img = cv2.imread('example.jpg')
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# lower mask (0-10)
lower_red = np.array([0, 50, 50])
upper_red = np.array([10, 255, 255]
mask0 = cv2.inRange(img_hsv, lower_red, upper_red)
# upper mask (170-180)
lower_red = np.array([170, 50, 50])
upper_red = np.array([180, 255, 255])
mask1 = cv2.inRange(img_hsv, lower_red, upper_red)
# join my masks
mask = mask0 + mask1
height = mask.shape[0]
width = mask.shape[1]
# iterate over every pixel
for i in range(height):
for j in range(width):
px = mask[i,j]
print px
# check if pixel is white or black
if (px[2] >= 0 and px[2] <= 40):
In the above example 'px' is a pixel in BGR. I need to convert the value to HSV because I want to check if the pixel is in a certain color range.
I already tried
colorsys.rgb_to_hsv(px[2], px[1], px[0})
which evokes the error: invalid index to scalar variable
Thanks!
From the docs:
# Convert BGR to HSV
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# define range of blue color in HSV
lower_blue = np.array([110,50,50])
upper_blue = np.array([130,255,255])
# Threshold the HSV image to get only blue colors
mask = cv2.inRange(hsv, lower_blue, upper_blue)
You can just convert your whole img
to hsv using the built in method:
hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)