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| from pathlib import Path | |
| import torch | |
| import argparse | |
| import os | |
| import cv2 | |
| import numpy as np | |
| from hmr2.configs import get_config | |
| from hmr2.models import HMR2 | |
| from hmr2.utils import recursive_to | |
| from hmr2.datasets.vitdet_dataset import ViTDetDataset, DEFAULT_MEAN, DEFAULT_STD | |
| from hmr2.utils.renderer import Renderer, cam_crop_to_full | |
| LIGHT_BLUE=(0.65098039, 0.74117647, 0.85882353) | |
| # DEFAULT_CHECKPOINT='logs/train/multiruns/20b1_mix11_a1/0/checkpoints/epoch=30-step=1000000.ckpt' | |
| DEFAULT_CHECKPOINT='logs/train/multiruns/hmr2/0/checkpoints/epoch=35-step=1000000.ckpt' | |
| parser = argparse.ArgumentParser(description='HMR2 demo code') | |
| parser.add_argument('--checkpoint', type=str, default=DEFAULT_CHECKPOINT, help='Path to pretrained model checkpoint') | |
| parser.add_argument('--img_folder', type=str, default='example_data/images', help='Folder with input images') | |
| parser.add_argument('--out_folder', type=str, default='demo_out', help='Output folder to save rendered results') | |
| parser.add_argument('--side_view', dest='side_view', action='store_true', default=False, help='If set, render side view also') | |
| parser.add_argument('--batch_size', type=int, default=1, help='Batch size for inference/fitting') | |
| args = parser.parse_args() | |
| # Setup HMR2.0 model | |
| device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') | |
| model_cfg = str(Path(args.checkpoint).parent.parent / 'model_config.yaml') | |
| model_cfg = get_config(model_cfg) | |
| model = HMR2.load_from_checkpoint(args.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) | |
| # Make output directory if it does not exist | |
| os.makedirs(args.out_folder, exist_ok=True) | |
| # Iterate over all images in folder | |
| for img_path in Path(args.img_folder).glob('*.png'): | |
| img_cv2 = cv2.imread(str(img_path), cv2.IMREAD_COLOR) | |
| # Detect humans in image | |
| det_out = detector(img_cv2) | |
| det_instances = det_out['instances'] | |
| valid_idx = (det_instances.pred_classes==0) & (det_instances.scores > 0.5) | |
| boxes=det_instances.pred_boxes.tensor[valid_idx].cpu().numpy() | |
| # Run HMR2.0 on all detected humans | |
| dataset = ViTDetDataset(model_cfg, img_cv2.copy(), boxes) | |
| dataloader = torch.utils.data.DataLoader(dataset, batch_size=8, shuffle=False, num_workers=0) | |
| all_verts = [] | |
| all_cam_t = [] | |
| 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() | |
| regression_img = renderer(out['pred_vertices'][n].detach().cpu().numpy(), | |
| out['pred_cam_t'][n].detach().cpu().numpy(), | |
| batch['img'][n], | |
| mesh_base_color=LIGHT_BLUE, | |
| scene_bg_color=(1, 1, 1), | |
| ) | |
| if args.side_view: | |
| side_img = renderer(out['pred_vertices'][n].detach().cpu().numpy(), | |
| out['pred_cam_t'][n].detach().cpu().numpy(), | |
| white_img, | |
| mesh_base_color=LIGHT_BLUE, | |
| scene_bg_color=(1, 1, 1), | |
| side_view=True) | |
| final_img = np.concatenate([input_patch, regression_img, side_img], axis=1) | |
| else: | |
| final_img = np.concatenate([input_patch, regression_img], axis=1) | |
| verts = out['pred_vertices'][n].detach().cpu().numpy() | |
| cam_t = pred_cam_t[n] | |
| all_verts.append(verts) | |
| all_cam_t.append(cam_t) | |
| misc_args = dict( | |
| mesh_base_color=LIGHT_BLUE, | |
| scene_bg_color=(1, 1, 1), | |
| ) | |
| # Render front view | |
| if len(all_verts) > 0: | |
| cam_view = renderer.render_rgba_multiple(all_verts, cam_t=all_cam_t, render_res=render_size[n], **misc_args) | |
| # Overlay image | |
| input_img = img_cv2.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:] | |
| # cv2.imwrite(os.path.join(args.out_folder, f'{img_fn}_{person_id}.jpg'), 255*final_img[:, :, ::-1]) | |
| cv2.imwrite(os.path.join(args.out_folder, f'rend_{img_fn}.jpg'), 255*input_img_overlay[:, :, ::-1]) | |