def __getitem__(self, index):
image = read_image(self.file_paths[index])
if len(image.shape) < 3:
image = image[..., np.newaxis]
image = torch.Tensor(image.transpose(2, 0, 1))
if self.has_target:
return image, self.target[index]
return image
def __getitem__(self, index):
image = read_image(self.file_paths[index])
if len(image.shape) < 3:
image = image[..., np.newaxis]
image = torch.Tensor(image.transpose(2, 0, 1))
if self.has_target:
return image, self.target[index]
return image
def _image_to_array(img_path):
"""Read the image from the path and return image object.
Return an image object.
Args:
img_path: image file name in images_dir_path.
"""
if os.path.exists(img_path):
img = read_image(img_path)
if len(img.shape) < 3:
img = img[..., np.newaxis]
return img
else:
raise ValueError("%s image does not exist" % img_path)
def _image_to_array(img_path):
"""Read the image from the path and return it as an numpy.ndarray.
Load the image file as an array
Args:
img_path: a string whose value is the image file name
"""
if os.path.exists(img_path):
img = read_image(img_path)
if len(img.shape) < 3:
img = img[..., np.newaxis]
return img
else:
raise ValueError("%s image does not exist" % img_path)
def read_images(img_file_names, images_dir_path):
"""Read the images from the path and return their numpy.ndarray instance.
Return a numpy.ndarray instance containing the training data.
Args:
img_file_names: List containing images names.
images_dir_path: Path to the directory containing images.
"""
x_train = []
if os.path.isdir(images_dir_path):
for img_file in img_file_names:
img_path = os.path.join(images_dir_path, img_file)
if os.path.exists(img_path):
img = read_image(img_path)
if len(img.shape) < 3:
img = img[..., np.newaxis]
x_train.append(img)
else:
raise ValueError("%s image does not exist" % img_file)
else:
raise ValueError("Directory containing images does not exist")
return np.asanyarray(x_train)
def read_images(img_file_names, images_dir_path):
"""Read the images from the path and return their numpy.ndarray instance.
Return a numpy.ndarray instance containing the training data.
Args:
img_file_names: List containing images names.
images_dir_path: Path to the directory containing images.
"""
x_train = []
if os.path.isdir(images_dir_path):
for img_file in img_file_names:
img_path = os.path.join(images_dir_path, img_file)
if os.path.exists(img_path):
img = read_image(img_path)
if len(img.shape) < 3:
img = img[..., np.newaxis]
x_train.append(img)
else:
raise ValueError("%s image does not exist" % img_file)
else:
raise ValueError("Directory containing images does not exist")
return np.asanyarray(x_train)
