使用OpenCV python跟踪黄色对象 [英] Tracking yellow color object with OpenCV python

问题描述

如何使用opencv在python中跟踪黄色对象？而且，如果可能的话，如何获得对象的位置？

How can I track a yellow object in python using opencv? And, if possible, how can I get the position of the object?

我尝试使用以下方法，但我不知道如何降低和升高

I've tried using the following method but i can't figure out how to lower and upper ranges work.

import numpy as np
import cv2


cap = cv2.VideoCapture(0)
while True:
    screen =  np.array(ImageGrab.grab())
    ret, img = cap.read()
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

    #Help
    lower = np.array([])
    upper = np.array([])

    mask = cv2.inRange(hsv, lower, upper)

    cv2.imshow('screen', mask)



    if cv2.waitKey(25) & 0xFF == ord('q'):
        cv2.destroyAllWindows()
        break

它应该找到黄色物体并可能获得它们的位置。

It should find yellow objects and possibly get their position.

推荐答案

您可以将图像转换为HSV，然后使用颜色阈值处理。使用此示例图像

You can convert the image to HSV then use color thresholding. Using this example image

上下限

lower = np.array([22, 93, 0])
upper = np.array([45, 255, 255])

我们可以隔离黄色

要获取对象的位置（我假设您想要一个边界框），可以在生成的蒙版上找到轮廓

To get the position of the object (I'm assuming you want a bounding box), you can find contours on the resulting mask

import numpy as np
import cv2

image = cv2.imread('yellow.jpg')
original = image.copy()
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
lower = np.array([22, 93, 0], dtype="uint8")
upper = np.array([45, 255, 255], dtype="uint8")
mask = cv2.inRange(image, lower, upper)

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

for c in cnts:
    x,y,w,h = cv2.boundingRect(c)
    cv2.rectangle(original, (x, y), (x + w, y + h), (36,255,12), 2)

cv2.imshow('mask', mask)
cv2.imshow('original', original)
cv2.waitKey()

您可以使用此脚本查找颜色阈值范围

You can use this script to find the color threshold ranges

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()

这篇关于使用OpenCV python跟踪黄色对象的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！