File size: 32,043 Bytes
3c9a806
 
5101aba
7148e12
5101aba
 
 
ab4c47c
3c9a806
afbb6e9
 
 
5101aba
 
 
 
 
 
 
d492a58
5101aba
b5a2293
d492a58
c1ff4cf
09ac26d
3c9a806
 
 
 
 
 
 
 
 
 
 
 
 
 
5101aba
3c9a806
 
 
 
 
 
8897e08
3c9a806
 
8897e08
 
 
3c9a806
8897e08
 
 
 
3c9a806
2337f0d
3c9a806
 
 
 
03177fd
 
2337f0d
3c9a806
2337f0d
3c9a806
04f27a5
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3c9a806
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2337f0d
3c9a806
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
5101aba
3c9a806
 
d74847a
3c9a806
 
 
 
d74847a
3c9a806
04f27a5
3c9a806
 
 
 
 
 
 
 
d74847a
3c9a806
d74847a
3c3d4fa
5101aba
3c9a806
 
2337f0d
3c9a806
 
 
 
 
 
 
d74847a
3c9a806
 
 
 
3c3d4fa
3c9a806
 
 
 
5101aba
3c9a806
d74847a
3c9a806
 
d74847a
3c9a806
d74847a
3c9a806
 
04f27a5
3c9a806
 
09ac26d
 
3c3d4fa
09ac26d
3c9a806
 
 
 
09ac26d
3c9a806
04f27a5
3c9a806
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3c3d4fa
3c9a806
 
 
 
 
 
 
 
 
 
 
3c3d4fa
3c9a806
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3c3d4fa
3c9a806
 
 
 
 
cf4af5e
3c9a806
cf4af5e
 
3c9a806
 
 
 
 
 
 
 
 
04f27a5
3c9a806
 
 
 
 
 
 
 
 
 
 
 
cf4af5e
3c9a806
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7d7aedf
3c9a806
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
04f27a5
3c9a806
 
 
8897e08
3c9a806
 
2337f0d
 
3c9a806
 
 
 
 
 
 
cf4af5e
3c9a806
3d7a60f
2337f0d
3d7a60f
3c9a806
 
 
 
 
5101aba
3c9a806
2337f0d
 
 
 
 
 
 
 
 
 
 
 
 
3c9a806
5101aba
3c9a806
 
 
2337f0d
 
3c9a806
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
aa2ac84
3c9a806
bb83e2d
3c9a806
 
 
 
0c4cb96
 
03177fd
3d7a60f
3c9a806
03177fd
 
 
 
04f27a5
3c9a806
47c5597
 
 
3c9a806
 
 
 
 
47c5597
 
 
3c9a806
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
22945de
3c9a806
 
47c5597
3c9a806
 
 
f370d19
3c9a806
 
 
f370d19
3c9a806
 
 
f370d19
3c9a806
 
 
5101aba
3c9a806
f370d19
3c9a806
 
 
f370d19
3c9a806
 
ab80b89
3c9a806
 
 
 
 
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
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
import os, sys
from huggingface_hub import snapshot_download

is_local_run = False

code_dir = snapshot_download("One-2-3-45/code", token=os.environ['TOKEN']) if not is_local_run else "../code"

sys.path.append(code_dir)

elev_est_dir = os.path.join(code_dir, "one2345_elev_est/")
sys.path.append(elev_est_dir)

if not is_local_run:
    import subprocess
    subprocess.run(["sh", os.path.join(elev_est_dir, "install.sh")], cwd=elev_est_dir)
    # export TORCH_CUDA_ARCH_LIST="7.0;7.2;8.0;8.6"
    # export IABN_FORCE_CUDA=1
    os.environ["TORCH_CUDA_ARCH_LIST"] = "8.0;8.6"
    os.environ["IABN_FORCE_CUDA"] = "1"
    os.environ["FORCE_CUDA"] = "1"
    subprocess.run(["pip", "install", "inplace_abn"]) 
    # FORCE_CUDA=1 pip install --no-cache-dir git+https://github.com/mit-han-lab/torchsparse.git@v1.4.0
    subprocess.run(["pip", "install", "--no-cache-dir", "git+https://github.com/mit-han-lab/torchsparse.git@v1.4.0"])

import inspect
import shutil
import torch
import fire
import gradio as gr
import numpy as np
import plotly.graph_objects as go
from functools import partial

from lovely_numpy import lo
import cv2
from PIL import Image
import trimesh
import tempfile
from zero123_utils import init_model, predict_stage1_gradio, zero123_infer
from sam_utils import sam_init, sam_out_nosave
from utils import image_preprocess_nosave, gen_poses
from one2345_elev_est.tools.estimate_wild_imgs import estimate_elev
from rembg import remove

