I am developing an app which detects major colour in the frame. I used python code and HSV colour ranges to do this. My sample code for detecting blue is as below.
import cv2
def detectBlue(frame):
# Convert BGR to HSV
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# define range of green color in HSV
lower_blue = np.array([101, 39, 64])
upper_blue = np.array([140, 255, 255])
mask = cv2.inRange(hsv, lower_blue, upper_blue)
kernel = np.ones((5,5),'int')
dilated = cv2.dilate(mask,kernel)
# Bitwise-AND mask and original image
res = cv2.bitwise_and(frame,frame, mask=mask)
ret,thrshed = cv2.threshold(cv2.cvtColor(res,cv2.COLOR_BGR2GRAY),3,255,cv2.THRESH_BINARY)
#img,contours,hier = cv2.findContours(thrshed,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
contours,hier = cv2.findContours(thrshed,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
area = [0]
for cnt in contours:
#Contour area is taken
area.append(cv2.contourArea(cnt))
return max(area)
I created similar functions for Red and Green with HSV color ranges as below.
Green:
lower_green = [65,60,60])
upper_green = [80,255,255]
Red:
lower_red = [170,120,70]
upper_red = [180,255,255]
Now I am trying to do the same for all VIBGYOR colors, but I am not getting proper color ranges for all these. I tried converting RGB code to HSV using this python code.
import colorsys
def rgb_hsv_converter(rgb):
(r,g,b) = rgb_normalizer(rgb)
hsv = colorsys.rgb_to_hsv(r,g,b)
(h,s,v) = hsv_normalizer(hsv)
upper_band = [h+10, s+40, v+40]
lower_band = [h-10, s-40, v-40]
return {
'upper_band': upper_band,
'lower_band': lower_band
}
def rgb_normalizer(rgb):
(r,g,b) = rgb
return (r/255, g/255, b/255)
def hsv_normalizer(hsv):
(h,s,v) = hsv
return (h*360, s*255, v*255)
rgb_hsv_converter((255,255,255))
But, these ranges did not work properly when tested. I searched a lot to find out best HSV ranges for all these colors, but no use. Can anybody suggest any approach to find best and more accurate color ranges for many colors.?
