Spaces:
kadirnar
/
Runtime error

File size: 6,673 Bytes
938e515
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
# Copyright (c) Facebook, Inc. and its affiliates.
import logging
import numpy as np
import cv2
import torch

Image = np.ndarray
Boxes = torch.Tensor


class MatrixVisualizer:
    """
    Base visualizer for matrix data
    """

    def __init__(
        self,
        inplace=True,
        cmap=cv2.COLORMAP_PARULA,
        val_scale=1.0,
        alpha=0.7,
        interp_method_matrix=cv2.INTER_LINEAR,
        interp_method_mask=cv2.INTER_NEAREST,
    ):
        self.inplace = inplace
        self.cmap = cmap
        self.val_scale = val_scale
        self.alpha = alpha
        self.interp_method_matrix = interp_method_matrix
        self.interp_method_mask = interp_method_mask

    def visualize(self, image_bgr, mask, matrix, bbox_xywh):
        self._check_image(image_bgr)
        self._check_mask_matrix(mask, matrix)
        if self.inplace:
            image_target_bgr = image_bgr
        else:
            image_target_bgr = image_bgr 
            image_target_bgr *= 0
        x, y, w, h = [int(v) for v in bbox_xywh]
        if w <= 0 or h <= 0:
            return image_bgr
        mask, matrix = self._resize(mask, matrix, w, h)
        mask_bg = np.tile((mask == 0)[:, :, np.newaxis], [1, 1, 3])
        matrix_scaled = matrix.astype(np.float32) * self.val_scale
        _EPSILON = 1e-6
        if np.any(matrix_scaled > 255 + _EPSILON):
            logger = logging.getLogger(__name__)
            logger.warning(
                f"Matrix has values > {255 + _EPSILON} after " f"scaling, clipping to [0..255]"
            )
        matrix_scaled_8u = matrix_scaled.clip(0, 255).astype(np.uint8)
        matrix_vis = cv2.applyColorMap(matrix_scaled_8u, self.cmap)
        matrix_vis[mask_bg] = image_target_bgr[y : y + h, x : x + w, :][mask_bg]
        image_target_bgr[y : y + h, x : x + w, :] = (
            image_target_bgr[y : y + h, x : x + w, :] * (1.0 - self.alpha) + matrix_vis * self.alpha
        )
        return image_target_bgr.astype(np.uint8)

    def _resize(self, mask, matrix, w, h):
        if (w != mask.shape[1]) or (h != mask.shape[0]):
            mask = cv2.resize(mask, (w, h), self.interp_method_mask)
        if (w != matrix.shape[1]) or (h != matrix.shape[0]):
            matrix = cv2.resize(matrix, (w, h), self.interp_method_matrix)
        return mask, matrix

    def _check_image(self, image_rgb):
        assert len(image_rgb.shape) == 3
        assert image_rgb.shape[2] == 3
        assert image_rgb.dtype == np.uint8

    def _check_mask_matrix(self, mask, matrix):
        assert len(matrix.shape) == 2
        assert len(mask.shape) == 2
        assert mask.dtype == np.uint8


class RectangleVisualizer:

    _COLOR_GREEN = (18, 127, 15)

    def __init__(self, color=_COLOR_GREEN, thickness=1):
        self.color = color
        self.thickness = thickness

    def visualize(self, image_bgr, bbox_xywh, color=None, thickness=None):
        x, y, w, h = bbox_xywh
        color = color or self.color
        thickness = thickness or self.thickness
        cv2.rectangle(image_bgr, (int(x), int(y)), (int(x + w), int(y + h)), color, thickness)
        return image_bgr


class PointsVisualizer:

    _COLOR_GREEN = (18, 127, 15)

    def __init__(self, color_bgr=_COLOR_GREEN, r=5):
        self.color_bgr = color_bgr
        self.r = r

    def visualize(self, image_bgr, pts_xy, colors_bgr=None, rs=None):
        for j, pt_xy in enumerate(pts_xy):
            x, y = pt_xy
            color_bgr = colors_bgr[j] if colors_bgr is not None else self.color_bgr
            r = rs[j] if rs is not None else self.r
            cv2.circle(image_bgr, (x, y), r, color_bgr, -1)
        return image_bgr


class TextVisualizer:

    _COLOR_GRAY = (218, 227, 218)
    _COLOR_WHITE = (255, 255, 255)

    def __init__(
        self,
        font_face=cv2.FONT_HERSHEY_SIMPLEX,
        font_color_bgr=_COLOR_GRAY,
        font_scale=0.35,
        font_line_type=cv2.LINE_AA,
        font_line_thickness=1,
        fill_color_bgr=_COLOR_WHITE,
        fill_color_transparency=1.0,
        frame_color_bgr=_COLOR_WHITE,
        frame_color_transparency=1.0,
        frame_thickness=1,
    ):
        self.font_face = font_face
        self.font_color_bgr = font_color_bgr
        self.font_scale = font_scale
        self.font_line_type = font_line_type
        self.font_line_thickness = font_line_thickness
        self.fill_color_bgr = fill_color_bgr
        self.fill_color_transparency = fill_color_transparency
        self.frame_color_bgr = frame_color_bgr
        self.frame_color_transparency = frame_color_transparency
        self.frame_thickness = frame_thickness

    def visualize(self, image_bgr, txt, topleft_xy):
        txt_w, txt_h = self.get_text_size_wh(txt)
        topleft_xy = tuple(map(int, topleft_xy))
        x, y = topleft_xy
        if self.frame_color_transparency < 1.0:
            t = self.frame_thickness
            image_bgr[y - t : y + txt_h + t, x - t : x + txt_w + t, :] = (
                image_bgr[y - t : y + txt_h + t, x - t : x + txt_w + t, :]
                * self.frame_color_transparency
                + np.array(self.frame_color_bgr) * (1.0 - self.frame_color_transparency)
            ).astype(float)
        if self.fill_color_transparency < 1.0:
            image_bgr[y : y + txt_h, x : x + txt_w, :] = (
                image_bgr[y : y + txt_h, x : x + txt_w, :] * self.fill_color_transparency
                + np.array(self.fill_color_bgr) * (1.0 - self.fill_color_transparency)
            ).astype(float)
        cv2.putText(
            image_bgr,
            txt,
            topleft_xy,
            self.font_face,
            self.font_scale,
            self.font_color_bgr,
            self.font_line_thickness,
            self.font_line_type,
        )
        return image_bgr

    def get_text_size_wh(self, txt):
        ((txt_w, txt_h), _) = cv2.getTextSize(
            txt, self.font_face, self.font_scale, self.font_line_thickness
        )
        return txt_w, txt_h


class CompoundVisualizer:
    def __init__(self, visualizers):
        self.visualizers = visualizers

    def visualize(self, image_bgr, data):
        assert len(data) == len(
            self.visualizers
        ), "The number of datas {} should match the number of visualizers" " {}".format(
            len(data), len(self.visualizers)
        )
        image = image_bgr
        for i, visualizer in enumerate(self.visualizers):
            image = visualizer.visualize(image, data[i])
        return image

    def __str__(self):
        visualizer_str = ", ".join([str(v) for v in self.visualizers])
        return "Compound Visualizer [{}]".format(visualizer_str)