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)