You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
130 lines
4.3 KiB
130 lines
4.3 KiB
# Copyright 2020 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 cv2
|
|
import numpy as np
|
|
from PIL import Image
|
|
import mindspore.dataset.vision.c_transforms as C
|
|
from mindspore import log as logger
|
|
|
|
|
|
def test_eager_decode():
|
|
img = np.fromfile("../data/dataset/apple.jpg", dtype=np.uint8)
|
|
logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
|
|
|
|
img = C.Decode()(img)
|
|
logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
|
|
|
|
assert img.shape == (2268, 4032, 3)
|
|
|
|
|
|
def test_eager_resize():
|
|
img = cv2.imread("../data/dataset/apple.jpg")
|
|
logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
|
|
|
|
img = C.Resize(size=(32, 32))(img)
|
|
logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
|
|
|
|
assert img.shape == (32, 32, 3)
|
|
|
|
def test_eager_rescale():
|
|
img = cv2.imread("../data/dataset/apple.jpg")
|
|
logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
|
|
pixel = img[0][0][0]
|
|
|
|
rescale_factor = 0.5
|
|
img = C.Rescale(rescale=rescale_factor, shift=0)(img)
|
|
logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
|
|
pixel_rescaled = img[0][0][0]
|
|
|
|
assert pixel*rescale_factor == pixel_rescaled
|
|
|
|
def test_eager_normalize():
|
|
img = Image.open("../data/dataset/apple.jpg").convert("RGB")
|
|
logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
|
|
pixel = img.getpixel((0, 0))[0]
|
|
|
|
mean_vec = [100, 100, 100]
|
|
std_vec = [2, 2, 2]
|
|
img = C.Normalize(mean=mean_vec, std=std_vec)(img)
|
|
logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
|
|
pixel_normalized = img[0][0][0]
|
|
|
|
assert (pixel - mean_vec[0]) / std_vec[0] == pixel_normalized
|
|
|
|
def test_eager_HWC2CHW():
|
|
img = cv2.imread("../data/dataset/apple.jpg")
|
|
logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
|
|
channel = img.shape
|
|
|
|
img = C.HWC2CHW()(img)
|
|
logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
|
|
channel_swaped = img.shape
|
|
|
|
assert channel == (channel_swaped[1], channel_swaped[2], channel_swaped[0])
|
|
|
|
def test_eager_pad():
|
|
img = Image.open("../data/dataset/apple.jpg").convert("RGB")
|
|
logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
|
|
|
|
img = C.Resize(size=(32, 32))(img)
|
|
logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
|
|
size = img.shape
|
|
|
|
pad = 4
|
|
img = C.Pad(padding=pad)(img)
|
|
logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.size))
|
|
size_padded = img.shape
|
|
|
|
assert size_padded == (size[0] + 2 * pad, size[1] + 2 * pad, size[2])
|
|
|
|
def test_eager_exceptions():
|
|
try:
|
|
img = "../data/dataset/apple.jpg"
|
|
img = C.Decode()(img)
|
|
assert False
|
|
except TypeError as e:
|
|
assert "Input should be an encoded image in 1-D NumPy format" in str(e)
|
|
|
|
try:
|
|
img = np.array(["a", "b", "c"])
|
|
img = C.Decode()(img)
|
|
assert False
|
|
except TypeError as e:
|
|
assert "Input should be an encoded image in 1-D NumPy format" in str(e)
|
|
|
|
try:
|
|
img = cv2.imread("../data/dataset/apple.jpg")
|
|
img = C.Resize(size=(-32, 32))(img)
|
|
assert False
|
|
except ValueError as e:
|
|
assert "not within the required interval" in str(e)
|
|
|
|
try:
|
|
img = "../data/dataset/apple.jpg"
|
|
img = C.Pad(padding=4)(img)
|
|
assert False
|
|
except TypeError as e:
|
|
assert "Input should be NumPy or PIL image" in str(e)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_eager_decode()
|
|
test_eager_resize()
|
|
test_eager_rescale()
|
|
test_eager_normalize()
|
|
test_eager_HWC2CHW()
|
|
test_eager_pad()
|
|
test_eager_exceptions()
|
|
|