def test_equalize_py(plot=False): """ Test Equalize py op """ logger.info("Test Equalize") # Original Images data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) transforms_original = mindspore.dataset.transforms.py_transforms.Compose( [F.Decode(), F.Resize((224, 224)), F.ToTensor()]) ds_original = data_set.map(operations=transforms_original, input_columns="image") ds_original = ds_original.batch(512) for idx, (image, _) in enumerate(ds_original): if idx == 0: images_original = np.transpose(image.asnumpy(), (0, 2, 3, 1)) else: images_original = np.append(images_original, np.transpose(image.asnumpy(), (0, 2, 3, 1)), axis=0) # Color Equalized Images data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) transforms_equalize = mindspore.dataset.transforms.py_transforms.Compose( [F.Decode(), F.Resize((224, 224)), F.Equalize(), F.ToTensor()]) ds_equalize = data_set.map(operations=transforms_equalize, input_columns="image") ds_equalize = ds_equalize.batch(512) for idx, (image, _) in enumerate(ds_equalize): if idx == 0: images_equalize = np.transpose(image.asnumpy(), (0, 2, 3, 1)) else: images_equalize = np.append(images_equalize, np.transpose(image.asnumpy(), (0, 2, 3, 1)), axis=0) num_samples = images_original.shape[0] mse = np.zeros(num_samples) for i in range(num_samples): mse[i] = diff_mse(images_equalize[i], images_original[i]) logger.info("MSE= {}".format(str(np.mean(mse)))) if plot: visualize_list(images_original, images_equalize)
def test_equalize_py_c(plot=False): """ Test Equalize Cpp op and python op """ logger.info("Test Equalize cpp and python op") # equalize Images in cpp data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) data_set = data_set.map(operations=[C.Decode(), C.Resize((224, 224))], input_columns=["image"]) ds_c_equalize = data_set.map(operations=C.Equalize(), input_columns="image") ds_c_equalize = ds_c_equalize.batch(512) for idx, (image, _) in enumerate(ds_c_equalize): if idx == 0: images_c_equalize = image.asnumpy() else: images_c_equalize = np.append(images_c_equalize, image.asnumpy(), axis=0) # Equalize images in python data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) data_set = data_set.map(operations=[C.Decode(), C.Resize((224, 224))], input_columns=["image"]) transforms_p_equalize = mindspore.dataset.transforms.py_transforms.Compose( [lambda img: img.astype(np.uint8), F.ToPIL(), F.Equalize(), np.array]) ds_p_equalize = data_set.map(operations=transforms_p_equalize, input_columns="image") ds_p_equalize = ds_p_equalize.batch(512) for idx, (image, _) in enumerate(ds_p_equalize): if idx == 0: images_p_equalize = image.asnumpy() else: images_p_equalize = np.append(images_p_equalize, image.asnumpy(), axis=0) num_samples = images_c_equalize.shape[0] mse = np.zeros(num_samples) for i in range(num_samples): mse[i] = diff_mse(images_p_equalize[i], images_c_equalize[i]) logger.info("MSE= {}".format(str(np.mean(mse)))) if plot: visualize_list(images_c_equalize, images_p_equalize, visualize_mode=2)
def test_equalize_md5_py(): """ Test Equalize py op with md5 check """ logger.info("Test Equalize") # First dataset data1 = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) transforms = mindspore.dataset.transforms.py_transforms.Compose( [F.Decode(), F.Equalize(), F.ToTensor()]) data1 = data1.map(operations=transforms, input_columns="image") # Compare with expected md5 from images filename = "equalize_01_result.npz" save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
def test_uniform_augment(plot=False, num_ops=2): """ Test UniformAugment """ logger.info("Test UniformAugment") # Original Images data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) transforms_original = mindspore.dataset.transforms.py_transforms.Compose([F.Decode(), F.Resize((224, 224)), F.ToTensor()]) ds_original = data_set.map(operations=transforms_original, input_columns="image") ds_original = ds_original.batch(512) for idx, (image, _) in enumerate(ds_original): if idx == 0: images_original = np.transpose(image.asnumpy(), (0, 2, 3, 1)) else: images_original = np.append(images_original, np.transpose(image.asnumpy(), (0, 2, 3, 1)), axis=0) # UniformAugment Images data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) transform_list = [F.RandomRotation(45), F.RandomColor(), F.RandomSharpness(), F.Invert(), F.AutoContrast(), F.Equalize()] transforms_ua = \ mindspore.dataset.transforms.py_transforms.Compose([F.Decode(), F.Resize((224, 224)), F.UniformAugment(transforms=transform_list, num_ops=num_ops), F.ToTensor()]) ds_ua = data_set.map(operations=transforms_ua, input_columns="image") ds_ua = ds_ua.batch(512) for idx, (image, _) in enumerate(ds_ua): if idx == 0: images_ua = np.transpose(image.asnumpy(), (0, 2, 3, 1)) else: images_ua = np.append(images_ua, np.transpose(image.asnumpy(), (0, 2, 3, 1)), axis=0) num_samples = images_original.shape[0] mse = np.zeros(num_samples) for i in range(num_samples): mse[i] = diff_mse(images_ua[i], images_original[i]) logger.info("MSE= {}".format(str(np.mean(mse)))) if plot: visualize_list(images_original, images_ua)