Spaces:
Runtime error
Runtime error
File size: 6,326 Bytes
2de1f98 010a8bc 2de1f98 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 |
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')
return vis_img, mesh_paths, smplx_paths
|