] - histogram[..., : -1]) ** 2, \
        axis = 1, dtype = float)
     return score

def correct_skew(image, delta = 0.1, limit = 5):
     thresh = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY_INV + \
        cv2.THRESH_OTSU)[1]
     angles = np.arange(-limit, limit + delta, delta)
     img_stack = np.stack([rotate_image(thresh, angle) for angle \
        in angles], axis = 0)
     scores = determine_score(img_stack)
     best_angle = angles[np.argmax(scores)]
     corrected = rotate_image(image, best_angle)
     return best_angle, corrected
if __name__ == "__main__":
    file_path=r'D:/_21.png'
    img = cv2.imread(file_path, 0)
    angle, corrected = correct_skew(img)
    print(angle)
    cv2.imwrite(r'D:/temp_' + file_path.split('/')[-1], corrected)