-
Notifications
You must be signed in to change notification settings - Fork 368
/
gradio_app_recon.py
438 lines (357 loc) · 16.4 KB
/
gradio_app_recon.py
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
import os
import torch
import fire
import gradio as gr
from PIL import Image
from functools import partial
import cv2
import time
import numpy as np
from rembg import remove
from segment_anything import sam_model_registry, SamPredictor
import os
import sys
import numpy
import torch
import rembg
import threading
import urllib.request
from PIL import Image
from typing import Dict, Optional, Tuple, List
from dataclasses import dataclass
import streamlit as st
import huggingface_hub
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from mvdiffusion.models.unet_mv2d_condition import UNetMV2DConditionModel
from mvdiffusion.data.single_image_dataset import SingleImageDataset as MVDiffusionDataset
from mvdiffusion.pipelines.pipeline_mvdiffusion_image import MVDiffusionImagePipeline
from diffusers import AutoencoderKL, DDPMScheduler, DDIMScheduler
from einops import rearrange
import numpy as np
import subprocess
from datetime import datetime
def save_image(tensor):
ndarr = tensor.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()
# pdb.set_trace()
im = Image.fromarray(ndarr)
return ndarr
def save_image_to_disk(tensor, fp):
ndarr = tensor.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()
# pdb.set_trace()
im = Image.fromarray(ndarr)
im.save(fp)
return ndarr
def save_image_numpy(ndarr, fp):
im = Image.fromarray(ndarr)
im.save(fp)
weight_dtype = torch.float16
_TITLE = '''Wonder3D: Single Image to 3D using Cross-Domain Diffusion'''
_DESCRIPTION = '''
<div>
Generate consistent multi-view normals maps and color images.
<a style="display:inline-block; margin-left: .5em" href='https://github.com/xxlong0/Wonder3D/'><img src='https://img.shields.io/github/stars/xxlong0/Wonder3D?style=social' /></a>
</div>
<div>
The demo does not include the mesh reconstruction part, please visit <a href="https://github.com/xxlong0/Wonder3D/">our github repo</a> to get a textured mesh.
</div>
'''
_GPU_ID = 0
if not hasattr(Image, 'Resampling'):
Image.Resampling = Image
def sam_init():
sam_checkpoint = os.path.join(os.path.dirname(__file__), "sam_pt", "sam_vit_h_4b8939.pth")
model_type = "vit_h"
sam = sam_model_registry[model_type](checkpoint=sam_checkpoint).to(device=f"cuda:{_GPU_ID}")
predictor = SamPredictor(sam)
return predictor
def sam_segment(predictor, input_image, *bbox_coords):
bbox = np.array(bbox_coords)
image = np.asarray(input_image)
start_time = time.time()
predictor.set_image(image)
masks_bbox, scores_bbox, logits_bbox = predictor.predict(box=bbox, multimask_output=True)
print(f"SAM Time: {time.time() - start_time:.3f}s")
out_image = np.zeros((image.shape[0], image.shape[1], 4), dtype=np.uint8)
out_image[:, :, :3] = image
out_image_bbox = out_image.copy()
out_image_bbox[:, :, 3] = masks_bbox[-1].astype(np.uint8) * 255
torch.cuda.empty_cache()
return Image.fromarray(out_image_bbox, mode='RGBA')
def expand2square(pil_img, background_color):
width, height = pil_img.size
if width == height:
return pil_img
elif width > height:
result = Image.new(pil_img.mode, (width, width), background_color)
result.paste(pil_img, (0, (width - height) // 2))
return result
else:
result = Image.new(pil_img.mode, (height, height), background_color)
result.paste(pil_img, ((height - width) // 2, 0))
