method_test.py

import cv2
import numpy as np
import time


t0 = time.time()
mask_gray = cv2.imread('pic/diamond.png', 0)
rows, cols = mask_gray.shape
# Get the contours
print(f'原图尺寸{mask_gray.shape}')
# 识别轮廓
_, mask_grayqqqq = cv2.threshold(mask_gray, 50, 255, cv2.THRESH_BINARY)
contours2, _ = cv2.findContours(mask_grayqqqq, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

mm = cv2.resize(mask_grayqqqq, (int(cols / 8 / 4), int(rows / 8 / 4)))
contours, _ = cv2.findContours(mm, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

raw_dist = np.zeros_like(mm, dtype=np.float32)

raw_dist2 = np.zeros_like(mask_grayqqqq, dtype=np.float32)

for i in contours:
    if 100 < cv2.contourArea(i):
        # print(cv2.contourArea(i))
        cont = i
        # 计算到轮廓的距离
        x, y, w, h = cv2.boundingRect(cont)
        for i in range(int(x + w / 4), int(x + w / 4 * 3)):
            for j in range(int(y + h / 4), int(y + h / 4 * 3)):
                raw_dist[i, j] = cv2.pointPolygonTest(cont, (j, i), True)

# 获取最大值即内接圆半径，中心点坐标
minVal, maxVal, _, maxDistPt = cv2.minMaxLoc(raw_dist)
print(f'缩放后的圆心{maxDistPt}')

for i in contours2:
    if 5000 < cv2.contourArea(i):
        # print(cv2.contourArea(i))
        cont2 = i
        for i in range(maxDistPt[1] * 8 * 4 - 25, maxDistPt[1] * 8 * 4 + 25):
            for j in range(maxDistPt[0] * 8 * 4 - 25, maxDistPt[0] * 8 * 4 + 25):
                raw_dist2[i, j] = cv2.pointPolygonTest(cont2, (j, i), True)
minVal, maxVal, _, maxDistPt = cv2.minMaxLoc(raw_dist2)
maxVal = abs(maxVal)
# 画出最大内接圆
result = cv2.cvtColor(mask_gray, cv2.COLOR_GRAY2BGR)
print(f'原图的圆心{maxDistPt}')

center_of_circle = maxDistPt
cv2.circle(result, maxDistPt, int(maxVal), (0, 255, 0), 2, 1, 0)
# cv2.imwrite('./sawww.bmp',result)
result = cv2.resize(result, (1024, 1024))

print(f'耗时{time.time() - t0}')
cv2.imshow('Maximum inscribed circle', result)
cv2.waitKey(0)