annotator/midas/utils.py

# -*- coding: utf-8 -*-
"""Utils for monoDepth."""
import re
import sys

import cv2
import numpy as np
import torch


def read_pfm(path):
    """Read pfm file.

    Args:
        path (str): path to file

    Returns:
        tuple: (data, scale)
    """
    with open(path, 'rb') as file:

        color = None
        width = None
        height = None
        scale = None
        endian = None

        header = file.readline().rstrip()
        if header.decode('ascii') == 'PF':
            color = True
        elif header.decode('ascii') == 'Pf':
            color = False
        else:
            raise Exception('Not a PFM file: ' + path)

        dim_match = re.match(r'^(\d+)\s(\d+)\s$',
                             file.readline().decode('ascii'))
        if dim_match:
            width, height = list(map(int, dim_match.groups()))
        else:
            raise Exception('Malformed PFM header.')

        scale = float(file.readline().decode('ascii').rstrip())
        if scale < 0:
            # little-endian
            endian = '<'
            scale = -scale
        else:
            # big-endian
            endian = '>'

        data = np.fromfile(file, endian + 'f')
        shape = (height, width, 3) if color else (height, width)

        data = np.reshape(data, shape)
        data = np.flipud(data)

        return data, scale


def write_pfm(path, image, scale=1):
    """Write pfm file.

    Args:
        path (str): pathto file
        image (array): data
        scale (int, optional): Scale. Defaults to 1.
    """

    with open(path, 'wb') as file:
        color = None

        if image.dtype.name != 'float32':
            raise Exception('Image dtype must be float32.')

        image = np.flipud(image)

        if len(image.shape) == 3 and image.shape[2] == 3:  # color image
            color = True
        elif (len(image.shape) == 2
              or len(image.shape) == 3 and image.shape[2] == 1):  # greyscale
            color = False
        else:
            raise Exception(
                'Image must have H x W x 3, H x W x 1 or H x W dimensions.')

        file.write('PF\n' if color else 'Pf\n'.encode())
        file.write('%d %d\n'.encode() % (image.shape[1], image.shape[0]))

        endian = image.dtype.byteorder

        if endian == '<' or endian == '=' and sys.byteorder == 'little':
            scale = -scale

        file.write('%f\n'.encode() % scale)

        image.tofile(file)


def read_image(path):
    """Read image and output RGB image (0-1).

    Args:
        path (str): path to file

    Returns:
        array: RGB image (0-1)
    """
    img = cv2.imread(path)

    if img.ndim == 2:
        img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)

    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0

    return img


def resize_image(img):
    """Resize image and make it fit for network.

    Args:
        img (array): image

    Returns:
        tensor: data ready for network
    """
    height_orig = img.shape[0]
    width_orig = img.shape[1]

    if width_orig > height_orig:
        scale = width_orig / 384
    else:
        scale = height_orig / 384

    height = (np.ceil(height_orig / scale / 32) * 32).astype(int)
    width = (np.ceil(width_orig / scale / 32) * 32).astype(int)

    img_resized = cv2.resize(img, (width, height),
                             interpolation=cv2.INTER_AREA)

    img_resized = (torch.from_numpy(np.transpose(
        img_resized, (2, 0, 1))).contiguous().float())
    img_resized = img_resized.unsqueeze(0)

    return img_resized


def resize_depth(depth, width, height):
    """Resize depth map and bring to CPU (numpy).

    Args:
        depth (tensor): depth
        width (int): image width
        height (int): image height

    Returns:
        array: processed depth
    """
    depth = torch.squeeze(depth[0, :, :, :]).to('cpu')

    depth_resized = cv2.resize(depth.numpy(), (width, height),
                               interpolation=cv2.INTER_CUBIC)

    return depth_resized


def write_depth(path, depth, bits=1):
    """Write depth map to pfm and png file.

    Args:
        path (str): filepath without extension
        depth (array): depth
    """
    write_pfm(path + '.pfm', depth.astype(np.float32))

    depth_min = depth.min()
    depth_max = depth.max()

    max_val = (2**(8 * bits)) - 1

    if depth_max - depth_min > np.finfo('float').eps:
        out = max_val * (depth - depth_min) / (depth_max - depth_min)
    else:
        out = np.zeros(depth.shape, dtype=depth.type)

    if bits == 1:
        cv2.imwrite(path + '.png', out.astype('uint8'))
    elif bits == 2:
        cv2.imwrite(path + '.png', out.astype('uint16'))

    return