Python手動實現Hough圓變換的示例代碼
Hough圓變換的原理很多博客都已經說得非常清楚瞭,但是手動實現的比較少,所以本文直接貼上手動實現的代碼。
這裡使用的圖片是一堆硬幣:
首先利用通過計算梯度來尋找邊緣,代碼如下:
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
需要註意的是,這裡使用的kernel內的數值比較大,所以得到瞭結果圖中的某些位置的數值超過255,但並不影響顯示,但如果想通過cv2.imshow來顯示,就需要將超過255的地方設為255即可(已經在代碼中用註釋標出),結果如下:
接下來就是要進行Hough圓變換,先看代碼:
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進度:', 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
其中輸入是我們之前得到的邊緣圖,以及確定強邊緣的閾值,以及一個包含著我們估計的半徑的數組;返回值是強邊緣圖以及參數域矩陣。代碼中首先遍歷邊緣圖,通過閾值留下那些較強的位置,這裡的閾值需要自己根據自己的輸入圖進行調節。接著就是進行Hough變換,這裡的候選半徑集合需要根據自己的輸入圖進行調節。在繪制參數域的過程中,隻遍歷瞭所需正方形區域(大小為 r*r)的 1/4,這是因為在坐出參數域上的一個點之後,由於圓的對稱性,就可以找到與之對稱的另外三個點,無需額外進行遍歷。
最後一步就是從參數域矩陣中提取出結果圓,代碼如下,其中篩選閾值需要根據你的輸入圖像自己調節:
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 進度:', 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('圓的數量:', 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
註意一下在這一步中需要將那些圓心相近的圓剔除掉,隻保留一個結果。
接著是main函數,這沒啥好說的:
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 # 需要註意的是,在img1中有些地方的像素值是高於255的,這是由於之前的kernel內的數更大 # 但這並不影響圖像的顯示 # 因此這裡的thresh要大於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
下面是我的運行結果:
到此這篇關於Python手動實現Hough圓變換的示例代碼的文章就介紹到這瞭,更多相關Python Hough圓變換內容請搜索WalkonNet以前的文章或繼續瀏覽下面的相關文章希望大傢以後多多支持WalkonNet!
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