def init_model(self):
self.model = TemporalModel(self.valid_poses[0].shape[-2],
self.valid_poses[0].shape[-1],
self.dataset.skeleton().num_joints(),
filter_widths=[3, 3, 3, 3, 3],
causal=False,
dropout=0.25,
channels=1024,
dense=False)
self.model.load_state_dict(self.checkpoint['model_pos'])
def videopose_model_load():
# load trained model
from common.model import TemporalModel
chk_filename = main_path + '/../checkpoint/cpn-pt-243.bin'
checkpoint = torch.load(chk_filename,
map_location=lambda storage, loc: storage)
model_pos = TemporalModel(17,
2,
17,
filter_widths=[3, 3, 3, 3, 3],
causal=False,
dropout=False,
channels=1024,
dense=False)
#bypass CUDA for now to run only on CPU
#model_pos = model_pos.cuda()
model_pos.load_state_dict(checkpoint['model_pos'])
# Print model's state_dict
print("Model's state_dict:")
for param_tensor in model_pos.state_dict():
print(param_tensor, "\t", model_pos.state_dict()[param_tensor].size())
receptive_field = model_pos.receptive_field()
return model_pos
def create_model():
# 加载模型
filter_widths = [int(x) for x in args.architecture.split(',')]
model_eval = TemporalModel(poses_valid_2d[0].shape[-2],
poses_valid_2d[0].shape[-1],
dataset.skeleton().num_joints(),
filter_widths=filter_widths,
causal=args.causal,
dropout=args.dropout,
channels=args.channels,
dense=args.dense)
receptive_field = model_eval.receptive_field()
print('INFO: Receptive field: {} frames'.format(receptive_field))
pad = (receptive_field - 1) // 2 # Padding on each side
if args.causal:
print('INFO: Using causal convolutions')
causal_shift = pad
else:
causal_shift = 0
model_params = 0
for parameter in model_eval.parameters():
model_params += parameter.numel()
print('INFO: Trainable parameter count:', model_params)
model_eval.to(device)
return model_eval, causal_shift, pad
def get_pose3d_predictor(ckpt_dir,
ckpt_name,
filter_widths,
causal=False,
channels=1024):
"""
加载3d关节点坐标预测器
Args:
channels:
ckpt_dir:
ckpt_name:
filter_widths:
causal:
Returns: pose3d_predictor
"""
ckpt_path = os.path.join(ckpt_dir, ckpt_name)
print('Loading checkpoint', ckpt_path)
checkpoint = torch.load(ckpt_path,
map_location=lambda storage, loc: storage)
print('This model was trained for {} epochs'.format(checkpoint['epoch']))
pose3d_predictor = TemporalModel(17,
2,
17,
filter_widths=filter_widths,
causal=causal,
channels=channels)
receptive_field = pose3d_predictor.receptive_field()
print('INFO: Receptive field: {} frames'.format(receptive_field))
pose3d_predictor.load_state_dict(checkpoint['model_pos'])
return pose3d_predictor.to(device).eval()
def videopose_model_load():
# load trained model
from common.model import TemporalModel
chk_filename = main_path + '/checkpoint/cpn-pt-243.bin'
checkpoint = torch.load(
chk_filename,
map_location=lambda storage, loc: storage) # 把loc映射到storage
model_pos = TemporalModel(17,
2,
17,
filter_widths=[3, 3, 3, 3, 3],
causal=False,
dropout=False,
channels=1024,
dense=False)
model_pos = model_pos.cuda()
model_pos.load_state_dict(checkpoint['model_pos'])
receptive_field = model_pos.receptive_field()
return model_pos
cameras_valid, poses_valid, poses_valid_2d = fetch(subjects_test, None)
filter_widths = [int(x) for x in args['architecture'].split(',')]
model_pos_train = TemporalModelOptimized1f(poses_valid_2d[0].shape[-2],
poses_valid_2d[0].shape[-1],
dataset.skeleton().num_joints(),
filter_widths=filter_widths,
causal=args['causal'],
dropout=args['dropout'],
channels=args['channels'])
model_pos = TemporalModel(poses_valid_2d[0].shape[-2],
poses_valid_2d[0].shape[-1],
dataset.skeleton().num_joints(),
