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