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# pip3 install opencv-contrib==4.5.1.48 opencv-contrib-python==4.5.1.48
import numpy as np
import cv2
src = cv2.imread('sample.jpg', 1)
blurred = cv2.GaussianBlur(src, (5, 5), 0)
blurred_float = blurred.astype(np.float32) / 255.0
# download model from https://github.com/opencv/opencv_extra/blob/5e3a56880fb115e757855f8e01e744c154791144/testdata/cv/ximgproc/model.yml.gz
edgeDetector = cv2.ximgproc.createStructuredEdgeDetection("model.yml")
edges = edgeDetector.detectEdges(blurred_float) * 255.0
cv2.imwrite('edge-raw.jpg', edges)
def filterOutSaltPepperNoise(edgeImg):
# Get rid of salt & pepper noise.
count = 0
lastMedian = edgeImg
median = cv2.medianBlur(edgeImg, 3)
while not np.array_equal(lastMedian, median):
# get those pixels that gets zeroed out
zeroed = np.invert(np.logical_and(median, edgeImg))
edgeImg[zeroed] = 0
count = count + 1
if count > 50:
break
lastMedian = median
median = cv2.medianBlur(edgeImg, 3)
edges_8u = np.asarray(edges, np.uint8)
filterOutSaltPepperNoise(edges_8u)
cv2.imwrite('edge.jpg', edges_8u)
def findLargestContour(edgeImg):
contours, hierarchy = cv2.findContours(
edgeImg,
cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE
)
# From among them, find the contours with large surface area.
contoursWithArea = []
for contour in contours:
area = cv2.contourArea(contour)
contoursWithArea.append([contour, area])
contoursWithArea.sort(key=lambda tupl: tupl[1], reverse=True)
largestContour = contoursWithArea[0][0]
return largestContour
contour = findLargestContour(edges_8u)
# Draw the contour on the original image
contourImg = np.copy(src)
cv2.drawContours(contourImg, [contour], 0, (0, 255, 0), 2, cv2.LINE_AA, maxLevel=1)
cv2.imwrite('contour.jpg', contourImg)
mask = np.zeros_like(edges_8u)
cv2.fillPoly(mask, [contour], 255)
# calculate sure foreground area by dilating the mask
mapFg = cv2.erode(mask, np.ones((5, 5), np.uint8), iterations=10)
# mark inital mask as "probably background"
# and mapFg as sure foreground
trimap = np.copy(mask)
trimap[mask == 0] = cv2.GC_BGD
trimap[mask == 255] = cv2.GC_PR_BGD
trimap[mapFg == 255] = cv2.GC_FGD
# visualize trimap
trimap_print = np.copy(trimap)
trimap_print[trimap_print == cv2.GC_PR_BGD] = 128
trimap_print[trimap_print == cv2.GC_FGD] = 255
cv2.imwrite('trimap.png', trimap_print)
# run grabcut
bgdModel = np.zeros((1, 65), np.float64)
fgdModel = np.zeros((1, 65), np.float64)
rect = (0, 0, mask.shape[0] - 1, mask.shape[1] - 1)
cv2.grabCut(src, trimap, rect, bgdModel, fgdModel, 5, cv2.GC_INIT_WITH_MASK)
# create mask again
mask2 = np.where(
(trimap == cv2.GC_FGD) | (trimap == cv2.GC_PR_FGD),
255,
0
).astype('uint8')
cv2.imwrite('mask2.jpg', mask2)
contour2 = findLargestContour(mask2)
mask3 = np.zeros_like(mask2)
cv2.fillPoly(mask3, [contour2], 255)
# blended alpha cut-out
mask3 = np.repeat(mask3[:, :, np.newaxis], 3, axis=2)
mask4 = cv2.GaussianBlur(mask3, (3, 3), 0)
alpha = mask4.astype(float) * 1.1 # making blend stronger
alpha[mask3 > 0] = 255.0
alpha[alpha > 255] = 255.0
foreground = np.copy(src).astype(float)
foreground[mask4 == 0] = 0
background = np.ones_like(foreground, dtype=float) * 255.0
cv2.imwrite('foreground.png', foreground)
cv2.imwrite('background.png', background)
cv2.imwrite('alpha.png', alpha)
# Normalize the alpha mask to keep intensity between 0 and 1
alpha = alpha / 255.0
# Multiply the foreground with the alpha matte
foreground = cv2.multiply(alpha, foreground)
# Multiply the background with ( 1 - alpha )
background = cv2.multiply(1.0 - alpha, background)
# Add the masked foreground and background.
cutout = cv2.add(foreground, background)
cv2.imwrite('cutout.jpg', cutout)
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