Color detection

Python
1

Hi, I am trying to detect between two types of White (Alaskan White and Pearl White) but I am not getting results, can anyone help me? This is my code and these are the two types of white.

WhatsApp Image 2021-07-22 at 10.45.40
WhatsApp Image 2021-07-22 at 10.45.40958Ă—1280 77.5 KB 

import cv2
import numpy as np

# capturing video through webcam

cap = cv2.VideoCapture(0)

while(1):
    _, img = cap.read()

    # converting frame(img == BGR) to HSV(hue-saturation-value)

    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

    # red color

    red_lower = np.array([136,87,111],np.uint8)
    red_upper = np.array([180,255,255],np.uint8)

    # blue color

    blue_lower = np.array([99,115,150],np.uint8)
    blue_upper = np.array([110,255,255],np.uint8)

    # yellow color

    yellow_lower = np.array([22,60,200],np.uint8)
    yellow_upper = np.array([60,255,255],np.uint8)

    # white color

    white_lower = np.array([0,0,200],np.uint8)
    white_upper = np.array([180,20,255],np.uint8)

    # black color

    black_lower = np.array([0,0,0],np.uint8)
    black_upper = np.array([180,255,30],np.uint8)

    # all color together

    red = cv2.inRange(hsv, red_lower, red_upper)
    blue = cv2.inRange(hsv, blue_lower, blue_upper)
    yellow = cv2.inRange(hsv, yellow_lower, yellow_upper)
    white = cv2.inRange(hsv, white_lower, white_upper)
    black = cv2.inRange(hsv, black_lower, black_upper)

    # Morphological Transform, Dilation

    kernal = np.ones((5, 5), "uint8")

    red = cv2.dilate(red, kernal)
    res_red = cv2.bitwise_and(img, img, mask = red)

    blue = cv2.dilate(blue, kernal)
    res_blue = cv2.bitwise_and(img, img, mask = blue)

    yellow = cv2.dilate(yellow, kernal)
    res_yellow = cv2.bitwise_and(img, img, mask = yellow)

    white = cv2.dilate(white, kernal)
    res_white = cv2.bitwise_and(img, img, mask = white)

    black = cv2.dilate(black, kernal)
    res_black = cv2.bitwise_and(img, img, mask = black)

    # Tracking red
    contours, hierarchy = cv2.findContours(red, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    for pic, contour in enumerate(contours):
        area = cv2.contourArea(contour)
        if(area > 300):
            x, y, w, h = cv2.boundingRect(contour)
            img = cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 2)
            cv2.putText(img, "ROJO: ", (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255))    

    # Tracking blue
    contours, hierarchy = cv2.findContours(blue, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    for pic, contour in enumerate(contours):
        area = cv2.contourArea(contour)
        if(area > 300):
            x, y, w, h = cv2.boundingRect(contour)
            img = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
            cv2.putText(img, "AZUL: ", (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 0))
            

    # Tracking yellow
    contours, hierarchy = cv2.findContours(yellow, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    for pic, contour in enumerate(contours):
        area = cv2.contourArea(contour)
        if(area > 300):
            x, y, w, h = cv2.boundingRect(contour)
            img = cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
            cv2.putText(img, "AMARILLO: ", (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 255, 0))

    # Tracking white
    contours, hierarchy = cv2.findContours(white, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    for pic, contour in enumerate(contours):
        area = cv2.contourArea(contour)
        if(area > 300):
            x, y, w, h = cv2.boundingRect(contour)
            img = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 255, 255), 2)
            cv2.putText(img, "BLANCO: ", (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255, 255, 255))

    # Tracking black
    contours, hierarchy = cv2.findContours(black, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    for pic, contour in enumerate(contours):
        area = cv2.contourArea(contour)
        if(area > 300):
            x, y, w, h = cv2.boundingRect(contour)
            img = cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 0), 2)
            cv2.putText(img, "NEGRO: ", (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 0))
    
    cv2.imshow("Color Tracking", img)
    if cv2.waitKey(10) & 0xFF == ord('q'):
        cap.release()
        cv2.destroyAllWindows()
        break
2

what exactly is the problem?

“not getting results” is non-specific

your code shows no evidence of even trying to distinguish between your two shades of white

are you aware of “white balance” and “color temperature”, which are settings of most cameras?

3

Currently the code only detects between blue, red, black, white and yellow. I would like to be able to detect both targets (I wanted to do it with different rgb) but it doesn’t work. I don’t know how to do it

4

Hi @Santispecia

First thing, painted colors change with light. If you illuminate with a red light, white paint will be seen red. You can detect colors seen by the camera, not the paint color.

You can distinguish those two whites using your code with inRange, if you control illumination, so the same color looks always the same way.

5

thank you very much, it was very helpful

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