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Running
on
L40S
| import os | |
| import sys | |
| import os.path as osp | |
| import argparse | |
| import numpy as np | |
| import torchvision.transforms as transforms | |
| import torch.backends.cudnn as cudnn | |
| import torch | |
| CUR_DIR = osp.dirname(os.path.abspath(__file__)) | |
| sys.path.insert(0, osp.join(CUR_DIR, '..', 'main')) | |
| sys.path.insert(0, osp.join(CUR_DIR , '..', 'common')) | |
| from config import cfg | |
| import cv2 | |
| from tqdm import tqdm | |
| import json | |
| from typing import Literal, Union | |
| from mmdet.apis import init_detector, inference_detector | |
| from utils.inference_utils import process_mmdet_results, non_max_suppression | |
| class Inferer: | |
| def __init__(self, pretrained_model, num_gpus, output_folder): | |
| self.output_folder = output_folder | |
| self.device = torch.device('cuda') if (num_gpus > 0) else torch.device('cpu') | |
| config_path = osp.join(CUR_DIR, './config', f'config_{pretrained_model}.py') | |
| ckpt_path = osp.join(CUR_DIR, '../pretrained_models', f'{pretrained_model}.pth.tar') | |
| cfg.get_config_fromfile(config_path) | |
| cfg.update_config(num_gpus, ckpt_path, output_folder, self.device) | |
| self.cfg = cfg | |
| cudnn.benchmark = True | |
| # load model | |
| from base import Demoer | |
| demoer = Demoer() | |
| demoer._make_model() | |
| demoer.model.eval() | |
| self.demoer = demoer | |
| checkpoint_file = osp.join(CUR_DIR, '../pretrained_models/mmdet/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth') | |
| config_file= osp.join(CUR_DIR, '../pretrained_models/mmdet/mmdet_faster_rcnn_r50_fpn_coco.py') | |
| model = init_detector(config_file, checkpoint_file, device=self.device) # or device='cuda:0' | |
| self.model = model | |
| def infer(self, original_img, iou_thr, frame, multi_person=False, mesh_as_vertices=False): | |
| from utils.preprocessing import process_bbox, generate_patch_image | |
| # from utils.vis import render_mesh, save_obj | |
| from utils.human_models import smpl_x | |
| mesh_paths = [] | |
| smplx_paths = [] | |
| # prepare input image | |
| transform = transforms.ToTensor() | |
| vis_img = original_img.copy() | |
| original_img_height, original_img_width = original_img.shape[:2] | |
| ## mmdet inference | |
| mmdet_results = inference_detector(self.model, original_img) | |
| pred_instance = mmdet_results.pred_instances.cpu().numpy() | |
| bboxes = np.concatenate( | |
| (pred_instance.bboxes, pred_instance.scores[:, None]), axis=1) | |
| bboxes = bboxes[pred_instance.labels == 0] | |
| bboxes = np.expand_dims(bboxes, axis=0) | |
| mmdet_box = process_mmdet_results(bboxes, cat_id=0, multi_person=True) | |
| # save original image if no bbox | |
| if len(mmdet_box[0])<1: | |
| return original_img, [], [] | |
| # if not multi_person: | |
| # only select the largest bbox | |
| num_bbox = 1 | |
| mmdet_box = mmdet_box[0] | |
| # else: | |
| # # keep bbox by NMS with iou_thr | |
| # mmdet_box = non_max_suppression(mmdet_box[0], iou_thr) | |
| # num_bbox = len(mmdet_box) | |
| ## loop all detected bboxes | |
| for bbox_id in range(num_bbox): | |
| mmdet_box_xywh = np.zeros((4)) | |
| mmdet_box_xywh[0] = mmdet_box[bbox_id][0] | |
| mmdet_box_xywh[1] = mmdet_box[bbox_id][1] | |
| mmdet_box_xywh[2] = abs(mmdet_box[bbox_id][2]-mmdet_box[bbox_id][0]) | |
| mmdet_box_xywh[3] = abs(mmdet_box[bbox_id][3]-mmdet_box[bbox_id][1]) | |
| # skip small bboxes by bbox_thr in pixel | |
| if mmdet_box_xywh[2] < 50 or mmdet_box_xywh[3] < 150: | |
| continue | |
| bbox = process_bbox(mmdet_box_xywh, original_img_width, original_img_height) | |
| img, img2bb_trans, bb2img_trans = generate_patch_image(original_img, bbox, 1.0, 0.0, False, self.cfg.input_img_shape) | |
| img = transform(img.astype(np.float32))/255 | |
| img = img.to(cfg.device)[None,:,:,:] | |
| inputs = {'img': img} | |
| targets = {} | |
| meta_info = {} | |
| # mesh recovery | |
| with torch.no_grad(): | |
| out = self.demoer.model(inputs, targets, meta_info, 'test') | |
| # mesh = out['smplx_mesh_cam'].detach().cpu().numpy()[0] | |
| ## save mesh | |
| # save_path_mesh = os.path.join(self.output_folder, 'mesh') | |
| # os.makedirs(save_path_mesh, exist_ok= True) | |
| # obj_path = os.path.join(save_path_mesh, f'{frame:05}_{bbox_id}.obj') | |
| # save_obj(mesh, smpl_x.face, obj_path) | |
| # mesh_paths.append(obj_path) | |
| ## save single person param | |
| smplx_pred = {} | |
| smplx_pred['global_orient'] = out['smplx_root_pose'].reshape(-1,3).cpu().numpy() | |
| smplx_pred['body_pose'] = out['smplx_body_pose'].reshape(-1,3).cpu().numpy() | |
| smplx_pred['left_hand_pose'] = out['smplx_lhand_pose'].reshape(-1,3).cpu().numpy() | |
| smplx_pred['right_hand_pose'] = out['smplx_rhand_pose'].reshape(-1,3).cpu().numpy() | |
| smplx_pred['jaw_pose'] = out['smplx_jaw_pose'].reshape(-1,3).cpu().numpy() | |
| smplx_pred['leye_pose'] = np.zeros((1, 3)) | |
| smplx_pred['reye_pose'] = np.zeros((1, 3)) | |
| smplx_pred['betas'] = out['smplx_shape'].reshape(-1,10).cpu().numpy() | |
| smplx_pred['expression'] = out['smplx_expr'].reshape(-1,10).cpu().numpy() | |
| smplx_pred['transl'] = out['cam_trans'].reshape(-1,3).cpu().numpy() | |
| save_path_smplx = os.path.join(self.output_folder, 'smplx') | |
| os.makedirs(save_path_smplx, exist_ok= True) | |
| npz_path = os.path.join(save_path_smplx, f'{frame:05}_{bbox_id}.npz') | |
| np.savez(npz_path, **smplx_pred) | |
| smplx_paths.append(npz_path) | |
| ## render single person mesh | |
| # focal = [self.cfg.focal[0] / self.cfg.input_body_shape[1] * bbox[2], self.cfg.focal[1] / self.cfg.input_body_shape[0] * bbox[3]] | |
| # princpt = [self.cfg.princpt[0] / self.cfg.input_body_shape[1] * bbox[2] + bbox[0], self.cfg.princpt[1] / self.cfg.input_body_shape[0] * bbox[3] + bbox[1]] | |
| # vis_img = render_mesh(vis_img, mesh, smpl_x.face, {'focal': focal, 'princpt': princpt}, | |
| # mesh_as_vertices=mesh_as_vertices) | |
| # vis_img = vis_img.astype('uint8') | |
| vis_img = None | |
| mesh_paths = None | |
| return vis_img, mesh_paths, smplx_paths | |