这是一种可行的方法，它仍然使用HSV 颜色空间，您必须正确获取HSV 范围值。查找目标颜色的 RGB -> HSV 等效值。您绝对可以从一些预处理中受益，以更好地清理您的面具。您还可以实现轮廓过滤器，因为您正在寻找的感兴趣的斑点（圆盘）具有非常不同的属性，例如，纵横比、面积和当然，循环性。我建议以下步骤：
- 为您正在寻找的每个目标冰球获取
HSV 值
- 定义
upper 和lower 范围值
- 对
HSV 图像设置阈值以获得二进制掩码
- 应用区域过滤器去除小噪音
- 应用一些形态学 (
Dilate+Erode) 来改进您的目标 blob
- 获取外轮廓（忽略内轮廓）
- 将这些轮廓转换为
bounding rectangles
- 获取两个
bounding rectangles 属性：aspect ratio 和area
- 根据阈值属性值过滤边界矩形
让我们看看代码：
# importing cv2 and numpy:
import numpy as np
import cv2
# image path
path = "C://opencvImages//"
fileName = "board.png"
# Reading an image in default mode:
inputImage = cv2.imread(path + fileName)
# Convert the image to HSV:
frame_hsv = cv2.cvtColor(inputImage, cv2.COLOR_BGR2HSV)
# Prepare a dictionary to store the lower and upper
# HSV thresholds:
rangeDictionary = {}
# brown color
lower_values = np.array([6, 63, 0])
upper_values = np.array([23, 255, 81])
# push it into the dictionary:
rangeDictionary[0] = (lower_values, upper_values, "brown")
# gray color
lower_values = np.array([23, 0, 0])
upper_values = np.array([80, 105, 107])
# push it into the dictionary:
rangeDictionary[1] = (lower_values, upper_values, "gray")
# white color
lower_values = np.array([37, 0, 131])
upper_values = np.array([170, 25, 152])
# push it into the dictionary:
rangeDictionary[2] = (lower_values, upper_values, "white")
# Store results here:
targetRectangles = []
到目前为止，我有目标颜色的 looked up HSV 等价物。我为这些颜色定义了upper 和lower 阈值，并将它们存储在dictionary 中。这个想法是循环遍历这个字典并相应地提取每个颜色范围：
# Loop through the dictionary and locate each circle:
for i in rangeDictionary:
# Get current lower and upper range values:
current_LowRange = rangeDictionary[i][0]
current_UppRange = rangeDictionary[i][1]
# Create the HSV mask
mask = cv2.inRange(frame_hsv, current_LowRange, current_UppRange)
# Run a minimum area filter:
minArea = 800
mask = areaFilter(minArea, mask)
对于第一种颜色，这是未过滤的二进制掩码：
你在这里看到我已经实现了一个areaFilter。这将消除小于800 的斑点，让我们开始以正确的方式清洁你的面具。此功能在文章末尾定义。接下来是一些morphology 来进一步定义目标blob：
# Pre-process mask:
kernelSize = 3
structuringElement = cv2.getStructuringElement(cv2.MORPH_RECT, (kernelSize, kernelSize))
iterations = 10
mask = cv2.morphologyEx(mask, cv2.MORPH_DILATE, structuringElement, None, None, iterations, cv2.BORDER_REFLECT101)
mask = cv2.morphologyEx(mask, cv2.MORPH_ERODE, structuringElement, None, None, iterations, cv2.BORDER_REFLECT101)
这是过滤后的掩码：
非常好，嗯？没什么特别的，只是一个非常激进的dilation + erosion 连锁店。我想定义漂亮和干净的冰球。根据输入图像的大小，您可能需要调整 iterations 值。让我们继续。接下来的步骤（仍在循环内部）是计算contours（仅外部的）并将每个contour近似为polygon，然后近似为rectangle：
# Find the big contours/blobs on the filtered image:
contours, hierarchy = cv2.findContours(mask, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
# List to store all the bounding rectangles:
contours_poly = [None] * len(contours)
boundRect = []
# Alright, just look for the outer bounding boxes:
for b, c in enumerate(contours):
if hierarchy[0][b][3] == -1:
# Approximate the contour to a polygon:
contours_poly = cv2.approxPolyDP(c, 3, True)
# Convert the polygon to a bounding rectangle:
boundRect = cv2.boundingRect(contours_poly)
现在我们正在处理bounding rectangles，操作变得非常简单。让我们获取矩形的尺寸并计算几个参数：aspectRatio 和 area。使用一些启发式方法，我已经设置了用于过滤矩形的最小阈值：
# Get the dimensions of the bounding rect:
rectX = boundRect[0]
rectY = boundRect[1]
rectWidth = boundRect[2]
rectHeight = boundRect[3]
rectArea = rectWidth * rectHeight
# Calculate the aspect ratio:
aspectRatio = rectWidth / rectHeight
delta = abs(1.0 - aspectRatio)
# Set the min threshold values to identify the
# blob of interest:
minArea = 1000
epsilon = 0.2
# Is this bounding rectangle one the one we
# are looking for?
if rectArea > minArea and delta < epsilon:
# Set a color:
color = (0, 255, 0)
inputCopy = inputImage.copy()
# Draw the current rectangle on a copy of the BGR input:
cv2.rectangle(inputCopy, (int(rectX), int(rectY)),
(int(rectX + rectWidth), int(rectY + rectHeight)), color, 2)
# Store this bounding rectangle:
targetRectangles.append(boundRect)
# Label the current mask:
currentColor = rangeDictionary[i][2]
org = (rectX, rectY -10)
font = cv2.FONT_HERSHEY_SIMPLEX
color = (255, 0, 0)
cv2.putText(inputCopy, currentColor, org, font,
0.5, color, 1, cv2.LINE_AA)
cv2.imwrite(path + "colorMask_"+currentColor+".png", inputCopy)
我还在输入的深层副本上绘制了目标矩形，并绘制了漂亮的文本来识别颜色，查看结果：
“B-b-但是伙计，黑冰球呢？！”如果您一直关注到现在，应该很容易获得额外的面具。这是areaFilter函数的定义和实现：
def areaFilter(minArea, inputImage):
# Perform an area filter on the binary blobs:
componentsNumber, labeledImage, componentStats, componentCentroids = \
cv2.connectedComponentsWithStats(inputImage, connectivity=4)
# Get the indices/labels of the remaining components based on the area stat
# (skip the background component at index 0)
remainingComponentLabels = [i for i in range(1, componentsNumber) if componentStats[i][4] >= minArea]
# Filter the labeled pixels based on the remaining labels,
# assign pixel intensity to 255 (uint8) for the remaining pixels
filteredImage = np.where(np.isin(labeledImage, remainingComponentLabels) == True, 255, 0).astype('uint8')
return filteredImage
该死的，看看这一切，我可能应该在你的项目报告中的某个地方得到承认。希望这些信息对您有用。