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demo.py
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demo.py
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# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
# This program is free software; you can redistribute it and/or modify it
# under the terms of the MIT license.
# This program is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
# PARTICULAR PURPOSE. See the MIT License for more details.
'''
detector for single person detection:
https://github.com/open-mmlab/mmdetection/tree/master/configs/yolox
tracker for multi-person tracking and ReID:
https://github.com/open-mmlab/mmtracking/tree/master/configs/mot/bytetrack
'''
import os
import os.path as osp
import cv2
import copy
import glob
import argparse
import numpy as np
from tqdm import tqdm
import torch
import torchgeometry as tgm
from torch.utils.data import DataLoader
import smplx
from models.smpl import SMPL
from models.cliff_hr48.cliff import CLIFF as cliff_hr48
from models.cliff_res50.cliff import CLIFF as cliff_res50
from common import constants
from common.renderer_pyrd import Renderer
from common.mocap_dataset import MocapDataset
from common.utils import estimate_focal_length
from common.utils import strip_prefix_if_present, cam_crop2full, video_to_images
import mmcv
from mmtrack.apis import inference_mot, init_model
from mmtrack.core import results2outs
from mmdet.apis import inference_detector, init_detector
def perspective_projection(points, rotation, translation, focal_length,
camera_center):
"""This function computes the perspective projection of a set of points.
Input:
points (bs, N, 3): 3D points
rotation (bs, 3, 3): Camera rotation
translation (bs, 3): Camera translation
focal_length (bs,) or scalar: Focal length
camera_center (bs, 2): Camera center
"""
batch_size = points.shape[0]
K = torch.zeros([batch_size, 3, 3], device=points.device)
K[:, 0, 0] = focal_length
K[:, 1, 1] = focal_length
K[:, 2, 2] = 1.
K[:, :-1, -1] = camera_center
# Transform points
points = torch.einsum('bij,bkj->bki', rotation, points)
# ATTENTION: the line shoule be commented out as the points have been aligned
# points = points + translation.unsqueeze(1)
# Apply perspective distortion
projected_points = points / points[:, :, -1].unsqueeze(-1)
# Apply camera intrinsics
projected_points = torch.einsum('bij,bkj->bki', K, projected_points)
return projected_points[:, :, :-1]
def main(args):
if args.smooth:
from mmhuman3d.utils.demo_utils import smooth_process
device = torch.device('cuda:{}'.format(args.gpu)) if torch.cuda.is_available() else torch.device('cpu')
print("Input path:", args.input_path)
print("Input type:", args.input_type)
if args.input_type == "image":
img_path_list = [args.input_path]
base_dir = osp.dirname(osp.abspath(args.input_path))
front_view_dir = side_view_dir = bbox_dir = base_dir
result_filepath = f"{args.input_path[:-4]}_cliff_{args.backbone}.npz"
else:
if args.input_type == "video":
basename = osp.basename(args.input_path).split('.')[0]
base_dir = osp.join(osp.dirname(osp.abspath(args.input_path)), basename)
img_dir = osp.join(base_dir, "imgs")
front_view_dir = osp.join(base_dir, "front_view_%s" % args.backbone)
side_view_dir = osp.join(base_dir, "side_view_%s" % args.backbone)
bbox_dir = osp.join(base_dir, "bbox")
result_filepath = osp.join(base_dir, f"{basename}_cliff_{args.backbone}.npz")
if osp.exists(img_dir):
print(f"Skip extracting images from video, because \"{img_dir}\" already exists")
else:
os.makedirs(img_dir, exist_ok=True)
video_to_images(args.input_path, img_folder=img_dir)
elif args.input_type == "folder":
img_dir = osp.join(args.input_path, "imgs")
front_view_dir = osp.join(args.input_path, "front_view_%s" % args.backbone)
side_view_dir = osp.join(args.input_path, "side_view_%s" % args.backbone)
bbox_dir = osp.join(args.input_path, "bbox")
basename = args.input_path.split('/')[-1]
result_filepath = osp.join(args.input_path, f"{basename}_cliff_{args.backbone}.npz")
# get all image paths
img_path_list = glob.glob(osp.join(img_dir, '*.jpg'))
img_path_list.extend(glob.glob(osp.join(img_dir, '*.png')))
img_path_list.sort()
# load all images
print("Loading images ...")
