3H proficient in opencv (VII) - color detection

0、 preparation

Right click the new project , choice Python File, Create a new one Python file , Then at the beginning import cv2 Import cv2 library ,import numpy And rename to np.

import cv2import numpy as np

We also need to know where OpenCV in , The direction of the coordinate axis is x Shaft to the right ,y Axis down , The coordinate origin is in the upper left corner , For example, the following one is 640 Pixels , Wide for 480 Pixel image .OK, Let's begin with this section .

1、 Implementation code

• stackImages() It is the library function written by ourselves that we used in the previous section , The following lines createTrackbar() Is defining windows and sliders .createTrackbar("Hue Min","TrackBars",0,179,empty) in 0 Is the default value of the slider ,179 Is its range of values ,empty If it is empty pass Whatever it is .
• Hue It's hue ,Sat It's saturation ,Val Is the pixel values . The image will show the pixels between the minimum and maximum values of these three attributes .
• while Loop is when the slider is dragged , The image will also change the display , At the same time, the value will also update the output .

def empty(a):    passdef stackImages(scale,imgArray):    rows = len(imgArray)    cols = len(imgArray[0])    rowsAvailable = isinstance(imgArray[0], list)    width = imgArray[0][0].shape[1]    height = imgArray[0][0].shape[0]    if rowsAvailable:        for x in range ( 0, rows):            for y in range(0, cols):                if imgArray[x][y].shape[:2] == imgArray[0][0].shape [:2]:                    imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 0), None, scale, scale)                else:                    imgArray[x][y] = cv2.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]), None, scale, scale)                if len(imgArray[x][y].shape) == 2: imgArray[x][y]= cv2.cvtColor( imgArray[x][y], cv2.COLOR_GRAY2BGR)        imageBlank = np.zeros((height, width, 3), np.uint8)        hor = [imageBlank]*rows        hor_con = [imageBlank]*rows        for x in range(0, rows):            hor[x] = np.hstack(imgArray[x])        ver = np.vstack(hor)    else:        for x in range(0, rows):            if imgArray[x].shape[:2] == imgArray[0].shape[:2]:                imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)            else:                imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None,scale, scale)            if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)        hor= np.hstack(imgArray)        ver = hor    return verpath="Resources/lambo.png"cv2.namedWindow("TrackBars")cv2.resizeWindow("TrackBars",640,240)cv2.createTrackbar("Hue Min","TrackBars",0,179,empty)cv2.createTrackbar("Hue Max","TrackBars",19,179,empty)cv2.createTrackbar("Sat Min","TrackBars",110,255,empty)cv2.createTrackbar("Sat Max","TrackBars",240,255,empty)cv2.createTrackbar("Val Min","TrackBars",153,255,empty)cv2.createTrackbar("Val Max","TrackBars",255,255,empty)while True:    img = cv2.imread(path)    imgHSV = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)    h_min = cv2.getTrackbarPos("Hue Min","TrackBars")    h_max = cv2.getTrackbarPos("Hue Max", "TrackBars")    s_min = cv2.getTrackbarPos("Sat Min", "TrackBars")    s_max = cv2.getTrackbarPos("Sat Max", "TrackBars")    v_min = cv2.getTrackbarPos("Val Min", "TrackBars")    v_max = cv2.getTrackbarPos("Val Max", "TrackBars")    print(h_min,h_max,s_min,s_max,v_min,v_max)    lower = np.array([h_min,s_min,v_min])    upper = np.array([h_max,s_max,v_max])    mask = cv2.inRange(img,lower,upper)    imgResult = cv2.bitwise_and(img,img,mask=mask)    imgStack = stackImages(0.6,([img,imgHSV],[mask,imgResult]))    cv2.imshow("Stacked Images", imgStack)    cv2.waitKey(1)

2、 Running effect

Run to see the effect , At first, it's the default value , We change the minimum and maximum hue values to 0, It must be dark .

We will the hue of Max Drag onto 116, I found the color of the vehicle displayed .

Let's change the saturation Min, It's on 110, Many color dots in the image have disappeared .

We're going to saturate Max Set it to the maximum , At this time, all the images are displayed again .

Finally, adjust the pixel value , The car color display is very comprehensive , That means orange and white are in this range .

Reduce the pixel value range , The trolley will only display the colors in this range .