_GPU_INDEX = 0

_TITLE = '''One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization'''

_DESCRIPTION = '''
We reconstruct a 3D textured mesh from a single image by initially predicting multi-view images and then lifting them to 3D. 
[<a href="http://One-2-3-45.com">Project</a>] 
[<a href="https://github.com/One-2-3-45/One-2-3-45">GitHub</a>] 
'''
# _HTML = '''<p>[<a href="https://github.com/One-2-3-45/One-2-3-45">GitHub</a>] 
# <object alt="GitHub Repo stars" src="https://img.shields.io/github/stars/One-2-3-45/One-2-3-45?style=social&link=https%3A%2F%2Fgithub.com%2FOne-2-3-45%2FOne-2-3-45">
# </p>'''
# _HTML = '<script async defer src="https://buttons.github.io/buttons.js"></script> <a class="github-button" href="https://github.com/One-2-3-45/One-2-3-45" data-icon="octicon-star" data-show-count="true" aria-label="Star One-2-3-45/One-2-3-45 on GitHub">Star</a><p>'

_USER_GUIDE = "Please upload an image in the block above (or choose an example above) and click **Run Generation**." 
_BBOX_1 = "Predicting bounding box for the input image..."
_BBOX_2 = "Bounding box adjusted. Continue adjusting or **Run Generation**."
_BBOX_3 = "Bounding box predicted. Adjust it using sliders or **Run Generation**."
_SAM = "Preprocessing the input image... (safety check, SAM segmentation, *etc*.)"
_GEN_1 = "Predicting multi-view images... (may take \~13 seconds) <br> Images will be shown in the bottom right blocks."
_GEN_2 = "Predicting nearby views and generating mesh... (may take \~35 seconds) <br> Mesh will be shown on the right."
_DONE = "Done! Mesh is shown on the right. <br> If it is not satisfactory, please select **Retry view** checkboxes for inaccurate views and click **Regenerate selected view(s)** at the bottom."
_REGEN_1 = "Selected view(s) are regenerated. You can click **Regenerate nearby views and mesh**. <br> Alternatively, if the regenerated view(s) are still not satisfactory, you can repeat the previous step (select the view and regenerate)."
_REGEN_2 = "Regeneration done. Mesh is shown on the right."


def calc_cam_cone_pts_3d(polar_deg, azimuth_deg, radius_m, fov_deg):
    '''
    :param polar_deg (float).
    :param azimuth_deg (float).
    :param radius_m (float).
    :param fov_deg (float).
    :return (5, 3) array of float with (x, y, z).
    '''
    polar_rad = np.deg2rad(polar_deg)
    azimuth_rad = np.deg2rad(azimuth_deg)
    fov_rad = np.deg2rad(fov_deg)
    polar_rad = -polar_rad  # NOTE: Inverse of how used_x relates to x.

    # Camera pose center:
    cam_x = radius_m * np.cos(azimuth_rad) * np.cos(polar_rad)
    cam_y = radius_m * np.sin(azimuth_rad) * np.cos(polar_rad)
    cam_z = radius_m * np.sin(polar_rad)

    # Obtain four corners of camera frustum, assuming it is looking at origin.
    # First, obtain camera extrinsics (rotation matrix only):
    camera_R = np.array([[np.cos(azimuth_rad) * np.cos(polar_rad),
                          -np.sin(azimuth_rad),
                          -np.cos(azimuth_rad) * np.sin(polar_rad)],
                         [np.sin(azimuth_rad) * np.cos(polar_rad),
                          np.cos(azimuth_rad),
                          -np.sin(azimuth_rad) * np.sin(polar_rad)],
                         [np.sin(polar_rad),
                          0.0,
                          np.cos(polar_rad)]])

    # Multiply by corners in camera space to obtain go to space:
    corn1 = [-1.0, np.tan(fov_rad / 2.0), np.tan(fov_rad / 2.0)]
    corn2 = [-1.0, -np.tan(fov_rad / 2.0), np.tan(fov_rad / 2.0)]
    corn3 = [-1.0, -np.tan(fov_rad / 2.0), -np.tan(fov_rad / 2.0)]
    corn4 = [-1.0, np.tan(fov_rad / 2.0), -np.tan(fov_rad / 2.0)]
    corn1 = np.dot(camera_R, corn1)
    corn2 = np.dot(camera_R, corn2)
    corn3 = np.dot(camera_R, corn3)
    corn4 = np.dot(camera_R, corn4)

