使用opencv截取旋转框目标

• 1、第一种方法
• 2、第二种方法
• 3、两种方法的简单对比
• 4、opencv 最小面积矩形返回角度的理解
• • 4.1、version4.2之前
  • 4.1、version4.2之后

标注文件有四个点的坐标：

1、第一种方法

总体思路是，找最小面积矩形，接着旋转，最后crop

import cv2
import numpy as np
import matplotlib.pyplot as pltdef crop_rect(img, rect):# get the parameter of the small rectanglecenter, size, angle = rect[0], rect[1], rect[2]center, size = tuple(map(int, center)), tuple(map(int, size))# get row and col num in imgheight, width = img.shape[0], img.shape[1]# calculate the rotation matrixM = cv2.getRotationMatrix2D(center, angle, 1)# rotate the original imageimg_rot = cv2.warpAffine(img, M, (width, height))# now rotated rectangle becomes vertical, and we crop itimg_crop = cv2.getRectSubPix(img_rot, size, center)return img_crop, img_rotcnts = []
labels=[]
with open('rodog.txt') as f:lines = f.read().strip().splitlines()
for line in lines:data = line.split()cnt=list(map(int,data[:8]))label=data[8]cnts.append(cnt)labels.append(label)num=len(cnts)
plt.figure(figsize=(15,num*5))
for i,cnt in enumerate(cnts):img = cv2.imread("rodog.jpeg")cnt = np.reshape(cnt,[4,2])# print("cnt:",cnt)# find the exact rectangle enclosing the text area# rect is a tuple consisting of 3 elements: the first element is the center# of the rectangle, the second element is the width, height, and the# third element is the detected rotation angle.# Example output: ((227.5, 187.50003051757812),# (94.57575225830078, 417.98736572265625), -36.982906341552734)rect = cv2.minAreaRect(cnt)print("rect: {}".format(rect))# the order of the box points: bottom left, top left, top right,box = cv2.boxPoints(rect)box = np.int0(box)print("box:",box)# print("bounding box: {}".format(box))cv2.drawContours(img, [box], 0, (255, 0, 0), 2)# img_crop will the cropped rectangle, img_rot is the rotated imageimg_crop, img_rot = crop_rect(img, rect)plt.subplot(num,3,1+i*3)plt.imshow(img)plt.title('orig')plt.subplot(num,3,2+i*3)plt.imshow(img_rot)plt.title('rotate')plt.subplot(num,3,3+i*3)plt.imshow(img_crop)plt.title('crop')# cv2.imwrite(f"orig_img_{i}.jpg", img)# cv2.imwrite(f"rotate_img_{i}.jpg", img_rot)# cv2.imwrite(f"cropped_img_{i}.jpg", img_crop)# cv2.waitKey(0)

rect: ((370.5, 164.50001525878906), (140.75865173339844, 306.3152160644531), 43.54475784301758)
box: [[213 227][425   5][527 101][316 324]]
rect: ((266.9999694824219, 53.499996185302734), (38.984642028808594, 44.95772933959961), 24.22774314880371)
box: [[239  65][258  25][293  41][275  81]]
rect: ((277.5, 132.0), (533.65625, 161.2079315185547), 11.457330703735352)
box: [[  0 157][ 32   0][555 106][523 264]]

显示是用的bgr图，没转rgb

这种方法目前还没有遇到问题，如果遇到问题，可能需要在原图上做pad，相应的标签文件也做好修改再进行处理

2、第二种方法

思路是先找最小矩形，接着透视变换得到目标图。

import cv2
import numpy as np
import matplotlib.pyplot as pltcnts = []
labels=[]
with open('rodog.txt') as f:lines = f.read().strip().splitlines()
for line in lines:data = line.split()cnt=list(map(int,data[:8]))label=data[8]cnts.append(cnt)labels.append(label)num=len(cnts)
plt.figure(figsize=(10,num*5))
for i,cnt in enumerate(cnts):img = cv2.imread("rodog.jpeg")cnt = np.reshape(cnt,[4,2])# print("cnt:",cnt)# find the exact rectangle enclosing the text area# rect is a tuple consisting of 3 elements: the first element is the center# of the rectangle, the second element is the width, height, and the# third element is the detected rotation angle.# Example output: ((227.5, 187.50003051757812),# (94.57575225830078, 417.98736572265625), -36.982906341552734)rect = cv2.minAreaRect(cnt)# print("rect: {}".format(rect))# the order of the box points: bottom left, top left, top right,# bottom rightbox = cv2.boxPoints(rect)box = np.int0(box)# print('box:',box)# print("bounding box: {}".format(box))cv2.drawContours(img, [box], 0, (0, 255, 0), 2)# get width and height of the detected rectanglewidth = int(rect[1][0])height = int(rect[1][1])# print("width,height:",width,height)src_pts = box.astype("float32")# coordinate of the points in box points after the rectangle has been# straighteneddst_pts = np.array([[0, height-1],[0, 0],[width-1, 0],[width-1, height-1]], dtype="float32")# the perspective transformation matrixM = cv2.getPerspectiveTransform(src_pts, dst_pts)# directly warp the rotated rectangle to get the straightened rectanglewarped = cv2.warpPerspective(img, M, (width, height))# cv2.imwrite("crop_img.jpg", warped)# cv2.waitKey(0)plt.subplot(num,2,1+i*2)plt.imshow(img)plt.title('orig')plt.subplot(num,2,2+i*2)plt.imshow(warped)plt.title('crop')

3、两种方法的简单对比

img_crop.shape

(161, 533, 3)

warped.shape

(161, 533, 3)

两种方式看效果是一样的，图像尺寸也一样，但要对比像素值，还是不一样，如下代码，绝对误差大于20有4.56%

try:np.testing.assert_allclose(img_crop,warped,atol=20)
except Exception as e:print(e)

Not equal to tolerance rtol=1e-07, atol=20Mismatched elements: 11739 / 257439 (4.56%)
Max absolute difference: 255
Max relative difference: 255.x: array([[[255,   0,   0],[255,   0,   0],[237,  15,  18],...y: array([[[  0, 255,   0],[  0, 255,   0],[  0, 255,   0],...

4、opencv 最小面积矩形返回角度的理解

center, size, angle=cv2.minAreaRect(points)

points就是一系的（x,y)的点，center,size,angle分别是最小矩形的中心点，宽高，及angle。对于opencv的这个角度的理解，这里简单写一些，实际是与opencv版本有关，以opencv4.2为分界

4.1、version4.2之前

https://blog.csdn.net/weixin_43229348/article/details/125986969 (https://theailearner.com/tag/cv2-minarearect/) 参考这个博客就好，一句话说，就是x轴逆时针转，接触到的第一条边就是宽边w(不在意长短）,转的过程就是角度从0到-90 ，不包括0。
那么最小面积矩形的四个点及顺序是啥，用以下代码获取：

rect = CV2.minAreaRect(cnt)
box = cv2.boxPoints(rect)

box就是四个点的坐标，我们会用这四个点做旋转或透视变换都是需要的。那么顺序是什么呢？

可以理解为x轴与w边相交，箭头反方向的点为起点，接着顺时针。

4.1、version4.2之后

x轴顺时针转，接触到的第一条边就是宽边w(不在意长短）,转的过程就是角度从0到90 ，不包括0。x轴也与4.2之前的相反。
点顺序如下：

可以理解为x轴与w边相交，箭头同方向的点为起点，接着顺时针。

本文另一篇参考文章：https://jdhao.github.io/2019/02/23/crop_rotated_rectangle_opencv/#fn:2