# Copyright 2022 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Example for natural robustness methods.""" import numpy as np import cv2 from mindarmour.natural_robustness.transform.image import Translate, Curve, Perspective, Scale, Shear, Rotate, SaltAndPepperNoise, \ NaturalNoise, GaussianNoise, UniformNoise, MotionBlur, GaussianBlur, GradientBlur, Contrast, GradientLuminance def test_perspective(image): """Test perspective.""" ori_pos = [[0, 0], [0, 800], [800, 0], [800, 800]] dst_pos = [[50, 0], [0, 800], [780, 0], [800, 800]] trans = Perspective(ori_pos, dst_pos) dst = trans(image) cv2.imshow('dst', dst) cv2.waitKey() def test_uniform_noise(image): """Test uniform noise.""" trans = UniformNoise(factor=0.1) dst = trans(image) cv2.imshow('dst', dst) cv2.waitKey() def test_gaussian_noise(image): """Test gaussian noise.""" trans = GaussianNoise(factor=0.1) dst = trans(image) cv2.imshow('dst', dst) cv2.waitKey() def test_contrast(image): """Test contrast.""" trans = Contrast(alpha=2, beta=0) dst = trans(image) cv2.imshow('dst', dst) cv2.waitKey() def test_gaussian_blur(image): """Test gaussian blur.""" trans = GaussianBlur(ksize=5) dst = trans(image) cv2.imshow('dst', dst) cv2.waitKey() def test_salt_and_pepper_noise(image): """Test salt and pepper noise.""" trans = SaltAndPepperNoise(factor=0.01) dst = trans(image) cv2.imshow('dst', dst) cv2.waitKey() def test_translate(image): """Test translate.""" trans = Translate(x_bias=0.1, y_bias=0.1) dst = trans(image) cv2.imshow('dst', dst) cv2.waitKey() def test_scale(image): """Test scale.""" trans = Scale(factor_x=0.7, factor_y=0.7) dst = trans(image) cv2.imshow('dst', dst) cv2.waitKey() def test_shear(image): """Test shear.""" trans = Shear(factor=0.2) dst = trans(image) cv2.imshow('dst', dst) cv2.waitKey() def test_rotate(image): """Test rotate.""" trans = Rotate(angle=20) dst = trans(image) cv2.imshow('dst', dst) cv2.waitKey() def test_curve(image): """Test curve.""" trans = Curve(curves=2, depth=1.5, mode='horizontal') dst = trans(image) cv2.imshow('dst', dst) cv2.waitKey() def test_natural_noise(image): """Test natural noise.""" trans = NaturalNoise(ratio=0.0001, k_x_range=(1, 30), k_y_range=(1, 10), auto_param=True) dst = trans(image) cv2.imshow('dst', dst) cv2.waitKey() def test_gradient_luminance(image): """Test gradient luminance.""" height, width = image.shape[:2] point = (height // 4, width // 2) start = (255, 255, 255) end = (0, 0, 0) scope = 0.3 bright_rate = 0.4 trans = GradientLuminance(start, end, start_point=point, scope=scope, pattern='dark', bright_rate=bright_rate, mode='horizontal') dst = trans(image) cv2.imshow('dst', dst) cv2.waitKey() def test_motion_blur(image): """Test motion blur.""" angle = -10.5 i = 10 trans = MotionBlur(degree=i, angle=angle) dst = trans(image) cv2.imshow('dst', dst) cv2.waitKey() def test_gradient_blur(image): """Test gradient blur.""" number = 10 h, w = image.shape[:2] point = (int(h / 5), int(w / 5)) center = False trans = GradientBlur(point, number, center) dst = trans(image) cv2.imshow('dst', dst) cv2.waitKey() if __name__ == '__main__': img = cv2.imread('1.jpeg') img = np.array(img) test_uniform_noise(img) test_gaussian_noise(img) test_motion_blur(img) test_gradient_blur(img) test_gradient_luminance(img) test_natural_noise(img) test_curve(img) test_rotate(img) test_shear(img) test_scale(img) test_translate(img) test_salt_and_pepper_noise(img) test_gaussian_blur(img) test_contrast(img) test_perspective(img)