    # Now attach as offset to actual 3D camera position:
    corn1 = np.array(corn1) / np.linalg.norm(corn1, ord=2)
    corn_x1 = cam_x + corn1[0]
    corn_y1 = cam_y + corn1[1]
    corn_z1 = cam_z + corn1[2]
    corn2 = np.array(corn2) / np.linalg.norm(corn2, ord=2)
    corn_x2 = cam_x + corn2[0]
    corn_y2 = cam_y + corn2[1]
    corn_z2 = cam_z + corn2[2]
    corn3 = np.array(corn3) / np.linalg.norm(corn3, ord=2)
    corn_x3 = cam_x + corn3[0]
    corn_y3 = cam_y + corn3[1]
    corn_z3 = cam_z + corn3[2]
    corn4 = np.array(corn4) / np.linalg.norm(corn4, ord=2)
    corn_x4 = cam_x + corn4[0]
    corn_y4 = cam_y + corn4[1]
    corn_z4 = cam_z + corn4[2]

    xs = [cam_x, corn_x1, corn_x2, corn_x3, corn_x4]
    ys = [cam_y, corn_y1, corn_y2, corn_y3, corn_y4]
    zs = [cam_z, corn_z1, corn_z2, corn_z3, corn_z4]

    return np.array([xs, ys, zs]).T

class CameraVisualizer:
    def __init__(self, gradio_plot):
        self._gradio_plot = gradio_plot
        self._fig = None
        self._polar = 0.0
        self._azimuth = 0.0
        self._radius = 0.0
        self._raw_image = None
        self._8bit_image = None
        self._image_colorscale = None

    def encode_image(self, raw_image, elev=90):
        '''
        :param raw_image (H, W, 3) array of uint8 in [0, 255].
        '''
        # https://stackoverflow.com/questions/60685749/python-plotly-how-to-add-an-image-to-a-3d-scatter-plot

        dum_img = Image.fromarray(np.ones((3, 3, 3), dtype='uint8')).convert('P', palette='WEB')
        idx_to_color = np.array(dum_img.getpalette()).reshape((-1, 3))

        self._raw_image = raw_image
        self._8bit_image = Image.fromarray(raw_image).convert('P', palette='WEB', dither=None)
        # self._8bit_image = Image.fromarray(raw_image.clip(0, 254)).convert(
        #     'P', palette='WEB', dither=None)
        self._image_colorscale = [
            [i / 255.0, 'rgb({}, {}, {})'.format(*rgb)] for i, rgb in enumerate(idx_to_color)]
        self._elev = elev
        # return self.update_figure()

    def update_figure(self):
        fig = go.Figure()

        if self._raw_image is not None:
            (H, W, C) = self._raw_image.shape

            x = np.zeros((H, W))
            (y, z) = np.meshgrid(np.linspace(-1.0, 1.0, W), np.linspace(1.0, -1.0, H) * H / W)
            
            angle_deg = self._elev-90
            angle = np.radians(90-self._elev)
            rotation_matrix = np.array([
                [np.cos(angle), 0, np.sin(angle)],
                [0, 1, 0],
                [-np.sin(angle), 0, np.cos(angle)]
            ])
            # Assuming x, y, z are the original 3D coordinates of the image
            coordinates = np.stack((x, y, z), axis=-1)  # Combine x, y, z into a single array
            # Apply the rotation matrix
            rotated_coordinates = np.matmul(coordinates, rotation_matrix)
            # Extract the new x, y, z coordinates from the rotated coordinates
            x, y, z = rotated_coordinates[..., 0], rotated_coordinates[..., 1], rotated_coordinates[..., 2]


            print('x:', lo(x))
            print('y:', lo(y))
            print('z:', lo(z))

            fig.add_trace(go.Surface(
                x=x, y=y, z=z,
                surfacecolor=self._8bit_image,
                cmin=0,
                cmax=255,
                colorscale=self._image_colorscale,
                showscale=False,
                lighting_diffuse=1.0,
                lighting_ambient=1.0,
                lighting_fresnel=1.0,
                lighting_roughness=1.0,
                lighting_specular=0.3))

            scene_bounds = 3.5
            base_radius = 2.5
            zoom_scale = 1.5  # Note that input radius offset is in [-0.5, 0.5].
            fov_deg = 50.0
            edges = [(0, 1), (0, 2), (0, 3), (0, 4), (1, 2), (2, 3), (3, 4), (4, 1)]

