我正在嘗試檢測白色/灰色背景上的彩色點。這些點是 3 種不同顏色（黃色、紫色、藍色）的不同大小。這是原始圖像：我將圖像轉換為 HSV 并找到每個圖像的下限和上限，然后應用輪廓檢測來找到這些點。以下代碼檢測到大部分點：import cv2import numpy as npfrom matplotlib import pyplot as pltimg = 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)以下是檢測到的輪廓：問題是沒有檢測到一些彩色點。我理解通過微調顏色范圍（上下）可以檢測到更多的點。但這非常耗時，并且不能推廣到類似的圖像。例如，下面的圖像看起來與上面的第一張圖像相似，并且具有相同的彩色點，但背景略有不同，一旦我通過上面的代碼運行它，它甚至無法檢測到一個點。我在正確的軌道上嗎？是否有更可擴展和更可靠的解決方案，無需調整顏色參數即可解決此問題？這是我嘗試的另一張圖片：