HoughCircles使用與說明

• 1. HoughCircles說明
• 2. 代碼
• 3.結果

cv2提供了一種圓檢測的方法：HoughCircles。該函數的返回結果與參數設置有很大的關系。
檢測的圖像時9枚錢幣，分別使用了阈值（大津法和三角法）、均值偏移濾波以及未處理圖像。實驗的結果是只要調整param1和param2兩個參數，上述方法都能准確的識別圖像中的圓形。與圓最貼切的是大津法阈值。使用該方法同時需要使用cv2.THRESHOLD_TRUNC。

1. HoughCircles說明

函數定義如下：

HoughCircles(image, method, dp, minDist, circles=None, param1=None, param2=None, minRadius=None, maxRadius=None)

參數含義image原始圖像method目前只支持cv2.HOUGH_GRADIENTdp圖像解析的反向比例。1為原始大小，2為原始大小的一半minDist圓心之間的最小距離。過小會增加圓的誤判，過大會丟失存在的圓param1Canny檢測器的高阈值param2檢測階段圓心的累加器阈值。越小的話，會增加不存在的圓；越大的話，則檢測到的圓就更加接近完美的圓形minRadius檢測的最小圓的半徑maxRadius檢測的最大圓的半徑

2. 代碼

# coding:utf8
import cv2
import numpy as np
def row_method(src):
image = np.array(src)
cimage = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # 灰度圖
circles = cv2.HoughCircles(cimage, cv2.HOUGH_GRADIENT, 1, 40, param1=250, param2=58, minRadius=0)
circles = np.uint16(np.around(circles)) # 取整
for i in circles[0, :]:
cv2.circle(image, (i[0], i[1]), i[2], (0, 0, 255), 2) # 在原圖上畫圓，圓心，半徑，顏色，線框
cv2.circle(image, (i[0], i[1]), 2, (255, 0, 0), 2) # 畫圓心
cv2.putText(image, "param1=250, param2=58", (20, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
cv2.imshow("row_circles", image)
def threshold_OTSU_method(src):
image = np.array(src)
cimage = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY) # 灰度圖
th, dst = cv2.threshold(cimage, 200, 255, cv2.THRESH_BINARY + cv2.THRESH_TRUNC + cv2.THRESH_OTSU)
circles = cv2.HoughCircles(dst, cv2.HOUGH_GRADIENT, 1, 40, param1=50, param2=47, minRadius=0)
circles = np.uint16(np.around(circles)) # 取整
for i in circles[0, :]:
cv2.circle(image, (i[0], i[1]), i[2], (0, 0, 255), 2) # 在原圖上畫圓，圓心，半徑，顏色，線框
cv2.circle(image, (i[0], i[1]), 2, (255, 0, 0), 2) # 畫圓心
cv2.putText(image, "param1=50, param2=47", (20, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
cv2.imshow("otsu_circles", image)
def threshold_triangle_method(src):
image = np.array(src)
cimage = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY) # 灰度圖
th, dst = cv2.threshold(cimage, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_TRIANGLE)
circles = cv2.HoughCircles(dst, cv2.HOUGH_GRADIENT, 1, 40, param1=50, param2=17, minRadius=0)
circles = np.uint16(np.around(circles)) # 取整
for i in circles[0, :]:
cv2.circle(image, (i[0], i[1]), i[2], (0, 0, 255), 2) # 在原圖上畫圓，圓心，半徑，顏色，線框
cv2.circle(image, (i[0], i[1]), 2, (255, 0, 0), 2) # 畫圓心
cv2.putText(image, "param1=50, param2=17", (20, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
cv2.imshow("triangle_circles", image)
def mean_circles(src):
image = np.array(src)
dst = cv2.pyrMeanShiftFiltering(image, 10, 100) # 均值偏移濾波
cimage = cv2.cvtColor(dst, cv2.COLOR_BGR2GRAY) # 灰度圖
circles = cv2.HoughCircles(cimage, cv2.HOUGH_GRADIENT, 1, 40, param1=50, param2=20, minRadius=0)
circles = np.uint16(np.around(circles)) # 取整
for i in circles[0, :]:
cv2.circle(image, (i[0], i[1]), i[2], (0, 0, 255), 2) # 在原圖上畫圓，圓心，半徑，顏色，線框
cv2.circle(image, (i[0], i[1]), 2, (255, 0, 0), 2) # 畫圓心
cv2.putText(image, "param1=50, param2=20", (20, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
cv2.imshow("mean_circles", image)
src = cv2.imread("circle.png") # 讀取圖片位置
cv2.namedWindow("input image", cv2.WINDOW_AUTOSIZE)
cv2.imshow("input image", src)
threshold_OTSU_method(src)
threshold_triangle_method(src)
mean_circles(src)
row_method(src)
cv2.waitKey(0)
cv2.destroyAllWindows()

3.結果