return result
def preprocess(predictor, input_image, chk_group=None, segment=True, rescale=False):
RES = 1024
input_image.thumbnail([RES, RES], Image.Resampling.LANCZOS)
if chk_group is not None:
segment = "Background Removal" in chk_group
rescale = "Rescale" in chk_group
if segment:
image_rem = input_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())
input_image = sam_segment(predictor, input_image.convert('RGB'), x_min, y_min, x_max, y_max)
# Rescale and recenter
if rescale:
image_arr = np.array(input_image)
in_w, in_h = image_arr.shape[:2]
out_res = min(RES, max(in_w, in_h))
ret, mask = cv2.threshold(np.array(input_image.split()[-1]), 0, 255, cv2.THRESH_BINARY)
x, y, w, h = cv2.boundingRect(mask)
max_size = max(w, h)
ratio = 0.75
side_len = int(max_size / ratio)
padded_image = np.zeros((side_len, side_len, 4), dtype=np.uint8)
center = side_len // 2
padded_image[center - h // 2 : center - h // 2 + h, center - w // 2 : center - w // 2 + w] = image_arr[y : y + h, x : x + w]
rgba = Image.fromarray(padded_image).resize((out_res, out_res), Image.LANCZOS)
rgba_arr = np.array(rgba) / 255.0
rgb = rgba_arr[..., :3] * rgba_arr[..., -1:] + (1 - rgba_arr[..., -1:])
input_image = Image.fromarray((rgb * 255).astype(np.uint8))
else:
input_image = expand2square(input_image, (127, 127, 127, 0))
return input_image, input_image.resize((320, 320), Image.Resampling.LANCZOS)
def load_wonder3d_pipeline(cfg):
pipeline = MVDiffusionImagePipeline.from_pretrained(
cfg.pretrained_model_name_or_path,
torch_dtype=weight_dtype
)
# pipeline.to('cuda:0')
pipeline.unet.enable_xformers_memory_efficient_attention()
if torch.cuda.is_available():
pipeline.to('cuda:0')
# sys.main_lock = threading.Lock()
return pipeline
from mvdiffusion.data.single_image_dataset import SingleImageDataset
def prepare_data(single_image, crop_size):
dataset = SingleImageDataset(root_dir='', num_views=6, img_wh=[256, 256], bg_color='white', crop_size=crop_size, single_image=single_image)
return dataset[0]
scene = 'scene'
def run_pipeline(pipeline, cfg, single_image, guidance_scale, steps, seed, crop_size, chk_group=None):
import pdb
global scene
# pdb.set_trace()
if chk_group is not None:
write_image = "Write Results" in chk_group
batch = prepare_data(single_image, crop_size)
pipeline.set_progress_bar_config(disable=True)
seed = int(seed)
generator = torch.Generator(device=pipeline.unet.device).manual_seed(seed)
# repeat (2B, Nv, 3, H, W)
imgs_in = torch.cat([batch['imgs_in']] * 2, dim=0).to(weight_dtype)
# (2B, Nv, Nce)
camera_embeddings = torch.cat([batch['camera_embeddings']] * 2, dim=0).to(weight_dtype)
task_embeddings = torch.cat([batch['normal_task_embeddings'], batch['color_task_embeddings']], dim=0).to(weight_dtype)
camera_embeddings = torch.cat([camera_embeddings, task_embeddings], dim=-1).to(weight_dtype)
# (B*Nv, 3, H, W)
imgs_in = rearrange(imgs_in, "Nv C H W -> (Nv) C H W")
# (B*Nv, Nce)
# camera_embeddings = rearrange(camera_embeddings, "B Nv Nce -> (B Nv) Nce")
out = pipeline(
imgs_in,
camera_embeddings,
generator=generator,
guidance_scale=guidance_scale,
num_inference_steps=steps,
output_type='pt',
num_images_per_prompt=1,
**cfg.pipe_validation_kwargs,
).images
bsz = out.shape[0] // 2
normals_pred = out[:bsz]
images_pred = out[bsz:]
num_views = 6
if write_image:
VIEWS = ['front', 'front_right', 'right', 'back', 'left', 'front_left']
cur_dir = os.path.join("./outputs", f"cropsize-{int(crop_size)}-cfg{guidance_scale:.1f}")