orig_img_bgr_all = [cv2.imread(img_path) for img_path in tqdm(img_path_list)]
print("Image number:", len(img_path_list))
# multi-person
if args.multi:
# the number of tracked person
# set N=2 for 2 person interactive videos
N = -1
# https://github.com/open-mmlab/mmtracking/tree/master/configs/mot/bytetrack
mot_config = './mmtracking/configs/mot/bytetrack/bytetrack_yolox_x_crowdhuman_mot17-private.py'
checkpoint = './mmtracking/checkpoints/bytetrack_yolox_x_crowdhuman_mot17-private-half_20211218_205500-1985c9f0.pth'
# load model
mot_model = init_model(mot_config, checkpoint, device)
# save results
# [frame_id, x1, y1, x2, y2, conf_score, nms_threshold, person_id]
detection_all = []
# mmtracking procedure
imgs = mmcv.VideoReader(args.input_path)
prog_bar = mmcv.ProgressBar(len(imgs))
for i, img in enumerate(imgs):
result = inference_mot(mot_model, img, frame_id=i)
track_masks = result.get('track_masks', None)
track_bboxes = result.get('track_bboxes', None)
outs_track = results2outs(bbox_results=track_bboxes,
mask_results=track_masks,
mask_shape=img.shape[:2])
ids = outs_track.get('ids', None)
bboxes = outs_track.get('bboxes', None)
# make id starting from 0 in order
ids, bboxes = (list(t) for t in zip(*sorted(zip(ids, bboxes))))
# for convinience, just keep the bbox with the highest conf for each person
existed_ids = []
for j in range(len(bboxes)):
if ids[j] in existed_ids:
continue
if N != -1 and ids[j] > N:
continue
x1, y1, x2, y2, score = bboxes[j]
if score < 0.5:
continue
detection_all.append([i, x1, y1, x2, y2, score, 0.99, ids[j]])
existed_ids.append(ids[j])
# list to array
detection_all = np.array(detection_all)
# single-person
else:
# https://github.com/open-mmlab/mmdetection/tree/master/configs/yolox
config = './mmdetection/configs/yolox/yolox_x_8x8_300e_coco.py'
checkpoint = './mmdetection/checkpoints/yolox_x_8x8_300e_coco_20211126_140254-1ef88d67.pth'
# load detector
model = init_detector(config, checkpoint, device)
# save results
detection_all = []
# mmdetection procedure
imgs = mmcv.VideoReader(args.input_path)
prog_bar = mmcv.ProgressBar(len(imgs))
# only take-out person (id=0)
class_id = 0
for i, img in enumerate(imgs):
result = inference_detector(model, img)
if len(result[class_id]) == 0:
continue
x1, y1, x2, y2, score = result[class_id][0]
detection_all.append([i, x1, y1, x2, y2, score, 0.99, 0])
# list to array
detection_all = np.array(detection_all)
print("--------------------------- 3D HPS estimation ---------------------------")
# Create the model instance
cliff = eval("cliff_" + args.backbone)
cliff_model = cliff(constants.SMPL_MEAN_PARAMS).to(device)
# Load the pretrained model
print("Load the CLIFF checkpoint from path:", args.ckpt)
state_dict = torch.load(args.ckpt)['model']
state_dict = strip_prefix_if_present(state_dict, prefix="module.")