            input_cone = calc_cam_cone_pts_3d(
                angle_deg, 0.0, base_radius, fov_deg)  # (5, 3).
            output_cone = calc_cam_cone_pts_3d(
                self._polar, self._azimuth, base_radius + self._radius * zoom_scale, fov_deg)  # (5, 3).
            output_cones = []
            for i in range(1,4):
                output_cones.append(calc_cam_cone_pts_3d(
                    angle_deg, i*90, base_radius + self._radius * zoom_scale, fov_deg))
            delta_deg = 30 if angle_deg <= -15 else -30
            for i in range(4):
                output_cones.append(calc_cam_cone_pts_3d(
                    angle_deg+delta_deg, 30+i*90, base_radius + self._radius * zoom_scale, fov_deg))

            cones = [(input_cone, 'rgb(174, 54, 75)', 'Input view (Predicted view 1)')]
            for i in range(len(output_cones)):
                cones.append((output_cones[i], 'rgb(32, 77, 125)', f'Predicted view {i+2}'))

            for idx, (cone, clr, legend) in enumerate(cones):

                for (i, edge) in enumerate(edges):
                    (x1, x2) = (cone[edge[0], 0], cone[edge[1], 0])
                    (y1, y2) = (cone[edge[0], 1], cone[edge[1], 1])
                    (z1, z2) = (cone[edge[0], 2], cone[edge[1], 2])
                    fig.add_trace(go.Scatter3d(
                        x=[x1, x2], y=[y1, y2], z=[z1, z2], mode='lines',
                        line=dict(color=clr, width=3),
                        name=legend, showlegend=(i == 1) and (idx <= 1)))

                # Add label.
                if cone[0, 2] <= base_radius / 2.0:
                    fig.add_trace(go.Scatter3d(
                        x=[cone[0, 0]], y=[cone[0, 1]], z=[cone[0, 2] - 0.05], showlegend=False,
                        mode='text', text=legend, textposition='bottom center'))
                else:
                    fig.add_trace(go.Scatter3d(
                        x=[cone[0, 0]], y=[cone[0, 1]], z=[cone[0, 2] + 0.05], showlegend=False,
                        mode='text', text=legend, textposition='top center'))

            # look at center of scene
            fig.update_layout(
                # width=640,
                # height=480,
                # height=400,
                height=450,
                autosize=True,
                hovermode=False,
                margin=go.layout.Margin(l=0, r=0, b=0, t=0),
                showlegend=False,
                legend=dict(
                    yanchor='bottom',
                    y=0.01,
                    xanchor='right',
                    x=0.99,
                ),
                scene=dict(
                    aspectmode='manual',
                    aspectratio=dict(x=1, y=1, z=1.0),
                    camera=dict(
                        eye=dict(x=base_radius - 1.6, y=0.0, z=0.6),
                        center=dict(x=0.0, y=0.0, z=0.0),
                        up=dict(x=0.0, y=0.0, z=1.0)),
                    xaxis_title='',
                    yaxis_title='',
                    zaxis_title='',
                    xaxis=dict(
                        range=[-scene_bounds, scene_bounds],
                        showticklabels=False,
                        showgrid=True,
                        zeroline=False,
                        showbackground=True,
                        showspikes=False,
                        showline=False,
                        ticks=''),
                    yaxis=dict(
                        range=[-scene_bounds, scene_bounds],
                        showticklabels=False,
                        showgrid=True,
                        zeroline=False,
                        showbackground=True,
                        showspikes=False,
                        showline=False,
                        ticks=''),
                    zaxis=dict(
                        range=[-scene_bounds, scene_bounds],
                        showticklabels=False,
                        showgrid=True,
                        zeroline=False,
                        showbackground=True,
                        showspikes=False,
                        showline=False,
                        ticks='')))

        self._fig = fig
        return fig
    

def stage1_run(models, device, cam_vis, tmp_dir,
               input_im, scale, ddim_steps, elev=None, rerun_all=[],
               *btn_retrys):
    is_rerun = True if cam_vis is None else False
    model = models['turncam'].half()

    stage1_dir = os.path.join(tmp_dir, "stage1_8")
    if not is_rerun:
        os.makedirs(stage1_dir, exist_ok=True)
        output_ims = predict_stage1_gradio(model, input_im, save_path=stage1_dir, adjust_set=list(range(4)), device=device, ddim_steps=ddim_steps, scale=scale)
        stage2_steps = 50 # ddim_steps
        zero123_infer(model, tmp_dir, indices=[0], device=device, ddim_steps=stage2_steps, scale=scale)
        elev_output = estimate_elev(tmp_dir)
        gen_poses(tmp_dir, elev_output)
        show_in_im1 = np.asarray(input_im, dtype=np.uint8)
        cam_vis.encode_image(show_in_im1, elev=elev_output)
        new_fig = cam_vis.update_figure()

