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Running
on
Zero
| import os | |
| import sys | |
| os.environ["PYOPENGL_PLATFORM"] = "egl" | |
| os.environ["MESA_GL_VERSION_OVERRIDE"] = "4.1" | |
| # os.system('pip install /home/user/app/pyrender') | |
| # sys.path.append('/home/user/app/pyrender') | |
| import gradio as gr | |
| import spaces | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| from ultralytics import YOLO | |
| from pathlib import Path | |
| import argparse | |
| import json | |
| from typing import Dict, Optional | |
| from wilor.models import WiLoR, load_wilor | |
| from wilor.utils import recursive_to | |
| from wilor.datasets.vitdet_dataset import ViTDetDataset, DEFAULT_MEAN, DEFAULT_STD | |
| from wilor.utils.renderer import Renderer, cam_crop_to_full | |
| device = torch.device('cpu') if torch.cuda.is_available() else torch.device('cuda') | |
| LIGHT_PURPLE=(0.25098039, 0.274117647, 0.65882353) | |
| model, model_cfg = load_wilor(checkpoint_path = './pretrained_models/wilor_final.ckpt' , cfg_path= './pretrained_models/model_config.yaml') | |
| # Setup the renderer | |
| renderer = Renderer(model_cfg, faces=model.mano.faces) | |
| model = model.to(device) | |
| model.eval() | |
| detector = YOLO('./pretrained_models/detector.pt').to(device) | |
| def render_reconstruction(image, conf, IoU_threshold=0.5): | |
| input_img, num_dets, reconstructions = run_wilow_model(image, conf, IoU_threshold=0.5) | |
| if num_dets> 0: | |
| # Render front view | |
| misc_args = dict( | |
| mesh_base_color=LIGHT_PURPLE, | |
| scene_bg_color=(1, 1, 1), | |
| focal_length=reconstructions['focal'], | |
| ) | |
| cam_view = renderer.render_rgba_multiple(reconstructions['verts'], | |
| cam_t=reconstructions['cam_t'], | |
| render_res=reconstructions['img_size'], | |
| is_right=reconstructions['right'], **misc_args) | |
| # Overlay image | |
| 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:] | |
| return input_img_overlay, f'{num_dets} hands detected' | |
| else: | |
| return input_img, f'{num_dets} hands detected' | |
| def run_wilow_model(image, conf, IoU_threshold=0.5): | |
| img_cv2 = image[...,::-1] | |
| img_vis = image.copy() | |
| detections = detector(img_cv2, conf=conf, verbose=False, iou=IoU_threshold)[0] | |
| bboxes = [] | |
| is_right = [] | |
| for det in detections: | |
| Bbox = det.boxes.data.cpu().detach().squeeze().numpy() | |
| Conf = det.boxes.conf.data.cpu().detach()[0].numpy().reshape(-1).astype(np.float16) | |
| Side = det.boxes.cls.data.cpu().detach() | |
| #Bbox[:2] -= np.int32(0.1 * Bbox[:2]) | |
| #Bbox[2:] += np.int32(0.1 * Bbox[ 2:]) | |
| is_right.append(det.boxes.cls.cpu().detach().squeeze().item()) | |
| bboxes.append(Bbox[:4].tolist()) | |
| color = (255*0.208, 255*0.647 ,255*0.603 ) if Side==0. else (255*1, 255*0.78039, 255*0.2353) | |
| label = f'L - {Conf[0]:.3f}' if Side==0 else f'R - {Conf[0]:.3f}' | |
| cv2.rectangle(img_vis, (int(Bbox[0]), int(Bbox[1])), (int(Bbox[2]), int(Bbox[3])), color , 3) | |
| (w, h), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 1) | |
| cv2.rectangle(img_vis, (int(Bbox[0]), int(Bbox[1]) - 20), (int(Bbox[0]) + w, int(Bbox[1])), color, -1) | |
| cv2.putText(img_vis, label, (int(Bbox[0]), int(Bbox[1]) - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0,0,0), 2) | |
| if len(bboxes) != 0: | |
| boxes = np.stack(bboxes) | |
| right = np.stack(is_right) | |
| dataset = ViTDetDataset(model_cfg, img_cv2, boxes, right, rescale_factor=2.0 ) | |
| dataloader = torch.utils.data.DataLoader(dataset, batch_size=32, shuffle=False, num_workers=0) | |
| all_verts = [] | |
| all_cam_t = [] | |
| all_right = [] | |
| all_joints= [] | |
| for batch in dataloader: | |
| batch = recursive_to(batch, device) | |
| with torch.no_grad(): | |
| out = model(batch) | |
| print('CUDA AVAILABLE', torch.cuda.is_available()) | |
| print(out['pred_vertices']) | |