cliff_model.load_state_dict(state_dict, strict=True)
cliff_model.eval()
# Setup the SMPL model
smpl_model = SMPL(constants.SMPL_MODEL_DIR).to(device)
pred_vert_arr = []
if args.save_results:
smpl_pose = []
smpl_betas = []
smpl_trans = []
smpl_joints = []
cam_focal_l = []
mocap_db = MocapDataset(orig_img_bgr_all, detection_all)
mocap_data_loader = DataLoader(mocap_db, batch_size=min(args.batch_size, len(detection_all)), num_workers=0)
for batch in tqdm(mocap_data_loader):
norm_img = batch["norm_img"].to(device).float()
center = batch["center"].to(device).float()
scale = batch["scale"].to(device).float()
img_h = batch["img_h"].to(device).float()
img_w = batch["img_w"].to(device).float()
focal_length = batch["focal_length"].to(device).float()
cx, cy, b = center[:, 0], center[:, 1], scale * 200
bbox_info = torch.stack([cx - img_w / 2., cy - img_h / 2., b], dim=-1)
bbox_info[:, :2] = bbox_info[:, :2] / focal_length.unsqueeze(-1) * 2.8 # [-1, 1]
bbox_info[:, 2] = (bbox_info[:, 2] - 0.24 * focal_length) / (0.06 * focal_length) # [-1, 1]
with torch.no_grad():
pred_rotmat, pred_betas, pred_cam_crop = cliff_model(norm_img, bbox_info)
# convert the camera parameters from the crop camera to the full camera
full_img_shape = torch.stack((img_h, img_w), dim=-1)
pred_cam_full = cam_crop2full(pred_cam_crop, center, scale, full_img_shape, focal_length)
pred_output = smpl_model(betas=pred_betas,
body_pose=pred_rotmat[:, 1:],
global_orient=pred_rotmat[:, [0]],
pose2rot=False,
transl=pred_cam_full)
pred_vertices = pred_output.vertices
pred_vert_arr.extend(pred_vertices.cpu().numpy())
# re-project to 2D keypoints on image plane for calculating reprojection loss
'''
# visualize
for index, (px, py) in enumerate(pred_keypoints2d[0]):
cv2.circle(img, (int(px), int(py)), 1, [255, 128, 0], 2)
cv2.imwrite("front_view_kpt.jpg", img)
'''
pred_keypoints3d = pred_output.joints[:,:24,:]
camera_center = torch.hstack((img_w[:,None], img_h[:,None])) / 2
pred_keypoints2d = perspective_projection(
pred_keypoints3d,
rotation=torch.eye(3, device=device).unsqueeze(0).expand(pred_keypoints3d.shape[0], -1, -1),
translation=pred_cam_full,
focal_length=focal_length,
camera_center=camera_center)
if args.save_results:
# default pose_format is rotation matrix instead of axis-angle
if args.pose_format == "aa":
rot_pad = torch.tensor([0, 0, 1], dtype=torch.float32, device=device).view(1, 3, 1)
rot_pad = rot_pad.expand(pred_rotmat.shape[0] * 24, -1, -1)
rotmat = torch.cat((pred_rotmat.view(-1, 3, 3), rot_pad), dim=-1)
pred_pose = tgm.rotation_matrix_to_angle_axis(rotmat).contiguous().view(-1, 72) # N*72
else:
pred_pose = pred_rotmat # N*24*3*3
smpl_pose.extend(pred_pose.cpu().numpy())
smpl_betas.extend(pred_betas.cpu().numpy())
smpl_trans.extend(pred_cam_full.cpu().numpy())
smpl_joints.extend(pred_output.joints.cpu().numpy())
cam_focal_l.extend(focal_length.cpu().numpy())
if args.infill:
print("Do motion interpolation.")
from scipy.interpolate import interp1d
# infilled smpl joints and vertices
smpl_joints_fill = np.copy(smpl_joints)
smpl_vertices_fill = np.copy(pred_vert_arr)
detection_all_fill = np.copy(detection_all)
# person number, only support infill for 1 or 2 persons now
person_count = len(set(detection_all[:,-1]))
# seperate multiple motions and infill each
for person in range(person_count):
choose_frame = []
choose_index = []
choose_joints = []
choose_vertices = []
for i in range(len(detection_all)):
frame_id = detection_all[i][0]
person_id = detection_all[i][-1]
if person_id == person:
choose_frame.append(int(frame_id))
choose_index.append(len(choose_index))
choose_joints.append(smpl_joints[i])
choose_vertices.append(pred_vert_arr[i])
if len(choose_frame) < 3:
continue
# existed frames that do not need infill
existed_list = copy.copy(choose_frame)
# chosen frame ids with interval
interval = 10
choose_frame = choose_frame[0::interval]
choose_index = choose_index[0::interval]
# stack results
choose_joints = np.stack(choose_joints, axis=0) # (N, J_NUM, 3)
choose_vertices = np.stack(choose_vertices, axis=0) # (N, V_NUM, 3)
# linear interpolation
choose_joints = interp1d(choose_frame, choose_joints[np.array(choose_index), :, :].transpose(1, 2, 0),
kind='linear')(range(int(min(choose_frame)), int(max(choose_frame)))).transpose(2, 0, 1)
choose_vertices = interp1d(choose_frame, choose_vertices[np.array(choose_index), :, :].transpose(1, 2, 0),
kind='linear')(range(int(min(choose_frame)), int(max(choose_frame)))).transpose(2, 0, 1)
if args.smooth:
# limit memory, only smooth smpl joints
print("Do motion smooth on person {}.".format(person))
choose_joints = smooth_process(choose_joints,
smooth_type='smoothnet_windowsize8',
cfg_base_dir='configs/_base_/post_processing/')
