# Copyright 2019 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. # ============================================================================== import mindspore.dataset.transforms.vision.c_transforms as vision import mindspore.dataset.transforms.vision.py_transforms as py_vision import numpy as np import matplotlib.pyplot as plt import mindspore.dataset as ds from mindspore import log as logger from util import diff_mse, visualize, save_and_check_md5 GENERATE_GOLDEN = False DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"] SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json" def test_center_crop_op(height=375, width=375, plot=False): """ Test random_vertical """ logger.info("Test CenterCrop") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"]) decode_op = vision.Decode() # 3 images [375, 500] [600, 500] [512, 512] center_crop_op = vision.CenterCrop([height, width]) data1 = data1.map(input_columns=["image"], operations=decode_op) data1 = data1.map(input_columns=["image"], operations=center_crop_op) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"]) data2 = data2.map(input_columns=["image"], operations=decode_op) image_cropped = [] image = [] for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): image_cropped.append(item1["image"].copy()) image.append(item2["image"].copy()) if plot: visualize(image, image_cropped) def test_center_crop_md5(height=375, width=375): """ Test random_vertical """ logger.info("Test CenterCrop") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle =False) decode_op = vision.Decode() # 3 images [375, 500] [600, 500] [512, 512] center_crop_op = vision.CenterCrop([height, width]) data1 = data1.map(input_columns=["image"], operations=decode_op) data1 = data1.map(input_columns=["image"], operations=center_crop_op) # expected md5 from images filename = "test_center_crop_01_result.npz" parameters = {"params": {}} save_and_check_md5(data1, parameters, filename, generate_golden=GENERATE_GOLDEN) def test_center_crop_comp(height=375, width=375, plot=False): """ Test random_vertical between python and c image augmentation """ logger.info("Test CenterCrop") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) decode_op = vision.Decode() center_crop_op = vision.CenterCrop([height, width]) data1 = data1.map(input_columns=["image"], operations=decode_op) data1 = data1.map(input_columns=["image"], operations=center_crop_op) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms = [ py_vision.Decode(), py_vision.CenterCrop([height, width]), py_vision.ToTensor() ] transform = py_vision.ComposeOp(transforms) data2 = data2.map(input_columns=["image"], operations=transform()) image_cropped = [] image = [] for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): c_image = item1["image"] py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8) # the images aren't exactly the same due to rouding error assert (diff_mse(py_image, c_image) < 0.001) image_cropped.append(item1["image"].copy()) image.append(item2["image"].copy()) if plot: visualize(image, image_cropped) if __name__ == "__main__": test_center_crop_op(600, 600) test_center_crop_op(300, 600) test_center_crop_op(600, 300) test_center_crop_md5(600, 600) test_center_crop_comp()