回答您的主要问题，分水岭不会删除单个对象。分水岭在您的算法中运行良好。它接收预定义的标签并相应地执行分割。

问题是您为距离变换设置的阈值太高，它消除了单个对象的弱信号，从而阻止了对象被标记并发送到分水岭算法。

距离变换信号弱的原因是颜色分割阶段分割不当，难以设置单一阈值去除噪声和提取信号。

为了解决这个问题，我们需要执行适当的颜色分割，并在分割距离变换信号时使用自适应阈值而不是单个阈值。

这是我修改的代码。我在代码中结合了@user1269942的颜色分割方法。额外的解释在代码中。

import cv2
import numpy as np
import matplotlib.pyplot as plt
from scipy.ndimage import label
import urllib.request


# https://stackoverflow.com/a/14617359/7690982


def segment_on_dt(a, img, img_gray):

    # Added several elliptical structuring element for better morphology process
    struct_big = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))
    struct_small = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))

    # increase border size
    border = cv2.dilate(img, struct_big, iterations=5)
    border = border - cv2.erode(img, struct_small)




    dt = cv2.distanceTransform(img, cv2.DIST_L2, 3)
    dt = ((dt - dt.min()) / (dt.max() - dt.min()) * 255).astype(np.uint8)

    # blur the signal lighty to remove noise
    dt = cv2.GaussianBlur(dt,(7,7),-1)

    # Adaptive threshold to extract local maxima of distance trasnform signal
    dt = cv2.adaptiveThreshold(dt, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 21, -9)
    #_ , dt = cv2.threshold(dt, 2, 255, cv2.THRESH_BINARY)


    # Morphology operation to clean the thresholded signal
    dt = cv2.erode(dt,struct_small,iterations = 1)
    dt = cv2.dilate(dt,struct_big,iterations = 10)

    plt.imshow(dt)
    plt.show()

    # Labeling
    lbl, ncc = label(dt)
    lbl = lbl * (255 / (ncc + 1))
    # Completing the markers now.
    lbl[border == 255] = 255

    plt.imshow(lbl)
    plt.show()

    lbl = lbl.astype(np.int32)
    cv2.watershed(a, lbl)
    print("[INFO] {} unique segments found".format(len(np.unique(lbl)) - 1))
    lbl[lbl == -1] = 0
    lbl = lbl.astype(np.uint8)
    return 255 - lbl

# Open Image
resp = urllib.request.urlopen("https://i.stack.imgur.com/YUgob.jpg")
img = np.asarray(bytearray(resp.read()), dtype="uint8")
img = cv2.imdecode(img, cv2.IMREAD_COLOR)


## Yellow slicer
# blur to remove noise
img = cv2.blur(img, (9,9))

# proper color segmentation
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)  
mask = cv2.inRange(hsv, (0, 140, 160), (35, 255, 255)) 
#mask = cv2.inRange(img, (0, 0, 0), (55, 255, 255))

imask = mask > 0
slicer = np.zeros_like(img, np.uint8)
slicer[imask] = img[imask]



# Image Binarization
img_gray = cv2.cvtColor(slicer, cv2.COLOR_BGR2GRAY)

_, img_bin = cv2.threshold(img_gray, 140, 255,
             cv2.THRESH_BINARY)


plt.imshow(img_bin)
plt.show()
# Morphological Gradient
# added
cv2.morphologyEx(img_bin, cv2.MORPH_OPEN,cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3)),img_bin,(-1,-1),10)
cv2.morphologyEx(img_bin, cv2.MORPH_ERODE,cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3)),img_bin,(-1,-1),3)

plt.imshow(img_bin)
plt.show()

# Segmentation
result = segment_on_dt(img, img_bin, img_gray)
plt.imshow(np.hstack([result, img_gray]), cmap='Set3')
plt.show()

# Final Picture
result[result != 255] = 0
result = cv2.dilate(result, None)
img[result == 255] = (0, 0, 255)
plt.imshow(result)
plt.show()

最终结果：找到124个独特的项目。发现了一个额外的项目，因为其中一个对象被分成2。通过适当的参数调整，你可能会得到你正在寻找的确切数字。但是我建议买一个更好的相机。

看看你的代码，它是完全合理的，所以我只想提出一个小建议，那就是使用HSV颜色空间来做你的“inRange”。

关于颜色空间的OpenCV文档：

https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_colorspaces/py_colorspaces.html

另一个将inRange与HSV一起使用的SO示例：

如何检测两种不同的颜色使用'cv2. inRange'在Python-OpenCV？

并为您进行小代码编辑：

img = cv2.blur(img, (5,5))  #new addition just before "##yellow slicer"

## Yellow slicer
#mask = cv2.inRange(img, (0, 0, 0), (55, 255, 255))   #your line: comment out.
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)  #new addition...convert to hsv
mask = cv2.inRange(hsv, (0, 120, 120), (35, 255, 255))  #new addition use hsv for inRange and an adjustment to the values.

检测丢失的物体

im_1im_2im_3

我数了12个丢失的物体：2、7、8、11、65、77、78、84、92、95、96。编辑：85也是

发现117个，失踪12个，错6个

1°尝试：降低面具敏感度

#mask = cv2.inRange(img, (0, 0, 0), (55, 255, 255))  #Current
mask = cv2.inRange(img, (0, 0, 0), (80, 255, 255))   #1' Attempt

inRange文档

im_4im_5im_6im_7

[INFO] 120 unique segments found

找到120，少9，错6