        flag_lower_cam = elev_output <= 75
        if flag_lower_cam:
            output_ims_2 = predict_stage1_gradio(model, input_im, save_path=stage1_dir, adjust_set=list(range(4,8)), device=device, ddim_steps=ddim_steps, scale=scale)
        else:
            output_ims_2 = predict_stage1_gradio(model, input_im, save_path=stage1_dir, adjust_set=list(range(8,12)), device=device, ddim_steps=ddim_steps, scale=scale)
        torch.cuda.empty_cache()
        return (90-elev_output, new_fig, *output_ims, *output_ims_2)
    else:
        rerun_idx = [i for i in range(len(btn_retrys)) if btn_retrys[i]]
        if 90-int(elev["label"]) > 75:
            rerun_idx_in = [i if i < 4 else i+4 for i in rerun_idx]
        else:
            rerun_idx_in = rerun_idx
        for idx in rerun_idx_in:
            if idx not in rerun_all:
                rerun_all.append(idx)
        print("rerun_idx", rerun_all)
        output_ims = predict_stage1_gradio(model, input_im, save_path=stage1_dir, adjust_set=rerun_idx_in, device=device, ddim_steps=ddim_steps, scale=scale)
        outputs = [gr.update(visible=True)] * 8
        for idx, view_idx in enumerate(rerun_idx):
            outputs[view_idx] = output_ims[idx]
        reset = [gr.update(value=False)] * 8
        torch.cuda.empty_cache()
        return (rerun_all, *reset, *outputs)
    
def stage2_run(models, device, tmp_dir,
               elev, scale, rerun_all=[], stage2_steps=50):
    flag_lower_cam = 90-int(elev["label"]) <= 75
    is_rerun = True if rerun_all else False
    model = models['turncam'].half()
    if not is_rerun:
        if flag_lower_cam:
            zero123_infer(model, tmp_dir, indices=list(range(1,8)), device=device, ddim_steps=stage2_steps, scale=scale)
        else:
            zero123_infer(model, tmp_dir, indices=list(range(1,4))+list(range(8,12)), device=device, ddim_steps=stage2_steps, scale=scale)
    else:
        print("rerun_idx", rerun_all)
        zero123_infer(model, tmp_dir, indices=rerun_all, device=device, ddim_steps=stage2_steps, scale=scale)
    
    dataset = tmp_dir
    main_dir_path = os.path.dirname(os.path.abspath(
            inspect.getfile(inspect.currentframe())))
    torch.cuda.empty_cache()
    os.chdir(os.path.join(code_dir, 'SparseNeuS_demo_v1/'))

    bash_script = f'CUDA_VISIBLE_DEVICES={_GPU_INDEX} python exp_runner_generic_blender_val.py --specific_dataset_name {dataset} --mode export_mesh --conf confs/one2345_lod0_val_demo.conf  --is_continue'
    print(bash_script)
    os.system(bash_script)
    os.chdir(main_dir_path)

    ply_path = os.path.join(tmp_dir, f"meshes_val_bg/lod0/mesh_00215000_gradio_lod0.ply")
    mesh_path = os.path.join(tmp_dir, "mesh.obj")
    # Read the textured mesh from .ply file
    mesh = trimesh.load_mesh(ply_path)
    axis = [1, 0, 0]
    angle = np.radians(90)
    rotation_matrix = trimesh.transformations.rotation_matrix(angle, axis)
    mesh.apply_transform(rotation_matrix)
    axis = [0, 0, 1]
    angle = np.radians(180)
    rotation_matrix = trimesh.transformations.rotation_matrix(angle, axis)
    mesh.apply_transform(rotation_matrix)
    # flip x
    mesh.vertices[:, 0] = -mesh.vertices[:, 0]
    mesh.faces = np.fliplr(mesh.faces)
    # Export the mesh as .obj file with colors
    mesh.export(mesh_path, file_type='obj', include_color=True)
    torch.cuda.empty_cache()

    if not is_rerun:
        return (mesh_path)
    else:
        return (mesh_path, [], gr.update(visible=False), gr.update(visible=False))

