如何使用OpenCV检测具有相同颜色的像素区域

Question

使用OpenCV Python，我想知道哪种方法最好的方法是识别图像中具有特定颜色像素高度集中的区域，并可能通过在它们周围绘制一个圆来“标记”它们。

我尝试使用findContours方法，但是很混乱……

我的直觉告诉我，我必须设置一种颜色的相邻像素的范围[min：max]，然后确定该区域的中心，并在其中绘制'O'。

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第一张图像是处理BGR图像（至HSV并处理少量色罩）后得到的示例：

检测前的图像

一旦检测到该区域，第二张图像就是我要绘制的图像。是的，我自己添加了黑色圆圈作为示例:-)

检测后的图像

谢谢 ！

Answer 1

颜色阈值cv2.inRange()应该在这里工作

这是主要思想

• 将图像转换为HSV格式
• 使用较低/较高阈值执行颜色分割
• 进行形态学转换以消除小噪声
• 查找轮廓并求和轮廓区域

我假设您要检测黄色区域。我们首先将图像转换为HSV格式，然后使用较低/较高范围的颜色阈值

lower = np.array([33, 0, 238], dtype="uint8")
upper = np.array([135, 189, 255], dtype="uint8")

这导致分段的蒙版

从这里我们执行形态变换以消除小噪声

接下来，我们找到轮廓，并用求和cv2.contourArea()。检测到的区域以黑色突出显示

总面积

87781.5

import numpy as np
import cv2

image = cv2.imread('2.jpg')
original = image.copy()
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
lower = np.array([33, 0, 238], dtype="uint8")
upper = np.array([135, 189, 255], dtype="uint8")
mask = cv2.inRange(image, lower, upper)

kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))
opening = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=1)

cnts = cv2.findContours(opening, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]

area = 0
for c in cnts:
    area += cv2.contourArea(c)
    cv2.drawContours(original,[c], 0, (0,0,0), 2)

print(area)
cv2.imshow('mask', mask)
cv2.imshow('original', original)
cv2.imshow('opening', opening)
cv2.waitKey()

您可以使用此脚本查找颜色阈值范围

import cv2
import sys
import numpy as np

def nothing(x):
    pass

useCamera=False

# Check if filename is passed
if (len(sys.argv) <= 1) :
    print("'Usage: python hsvThresholder.py <ImageFilePath>' to ignore camera and use a local image.")
    useCamera = True

# Create a window
cv2.namedWindow('image')

# create trackbars for color change
cv2.createTrackbar('HMin','image',0,179,nothing) # Hue is from 0-179 for Opencv
cv2.createTrackbar('SMin','image',0,255,nothing)
cv2.createTrackbar('VMin','image',0,255,nothing)
cv2.createTrackbar('HMax','image',0,179,nothing)
cv2.createTrackbar('SMax','image',0,255,nothing)
cv2.createTrackbar('VMax','image',0,255,nothing)

# Set default value for MAX HSV trackbars.
cv2.setTrackbarPos('HMax', 'image', 179)
cv2.setTrackbarPos('SMax', 'image', 255)
cv2.setTrackbarPos('VMax', 'image', 255)

# Initialize to check if HSV min/max value changes
hMin = sMin = vMin = hMax = sMax = vMax = 0
phMin = psMin = pvMin = phMax = psMax = pvMax = 0

# Output Image to display
if useCamera:
    cap = cv2.VideoCapture(0)
    # Wait longer to prevent freeze for videos.
    waitTime = 330
else:
    img = cv2.imread(sys.argv[1])
    output = img
    waitTime = 33

while(1):

    if useCamera:
        # Capture frame-by-frame
        ret, img = cap.read()
        output = img

    # get current positions of all trackbars
    hMin = cv2.getTrackbarPos('HMin','image')
    sMin = cv2.getTrackbarPos('SMin','image')
    vMin = cv2.getTrackbarPos('VMin','image')

    hMax = cv2.getTrackbarPos('HMax','image')
    sMax = cv2.getTrackbarPos('SMax','image')
    vMax = cv2.getTrackbarPos('VMax','image')

    # Set minimum and max HSV values to display
    lower = np.array([hMin, sMin, vMin])
    upper = np.array([hMax, sMax, vMax])

    # Create HSV Image and threshold into a range.
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(hsv, lower, upper)
    output = cv2.bitwise_and(img,img, mask= mask)

    # Print if there is a change in HSV value
    if( (phMin != hMin) | (psMin != sMin) | (pvMin != vMin) | (phMax != hMax) | (psMax != sMax) | (pvMax != vMax) ):
        print("(hMin = %d , sMin = %d, vMin = %d), (hMax = %d , sMax = %d, vMax = %d)" % (hMin , sMin , vMin, hMax, sMax , vMax))
        phMin = hMin
        psMin = sMin
        pvMin = vMin
        phMax = hMax
        psMax = sMax
        pvMax = vMax

    # Display output image
    cv2.imshow('image',output)

    # Wait longer to prevent freeze for videos.
    if cv2.waitKey(waitTime) & 0xFF == ord('q'):
        break

# Release resources
if useCamera:
    cap.release()
cv2.destroyAllWindows()