Python手动实现Hough圆变换的示例代码
Hough圆变换的原理相信大家都非常清楚了,但是手动实现的比较少。这篇文章将为大家介绍手动实现Hough圆变换的示例代码,需要的可以了解一下
Hough圆变换的原理很多博客都已经说得非常清楚了,但是手动实现的比较少,所以本文直接贴上手动实现的代码。
这里使用的图片是一堆硬币:
首先利用通过计算梯度来寻找边缘,代码如下:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | def detect_edges(image): h = image.shape[ 0 ] w = image.shape[ 1 ] sobeling = np.zeros((h, w), np.float64) sobelx = [[ - 3 , 0 , 3 ], [ - 10 , 0 , 10 ], [ - 3 , 0 , 3 ]] sobelx = np.array(sobelx) sobely = [[ - 3 , - 10 , - 3 ], [ 0 , 0 , 0 ], [ 3 , 10 , 3 ]] sobely = np.array(sobely) gx = 0 gy = 0 testi = 0 for i in range ( 1 , h - 1 ): for j in range ( 1 , w - 1 ): edgex = 0 edgey = 0 for k in range ( - 1 , 2 ): for l in range ( - 1 , 2 ): edgex + = image[k + i, l + j] * sobelx[ 1 + k, 1 + l] edgey + = image[k + i, l + j] * sobely[ 1 + k, 1 + l] gx = abs (edgex) gy = abs (edgey) sobeling[i, j] = gx + gy # if you want to imshow ,run codes below first # if sobeling[i,j]>255: # sobeling[i, j]=255 # sobeling[i, j] = sobeling[i,j]/255 return sobeling |
需要注意的是,这里使用的kernel内的数值比较大,所以得到了结果图中的某些位置的数值超过255,但并不影响显示,但如果想通过cv2.imshow来显示,就需要将超过255的地方设为255即可(已经在代码中用注释标出),结果如下:
接下来就是要进行Hough圆变换,先看代码:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | def hough_circles(edge_image, edge_thresh, radius_values): h = edge_image.shape[ 0 ] w = edge_image.shape[ 1 ] # print(h,w) edgimg = np.zeros((h, w), np.int64) for i in range (h): for j in range (w): if edge_image[i][j] > edge_thresh: edgimg[i][j] = 255 else : edgimg[i][j] = 0 accum_array = np.zeros(( len (radius_values), h, w)) # return edgimg , [] for i in range (h): print ( 'Hough Transform进度:' , i, '/' , h) for j in range (w): if edgimg[i][j] ! = 0 : for r in range ( len (radius_values)): rr = radius_values[r] hdown = max ( 0 , i - rr) for a in range (hdown, i): b = round (j + math.sqrt(rr * rr - (a - i) * (a - i))) if b> = 0 and b< = w - 1 : accum_array[r][a][b] + = 1 if 2 * i - a > = 0 and 2 * i - a < = h - 1 : accum_array[r][ 2 * i - a][b] + = 1 if 2 * j - b > = 0 and 2 * j - b < = w - 1 : accum_array[r][a][ 2 * j - b] + = 1 if 2 * i - a > = 0 and 2 * i - a < = h - 1 and 2 * j - b > = 0 and 2 * j - b < = w - 1 : accum_array[r][ 2 * i - a][ 2 * j - b] + = 1 return edgimg, accum_array |
其中输入是我们之前得到的边缘图,以及确定强边缘的阈值,以及一个包含着我们估计的半径的数组;返回值是强边缘图以及参数域矩阵。代码中首先遍历边缘图,通过阈值留下那些较强的位置,这里的阈值需要自己根据自己的输入图进行调节。接着就是进行Hough变换,这里的候选半径集合需要根据自己的输入图进行调节。在绘制参数域的过程中,只遍历了所需正方形区域(大小为 r*r)的 1/4,这是因为在坐出参数域上的一个点之后,由于圆的对称性,就可以找到与之对称的另外三个点,无需额外进行遍历。
最后一步就是从参数域矩阵中提取出结果圆,代码如下,其中筛选阈值需要根据你的输入图像自己调节:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | def find_circles(image, accum_array, radius_values, hough_thresh): returnlist = [] hlist = [] wlist = [] rlist = [] returnimg = deepcopy(image) for r in range (accum_array.shape[ 0 ]): print ( 'Find Circles 进度:' , r, '/' , accum_array.shape[ 0 ]) for h in range (accum_array.shape[ 1 ]): for w in range (accum_array.shape[ 2 ]): if accum_array[r][h][w] > hough_thresh: tmp = 0 for i in range ( len (hlist)): if abs (w - wlist[i])< 10 and abs (h - hlist[i])< 10 : tmp = 1 break if tmp = = 0 : #print(accum_array[r][h][w]) rr = radius_values[r] flag = '(h,w,r)is:(' + str (h) + ',' + str (w) + ',' + str (rr) + ')' returnlist.append(flag) hlist.append(h) wlist.append(w) rlist.append(rr) print ( '圆的数量:' , len (hlist)) for i in range ( len (hlist)): center = (wlist[i], hlist[i]) rr = rlist[i] color = ( 0 , 255 , 0 ) thickness = 2 cv2.circle(returnimg, center, rr, color, thickness) return returnlist, returnimg |
注意一下在这一步中需要将那些圆心相近的圆剔除掉,只保留一个结果。
接着是main函数,这没啥好说的:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | def main(argv): img_name = argv[ 0 ] img = cv2.imread( 'data/' + img_name + '.png' , cv2.IMREAD_COLOR) # print(img.shape[0], img.shape[1]) gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # print(gray_image.shape[0], gray_image.shape[1]) img1 = detect_edges(gray_image) cv2.imwrite( 'output/' + img_name + "_after_find_detect.png" , img1) thresh = 1500 # 需要注意的是,在img1中有些地方的像素值是高于255的,这是由于之前的kernel内的数更大 # 但这并不影响图像的显示 # 因此这里的thresh要大于255 radius_values = [] for i in range ( 10 ): radius_values.append( 20 + i) edgeimg, accum_array = hough_circles(img1, thresh, radius_values) cv2.imwrite( 'output/' + img_name + "_after_binary.png" , edgeimg) # Findcircle hough_thresh = 70 resultlist, resultimg = find_circles(img, accum_array, radius_values, hough_thresh) print (resultlist) cv2.imwrite( 'output/' + img_name + "_circles.png" , resultimg) if __name__ = = '__main__' : sys.argv.append( "coins" ) main(sys.argv[ 1 :]) # TODO |
下面是我的运行结果:
到此这篇关于Python手动实现Hough圆变换的示例代码的文章就介绍到这了
原文链接:https://blog.csdn.net/aRossoneri/article/details/122527480