def main():
global img_list
global gmap
global weight
img_list = utils.read_images("../../test_data/cafe", N, downsample=3)
full_img_list = utils.read_images("../../test_data/cafe", N)
gray_imgs = utils.read_images("../../test_data/cafe", N, gray=True)
x0 = np.full(N, 1.0/N)
(gmap, weight) = gradient_map(gray_imgs)
bnds = []
for i in range(len(img_list)):
bnds.append((0, 1))
lambdas = minimize(edge_light, x0, method='TNC', jac=False,
bounds=bnds)
ret_image = sum_images(lambdas.x, full_img_list)
print lambdas.message
print "Choice of lambdas = %s" % (lambdas.x)
cv2.imwrite('output_edge.png', utils.denormalize_img(ret_image))
cv2.imshow('image', ret_image)
cv2.waitKey(0)
def main():
for i in range(0, N):
img_name = "../../../input_image/basket/images/%03d.png" % (i)
# img_name = "../test_data/small/%03d_small.png" % (i)
img = cv2.imread(img_name)
gray_image = cv2.cvtColor(img, cv2.cv.CV_BGR2GRAY)
img_list.append(utils.normalize(gray_image))
gradient_map = my_gradient_map.gradient_map(img_list)
cv2.imwrite('output_diffuse.png', utils.denormalize(gradient_map))
cv2.imshow('image', gradient_map)
cv2.waitKey(0)
