def stylize_lines(filename, N, points):
# Creating the L-channel
img = cv2.imread(filename)
cv2.imshow(filename[:-4] + '_input', img)
img_Lab = cv2.cvtColor(img, cv2.COLOR_BGR2Lab)
img_L = img_Lab[:, :, 0]
img_A = img_Lab[:, :, 1]
img_B = img_Lab[:, :, 2]
#Creating Base and depth layers
base = cv2.bilateralFilter(img_L, 9, 75, 75)
detail = cv2.subtract(img_L, base)
#Creating the depth map.
F_b = 0.8
F_d = 0.6
base_N = base / 255
detail_N = detail / 255
depth = F_b * base_N + F_d * detail_N
#Creating random points
no_rpoints = 300
rpoints = numpy.random.rand(no_rpoints, 2)
rpoints[:, 0] = rpoints[:, 0] * (depth.shape[0] - 1)
rpoints[:, 1] = rpoints[:, 1] * (depth.shape[1] - 1)
rpoints = rpoints.astype(int)
#increasing depth of random points
for i in range(no_rpoints):
point = rpoints[i]
depth[point[0]][point[1]] = max(points[:, 2])
#Creating new image based on visibility.
d_img = numpy.zeros((N, img_L.shape[0], img_L.shape[1]), dtype=img_L.dtype)
for n in range(N):
for i in range(img_L.shape[0]):
for j in range(img_L.shape[1]):
q = (i, j, depth[i][j])
if (visibility(points[n], q, depth)):
d_img[n][i][j] = img_L[i][j]
f_d_img = numpy.zeros(img_L.shape, dtype='float64')
for i in range(N):
f_d_img += d_img[i] / N
f_img = numpy.zeros(img_Lab.shape, dtype=img_Lab.dtype)
f_img[:, :, 0] = f_d_img.astype(img_L.dtype)
f_img[:, :, 1] = img_A[:, :]
f_img[:, :, 2] = img_B[:, :]
f_img = cv2.cvtColor(f_img, cv2.COLOR_Lab2BGR)
cv2.imshow(filename[:-4] + '_final', f_img)
cv2.imwrite(filename[:-4] + '_out.jpg', f_img)
detail = cv2.subtract(img_L, base)
#Creating the depth map.
F_b = 0.8
F_d = 0.6
base_N = base / 255
detail_N = detail / 255
depth = F_b * base_N + F_d * detail_N
#Creating new image based on visibility.
d_img = numpy.zeros((N, img_L.shape[0], img_L.shape[1]), dtype=img_L.dtype)
for n in range(N):
for i in range(img_L.shape[0]):
for j in range(img_L.shape[1]):
q = (i, j, depth[i][j])
if (visibility(points[n], q, depth)):
d_img[n][i][j] = 255
f_d_img = numpy.zeros(img_L.shape, dtype='float64')
for i in range(N):
f_d_img += d_img[i] / N
f_img = numpy.zeros(img_Lab.shape, dtype=img_Lab.dtype)
f_img[:, :, 0] = f_d_img.astype(img_L.dtype)
f_img[:, :, 1] = img_A[:, :]
f_img[:, :, 2] = img_B[:, :]
f_img = cv2.cvtColor(f_img, cv2.COLOR_Lab2BGR)
cv2.imshow(filename[:-4] + '_final', f_img)
cv2.imwrite(filename[:-4] + '_out.jpg', f_img)