Python 18.opencv 轮廓相关，凸包检测，外接矩形、圆，极点，平均颜色，轮廓内像素点_计算外接圆内颜色平均值

import cv2
import numpy as np
 
img = cv2.imread('pic5.PNG')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray, 127, 255, 0)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
img = cv2.drawContours(img, contours, -1, (0, 255, 0), 3)
# img = cv2.drawContours(img, contours, 3, (0, 255, 0), 3)
 
# 获取图像的矩，可以计算出对象的重心
cnt = contours[0]
M = cv2.moments(cnt)
print(M)
 
# 获取轮廓的面积
area = cv2.contourArea(cnt)
print(area)
 
# 获取轮廓周长
perimeter = cv2.arcLength(cnt, True)
print(perimeter)
 
# 轮廓近似（获取最大轮廓）,如带缺口的矩形识别成矩形
epsilon = 0.1*cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt, epsilon, True)
 
# 凸包， 凸检验
# 获取凸包
hull = cv2.convexHull(cnt)
# 检测是否是凸包
k = cv2.isContourConvex(cnt)
print(k)
 
# 边界矩形，因为我的矩形轮廓所以无效果
# 直边界矩形，未考虑旋转
x, y, w, h = cv2.boundingRect(cnt)
img = cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)
# 旋转的边界矩形，考虑对象旋转
x, y, w, h = cv2.boundingRect(cnt)
img = cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)
# 边界矩形的长宽比
aspect_ratio = float(w)/h
# 边界矩形的面积
# 轮廓面积与凸包面积的比
hull_area = cv2.contourArea(hull)
solidity = float(area)/hull_area
 
# 返回对象的方向，长轴和短轴的长度，有问题
# (x, y), (MA, ma), angle = cv2.fitEllipse(cnt)
 
# 最小外接圆
(x, y), radius = cv2.minEnclosingCircle(cnt)
center = (int(x), int(y))
radius = int(radius)
#img = cv2.circle(img, center, radius, (0, 255, 0), 2)
 
# 椭圆拟合,注意椭圆不要超出图像边界，可能报错
#ellipse = cv2.fitEllipse(cnt)
#img = cv2.ellipse(img, ellipse, (0, 255, 0), 2)
 
# 直线拟合
rows, cols = img.shape[:2]
[vx, vy, x, y] = cv2.fitLine(cnt, cv2.DIST_L2, 0, 0.01, 0.01)
lefty = int((-x*vy/vx) + y)
righty = int(((cols - x)*vy/vx)+y)
# img = cv2.line(img, (cols-1, righty), (0, lefty), (0, 255, 0), 2)
 
# 获取构成对象的所有像素点
mask = np.zeros(gray.shape, np.uint8)
cv2.drawContours(mask, [cnt], 0, 255, -1)
pixelpoints = np.transpose(np.nonzero(mask))
print(pixelpoints)
# 最大值最小值及它们的位置
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(gray, mask=mask)
 
# 获取图像的平均颜色及平均灰度
mean_val = cv2.mean(img, mask=mask)
print(mean_val)
 
# 极点-轮廓的最左右上下的点
leftmost = tuple(cnt[cnt[:, :, 0].argmin()][0])
rightmost = tuple(cnt[cnt[:, :, 0].argmax()][0])
topmost = tuple(cnt[cnt[:, :, 1].argmin()][0])
bottommost = tuple(cnt[cnt[:, :, 1].argmax()][0])
 
cv2.imshow("img", mask)
cv2.waitKey(0)
cv2.destroyAllWindows()