def test_random_sharpness_c_py(degrees=(1.0, 1.0), plot=False): """ Test Random Sharpness C and python Op """ logger.info("Test RandomSharpness C and python Op") # RandomSharpness Images data = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) data = data.map(input_columns=["image"], operations=[C.Decode(), C.Resize((200, 300))]) python_op = F.RandomSharpness(degrees) c_op = C.RandomSharpness(degrees) transforms_op = F.ComposeOp( [lambda img: F.ToPIL()(img.astype(np.uint8)), python_op, np.array])() ds_random_sharpness_py = data.map(input_columns="image", operations=transforms_op) ds_random_sharpness_py = ds_random_sharpness_py.batch(512) for idx, (image, _) in enumerate(ds_random_sharpness_py): if idx == 0: images_random_sharpness_py = image else: images_random_sharpness_py = np.append(images_random_sharpness_py, image, axis=0) data = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) data = data.map(input_columns=["image"], operations=[C.Decode(), C.Resize((200, 300))]) ds_images_random_sharpness_c = data.map(input_columns="image", operations=c_op) ds_images_random_sharpness_c = ds_images_random_sharpness_c.batch(512) for idx, (image, _) in enumerate(ds_images_random_sharpness_c): if idx == 0: images_random_sharpness_c = image else: images_random_sharpness_c = np.append(images_random_sharpness_c, image, axis=0) num_samples = images_random_sharpness_c.shape[0] mse = np.zeros(num_samples) for i in range(num_samples): mse[i] = diff_mse(images_random_sharpness_c[i], images_random_sharpness_py[i]) logger.info("MSE= {}".format(str(np.mean(mse)))) if plot: visualize_list(images_random_sharpness_c, images_random_sharpness_py, visualize_mode=2)
def test_auto_contrast_c(plot=False): """ Test AutoContrast C Op """ logger.info("Test AutoContrast C Op") # AutoContrast Images ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[C.Decode(), C.Resize((224, 224))]) python_op = F.AutoContrast() c_op = C.AutoContrast() transforms_op = F.ComposeOp( [lambda img: F.ToPIL()(img.astype(np.uint8)), python_op, np.array])() ds_auto_contrast_py = ds.map(input_columns="image", operations=transforms_op) ds_auto_contrast_py = ds_auto_contrast_py.batch(512) for idx, (image, _) in enumerate(ds_auto_contrast_py): if idx == 0: images_auto_contrast_py = image else: images_auto_contrast_py = np.append(images_auto_contrast_py, image, axis=0) ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[C.Decode(), C.Resize((224, 224))]) ds_auto_contrast_c = ds.map(input_columns="image", operations=c_op) ds_auto_contrast_c = ds_auto_contrast_c.batch(512) for idx, (image, _) in enumerate(ds_auto_contrast_c): if idx == 0: images_auto_contrast_c = image else: images_auto_contrast_c = np.append(images_auto_contrast_c, image, axis=0) num_samples = images_auto_contrast_c.shape[0] mse = np.zeros(num_samples) for i in range(num_samples): mse[i] = diff_mse(images_auto_contrast_c[i], images_auto_contrast_py[i]) logger.info("MSE= {}".format(str(np.mean(mse)))) np.testing.assert_equal(np.mean(mse), 0.0) if plot: visualize_list(images_auto_contrast_c, images_auto_contrast_py, visualize_mode=2)
def test_compare_random_color_op(degrees=None, plot=False): """ Compare Random Color op in Python and Cpp """ logger.info("test_random_color_op") original_seed = config_get_set_seed(5) original_num_parallel_workers = config_get_set_num_parallel_workers(1) # Decode with rgb format set to True data1 = ds.TFRecordDataset(C_DATA_DIR, C_SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = ds.TFRecordDataset(C_DATA_DIR, C_SCHEMA_DIR, columns_list=["image"], shuffle=False) if degrees is None: c_op = vision.RandomColor() p_op = F.RandomColor() else: c_op = vision.RandomColor(degrees) p_op = F.RandomColor(degrees) transforms_random_color_py = F.ComposeOp( [lambda img: img.astype(np.uint8), F.ToPIL(), p_op, np.array]) data1 = data1.map(input_columns=["image"], operations=[vision.Decode(), c_op]) data2 = data2.map(input_columns=["image"], operations=[vision.Decode()]) data2 = data2.map(input_columns=["image"], operations=transforms_random_color_py()) image_random_color_op = [] image = [] for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): actual = item1["image"] expected = item2["image"] image_random_color_op.append(actual) image.append(expected) assert actual.shape == expected.shape mse = diff_mse(actual, expected) logger.info("MSE= {}".format(str(np.mean(mse)))) # Restore configuration ds.config.set_seed(original_seed) ds.config.set_num_parallel_workers(original_num_parallel_workers) if plot: visualize_list(image, image_random_color_op)
