Result = cv2.cvtColor(img,cv2.COLOR_BGR2BGRA)Ĭv2.imwrite('greenscreen_thresh.png', thresh)Ĭv2.imwrite('greenscreen_mask.png', mask)Ĭv2.imwrite('greenscreen_antialiased. Thresh = cv2.threshold(A, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)īlur = cv2.GaussianBlur(thresh, (0,0), sigmaX=5, sigmaY=5, borderType = cv2.BORDER_DEFAULT) Lab = cv2.cvtColor(img,cv2.COLOR_BGR2LAB) It has the benefit of reducing some green and smoothing the result so that it will blend smoothly into any background image.Īdjust the blur sigma values and/or the input clip values in the rescale_intensity to adjust the smoothing and amount of green showing. It does the same thresholding in LAB colorspace, but I simply anti-alias using a blur and a stretch of half the input range of gray values on the A channel.

Here is a variation on Luke's answer using Python/OpenCV.

Sophisticated methods will be needed to get rid of those. The green shade present within the person's face still remains.

You can try varying threshold_value and experiment. The noisy green outline around the border has improved to a certain extent. The resulting image looks like nothing much has changed, so here is a zoomed in comparison: Now in the above image, we do not see the dark borders around the person.Ĭonvert the image to BGR and the pixels that were dark (0) in the threshold image are set to white (255, 255, 255) in the color image img2 = cv2.cvtColor(mlab, cv2.COLOR_LAB2BGR) We will set intensity value in the a-channel of the selected pixels to 127: mlab2 = py() In the a-channel, having green color represents the lower end of the range, while red color represents the higher end. threshold_value = 100ĭst_th = cv2.threshold(dst, threshold_value, 255, cv2.THRESH_BINARY_INV)Ībove is the threshold image to segment green shaded regions. > Inverse binary threshold to select area with green borders mlab = cv2.cvtColor(masked, cv2.COLOR_BGR2LAB)ĭst = cv2.normalize(mlab, dst=None, alpha=0, beta=255,norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U)Ībove image is the a-channel with dark pixels around the border. > Normalize the a-channel mlab to use the entire intensity range between Masked = cv2.bitwise_and(img, img, mask = th) # contains dark background Th = cv2.threshold(a_channel,127,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU) Lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB) > Masking original image with binary mask img = cv2.imread('green_background.jpg') > Threshold a-channel to isolate green background We will use the above info as a base to approach the problem at hand: b-axis shows blue/yellow color on its extremesĪpplying a suitable threshold in the corresponding channels makes it easy for us to segment these colors.a-axis clearly shows red/green color on its extremes.B-channel: represents the amount of blue/yellow color.A-channel: represents the amount of red/green color.LAB space has 3 channels, out of which only 2 are color channels while 1 is a brightness channel: Background:ĭominant colors like red, green, blue and yellow can be segmented pretty easily when analyzed in LAB color space.

Green tint over monity