scene = 'scene'+datetime.now().strftime('@%Y%m%d-%H%M%S')
scene_dir = os.path.join(cur_dir, scene)
normal_dir = os.path.join(scene_dir, "normals")
masked_colors_dir = os.path.join(scene_dir, "masked_colors")
os.makedirs(normal_dir, exist_ok=True)
os.makedirs(masked_colors_dir, exist_ok=True)
for j in range(num_views):
view = VIEWS[j]
normal = normals_pred[j]
color = images_pred[j]
normal_filename = f"normals_000_{view}.png"
rgb_filename = f"rgb_000_{view}.png"
normal = save_image_to_disk(normal, os.path.join(normal_dir, normal_filename))
color = save_image_to_disk(color, os.path.join(scene_dir, rgb_filename))
rm_normal = remove(normal)
rm_color = remove(color)
save_image_numpy(rm_normal, os.path.join(scene_dir, normal_filename))
save_image_numpy(rm_color, os.path.join(masked_colors_dir, rgb_filename))
normals_pred = [save_image(normals_pred[i]) for i in range(bsz)]
images_pred = [save_image(images_pred[i]) for i in range(bsz)]
out = images_pred + normals_pred
return out
def process_3d(mode, data_dir, guidance_scale, crop_size):
dir = None
global scene
cur_dir = os.path.dirname(os.path.abspath(__file__))
subprocess.run(
f'cd instant-nsr-pl && python launch.py --config configs/neuralangelo-ortho-wmask.yaml --gpu 0 --train dataset.root_dir=../{data_dir}/cropsize-{int(crop_size)}-cfg{guidance_scale:.1f}/ dataset.scene={scene} && cd ..',
shell=True,
)
import glob
# import pdb
# pdb.set_trace()
obj_files = glob.glob(f'{cur_dir}/instant-nsr-pl/exp/{scene}/*/save/*.obj', recursive=True)
print(obj_files)
if obj_files:
dir = obj_files[0]
return dir
@dataclass
class TestConfig:
pretrained_model_name_or_path: str
pretrained_unet_path: str
revision: Optional[str]
validation_dataset: Dict
save_dir: str
seed: Optional[int]
validation_batch_size: int
dataloader_num_workers: int
local_rank: int
pipe_kwargs: Dict
pipe_validation_kwargs: Dict
unet_from_pretrained_kwargs: Dict
validation_guidance_scales: List[float]
validation_grid_nrow: int
camera_embedding_lr_mult: float
num_views: int
camera_embedding_type: str
pred_type: str # joint, or ablation
enable_xformers_memory_efficient_attention: bool
cond_on_normals: bool
cond_on_colors: bool
def run_demo():
from utils.misc import load_config
from omegaconf import OmegaConf
# parse YAML config to OmegaConf
cfg = load_config("./configs/mvdiffusion-joint-ortho-6views.yaml")
# print(cfg)
schema = OmegaConf.structured(TestConfig)
cfg = OmegaConf.merge(schema, cfg)
pipeline = load_wonder3d_pipeline(cfg)
torch.set_grad_enabled(False)
pipeline.to(f'cuda:{_GPU_ID}')
predictor = sam_init()
custom_theme = gr.themes.Soft(primary_hue="blue").set(
button_secondary_background_fill="*neutral_100", button_secondary_background_fill_hover="*neutral_200"
)
custom_css = '''#disp_image {
text-align: center; /* Horizontally center the content */
}'''
with gr.Blocks(title=_TITLE, theme=custom_theme, css=custom_css) as demo:
with gr.Row():
with gr.Column(scale=1):
gr.Markdown('# ' + _TITLE)
gr.Markdown(_DESCRIPTION)
with gr.Row(variant='panel'):
with gr.Column(scale=1):
input_image = gr.Image(type='pil', image_mode='RGBA', height=320, label='Input image', tool=None)
with gr.Column(scale=1):
processed_image = gr.Image(
type='pil',
label="Processed Image",
interactive=False,
height=320,
tool=None,
image_mode='RGBA',
elem_id="disp_image",
visible=True,
)
with gr.Column(scale=1):
## add 3D Model
obj_3d = gr.Model3D(
