File size: 2,214 Bytes
ab854b9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
# Ultralytics YOLO 🚀, AGPL-3.0 license
"""
Monkey patches to update/extend functionality of existing functions
"""

from pathlib import Path

import cv2
import numpy as np
import torch

# OpenCV Multilanguage-friendly functions ------------------------------------------------------------------------------
_imshow = cv2.imshow  # copy to avoid recursion errors


def imread(filename: str, flags: int = cv2.IMREAD_COLOR):
    """Read an image from a file.

    Args:
        filename (str): Path to the file to read.
        flags (int, optional): Flag that can take values of cv2.IMREAD_*. Defaults to cv2.IMREAD_COLOR.

    Returns:
        (np.ndarray): The read image.
    """
    return cv2.imdecode(np.fromfile(filename, np.uint8), flags)


def imwrite(filename: str, img: np.ndarray, params=None):
    """Write an image to a file.

    Args:
        filename (str): Path to the file to write.
        img (np.ndarray): Image to write.
        params (list of ints, optional): Additional parameters. See OpenCV documentation.

    Returns:
        (bool): True if the file was written, False otherwise.
    """
    try:
        cv2.imencode(Path(filename).suffix, img, params)[1].tofile(filename)
        return True
    except Exception:
        return False


def imshow(winname: str, mat: np.ndarray):
    """Displays an image in the specified window.

    Args:
        winname (str): Name of the window.
        mat (np.ndarray): Image to be shown.
    """
    _imshow(winname.encode('unicode_escape').decode(), mat)


# PyTorch functions ----------------------------------------------------------------------------------------------------
_torch_save = torch.save  # copy to avoid recursion errors


def torch_save(*args, **kwargs):
    """Use dill (if exists) to serialize the lambda functions where pickle does not do this.

    Args:
        *args (tuple): Positional arguments to pass to torch.save.
        **kwargs (dict): Keyword arguments to pass to torch.save.
    """
    try:
        import dill as pickle  # noqa
    except ImportError:
        import pickle

    if 'pickle_module' not in kwargs:
        kwargs['pickle_module'] = pickle  # noqa
    return _torch_save(*args, **kwargs)