def nsfw_check(models, raw_im, device='cuda'):
    safety_checker_input = models['clip_fe'](raw_im, return_tensors='pt').to(device)
    (_, has_nsfw_concept) = models['nsfw'](
        images=np.ones((1, 3)), clip_input=safety_checker_input.pixel_values)
    print('has_nsfw_concept:', has_nsfw_concept)
    del safety_checker_input
    if np.any(has_nsfw_concept):
        print('NSFW content detected.')
        # Define the image size and background color
        image_width = image_height = 256
        background_color = (255, 255, 255)  # White
        # Create a blank image
        image = Image.new("RGB", (image_width, image_height), background_color)
        from PIL import ImageDraw
        draw = ImageDraw.Draw(image)
        text = "Potential NSFW content was detected."
        text_color = (255, 0, 0)
        text_position = (10, 123)  
        draw.text(text_position, text, fill=text_color)
        text = "Please try again with a different image."
        text_position = (10, 133) 
        draw.text(text_position, text, fill=text_color)
        return image
    else:
        print('Safety check passed.')
        return False

def preprocess_run(predictor, models, raw_im, preprocess, *bbox_sliders):
    raw_im.thumbnail([512, 512], Image.Resampling.LANCZOS)
    check_results = nsfw_check(models, raw_im, device=predictor.device)
    if check_results:
        return check_results
    image_sam = sam_out_nosave(predictor, raw_im.convert("RGB"), *bbox_sliders)
    input_256 = image_preprocess_nosave(image_sam, lower_contrast=preprocess, rescale=True)
    torch.cuda.empty_cache()
    return input_256

def on_coords_slider(image, x_min, y_min, x_max, y_max, color=(88, 191, 131, 255)):
    """Draw a bounding box annotation for an image."""
    print("on_coords_slider, drawing bbox...")
    image.thumbnail([512, 512], Image.Resampling.LANCZOS)
    image_size = image.size
    if max(image_size) > 224:
        image.thumbnail([224, 224], Image.Resampling.LANCZOS)
        shrink_ratio = max(image.size) / max(image_size)
        x_min = int(x_min * shrink_ratio)
        y_min = int(y_min * shrink_ratio)
        x_max = int(x_max * shrink_ratio)
        y_max = int(y_max * shrink_ratio)
    image = cv2.cvtColor(np.array(image), cv2.COLOR_RGBA2BGRA)
    image = cv2.rectangle(image, (x_min, y_min), (x_max, y_max), color, int(max(max(image.shape) / 400*2, 2)))
    return cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA) # image[:, :, ::-1]

def init_bbox(image):
    image.thumbnail([512, 512], Image.Resampling.LANCZOS)
    width, height = image.size
    image_rem = image.convert('RGBA')
    image_nobg = remove(image_rem, alpha_matting=True)
    arr = np.asarray(image_nobg)[:,:,-1]
    x_nonzero = np.nonzero(arr.sum(axis=0))
    y_nonzero = np.nonzero(arr.sum(axis=1))
    x_min = int(x_nonzero[0].min())
    y_min = int(y_nonzero[0].min())
    x_max = int(x_nonzero[0].max())
    y_max = int(y_nonzero[0].max())
    image_mini = image.copy()
    image_mini.thumbnail([224, 224], Image.Resampling.LANCZOS)
    shrink_ratio = max(image_mini.size) / max(width, height)
    x_min_shrink = int(x_min * shrink_ratio)
    y_min_shrink = int(y_min * shrink_ratio)
    x_max_shrink = int(x_max * shrink_ratio)
    y_max_shrink = int(y_max * shrink_ratio)

    return [on_coords_slider(image_mini, x_min_shrink, y_min_shrink, x_max_shrink, y_max_shrink),
            gr.update(value=x_min, maximum=width),
            gr.update(value=y_min, maximum=height),
            gr.update(value=x_max, maximum=width),
            gr.update(value=y_max, maximum=height)]


def run_demo(
        device_idx=_GPU_INDEX,
        ckpt='zero123-xl.ckpt'):

    device = f"cuda:{device_idx}" if torch.cuda.is_available() else "cpu"
    models = init_model(device, os.path.join(code_dir, ckpt))
    # model = models['turncam']
    # sampler = DDIMSampler(model)

    # init sam model
    predictor = sam_init(device_idx)

    with open('instructions_12345.md', 'r') as f:
        article = f.read()

    # NOTE: Examples must match inputs
    example_folder = os.path.join(os.path.dirname(__file__), 'demo_examples')
    example_fns = os.listdir(example_folder)
    example_fns.sort()
    examples_full = [os.path.join(example_folder, x) for x in example_fns if x.endswith('.png')]

