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