def get_data(self, img_path):
"""
1. load image from img_path.
2. resize or oversampling.
3. transformer data: transpose, channel swap, sub mean.
return K x H x W ndarray.
img_path: image path.
"""
image = image_util.load_image(img_path, self.is_color)
# Another way to extract oversampled features is that
# cropping and averaging from large feature map which is
# calculated by large size of image.
# This way reduces the computation.
if self.oversample:
# image_util.resize_image: short side is self.resize_dim
image = image_util.resize_image(image, self.resize_dim)
image = np.array(image)
input = np.zeros((1, image.shape[0], image.shape[1], 3),
dtype=np.float32)
input[0] = image.astype(np.float32)
input = image_util.oversample(input, self.crop_dims)
else:
image = image.resize(self.crop_dims, Image.ANTIALIAS)
input = np.zeros((1, self.crop_dims[0], self.crop_dims[1], 3),
dtype=np.float32)
input[0] = np.array(image).astype(np.float32)
data_in = []
for img in input:
img = self.transformer.transformer(img).flatten()
data_in.append([img.tolist()])
# paddle input: [[[]],[[]],...], [[]] is one sample.
return data_in
def get_data(self, img_path):
"""
1. load image from img_path.
2. resize or oversampling.
3. transformer data: transpose, channel swap, sub mean.
return K x H x W ndarray.
img_path: image path.
"""
image = image_util.load_image(img_path, self.is_color)
# Another way to extract oversampled features is that
# cropping and averaging from large feature map which is
# calculated by large size of image.
# This way reduces the computation.
if self.oversample:
# image_util.resize_image: short side is self.resize_dim
image = image_util.resize_image(image, self.resize_dim)
image = np.array(image)
input = np.zeros(
(1, image.shape[0], image.shape[1], 3), dtype=np.float32)
input[0] = image.astype(np.float32)
input = image_util.oversample(input, self.crop_dims)
else:
image = image.resize(self.crop_dims, Image.ANTIALIAS)
input = np.zeros(
(1, self.crop_dims[0], self.crop_dims[1], 3), dtype=np.float32)
input[0] = np.array(image).astype(np.float32)
data_in = []
for img in input:
img = self.transformer.transformer(img).flatten()
data_in.append([img.tolist()])
# paddle input: [[[]],[[]],...], [[]] is one sample.
return data_in
def get_data(self, img_path):
"""
1. load image from img_path.
2. resize or oversampling.
3. transformer data: transpose, sub mean.
return K x H x W ndarray.
img_path: image path.
"""
image = image_util.load_image(img_path, self.is_color)
if self.oversample:
# image_util.resize_image: short side is self.resize_dim
image = image_util.resize_image(image, self.resize_dim)
image = np.array(image)
input = np.zeros((1, image.shape[0], image.shape[1], 3),
dtype=np.float32)
input[0] = image.astype(np.float32)
input = image_util.oversample(input, self.crop_dims)
else:
image = image.resize(self.crop_dims, Image.ANTIALIAS)
input = np.zeros((1, self.crop_dims[0], self.crop_dims[1], 3),
dtype=np.float32)
input[0] = np.array(image).astype(np.float32)
data_in = []
for img in input:
img = self.transformer.transformer(img).flatten()
data_in.append([img.tolist()])
return data_in
def get_data(self, img_path):
"""
1. load image from img_path.
2. resize or oversampling.
3. transformer data: transpose, sub mean.
return K x H x W ndarray.
img_path: image path.
"""
image = image_util.load_image(img_path, self.is_color)
if self.oversample:
# image_util.resize_image: short side is self.resize_dim
image = image_util.resize_image(image, self.resize_dim)
image = np.array(image)
input = np.zeros(
(1, image.shape[0], image.shape[1], 3), dtype=np.float32)
input[0] = image.astype(np.float32)
input = image_util.oversample(input, self.crop_dims)
else:
image = image.resize(self.crop_dims, Image.ANTIALIAS)
input = np.zeros(
(1, self.crop_dims[0], self.crop_dims[1], 3), dtype=np.float32)
input[0] = np.array(image).astype(np.float32)
data_in = []
for img in input:
img = self.transformer.transformer(img).flatten()
data_in.append([img.tolist()])
return data_in
