利用opencv边缘提取矩形框集合覆盖的图像

• contourArea因为以边缘像素中心为边长计算，所以面积会小于实际面积
• opencv python：https://docs.opencv.org/4.x/d3/d05/tutorial_py_table_of_contents_contours.html
• C++：https://docs.opencv.org/4.x/d3/dc0/group__imgproc__shape.html#gadf1ad6a0b82947fa1fe3c3d497f260e0

# coding=utf8
import cv2
import numpy as np

im=cv2.imread("lwd.jpg")
h,w,c=im.shape
# 全0图
raw=np.zeros((h,w,c))
# 把矩形框对应的图像复制到全0图，coco格式
for line in open("lwd.txt"):
	sp=line.split()
	x = int(float(sp[1])*w)
	y = int(float(sp[2])*h)
	ww = int(float(sp[3])*w)
	hh = int(float(sp[4])*h)
	raw[y:y+hh, x:x+ww] = im[y:y+hh, x:x+ww]
raw=np.uint8(raw)
cv2.imshow("hh", raw)
cv2.waitKey()
# 转化成灰度图
mask = cv2.cvtColor(raw, cv2.COLOR_BGR2GRAY)
# 转化成二值图
thresh, mask = cv2.threshold(mask, 0, 255, cv2.THRESH_BINARY)
# 提取轮廓 python2
image, contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# 画轮廓
image = cv2.drawContours(im, contours,-1,(0,0,255),2)
for contour in contours:
	print('*'*19)
	# 轮廓周长
	print(len(contour))
	# 轮廓面积
	print(cv2.contourArea(contour))
	# 轮廓准确面积
	x,y,w,h = cv2.boundingRect(contour)
	tmp = mask[y:y+h, x:x+w]
	print(cv2.countNonZero(tmp))
	# 以外接矩形提取轮廓内容，轮廓外的是0
	tmp = raw[y:y+h, x:x+w]
	cv2.imshow("hh", tmp)
	cv2.waitKey()