    # Compose demo layout & data flow.
    css = "#model-3d-out {height: 400px;} #plot-out {height: 450px;}"
    with gr.Blocks(title=_TITLE, css=css) as demo:
        gr.Markdown('# ' + _TITLE)
        gr.Markdown(_DESCRIPTION)
        # gr.HTML(_HTML)

        with gr.Row(variant='panel'):
            with gr.Column(scale=1.2):
                image_block = gr.Image(type='pil', image_mode='RGBA', label='Input image', tool=None).style(height=290)

                gr.Examples(
                    examples=examples_full,  # NOTE: elements must match inputs list!
                    inputs=[image_block],
                    outputs=[image_block],
                    cache_examples=False,
                    label='Examples (click one of the images below to start)',
                    examples_per_page=40
                )
                preprocess_chk = gr.Checkbox(
                        False, label='Reduce image contrast (mitigate shadows on the backside)')
                with gr.Accordion('Advanced options', open=False):
                    scale_slider = gr.Slider(0, 30, value=3, step=1,
                                             label='Diffusion guidance scale')
                    steps_slider = gr.Slider(5, 200, value=75, step=5,
                                             label='Number of diffusion inference steps')

                run_btn = gr.Button('Run Generation', variant='primary', interactive=False)
                guide_text = gr.Markdown(_USER_GUIDE, visible=True)
                
            with gr.Column(scale=.8):
                with gr.Row():
                    bbox_block = gr.Image(type='pil', label="Bounding box", interactive=False).style(height=290)
                    sam_block = gr.Image(type='pil', label="SAM output", interactive=False)
                max_width = max_height = 256
                with gr.Row():
                    x_min_slider = gr.Slider(label="X min", interactive=True, value=0, minimum=0, maximum=max_width, step=1)
                    y_min_slider = gr.Slider(label="Y min", interactive=True, value=0, minimum=0, maximum=max_height, step=1)
                with gr.Row():
                    x_max_slider = gr.Slider(label="X max", interactive=True, value=max_width, minimum=0, maximum=max_width, step=1)
                    y_max_slider = gr.Slider(label="Y max", interactive=True, value=max_height, minimum=0, maximum=max_height, step=1)
                bbox_sliders = [x_min_slider, y_min_slider, x_max_slider, y_max_slider]

                mesh_output = gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="One-2-3-45's Textured Mesh", elem_id="model-3d-out")
        
        with gr.Row(variant='panel'):
            with gr.Column(scale=0.85):
                elev_output = gr.Label(label='Estimated elevation (degree, w.r.t. the horizontal plane)')
                vis_output = gr.Plot(label='Camera poses of the input view (red) and predicted views (blue)', elem_id="plot-out")
                
            with gr.Column(scale=1.15):
                gr.Markdown('Predicted multi-view images')
                with gr.Row():
                    view_1 = gr.Image(interactive=False, show_label=False).style(height=200)
                    view_2 = gr.Image(interactive=False, show_label=False).style(height=200)
                    view_3 = gr.Image(interactive=False, show_label=False).style(height=200)
                    view_4 = gr.Image(interactive=False, show_label=False).style(height=200)
                with gr.Row():
                    btn_retry_1 = gr.Checkbox(label='Retry view 1')
                    btn_retry_2 = gr.Checkbox(label='Retry view 2')
                    btn_retry_3 = gr.Checkbox(label='Retry view 3')
                    btn_retry_4 = gr.Checkbox(label='Retry view 4')
                with gr.Row():
                    view_5 = gr.Image(interactive=False, show_label=False).style(height=200)
                    view_6 = gr.Image(interactive=False, show_label=False).style(height=200)
                    view_7 = gr.Image(interactive=False, show_label=False).style(height=200)
                    view_8 = gr.Image(interactive=False, show_label=False).style(height=200)
                with gr.Row():
                    btn_retry_5 = gr.Checkbox(label='Retry view 5')
                    btn_retry_6 = gr.Checkbox(label='Retry view 6')
                    btn_retry_7 = gr.Checkbox(label='Retry view 7')
                    btn_retry_8 = gr.Checkbox(label='Retry view 8')
                with gr.Row():
                    regen_view_btn = gr.Button('1. Regenerate selected view(s)', variant='secondary', visible=False)
                    regen_mesh_btn = gr.Button('2. Regenerate nearby views and mesh', variant='secondary', visible=False)

        update_guide = lambda GUIDE_TEXT: gr.update(value=GUIDE_TEXT)

        views = [view_1, view_2, view_3, view_4, view_5, view_6, view_7, view_8]
        btn_retrys = [btn_retry_1, btn_retry_2, btn_retry_3, btn_retry_4, btn_retry_5, btn_retry_6, btn_retry_7, btn_retry_8]
        
        rerun_idx = gr.State([])
        tmp_dir = gr.State('./demo_tmp/tmp_dir')

