'test'

app.py

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+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_unclip import StableUnCLIPImg2ImgPipeline
+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 = '''Era3D: High-Resolution Multiview Diffusion using Efficient Row-wise Attention'''
+_DESCRIPTION = '''
+<div>
+Generate consistent high-resolution multi-view normals maps and color images.
+<a style="display:inline-block; margin-left: .5em" href='https://github.com/pengHTYX/Era3D'></a>
+</div>
+<div>
+The demo does not include the mesh reconstruction part, please visit <a href="https://github.com/pengHTYX/Era3D">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((768, 768), Image.Resampling.LANCZOS)
+
+
+def load_era3d_pipeline(cfg):
+    # Load scheduler, tokenizer and models.
+    
+    pipeline = StableUnCLIPImg2ImgPipeline.from_pretrained(
+    '../MacLab-Era3D-512-6view',
+    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=[512, 512], 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)
+
+
+    imgs_in = torch.cat([batch['imgs_in']]*2, dim=0)
+    num_views = imgs_in.shape[1]
+    imgs_in = rearrange(imgs_in, "B Nv C H W -> (B Nv) C H W")# (B*Nv, 3, H, W)
+    
+    normal_prompt_embeddings, clr_prompt_embeddings = batch['normal_prompt_embeddings'], batch['color_prompt_embeddings'] 
+    prompt_embeddings = torch.cat([normal_prompt_embeddings, clr_prompt_embeddings], dim=0)
+    prompt_embeddings = rearrange(prompt_embeddings, "B Nv N C -> (B Nv) N C")
+    
+
+    out = pipeline(
+        imgs_in, 
+        None, 
+        prompt_embeds=prompt_embeddings,
+        generator=generator, 
+        guidance_scale=guidance_scale, 
+        output_type='pt', 
+        num_images_per_prompt=1, 
+        return_elevation_focal=cfg.log_elevation_focal_length,
+        **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("./mv_res", 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)
+        os.makedirs(scene_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_{view}_masked.png"
+            color_filename = f"color_{view}_masked.png"
+            normal = save_image_to_disk(normal, os.path.join(scene_dir, normal_filename))
+            color = save_image_to_disk(color, os.path.join(scene_dir, color_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 && bash run.sh 0 {scene} exp_demo && cd ..',
+        shell=True,
+    )
+    import glob
+    # import pdb
+
+    # pdb.set_trace()
+
+    obj_files = glob.glob(f'{cur_dir}/instant-nsr-pl/exp_demo/{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
+    # save_single_views: bool
+    save_mode: str
+    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
+    regress_elevation: bool
+    enable_xformers_memory_efficient_attention: bool
+
+    cond_on_normals: bool
+    cond_on_colors: bool
+    
+    regress_elevation: bool
+    regress_focal_length: bool
+    
+
+
+def run_demo():
+    from utils.misc import load_config
+    from omegaconf import OmegaConf
+
+    # parse YAML config to OmegaConf
+    cfg = load_config("./configs/test_unclip-512-6view.yaml")
+    # print(cfg)
+    schema = OmegaConf.structured(TestConfig)
+    cfg = OmegaConf.merge(schema, cfg)
+
+    pipeline = load_era3d_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=768, label='Input image')
+
+            with gr.Column(scale=1):
+                processed_image = gr.Image(
+                    type='pil',
+                    label="Processed Image",
+                    interactive=False,
+                    height=768,
+                    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)
+        with gr.Row(variant='panel'):
+            with gr.Column(scale=1):
+                example_folder = os.path.join(os.path.dirname(__file__), "./examples")
+                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=3, step=1, label='Classifier Free Guidance Scale')
+                        with gr.Column():
+                            steps_slider = gr.Slider(15, 100, value=40, step=1, label='Number of Diffusion Inference Steps')
+                    with gr.Row():
+                        with gr.Column():
+                            seed = gr.Number(600, label='Seed')
+                        with gr.Column():
+                            crop_size = gr.Number(420, label='Crop size')
+
+                        mode = gr.Textbox('train', visible=False)
+                        data_dir = gr.Textbox('outputs', visible=False)
+                    # 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)
+                # recon_btn = gr.Button('Reconstruct 3D model', variant='primary', interactive=True)
+                # gr.Markdown("<span style='color:red'>First click Generate button, then click Reconstruct button. Reconstruction may cost several minutes.</span>")
+        
+        with gr.Row():
+            view_1 = gr.Image(interactive=False, height=512, show_label=False)
+            view_2 = gr.Image(interactive=False, height=512, show_label=False)
+            view_3 = gr.Image(interactive=False, height=512, show_label=False)
+            view_4 = gr.Image(interactive=False, height=512, show_label=False)
+            view_5 = gr.Image(interactive=False, height=512, show_label=False)
+            view_6 = gr.Image(interactive=False, height=512, show_label=False)
+        with gr.Row():
+            normal_1 = gr.Image(interactive=False, height=512, show_label=False)
+            normal_2 = gr.Image(interactive=False, height=512, show_label=False)
+            normal_3 = gr.Image(interactive=False, height=512, show_label=False)
+            normal_4 = gr.Image(interactive=False, height=512, show_label=False)
+            normal_5 = gr.Image(interactive=False, height=512, show_label=False)
+            normal_6 = gr.Image(interactive=False, height=512, 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],
+        )
+        # recon_btn.click(
+        #     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)