File size: 8,525 Bytes
c7f0cc1
 
 
 
5b21c21
c7f0cc1
5b21c21
c7f0cc1
5b21c21
c7f0cc1
5b21c21
 
c7f0cc1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
from typing import Tuple
import PIL
import mmcv
import numpy as np
print('50\% \imported utils')
from detectron2.utils.colormap import colormap
print('60\% \imported utils')
from detectron2.utils.visualizer import VisImage, Visualizer
print('80\% \imported utils')
from mmdet.datasets.coco_panoptic import INSTANCE_OFFSET
print('100\% \imported utils')


CLASSES = [
    'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
    'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
    'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
    'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag',
    'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite',
    'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
    'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon',
    'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
    'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant',
    'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
    'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
    'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
    'hair drier', 'toothbrush', 'banner', 'blanket', 'bridge', 'cardboard',
    'counter', 'curtain', 'door-stuff', 'floor-wood', 'flower', 'fruit',
    'gravel', 'house', 'light', 'mirror-stuff', 'net', 'pillow', 'platform',
    'playingfield', 'railroad', 'river', 'road', 'roof', 'sand', 'sea',
    'shelf', 'snow', 'stairs', 'tent', 'towel', 'wall-brick', 'wall-stone',
    'wall-tile', 'wall-wood', 'water-other', 'window-blind', 'window-other',
    'tree-merged', 'fence-merged', 'ceiling-merged', 'sky-other-merged',
    'cabinet-merged', 'table-merged', 'floor-other-merged', 'pavement-merged',
    'mountain-merged', 'grass-merged', 'dirt-merged', 'paper-merged',
    'food-other-merged', 'building-other-merged', 'rock-merged',
    'wall-other-merged', 'rug-merged', 'background'
]

PREDICATES = [
    'over',
    'in front of',
    'beside',
    'on',
    'in',
    'attached to',
    'hanging from',
    'on back of',
    'falling off',
    'going down',
    'painted on',
    'walking on',
    'running on',
    'crossing',
    'standing on',
    'lying on',
    'sitting on',
    'flying over',
    'jumping over',
    'jumping from',
    'wearing',
    'holding',
    'carrying',
    'looking at',
    'guiding',
    'kissing',
    'eating',
    'drinking',
    'feeding',
    'biting',
    'catching',
    'picking',
    'playing with',
    'chasing',
    'climbing',
    'cleaning',
    'playing',
    'touching',
    'pushing',
    'pulling',
    'opening',
    'cooking',
    'talking to',
    'throwing',
    'slicing',
    'driving',
    'riding',
    'parked on',
    'driving on',
    'about to hit',
    'kicking',
    'swinging',
    'entering',
    'exiting',
    'enclosing',
    'leaning on',
]


def get_colormap(num_colors: int):
    return (np.resize(colormap(), (num_colors, 3))).tolist()


def draw_text(
    viz_img: VisImage = None,
    text: str = None,
    x: float = None,
    y: float = None,
    color: Tuple[float, float, float] = [0, 0, 0],
    size: float = 10,
    padding: float = 5,
    box_color: str = 'black',
    font: str = None,
) -> float:
    text_obj = viz_img.ax.text(
        x,
        y,
        text,
        size=size,
        # family="sans-serif",
        bbox={
            'facecolor': box_color,
            'alpha': 0.8,
            'pad': padding,
            'edgecolor': 'none',
        },
        verticalalignment='top',
        horizontalalignment='left',
        color=color,
        zorder=10,
        rotation=0,
    )
    viz_img.get_image()
    text_dims = text_obj.get_bbox_patch().get_extents()

    return text_dims.width


def show_result(img,
                result,
                is_one_stage,
                num_rel=20,
                show=False,
                out_dir=None,
                out_file=None):
    # Load image
    img = mmcv.imread(img)
    img = img.copy()  # (H, W, 3)
    img_h, img_w = img.shape[:-1]
    
    # Decrease contrast
    img = PIL.Image.fromarray(img)
    converter = PIL.ImageEnhance.Color(img)
    img = converter.enhance(0.01)
    if out_file is not None:
        mmcv.imwrite(np.asarray(img), 'bw'+out_file)

