[英]Detection of irregular shapes using houghcircle function opencv python
我目前正在對圖像進行圓檢測看起來像這樣,但是一些墨滴合並並形成一些不規則的形狀(原始圖像中的紅色標記)。 我在opencv中使用houghcircle函數來檢測圓圈。 對於那些不規則的形狀,該功能只能將它們檢測為幾個小圓圈,但我真的希望程序將不規則形狀視為一個完整的大形狀並得到一個像我在輸出圖像中繪制的大圓圈。
我的代碼將檢測所有圓圈並獲得它們的直徑。
這是我的代碼:
def circles(filename, p1, p2, minR, maxR):
# print(filename)
img = cv2.imread(filename, 0)
img = img[0:1000, 0:1360]
l = len(img)
w = len(img[1])
cimg = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)
circles = cv2.HoughCircles(img, cv2.HOUGH_GRADIENT, 1, 25,
param1 = int(p1) ,param2 = int(p2), minRadius = int(minR), maxRadius = int(maxR))
diameter = open(filename[:-4] + "_diamater.txt", "w")
diameter.write("Diameters(um)\n")
for i in circles[0,:]:
diameter.write(str(i[2] * 1.29 * 2) + "\n")
count = 0
d = []
area = []
for i in circles[0,:]:
cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2)
cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3)
count += 1
d += [i[2]*2]
area += [i[2]*i[2]*pi*1.286*1.286]
f = filename.split("/")[-1]
cv2.imwrite(filename[:-4] + "_circle.jpg", cimg)
# cv2.imwrite("test3/edge.jpg", edges)
print "Number of Circles is %d" % count
diaM = []
for i in d:
diaM += [i*1.286]
bWidth = range(int(min(diaM)) - 10, int(max(diaM)) + 10, 2)
txt = '''
Sample name: %s
Average diameter(um): %f std: %f
Drop counts: %d
Average coverage per drop(um^2): %f std: %f
''' % (f, np.mean(diaM), np.std(diaM), count, np.mean(area), np.std(area))
fig = plt.figure()
fig.suptitle('Histogram of Diameters', fontsize=14, fontweight='bold')
ax1 = fig.add_axes((.1,.4,.8,.5))
ax1.hist(diaM, bins = bWidth)
ax1.set_xlabel('Diameter(um)')
ax1.set_ylabel('Frequency')
fig.text(.1,.1,txt)
plt.savefig(filename[:-4] + '_histogram.jpg')
plt.clf()
print "Total area is %d" % (w*l)
print "Total covered area is %d" % (np.sum(area))
rt = "Number of Circles is " + str(count) + "\n" + "Coverage percent is " + str(np.divide(np.sum(area), (w*l))) + "\n"
return rt
如果您仍想使用HoughCircles功能,您可以看到兩個圓圈是否重疊並從中創建一個新圓圈。
您可以使用minEnclosingCircle 。 找到圖像的輪廓,然后應用該功能將形狀檢測為圓形。
下面是一個帶有c ++代碼的簡單示例。 在你的情況下,我覺得你應該使用Hough-circle和minEnclosingCircle的組合,因為你的圖像中的某些圓圈彼此非常接近,它們有可能被檢測為單個輪廓。
輸入圖像:
界:
Mat im = imread("circ.jpg");
Mat gr;
cvtColor(im, gr, CV_BGR2GRAY);
Mat bw;
threshold(gr, bw, 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);
vector<vector<Point>> contours;
vector<Vec4i> hierarchy;
findContours(bw, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
for(int idx = 0; idx >= 0; idx = hierarchy[idx][0])
{
Point2f center;
float radius;
minEnclosingCircle(contours[idx], center, radius);
circle(im, Point(center.x, center.y), radius, Scalar(0, 255, 255), 2);
}
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