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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')
print("Infer using device: ", self.device)
# # load model config
# 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
# load faster-rcnn as human detector
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)
mmdet_box = process_mmdet_results(mmdet_results, cat_id=0, multi_person=True)
# early return
# 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))
# xyxy -> xywh
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
# align these pre-processing steps
bbox = process_bbox(mmdet_box_xywh, original_img_width, original_img_height)
# test mmdet pipeline
if bbox is not None:
top_left = (int(bbox[0]), int(bbox[1]))
bottom_right = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3]))
cv2.rectangle(vis_img, top_left, bottom_right, (0, 0, 255), 2)
# human model inference
# 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')
return vis_img, bbox
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