        def refresh(tmp_dir):
            if os.path.exists(tmp_dir):
                shutil.rmtree(tmp_dir)
            tmp_dir = tempfile.TemporaryDirectory(dir=os.path.join(os.path.dirname(__file__), 'demo_tmp'))
            print("create tmp_dir", tmp_dir.name)
            clear = [gr.update(value=[])] + [None] * 5 + [gr.update(visible=False)] * 2 + [None] * 8 + [gr.update(value=False)] * 8
            return (tmp_dir.name, *clear)
        
        placeholder = gr.Image(visible=False)
        tmp_func = lambda x: False if not x else gr.update(visible=False)
        disable_func = lambda x: gr.update(interactive=False)
        enable_func = lambda x: gr.update(interactive=True)
        image_block.change(disable_func, inputs=run_btn, outputs=run_btn, queue=False
                           ).success(fn=refresh,
                           inputs=[tmp_dir],
                           outputs=[tmp_dir, rerun_idx, bbox_block, sam_block, elev_output, vis_output, mesh_output, regen_view_btn, regen_mesh_btn, *views, *btn_retrys],
                           queue=False
                           ).success(fn=tmp_func, inputs=[image_block], outputs=[placeholder], queue=False
                           ).success(fn=partial(update_guide, _BBOX_1), outputs=[guide_text], queue=False
                           ).success(fn=init_bbox,
                                     inputs=[image_block],
                                     outputs=[bbox_block, *bbox_sliders], queue=False
                           ).success(fn=partial(update_guide, _BBOX_3), outputs=[guide_text], queue=False
                           ).success(enable_func, inputs=run_btn, outputs=run_btn, queue=False)


        for bbox_slider in bbox_sliders:
            bbox_slider.release(fn=on_coords_slider,
                               inputs=[image_block, *bbox_sliders],
                               outputs=[bbox_block], 
                               queue=False
                               ).success(fn=partial(update_guide, _BBOX_2), outputs=[guide_text], queue=False)

        cam_vis = CameraVisualizer(vis_output)

        gr.Markdown(article)

        # Define the function to be called when any of the btn_retry buttons are clicked
        def on_retry_button_click(*btn_retrys):
            any_checked = any([btn_retry for btn_retry in btn_retrys])
            print('any_checked:', any_checked, [btn_retry for btn_retry in btn_retrys])
            if any_checked:
                return (gr.update(visible=True), gr.update(visible=True))
            else:
                return (gr.update(), gr.update())
        # make regen_btn visible when any of the btn_retry is checked
        for btn_retry in btn_retrys:
            # Add the event handlers to the btn_retry buttons
            btn_retry.change(fn=on_retry_button_click, inputs=[*btn_retrys], outputs=[regen_view_btn, regen_mesh_btn], queue=False)


        run_btn.click(fn=partial(update_guide, _SAM), outputs=[guide_text], queue=False
                      ).success(fn=partial(preprocess_run, predictor, models), 
                                inputs=[image_block, preprocess_chk, *bbox_sliders], 
                                outputs=[sam_block]
                      ).success(fn=partial(update_guide, _GEN_1), outputs=[guide_text], queue=False
                      ).success(fn=partial(stage1_run, models, device, cam_vis),
                                inputs=[tmp_dir, sam_block, scale_slider, steps_slider],
                                outputs=[elev_output, vis_output, *views]
                      ).success(fn=partial(update_guide, _GEN_2), outputs=[guide_text], queue=False
                      ).success(fn=partial(stage2_run, models, device),
                                inputs=[tmp_dir, elev_output, scale_slider],
                                outputs=[mesh_output]
                      ).success(fn=partial(update_guide, _DONE), outputs=[guide_text], queue=False)
    

        regen_view_btn.click(fn=partial(stage1_run, models, device, None),
                             inputs=[tmp_dir, sam_block, scale_slider, steps_slider, elev_output, rerun_idx, *btn_retrys],
                             outputs=[rerun_idx, *btn_retrys, *views]
                            ).success(fn=partial(update_guide, _REGEN_1), outputs=[guide_text], queue=False)
        regen_mesh_btn.click(fn=partial(stage2_run, models, device),
                             inputs=[tmp_dir, elev_output, scale_slider, rerun_idx],
                             outputs=[mesh_output, rerun_idx, regen_view_btn, regen_mesh_btn]
                            ).success(fn=partial(update_guide, _REGEN_2), outputs=[guide_text], queue=False)


    demo.launch(enable_queue=True, share=False, max_threads=80) # auth=("admin", os.environ['PASSWD'])


if __name__ == '__main__':

    fire.Fire(run_demo)