HMR2/app.py

import argparse
import os
from pathlib import Path
import tempfile
import sys
import cv2
import gradio as gr
import numpy as np
import torch
from PIL import Image
os.system('pip install /home/user/app/vendor/pyrender')
sys.path.append('/home/user/app/vendor/pyrender')
from hmr2.configs import get_config
from hmr2.datasets.vitdet_dataset import (DEFAULT_MEAN, DEFAULT_STD,
                                          ViTDetDataset)
from hmr2.models import HMR2
from hmr2.utils import recursive_to
from hmr2.utils.renderer import Renderer, cam_crop_to_full

os.environ["PYOPENGL_PLATFORM"] = "egl"
os.environ["MESA_GL_VERSION_OVERRIDE"] = "4.1"

try:
    import detectron2
except:
    import os 
    os.system('pip install git+https://github.com/facebookresearch/detectron2.git')

OUT_FOLDER = 'demo_out'
os.makedirs(OUT_FOLDER, exist_ok=True)

# Setup HMR2.0 model
LIGHT_BLUE=(0.65098039,  0.74117647,  0.85882353)
DEFAULT_CHECKPOINT='logs/train/multiruns/hmr2/0/checkpoints/epoch=35-step=1000000.ckpt'
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
model_cfg = str(Path(DEFAULT_CHECKPOINT).parent.parent / 'model_config.yaml')
model_cfg = get_config(model_cfg)
model = HMR2.load_from_checkpoint(DEFAULT_CHECKPOINT, strict=False, cfg=model_cfg).to(device)
model.eval()


# Load detector
from detectron2.config import LazyConfig

from hmr2.utils.utils_detectron2 import DefaultPredictor_Lazy

detectron2_cfg = LazyConfig.load(f"vendor/detectron2/projects/ViTDet/configs/COCO/cascade_mask_rcnn_vitdet_h_75ep.py")
detectron2_cfg.train.init_checkpoint = "https://dl.fbaipublicfiles.com/detectron2/ViTDet/COCO/cascade_mask_rcnn_vitdet_h/f328730692/model_final_f05665.pkl"
for i in range(3):
    detectron2_cfg.model.roi_heads.box_predictors[i].test_score_thresh = 0.25
detector = DefaultPredictor_Lazy(detectron2_cfg)

# Setup the renderer
renderer = Renderer(model_cfg, faces=model.smpl.faces)


import numpy as np


def infer(in_pil_img, in_threshold=0.8, out_pil_img=None):

    open_cv_image = np.array(in_pil_img)
    # Convert RGB to BGR
    open_cv_image = open_cv_image[:, :, ::-1].copy()
    print("EEEEE", open_cv_image.shape)
    det_out = detector(open_cv_image)
    det_instances = det_out['instances']
    valid_idx = (det_instances.pred_classes==0) & (det_instances.scores > in_threshold)
    boxes=det_instances.pred_boxes.tensor[valid_idx].cpu().numpy()

    # Run HMR2.0 on all detected humans
    dataset = ViTDetDataset(model_cfg, open_cv_image, boxes)
    dataloader = torch.utils.data.DataLoader(dataset, batch_size=8, shuffle=False, num_workers=0)

    all_verts = []
    all_cam_t = []
    all_mesh_paths = []

    temp_name = next(tempfile._get_candidate_names())
    
    for batch in dataloader:
        batch = recursive_to(batch, device)
        with torch.no_grad():
            out = model(batch)

        pred_cam = out['pred_cam']
        box_center = batch["box_center"].float()
        box_size = batch["box_size"].float()
        img_size = batch["img_size"].float()
        render_size = img_size
        pred_cam_t = cam_crop_to_full(pred_cam, box_center, box_size, render_size).detach().cpu().numpy()

        # Render the result
        batch_size = batch['img'].shape[0]
        for n in range(batch_size):
            # Get filename from path img_path
            # img_fn, _ = os.path.splitext(os.path.basename(img_path))
            person_id = int(batch['personid'][n])
            white_img = (torch.ones_like(batch['img'][n]).cpu() - DEFAULT_MEAN[:,None,None]/255) / (DEFAULT_STD[:,None,None]/255)
            input_patch = batch['img'][n].cpu() * (DEFAULT_STD[:,None,None]/255) + (DEFAULT_MEAN[:,None,None]/255)
            input_patch = input_patch.permute(1,2,0).numpy()


            verts = out['pred_vertices'][n].detach().cpu().numpy()
            cam_t = pred_cam_t[n]

            all_verts.append(verts)
            all_cam_t.append(cam_t)

            # Save all meshes to disk
            # if args.save_mesh:
            if True:
                camera_translation = cam_t.copy()
                tmesh = renderer.vertices_to_trimesh(verts, camera_translation, LIGHT_BLUE)

                temp_path = os.path.join(f'{OUT_FOLDER}/{temp_name}_{person_id}.obj')
                tmesh.export(temp_path)
                all_mesh_paths.append(temp_path)

    # Render front view
    if len(all_verts) > 0:
        misc_args = dict(
            mesh_base_color=LIGHT_BLUE,
            scene_bg_color=(1, 1, 1),
        )
        cam_view = renderer.render_rgba_multiple(all_verts, cam_t=all_cam_t, render_res=render_size[n], **misc_args)

        # Overlay image
        input_img = open_cv_image.astype(np.float32)[:,:,::-1]/255.0
        input_img = np.concatenate([input_img, np.ones_like(input_img[:,:,:1])], axis=2) # Add alpha channel
        input_img_overlay = input_img[:,:,:3] * (1-cam_view[:,:,3:]) + cam_view[:,:,:3] * cam_view[:,:,3:]

        # convert to PIL image
        out_pil_img =  Image.fromarray((input_img_overlay*255).astype(np.uint8))

        return out_pil_img, all_mesh_paths
    else:
        return None, []


with gr.Blocks(title="4DHumans", css=".gradio-container") as demo:

    gr.HTML("""<div style="font-weight:bold; text-align:center; color:royalblue;">HMR 2.0</div>""")

    with gr.Row():
        with gr.Column():
            input_image = gr.Image(label="Input image", type="pil")
        with gr.Column():
            output_image = gr.Image(label="Reconstructions", type="pil")
            output_meshes = gr.File(label="3D meshes")

    gr.HTML("""<br/>""")

    with gr.Row():
        threshold = gr.Slider(0, 1.0, value=0.6, label='Detection Threshold')
        send_btn = gr.Button("Infer")
        send_btn.click(fn=infer, inputs=[input_image, threshold], outputs=[output_image, output_meshes])

    # gr.Examples([
    #     ['assets/test1.png', 0.6], 
    #     ['assets/test2.jpg', 0.6], 
    #     ['assets/test3.jpg', 0.6], 
    #     ['assets/test4.jpg', 0.6], 
    #     ['assets/test5.jpg', 0.6], 
    #     ], 
    #     inputs=[input_image, threshold])

    gr.Examples([
        ['assets/test1.png'], 
        ['assets/test2.jpg'], 
        ['assets/test3.jpg'], 
        ['assets/test4.jpg'], 
        ['assets/test5.jpg'], 
        ], 
        inputs=[input_image, 0.6])


#demo.queue()
demo.launch(debug=True)




### EOF ###