# clear_color=[0.0, 0.0, 0.0, 0.0],
label="3D Model", height=320,
# camera_position=[0,0,2.0]
)
processed_image_highres = gr.Image(type='pil', image_mode='RGBA', visible=False, tool=None)
with gr.Row(variant='panel'):
with gr.Column(scale=1):
example_folder = os.path.join(os.path.dirname(__file__), "./example_images")
example_fns = [os.path.join(example_folder, example) for example in os.listdir(example_folder)]
gr.Examples(
examples=example_fns,
inputs=[input_image],
outputs=[input_image],
cache_examples=False,
label='Examples (click one of the images below to start)',
examples_per_page=30,
)
with gr.Column(scale=1):
with gr.Accordion('Advanced options', open=True):
with gr.Row():
with gr.Column():
input_processing = gr.CheckboxGroup(
['Background Removal'],
label='Input Image Preprocessing',
value=['Background Removal'],
info='untick this, if masked image with alpha channel',
)
with gr.Column():
output_processing = gr.CheckboxGroup(
['Write Results'], label='write the results in ./outputs folder', value=['Write Results']
)
with gr.Row():
with gr.Column():
scale_slider = gr.Slider(1, 5, value=1, step=1, label='Classifier Free Guidance Scale')
with gr.Column():
steps_slider = gr.Slider(15, 100, value=50, step=1, label='Number of Diffusion Inference Steps')
with gr.Row():
with gr.Column():
seed = gr.Number(42, label='Seed')
with gr.Column():
crop_size = gr.Number(192, label='Crop size')
mode = gr.Textbox('train', visible=False)
data_dir = gr.Textbox('outputs', visible=False)
# crop_size = 192
# with gr.Row():
# method = gr.Radio(choices=['instant-nsr-pl', 'NeuS'], label='Method (Default: instant-nsr-pl)', value='instant-nsr-pl')
# run_btn = gr.Button('Generate Normals and Colors', variant='primary', interactive=True)
run_btn = gr.Button('Reconstruct 3D model', variant='primary', interactive=True)
gr.Markdown("<span style='color:red'> Reconstruction may cost several minutes. Check results in instant-nsr-pl/exp/scene@{current-time}/ </span>")
with gr.Row():
view_1 = gr.Image(interactive=False, height=240, show_label=False)
view_2 = gr.Image(interactive=False, height=240, show_label=False)
view_3 = gr.Image(interactive=False, height=240, show_label=False)
view_4 = gr.Image(interactive=False, height=240, show_label=False)
view_5 = gr.Image(interactive=False, height=240, show_label=False)
view_6 = gr.Image(interactive=False, height=240, show_label=False)
with gr.Row():
normal_1 = gr.Image(interactive=False, height=240, show_label=False)
normal_2 = gr.Image(interactive=False, height=240, show_label=False)
normal_3 = gr.Image(interactive=False, height=240, show_label=False)
normal_4 = gr.Image(interactive=False, height=240, show_label=False)
normal_5 = gr.Image(interactive=False, height=240, show_label=False)
normal_6 = gr.Image(interactive=False, height=240, show_label=False)
run_btn.click(
fn=partial(preprocess, predictor), inputs=[input_image, input_processing], outputs=[processed_image_highres, processed_image], queue=True
).success(
fn=partial(run_pipeline, pipeline, cfg),
inputs=[processed_image_highres, scale_slider, steps_slider, seed, crop_size, output_processing],
outputs=[view_1, view_2, view_3, view_4, view_5, view_6, normal_1, normal_2, normal_3, normal_4, normal_5, normal_6],
).success(
process_3d, inputs=[mode, data_dir, scale_slider, crop_size], outputs=[obj_3d]
)
demo.queue().launch(share=True, max_threads=80)
if __name__ == '__main__':
fire.Fire(run_demo)