    # Draw masks
    pan_results = result.pan_results

    ids = np.unique(pan_results)[::-1]
    num_classes = 133
    legal_indices = (ids != num_classes)  # for VOID label
    ids = ids[legal_indices]

    # Get predicted labels
    labels = np.array([id % INSTANCE_OFFSET for id in ids], dtype=np.int64)
    labels = [CLASSES[l] for l in labels]

    #For psgtr
    rel_obj_labels = result.labels
    rel_obj_labels = [CLASSES[l - 1] for l in rel_obj_labels]

    # (N_m, H, W)
    segms = pan_results[None] == ids[:, None, None]
    # Resize predicted masks
    segms = [
        mmcv.image.imresize(m.astype(float), (img_w, img_h)) for m in segms
    ]
    # One stage segmentation
    masks = result.masks

    # Choose colors for each instance in coco
    colormap_coco = get_colormap(len(masks)) if is_one_stage else get_colormap(len(segms))
    colormap_coco = (np.array(colormap_coco) / 255).tolist()

    # Viualize masks
    viz = Visualizer(img)
    viz.overlay_instances(
        labels=rel_obj_labels if is_one_stage else labels,
        masks=masks if is_one_stage else segms,
        assigned_colors=colormap_coco,
    )
    viz_img = viz.get_output().get_image()
    if out_file is not None:
        mmcv.imwrite(viz_img, out_file)

    # Draw relations

    # Filter out relations
    n_rel_topk = num_rel
    # Exclude background class
    rel_dists = result.rel_dists[:, 1:]
    # rel_dists = result.rel_dists
    rel_scores = rel_dists.max(1)
    # rel_scores = result.triplet_scores
    # Extract relations with top scores
    rel_topk_idx = np.argpartition(rel_scores, -n_rel_topk)[-n_rel_topk:]
    rel_labels_topk = rel_dists[rel_topk_idx].argmax(1)
    rel_pair_idxes_topk = result.rel_pair_idxes[rel_topk_idx]
    relations = np.concatenate(
        [rel_pair_idxes_topk, rel_labels_topk[..., None]], axis=1)
    n_rels = len(relations)
    
    top_padding = 20
    bottom_padding = 20
    left_padding = 20
    text_size = 10
    text_padding = 5
    text_height = text_size + 2 * text_padding
    row_padding = 10
    height = (top_padding + bottom_padding + n_rels *
              (text_height + row_padding) - row_padding)
    width = img_w
    curr_x = left_padding
    curr_y = top_padding
    
    # # Adjust colormaps
    # colormap_coco = [adjust_text_color(c, viz) for c in colormap_coco]
    viz_graph = VisImage(np.full((height, width, 3), 255))
    
    all_rel_vis = []
    
    for i, r in enumerate(relations):
        s_idx, o_idx, rel_id = r
        s_label = rel_obj_labels[s_idx]
        o_label = rel_obj_labels[o_idx]
        rel_label = PREDICATES[rel_id]
        viz = Visualizer(img)
        viz.overlay_instances(
            labels=[s_label, o_label],
            masks=[masks[s_idx], masks[o_idx]],
            assigned_colors=[colormap_coco[s_idx], colormap_coco[o_idx]],
        )
        viz_masked_img = viz.get_output().get_image()

        viz_graph = VisImage(np.full((40, width, 3), 255))
        curr_x = 2
        curr_y = 2
        text_size = 25
        text_padding = 20
        font = 36
        text_width = draw_text(
            viz_img=viz_graph,
            text=s_label,
            x=curr_x,
            y=curr_y,
            color=colormap_coco[s_idx],
            size=text_size,
            padding=text_padding,
            font=font,
        )
        curr_x += text_width
        # Draw relation text
        text_width = draw_text(
            viz_img=viz_graph,
            text=rel_label,
            x=curr_x,
            y=curr_y,
            size=text_size,
            padding=text_padding,
            box_color='gainsboro',
            font=font,
        )
        curr_x += text_width

        # Draw object text
        text_width = draw_text(
            viz_img=viz_graph,
            text=o_label,
            x=curr_x,
            y=curr_y,
            color=colormap_coco[o_idx],
            size=text_size,
            padding=text_padding,
            font=font,
        )
        output_viz_graph = np.vstack([viz_masked_img, viz_graph.get_image()])
        if show:
           all_rel_vis.append(output_viz_graph)

    return all_rel_vis