filter_widths=filter_widths,
causal=args['causal'],
dropout=args['dropout'],
channels=args['channels'],
dense=args['dense'])
causal_shift = 0
if torch.cuda.is_available():
model_pos = model_pos.cuda()
model_pos_train = model_pos_train.cuda()
if args['resume'] or args['evaluate']:
chk_filename = os.path.join(
args['checkpoint'],
args['resume'] if args['resume'] else args['evaluate'])
print('Loading checkpoint', chk_filename)
checkpoint = torch.load(chk_filename,
elif stride > 1:
# Downsample as requested
for i in range(len(out_poses_2d)):
out_poses_2d[i] = out_poses_2d[i][::stride]
if out_poses_3d is not None:
out_poses_3d[i] = out_poses_3d[i][::stride]
return out_camera_params, out_poses_3d, out_poses_2d
cameras_valid, poses_valid, poses_valid_2d = fetch(['detectron2'], None)
model_pos = TemporalModel(poses_valid_2d[0].shape[-2],
poses_valid_2d[0].shape[-1],
dataset.skeleton().num_joints(),
filter_widths=[3, 3, 3, 3, 3],
causal=False,
dropout=0.25,
channels=1024,
dense=False)
receptive_field = model_pos.receptive_field()
pad = (receptive_field - 1) // 2
causal_shift = 0
if torch.cuda.is_available():
model_pos = model_pos.cuda()
checkpoint = torch.load(chk_filename,
map_location=lambda storage, loc: storage)
model_pos.load_state_dict(checkpoint['model_pos'])
test_generator = UnchunkedGenerator(cameras_valid,
def analyze_frame(h, frame):
boxes, keypoints = infer.inference_on_frame(h['predictor'], frame)
# step 4: prepare data.
# take 2d keypoints, that's it
# first element is empty array, second is our actual frame data, a 3d numpy array with first dimension 1, second and third being the 17 joints of 3 doubles each.
kp = keypoints[1][0][:2, :].T # extract (x, y) just like in prepare_data_2d_custom code
# what to do if kp is NaN or missing data or something?
# I guess just ignore it
# they do this at the end of step4. but we keep it simple, and take the data from step2 directly into a variable.
# output[canonical_name]['custom'] = [data[0]['keypoints'].astype('float32')]
#output_custom_canonical_bullshit = kp.astype('float32')
# this is what happens at the end of step4. which is a file that is loaded in the beginning of step 5.
# np.savez_compressed(os.path.join(args.dataoutputdir, output_prefix_2d + args.output), positions_2d=output, metadata=metadata)
# this is the bullshit they do in the original script.
# confusingly, keypoints is actually just data, until it is set to keypoints[positions_2d]
# keypoints = np.load('data/data_2d_' + args.dataset + '_' + args.keypoints + '.npz', allow_pickle=True)
# step 5: ..... all the other shit
# starting to copy stuff over from run.py
# extract dataset from the init dictionary
dataset = h['dataset']
keypoints_metadata = h['keypoints_metadata']
keypoints_symmetry = h['keypoints_symmetry']
kps_left = h['kps_left']
kps_right = h['kps_right']
joints_left = h['joints_left']
joints_right = h['joints_right']
# normalize
for i in range(len(kp)):
koord = kp[i]
kp[i] = normalize_screen_coordinates(koord, h['frame_metadata']['w'], h['frame_metadata']['h'])
#for kps in enumerate(keypoints):
# kps[..., :2] = normalize_screen_coordinates(kps[..., :2], frame_metadata['w'], frame_metadata['h'])
# this is taken from the args.architecture and run.py and just hardcoded, skipping a lot of nonsense
filter_widths = [int(x) for x in "3,3,3,3,3".split(',')]
skeleton_num_joints = dataset.skeleton().num_joints()
#skeleton_num_joints = 17
causal = True
dropout = 0.25
channels = 1024
dense = False
model_pos_train = TemporalModelOptimized1f(kp.shape[-2], kp.shape[-1], skeleton_num_joints,
