I want to extract a specific color from the image.

Thank you for your help.I'd like to extract red and green colors from the image and enclose the color dot group in a rectangle, but I can't try any HSV pattern.Blue is working well now, but it's partly off, and it's also hanging in unrelated places.I'm not sure if it's a bad way to find a color like a program or if I should change the scope.Please tell me the solution.Thank you for your cooperation.

col_img=cv2.imread("/home/pi/seisaku/2.jpg", cv2.IMREAD_COLOR)   
col_img=cv2.resize(col_img, None, fx=3, fy=3)
img=cv2.cvtColor(col_img, cv2.cv.CV_BGR2HSV)

h=copy.copy(img[:,:,0])
s=copy.copy(img[:,:,1])
v=copy.copy(img[:,:,2])

Blue=np.zeros (col_img.shape, dtype=np.uint8)
Red=np.zeros (col_img.shape, dtype=np.uint8)
Green=np.zeros (col_img.shape, dtype=np.uint8)
Blue [((h>200)|(h<260))&(s>100)] = 255
Red [((h<30)|(h>280))&(s>128)] = 255
Green [((h>80)|(h<230))&(s>170)] = 255

for colin [Red, Blue, Green]:
    gray=cv2.cvtColor(col,cv2.COLOR_BGR2GRAY)
    retval, bw = cv2.threshold (gray, 50, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
    cv2.imwrite("bw.jpg",bw)

    contents,_=cv2.findContours(bw,cv2.RETR_LIST,cv2.CHAIN_APPROX_NONE)

    ## treatment of each contour
    for i in range(0,len(contours)):

        # Compute contour areas
        area=cv2.contourArea(contours[i])

        # circumscribed rectangle
        if(len(contours[i])>0)&(100<area<400):
            rect=contours[i]
            x,y,w,h = cv2.boundingRect(rect)
            ifabs(w-h)<5:
                cv2.rectangle(col_img,(x,y),(x+w,y+h),(255,0,0),2)
            # print(int(x+w/2), int(y+h/2))

            cv2.imwrite("aux.jpg", col_img)
            # cv2.waitKey(0)

python opencv raspberry-pi

2022-09-30 16:11

1 Answers

Blue [((h>200)|(h<260))&(s>100)] = 255
Red [((h<30)|(h>280))&(s>128)] = 255
Green [((h>80)|(h<230))&(s>170)] = 255

Do you mean that you want to determine the hue (h) by the angle [0,360)?After OpenCV HSV conversion, the value range is 0 to 179, so this formula does not work as you expected.

Question We have improved the marker criteria based on the source code, assuming Python 3 + OpenCV3.

#!/usr/bin/env python3
import cv2
import numpy as np
import path

col_img = cv2.imread("q37984.jpg", cv2.IMREAD_COLOR)
col_img=cv2.resize(col_img, None, fx=3, fy=3)

# Decomposition by HSV channel
img=cv2.cvtColor(col_img,cv2.COLOR_BGR2HSV)
h = img [:, :, 0]
s=img[:,:,1]
v=img[:,:,2]

shape=(img.shape[0], img.shape[1], 1)
Red=np.zeros (shape, dtype=np.uint8)
Green=np.zeros (shape, dtype=np.uint8)
Blue=np.zeros (shape, dtype=np.uint8)
# Hue (Hue) detection of color components; + threshold determination of saturation (S) and brightness (V)
HF = 0.5
Red [((h<0*HF)|(h>300*HF))&(s>16)&(v>128)] = 255#330±30°
Green [((h>30*HF)&(h<90*HF))&(s>16)&(v>128)] = 255#60±30°
Blue [((h>180*HF)&(h<240*HF))&(s>16)&(v>128)] = 255#210±30°

# Reduced noise removal (5x5 circular kernel)
kernel=cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
Red=cv2.morphonyEx (Red, cv2.MORPH_OPEN, kernel)
Green=cv2.morphonyEx (Green, cv2.MORPH_OPEN, kernel)
Blue=cv2.morphologyEx (Blue, cv2.MORPH_OPEN, kernel)

Round_TH = 0.4# circle decision threshold

for target in [Red, Green, Blue]:
    _,contours,_=cv2.findContours(target,cv2.RETR_LIST,cv2.CHAIN_APPROX_NONE)
    for cnt in contents:
        # Calculate the circumscribed circular area area 1 of the detected contour
        (cx, cy), radius=cv2.minEnclosureCircle(cnt)
        area1=(radius*radius)*math.pi
        # Calculate the real area area 0 of the detected contour
        area0 = cv2.contourArea(cnt)
        # Estimate the "circularity" of the contour from the area ratio
        if(1-ROUND_TH)<area1/area0<(1+ROUND_TH):
            cv2.circle(col_img, (int(cx), int(cy))), int(radius), (0,0,0), 2)

cv2.imwrite("result.jpg", col_img)

Result:

2022-09-30 16:11

If you have any answers or tips

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