For CV2 Color to Grayscale Image Conversion

The task is simple: we want to verify the formula used in CV2 (OpenCV 2.0) for converting color image to grayscale. The formula to convert from RGB to gray can be found here: Y = 0.299*R + 0.587*G + 0.114*B

We use the following code to verify the formula. First we read in a color image as both color (img_color) and grayscale (img_gray). Then we use the equation above to convert the color image to gray (img_color2gray). As the final step, we compare img_gray and img_color2gray. This comparison allows to verify the equation. The output of the equation is floating number. We use rounding (np.round) to convert float to integer. np.round(1.499) = 1 and np.round(1.5) = 2. We have tried other quantization methods such as floor or ceil but they don't behave as well.

img_name = 'lena_color_512.tif';
img_color = cv2.imread(img_name, cv2.IMREAD_COLOR)
img_gray = cv2.imread(img_name, cv2.IMREAD_GRAYSCALE)

shape = img_gray.shape
img_color2gray = np.zeros(shape, dtype=np.int32)
img_color2gray[:,:] = np.round(0.299*img_color[:,:,2]+0.587*img_color[:,:,1]+0.114*img_color[:,:,0])
img_color2gray = np.clip(img_color2gray, 0, 255)

The comparison result is this: img_gray and img_color2gray are very similar but not identical. Out of 512*512=262144 pixels in total, 352 pixels don't match (%0.13 of total). The dots in the figure below are the unmatched pixels. This leaves a question of how exactly this conversion is done in OpenCV SW. My source code can be found here.