python - 打开 CV 分水岭没有正确分割椭圆对象

Question

尝试创建一种方法来处理图像以计算不同类型的平板电脑。以下代码对圆形对象运行良好，但是椭圆形会产生我无法找到解决方法的问题。

kernel = np.ones((5,5),np.uint8)


image = cv2.imread('sample.jpg')
shifted = cv2.GaussianBlur(image, (15, 15), 1)
shifted = cv2.pyrMeanShiftFiltering(shifted, 21, 51)
shifted = cv2.erode(shifted,kernel,iterations=1)
shifted = cv2.dilate(shifted,kernel,iterations=1)
cv2.imwrite("step1.jpg", shifted)
gray = cv2.cvtColor(shifted, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255,
    cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
cv2.imwrite("step2.jpg", thresh)
thresh = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)
cv2.imwrite("step3.jpg", thresh)
thresh = cv2.bitwise_not(thresh)
thresh = cv2.erode(thresh,kernel,iterations=1)
cv2.imwrite("step4.jpg", thresh)
D = ndimage.distance_transform_edt(thresh)
localMax = peak_local_max(D, indices=False, min_distance=10,
    labels=thresh)
markers = ndimage.label(localMax, structure=np.ones((3, 3)))[0]

labels = watershed(-D, markers, mask=thresh)
print("[INFO] {} unique segments found".format(len(np.unique(labels)) - 1))
for label in np.unique(labels):
    if label == 0:
        continue
    mask = np.zeros(gray.shape, dtype="uint8")
    mask[labels == label] = 255
    cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
        cv2.CHAIN_APPROX_SIMPLE)
    cnts = imutils.grab_contours(cnts)
    c = max(cnts, key=cv2.contourArea)
    ((x, y), r) = cv2.minEnclosingCircle(c)
    cv2.circle(image, (int(x), int(y)), int(r), (0, 255, 0), 2)
    cv2.putText(image, "#{}".format(label), (int(x) - 10, int(y)),
        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
cv2.imwrite("step5.jpg", image)
cv2.waitKey(0)

正在使用的图像是：

https://imgur.com/a/1U49DeT

过滤后的输出：

https://imgur.com/a/vXwrWlG

任何有关如何解决此问题的教学点将不胜感激。

标签： pythonopencvwatershed

Answer 1

我认为有更好的方法来使用分水岭运算符。

它依赖于良好的渐变，但如果图像与这张相似，您应该能够有效地做到这一点。此外，今天有非常强大的边缘检测器，比我在这个演示中使用的要好得多。

import cv2 
import numpy as np
import higra as hg
from skimage.segmentation import relabel_sequential
import matplotlib.pyplot as plt 

def main():                                                                                                                                                                                             
    img_path = "pills.jpg"                                                                                                                                                                              
    img = cv2.imread(img_path)                                                                                                                                                                          
    img = cv2.resize(img, (256, 256))                                                                                                                                                                   
    img = cv2.GaussianBlur(img, (9, 9), 0)                                                                                                                                                              

    edges = cv2.Canny(img, 100, 100)                                                                                                                                                                    

    size = img.shape[:2]                                                                                                                                                                                
    graph = hg.get_4_adjacency_graph(size)                                                                                                                                                              
    edge_weights = hg.weight_graph(graph, edges, hg.WeightFunction.mean)                                                                                                                                

    tree, altitudes = hg.watershed_hierarchy_by_area(graph, edge_weights)                                                                                                                               
    segments = hg.labelisation_horizontal_cut_from_threshold(tree, altitudes, 500)                                                                                                                      
    segments, _, _ = relabel_sequential(segments)                                                                                                                                                       
    print('The number of pills is ', segments.max() - 1)                                                                                                                                                
    plt.imshow(segments)                                                                                                                                                                                
    plt.show()                                                                                                                                                                                          

if __name__ == "__main__":                                                                                                                                                                              
    main()                                                                                                                                                                                              

最初，我调整图像大小以加快计算速度并应用模糊来减少背景渐变。我检测它的边缘（梯度）并用它作为边缘权重创建一个图；然后我计算按区域排序的分水岭层次结构并阈值它获得该级别的连接组件，从中您可以计算段数。