| multiplier = (2*batch['right']-1) | |
| pred_cam = out['pred_cam'] | |
| pred_cam[:,1] = multiplier*pred_cam[:,1] | |
| box_center = batch["box_center"].float() | |
| box_size = batch["box_size"].float() | |
| img_size = batch["img_size"].float() | |
| scaled_focal_length = model_cfg.EXTRA.FOCAL_LENGTH / model_cfg.MODEL.IMAGE_SIZE * img_size.max() | |
| pred_cam_t_full = cam_crop_to_full(pred_cam, box_center, box_size, img_size, scaled_focal_length).detach().cpu().numpy() | |
| batch_size = batch['img'].shape[0] | |
| for n in range(batch_size): | |
| verts = out['pred_vertices'][n].detach().cpu().numpy() | |
| joints = out['pred_keypoints_3d'][n].detach().cpu().numpy() | |
| is_right = batch['right'][n].cpu().numpy() | |
| verts[:,0] = (2*is_right-1)*verts[:,0] | |
| joints[:,0] = (2*is_right-1)*joints[:,0] | |
| cam_t = pred_cam_t_full[n] | |
| all_verts.append(verts) | |
| all_cam_t.append(cam_t) | |
| all_right.append(is_right) | |
| all_joints.append(joints) | |
| reconstructions = {'verts': all_verts, 'cam_t': all_cam_t, 'right': all_right, 'img_size': img_size[n], 'focal': scaled_focal_length} | |
| return img_vis.astype(np.float32)/255.0, len(detections), reconstructions | |
| else: | |
| return img_vis.astype(np.float32)/255.0, len(detections), None | |
| header = (''' | |
| <div class="embed_hidden" style="text-align: center;"> | |
| <h1> <b>WiLoR</b>: End-to-end 3D hand localization and reconstruction in-the-wild</h1> | |
| <h3> | |
| <a href="https://rolpotamias.github.io" target="_blank" rel="noopener noreferrer">Rolandos Alexandros Potamias</a><sup>1</sup>, | |
| <a href="" target="_blank" rel="noopener noreferrer">Jinglei Zhang</a><sup>2</sup>, | |
| <br> | |
| <a href="https://jiankangdeng.github.io/" target="_blank" rel="noopener noreferrer">Jiankang Deng</a><sup>1</sup>, | |
| <a href="https://wp.doc.ic.ac.uk/szafeiri/" target="_blank" rel="noopener noreferrer">Stefanos Zafeiriou</a><sup>1</sup> | |
| </h3> | |
| <h3> | |
| <sup>1</sup>Imperial College London; | |
| <sup>2</sup>Shanghai Jiao Tong University | |
| </h3> | |
| </div> | |
| <div style="display:flex; gap: 0.3rem; justify-content: center; align-items: center;" align="center"> | |
| <a href=''><img src='https://img.shields.io/badge/Arxiv-......-A42C25?style=flat&logo=arXiv&logoColor=A42C25'></a> | |
| <a href=''><img src='https://img.shields.io/badge/Paper-PDF-yellow?style=flat&logo=arXiv&logoColor=yellow'></a> | |
| <a href='https://rolpotamias.github.io/wilor/'><img src='https://img.shields.io/badge/Project-Page-%23df5b46?style=flat&logo=Google%20chrome&logoColor=%23df5b46'></a> | |
| <a href='https://github.com/rolpotamias/WiLoR'><img src='https://img.shields.io/badge/GitHub-Code-black?style=flat&logo=github&logoColor=white'></a> | |
| ''') | |
| with gr.Blocks(title="WiLoR: End-to-end 3D hand localization and reconstruction in-the-wild", css=".gradio-container") as demo: | |
| gr.Markdown(header) | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_image = gr.Image(label="Input image", type="numpy") | |
| threshold = gr.Slider(value=0.3, minimum=0.05, maximum=0.95, step=0.05, label='Detection Confidence Threshold') | |
| #nms = gr.Slider(value=0.5, minimum=0.05, maximum=0.95, step=0.05, label='IoU NMS Threshold') | |
| submit = gr.Button("Submit", variant="primary") | |
| with gr.Column(): | |
| reconstruction = gr.Image(label="Reconstructions", type="numpy") | |
| hands_detected = gr.Textbox(label="Hands Detected") | |
| submit.click(fn=render_reconstruction, inputs=[input_image, threshold], outputs=[reconstruction, hands_detected]) | |
| with gr.Row(): | |
| example_images = gr.Examples([ | |
| ['/home/user/app/assets/test1.jpg'], | |
| ['/home/user/app/assets/test2.png'], | |
| ['/home/user/app/assets/test3.jpg'], | |
| ['/home/user/app/assets/test4.jpg'], | |
| ['/home/user/app/assets/test5.jpeg'] | |
| ], | |
| inputs=input_image) | |
| demo.launch(debug=True) |