Python——基于OpenCV获取倾斜子图的一种方法

小鱼儿 2023-01-11 03:52 60阅读 0赞

# 解决方案  #

#!usr/bin/env python
    # -*- coding:utf-8 _*-
    """
    @version: 0.0.1
    author: ShenTuZhiGang
    @time: 2021/01/25 22:14
    @file: imageutil.py
    @function:
    @modify:
    """
    import cv2
    from math import *
    from PIL import Image
    import numpy as np
    def get_sub_image(image, box, adjust=False):
        """
        获取倾斜矫正后的子图
        :param image:
        :param box:
        :param adjust:
        :return:
        """
        height, width = image.shape[:2]
        x_length = int((box[6] - box[0]) * 0.1)
        y_length = int((box[7] - box[1]) * 0.2)
        if adjust:
            pt1 = (max(1, box[0] - x_length), max(1, box[1] - y_length))
            pt2 = (box[2], box[3])
            pt3 = (min(box[6] + x_length, width - 2),
                   min(height - 2, box[7] + y_length))
            pt4 = (box[4], box[5])
        else:
            pt1 = (max(1, box[0]), max(1, box[1]))
            pt2 = (box[2], box[3])
            pt3 = (min(box[6], width - 2), min(height - 2, box[7]))
            pt4 = (box[4], box[5])
        # 图像倾斜角度
        degree = degrees(atan2(pt2[1] - pt1[1], pt2[0] - pt1[0]))
        # 倾斜矫正
        height, width = image.shape[:2]
        height_new = int(width * fabs(sin(radians(degree))) +
                         height * fabs(cos(radians(degree))))
        width_new = int(height * fabs(sin(radians(degree))) +
                        width * fabs(cos(radians(degree))))
        mat_rotation = cv2.getRotationMatrix2D((width / 2, height / 2), degree, 1)
        mat_rotation[0, 2] += (width_new - width) / 2
        mat_rotation[1, 2] += (height_new - height) / 2
        img_rotation = cv2.warpAffine(
            image, mat_rotation, (width_new, height_new), borderValue=(255, 255, 255))
        pt1 = list(pt1)
        pt3 = list(pt3)
    
        [[pt1[0]], [pt1[1]]] = np.dot(mat_rotation,
                                      np.array([[pt1[0]], [pt1[1]], [1]]))
        [[pt3[0]], [pt3[1]]] = np.dot(mat_rotation,
                                      np.array([[pt3[0]], [pt3[1]], [1]]))
        y_dim, x_dim = img_rotation.shape[:2]
        img_out = img_rotation[max(1, int(pt1[1])):min(y_dim - 1, int(pt3[1])),
                  max(1, int(pt1[0])):min(x_dim - 1, int(pt3[0]))]
        # height,width=imgOut.shape[:2]
        return img_out

# 参考文章 #