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# Copyright (c) MONAI Consortium
# 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
import numpy as np
import torch
from monai.utils import first
from monai.utils.type_conversion import convert_to_numpy
def compute_scale_factor(autoencoder, train_loader, device):
with torch.no_grad():
check_data = first(train_loader)
z = autoencoder.encode_stage_2_inputs(check_data["image"].to(device))
scale_factor = 1 / torch.std(z)
return scale_factor.item()
def normalize_image_to_uint8(image):
"""
Normalize image to uint8
Args:
image: numpy array
"""
draw_img = image
if np.amin(draw_img) < 0:
draw_img[draw_img < 0] = 0
if np.amax(draw_img) > 0.1:
draw_img /= np.amax(draw_img)
draw_img = (255 * draw_img).astype(np.uint8)
return draw_img
def visualize_2d_image(image):
"""
Prepare a 2D image for visualization.
Args:
image: image numpy array, sized (H, W)
"""
image = convert_to_numpy(image)
# draw image
draw_img = normalize_image_to_uint8(image)
draw_img = np.stack([draw_img, draw_img, draw_img], axis=-1)
return draw_img
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