# it is also fine to smooth SMPL pose and translation, instead of joint directly
# refer to following code as reference, only SmoothNet has been tested.
'''
import copy
import numpy as np
from mmhuman3d.utils.demo_utils import smooth_process
pose = smpl_pose # (N,72), "pose" in npz file
trans = smpl_trans # (N,3), "global_t" in npz file
smooth_type = 'smoothnet_windowsize8'
# start from 0, the interval is 2
p0 = pose[::2]
t0 = trans[::2]
frame_num = p0.shape[0]
print(frame_num)
new_pose_0 = smooth_process(p0.reshape(frame_num,24,3),
smooth_type='smoothnet_windowsize8',
cfg_base_dir='configs/_base_/post_processing/').reshape(frame_num,72)
new_trans_0 = smooth_process(t0[:, np.newaxis],
smooth_type='smoothnet_windowsize8',
cfg_base_dir='configs/_base_/post_processing/').reshape(frame_num,3)
# start from 1, the interval is 2
p1 = pose[1::2]
t1 = trans[1::2]
frame_num = p1.shape[0]
new_pose_1 = smooth_process(p1.reshape(frame_num,24,3),
smooth_type='smoothnet_windowsize8',
cfg_base_dir='configs/_base_/post_processing/').reshape(frame_num,72)
new_trans_1 = smooth_process(t1[:, np.newaxis],
smooth_type='smoothnet_windowsize8',
cfg_base_dir='configs/_base_/post_processing/').reshape(frame_num,3)
new_pose = copy.copy(pose)
new_trans = copy.copy(trans)
new_pose[::2] = new_pose_0
new_pose[1::2] = new_pose_1
new_trans[::2] = new_trans_0
new_trans[1::2] = new_trans_1
np.savez('{}_smoothnet.npz'.format(name),
imgname=file['imgname'],
pose=new_pose,
shape=file['shape'],
global_t=new_trans,
pred_joints=file['pred_joints'],
focal_l=file['focal_l'],
detection_all=file['detection_all'])
'''
infill_frame_ids = []
for infill_frame_id in range(int(min(choose_frame)), int(max(choose_frame))):
if infill_frame_id not in existed_list:
infill_frame_ids.append(infill_frame_id)
print("Infill {} frames for person {}".format(len(infill_frame_ids), person))
infill_seq = list(range(int(min(choose_frame)), int(max(choose_frame))))
for infill_frame_id in infill_frame_ids:
smpl_joints_fill_item = choose_joints[infill_seq.index(infill_frame_id)]
smpl_joints_fill_item = smpl_joints_fill_item[np.newaxis, :]
smpl_joints_fill = np.append(smpl_joints_fill, smpl_joints_fill_item, axis=0)
smpl_vertices_fill_item = choose_vertices[infill_seq.index(infill_frame_id)]
smpl_vertices_fill_item = smpl_vertices_fill_item[np.newaxis, :]
smpl_vertices_fill = np.append(smpl_vertices_fill, smpl_vertices_fill_item, axis=0)
detection_all_fill_item = np.array([infill_frame_id, 0, 0, 0, 0, 0, 0, person])
detection_all_fill_item = detection_all_fill_item[np.newaxis, :]
detection_all_fill = np.append(detection_all_fill, detection_all_fill_item, axis=0)
smpl_joints = smpl_joints_fill
pred_vert_arr = smpl_vertices_fill
detection_all = detection_all_fill
if args.save_results:
if args.infill:
result_filepath = result_filepath[:-4]+'_infill.npz'
print(f"Save results to \"{result_filepath}\"")
np.savez(result_filepath, imgname=img_path_list,
pose=smpl_pose, shape=smpl_betas, global_t=smpl_trans,
pred_joints=smpl_joints, focal_l=cam_focal_l,
detection_all=detection_all)
print("--------------------------- Visualization ---------------------------")
# make the output directory
os.makedirs(front_view_dir, exist_ok=True)
print("Front view directory:", front_view_dir)
if args.show_sideView:
os.makedirs(side_view_dir, exist_ok=True)
print("Side view directory:", side_view_dir)
if args.show_bbox:
os.makedirs(bbox_dir, exist_ok=True)
print("Bounding box directory:", bbox_dir)
pred_vert_arr = np.array(pred_vert_arr)
for img_idx, orig_img_bgr in enumerate(tqdm(orig_img_bgr_all)):
chosen_mask = detection_all[:, 0] == img_idx
chosen_vert_arr = pred_vert_arr[chosen_mask]
