utils.py

import numpy as np
import matplotlib.pyplot as plt
from PIL import Image

cmap = plt.cm.viridis


def colored_depthmap(depth, d_min=None, d_max=None):
    if d_min is None:
        d_min = np.min(depth)
    if d_max is None:
        d_max = np.max(depth)
    depth_relative = (depth - d_min) / (d_max - d_min)
    return 255 * cmap(depth_relative)[:,:,:3] # H, W, C


def merge_into_row(input, depth_target, depth_pred):
    rgb = 255 * np.transpose(np.squeeze(input.cpu().numpy()), (1,2,0)) # H, W, C
    depth_target_cpu = np.squeeze(depth_target.cpu().numpy())
    depth_pred_cpu = np.squeeze(depth_pred.data.cpu().numpy())

    d_min = min(np.min(depth_target_cpu), np.min(depth_pred_cpu))
    d_max = max(np.max(depth_target_cpu), np.max(depth_pred_cpu))
    depth_target_col = colored_depthmap(depth_target_cpu, d_min, d_max)
    depth_pred_col = colored_depthmap(depth_pred_cpu, d_min, d_max)
    img_merge = np.hstack([rgb, depth_target_col, depth_pred_col])
    
    return img_merge


def merge_into_row_with_gt(input, depth_input, depth_target, depth_pred):
    rgb = 255 * np.transpose(np.squeeze(input.cpu().numpy()), (1,2,0)) # H, W, C
    depth_input_cpu = np.squeeze(depth_input.cpu().numpy())
    depth_target_cpu = np.squeeze(depth_target.cpu().numpy())
    depth_pred_cpu = np.squeeze(depth_pred.data.cpu().numpy())

    d_min = min(np.min(depth_input_cpu), np.min(depth_target_cpu), np.min(depth_pred_cpu))
    d_max = max(np.max(depth_input_cpu), np.max(depth_target_cpu), np.max(depth_pred_cpu))
    depth_input_col = colored_depthmap(depth_input_cpu, d_min, d_max)
    depth_target_col = colored_depthmap(depth_target_cpu, d_min, d_max)
    depth_pred_col = colored_depthmap(depth_pred_cpu, d_min, d_max)

    img_merge = np.hstack([rgb, depth_input_col, depth_target_col, depth_pred_col])

    return img_merge


def add_row(img_merge, row):
    return np.vstack([img_merge, row])


def save_image(img_merge, filename):
    img_merge = Image.fromarray(img_merge.astype('uint8'))
    img_merge.save(filename)