检测python中图像中的彩色点？

Question

我正在尝试检测白色/灰色背景上的彩色点。点是3种不同大小的不同颜色（黄色，紫色，蓝色）。这是原始图片：

我将图像转换为HSV，并找到每个图像的上下限，然后应用轮廓检测​​找到这些点。以下代码检测到大多数点：

import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('image1_1.png')

hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

lower_yellow = np.array([22,25,219])
upper_yellow = np.array([25,75,225])

lower_purple = np.array([141,31,223])
upper_purple = np.array([143,83,225])

lower_blue = np.array([92,32,202])
upper_blue = np.array([96,36,208])

mask_blue = cv2.inRange(hsv, lower_blue, upper_blue)
mask_purple = cv2.inRange(hsv, lower_purple, upper_purple)
mask_yellow = cv2.inRange(hsv, lower_yellow, upper_yellow)

res_blue = cv2.bitwise_and(img,img, mask=mask_blue)
res_purple = cv2.bitwise_and(img,img, mask=mask_purple)
res_yellow = cv2.bitwise_and(img,img, mask=mask_yellow)

gray_blue = cv2.cvtColor(res_blue, cv2.COLOR_BGR2GRAY)
gray_purple = cv2.cvtColor(res_purple, cv2.COLOR_BGR2GRAY)
gray_yellow = cv2.cvtColor(res_yellow, cv2.COLOR_BGR2GRAY)

_,thresh_blue = cv2.threshold(gray_blue,10,255,cv2.THRESH_BINARY)
_,thresh_purple = cv2.threshold(gray_purple,10,255,cv2.THRESH_BINARY)
_,thresh_yellow = cv2.threshold(gray_yellow,10,255,cv2.THRESH_BINARY)

contours_blue, hierarhy1 = cv2.findContours(thresh_blue,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
contours_purple, hierarhy2 = cv2.findContours(thresh_purple,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
contours_yellow, hierarhy3 = cv2.findContours(thresh_yellow,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

result = img.copy()
cv2.drawContours(result, contours_blue, -1, (0, 0, 255), 2)
cv2.drawContours(result, contours_purple, -1, (0, 0, 255), 2)
cv2.drawContours(result, contours_yellow, -1, (0, 0, 255), 2)
cv2.imwrite("_allContours.jpg", result)

这里是检测到的轮廓：

问题是某些色点未被检测到。我了解到，通过微调颜色范围（上下），可以检测到更多的点。但这非常耗时，并且不能推广到相似的图像。例如，下面的图像看起来与上面的第一个图像相似，并且具有相同的彩色点，但是背景略有不同，一旦我通过上面的代码运行它，就无法检测到其中一个点。我在正确的轨道上吗？是否有一种更具扩展性和可靠性的解决方案，而较少需要调整颜色参数来解决此问题？这是我尝试过的其他图像：

Answer 1

我建议在Python / OpenCV中简单地使用adaptiveThreshold

import cv2
import numpy as np

# read image
img = cv2.imread("dots.png")

# convert img to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# do adaptive threshold on gray image
thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 25, 6)

# write results to disk
cv2.imwrite("dots_thresh.jpg", thresh)

# display it
cv2.imshow("thresh", thresh)
cv2.waitKey(0)