# setup renderer for visualization
img_h, img_w, _ = orig_img_bgr.shape
focal_length = estimate_focal_length(img_h, img_w)
renderer = Renderer(focal_length=focal_length, img_w=img_w, img_h=img_h,
faces=smpl_model.faces,
same_mesh_color=False)
front_view = renderer.render_front_view(chosen_vert_arr,
bg_img_rgb=orig_img_bgr[:, :, ::-1].copy())
# save rendering results
basename = osp.basename(img_path_list[img_idx]).split(".")[0]
filename = basename + "_front_view_cliff_%s.jpg" % args.backbone
front_view_path = osp.join(front_view_dir, filename)
cv2.imwrite(front_view_path, front_view[:, :, ::-1])
if args.show_sideView:
side_view_img = renderer.render_side_view(chosen_vert_arr)
filename = basename + "_side_view_cliff_%s.jpg" % args.backbone
side_view_path = osp.join(side_view_dir, filename)
cv2.imwrite(side_view_path, side_view_img[:, :, ::-1])
# delete the renderer for preparing a new one
renderer.delete()
# draw the detection bounding boxes
if args.show_bbox:
chosen_detection = detection_all[chosen_mask]
bbox_info = chosen_detection[:, 1:6]
bbox_img_bgr = orig_img_bgr.copy()
for min_x, min_y, max_x, max_y, conf in bbox_info:
if conf == 0:
continue
ul = (int(min_x), int(min_y))
br = (int(max_x), int(max_y))
cv2.rectangle(bbox_img_bgr, ul, br, color=(0, 255, 0), thickness=2)
cv2.putText(bbox_img_bgr, "%.1f" % conf, ul,
cv2.FONT_HERSHEY_COMPLEX_SMALL, fontScale=1.0, color=(0, 0, 255), thickness=1)
filename = basename + "_bbox.jpg"
bbox_path = osp.join(bbox_dir, filename)
cv2.imwrite(bbox_path, bbox_img_bgr)
# make videos
if args.make_video:
print("--------------------------- Making videos ---------------------------")
from common.utils import images_to_video
images_to_video(front_view_dir, video_path=front_view_dir + ".mp4", frame_rate=args.frame_rate)
if args.show_sideView:
images_to_video(side_view_dir, video_path=side_view_dir + ".mp4", frame_rate=args.frame_rate)
if args.show_bbox:
images_to_video(bbox_dir, video_path=bbox_dir + ".mp4", frame_rate=args.frame_rate)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--input_type', default='image', choices=['image', 'folder', 'video'],
help='input type')
parser.add_argument('--input_path', default='test_samples/nba.jpg', help='path to the input data')
parser.add_argument('--ckpt',
default="data/ckpt/hr48-PA43.0_MJE69.0_MVE81.2_3dpw.pt",
help='path to the pretrained checkpoint')
parser.add_argument("--backbone", default="hr48", choices=['res50', 'hr48'],
help="the backbone architecture")
parser.add_argument('--batch_size', type=int, default=32,
help='batch size for detection and motion capture')
parser.add_argument('--save_results', action='store_true',
help='save the results as a npz file')
parser.add_argument('--pose_format', default='aa', choices=['aa', 'rotmat'],
help='aa for axis angle, rotmat for rotation matrix')
parser.add_argument('--show_bbox', action='store_true',
help='show the detection bounding boxes')
parser.add_argument('--show_sideView', action='store_true',
help='show the result from the side view')
parser.add_argument('--make_video', action='store_true',
help='make a video of the rendering results')
parser.add_argument('--frame_rate', type=int, default=30, help='frame rate')
# NEW!
parser.add_argument('--gpu', type=int, default=0, help='gpu id')
parser.add_argument('--multi', action='store_true', help='multi-person')
parser.add_argument('--infill', action='store_true', help='motion interpolation, only support linear interpolation now')
parser.add_argument('--smooth', action='store_true', help='motion smooth, support oneeuro, gaus1d, savgol, smoothnet')
args = parser.parse_args()
main(args)