def test_invert_py_c(plot=False): """ Test Invert Cpp op and python op """ logger.info("Test Invert cpp and python op") # Invert Images in cpp ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[C.Decode(), C.Resize((224, 224))]) ds_c_invert = ds.map(input_columns="image", operations=C.Invert()) ds_c_invert = ds_c_invert.batch(512) for idx, (image, _) in enumerate(ds_c_invert): if idx == 0: images_c_invert = image else: images_c_invert = np.append(images_c_invert, image, axis=0) # invert images in python ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[C.Decode(), C.Resize((224, 224))]) transforms_p_invert = F.ComposeOp( [lambda img: img.astype(np.uint8), F.ToPIL(), F.Invert(), np.array]) ds_p_invert = ds.map(input_columns="image", operations=transforms_p_invert()) ds_p_invert = ds_p_invert.batch(512) for idx, (image, _) in enumerate(ds_p_invert): if idx == 0: images_p_invert = image else: images_p_invert = np.append(images_p_invert, image, axis=0) num_samples = images_c_invert.shape[0] mse = np.zeros(num_samples) for i in range(num_samples): mse[i] = diff_mse(images_p_invert[i], images_c_invert[i]) logger.info("MSE= {}".format(str(np.mean(mse)))) if plot: visualize_list(images_c_invert, images_p_invert, visualize_mode=2)
def test_to_pil_01(): """ Test ToPIL Op with md5 comparison: input is already PIL image Expected to pass """ logger.info("test_to_pil_01") # Generate dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms = [ py_vision.Decode(), # If input is already PIL image. py_vision.ToPIL(), py_vision.CenterCrop(375), py_vision.ToTensor() ] transform = py_vision.ComposeOp(transforms) data1 = data1.map(input_columns=["image"], operations=transform()) # Compare with expected md5 from images filename = "to_pil_01_result.npz" save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
def test_auto_contrast_c(plot=False): """ Test AutoContrast C Op """ logger.info("Test AutoContrast C Op") # AutoContrast Images ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[C.Decode(), C.Resize((224, 224))]) python_op = F.AutoContrast(cutoff=10.0, ignore=[10, 20]) c_op = C.AutoContrast(cutoff=10.0, ignore=[10, 20]) transforms_op = F.ComposeOp( [lambda img: F.ToPIL()(img.astype(np.uint8)), python_op, np.array])() ds_auto_contrast_py = ds.map(input_columns="image", operations=transforms_op) ds_auto_contrast_py = ds_auto_contrast_py.batch(512) for idx, (image, _) in enumerate(ds_auto_contrast_py): if idx == 0: images_auto_contrast_py = image else: images_auto_contrast_py = np.append(images_auto_contrast_py, image, axis=0) ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[C.Decode(), C.Resize((224, 224))]) ds_auto_contrast_c = ds.map(input_columns="image", operations=c_op) ds_auto_contrast_c = ds_auto_contrast_c.batch(512) for idx, (image, _) in enumerate(ds_auto_contrast_c): if idx == 0: images_auto_contrast_c = image else: images_auto_contrast_c = np.append(images_auto_contrast_c, image, axis=0) num_samples = images_auto_contrast_c.shape[0] mse = np.zeros(num_samples) for i in range(num_samples): mse[i] = diff_mse(images_auto_contrast_c[i], images_auto_contrast_py[i]) logger.info("MSE= {}".format(str(np.mean(mse)))) np.testing.assert_equal(np.mean(mse), 0.0) # Compare with expected md5 from images filename = "autocontrast_01_result_c.npz" save_and_check_md5(ds_auto_contrast_c, filename, generate_golden=GENERATE_GOLDEN) if plot: visualize_list(images_auto_contrast_c, images_auto_contrast_py, visualize_mode=2)