# 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