filter_widths=filter_widths, causal=causal, dropout=dropout,
channels=channels)
model_pos = TemporalModel(kp.shape[-2], kp.shape[-1], skeleton_num_joints,
filter_widths=filter_widths, causal=causal, dropout=dropout,
channels=channels, dense=dense)
receptive_field = model_pos.receptive_field()
print('INFO: Receptive field: {} frames'.format(receptive_field))
pad = (receptive_field - 1) // 2 # Padding on each side
#if args.causal:
# print('INFO: Using causal convolutions')
# causal_shift = pad
#else:
# causal_shift = 0
causal_shift = pad
model_params = 0
for parameter in model_pos.parameters():
model_params += parameter.numel()
print('INFO: Trainable parameter count:', model_params)
if torch.cuda.is_available():
model_pos = model_pos.cuda()
model_pos_train = model_pos_train.cuda()
#if args.resume or args.evaluate:
if True:
chk_filename = "checkpoint/pretrained_h36m_detectron_coco.bin"
print('Loading checkpoint', chk_filename)
checkpoint = torch.load(chk_filename, map_location=lambda storage, loc: storage)
print('This model was trained for {} epochs'.format(checkpoint['epoch']))
model_pos_train.load_state_dict(checkpoint['model_pos'])
model_pos.load_state_dict(checkpoint['model_pos'])
# false in our particular case... we might benefit from getting rid of model_traj,
# unless it's super fast then we should just keep it in case we ever upgrade
if 'model_traj' in checkpoint:
# Load trajectory model if it contained in the checkpoint (e.g. for inference in the wild)
model_traj = TemporalModel(kp.shape[-2], kp.shape[-1], 1,
filter_widths=filter_widths, causal=causal, dropout=dropout, channels=channels,
dense=dense)
if torch.cuda.is_available():
model_traj = model_traj.cuda()
model_traj.load_state_dict(checkpoint['model_traj'])
else:
model_traj = None
test_generator = UnchunkedGenerator(None, None, kp,
pad=pad, causal_shift=causal_shift, augment=False,
kps_left=kps_left, kps_right=kps_right,
joints_left=joints_left, joints_right=joints_right)
print('INFO: Testing on {} frames'.format(test_generator.num_frames()))
# Evaluate
def evaluate(eval_generator, action=None, return_predictions=False, use_trajectory_model=False):
epoch_loss_3d_pos = 0
epoch_loss_3d_pos_procrustes = 0
epoch_loss_3d_pos_scale = 0
epoch_loss_3d_vel = 0
with torch.no_grad():
if not use_trajectory_model:
model_pos.eval()
else:
model_traj.eval()
N = 0
for _, batch, batch_2d in eval_generator.next_epoch():
inputs_2d = torch.from_numpy(batch_2d.astype('float32'))
if torch.cuda.is_available():
inputs_2d = inputs_2d.cuda()
# Positional model
if not use_trajectory_model:
predicted_3d_pos = model_pos(inputs_2d)
else:
predicted_3d_pos = model_traj(inputs_2d)
# Test-time augmentation (if enabled)
if eval_generator.augment_enabled():
# Undo flipping and take average with non-flipped version
predicted_3d_pos[1, :, :, 0] *= -1
if not use_trajectory_model:
predicted_3d_pos[1, :, joints_left + joints_right] = predicted_3d_pos[1, :, joints_right + joints_left]
predicted_3d_pos = torch.mean(predicted_3d_pos, dim=0, keepdim=True)
if return_predictions:
return predicted_3d_pos.squeeze(0).cpu().numpy()
inputs_3d = torch.from_numpy(batch.astype('float32'))
if torch.cuda.is_available():
inputs_3d = inputs_3d.cuda()
inputs_3d[:, :, 0] = 0
if eval_generator.augment_enabled():
inputs_3d = inputs_3d[:1]
error = mpjpe(predicted_3d_pos, inputs_3d)
epoch_loss_3d_pos_scale += inputs_3d.shape[0]*inputs_3d.shape[1] * n_mpjpe(predicted_3d_pos, inputs_3d).item()
epoch_loss_3d_pos += inputs_3d.shape[0]*inputs_3d.shape[1] * error.item()
N += inputs_3d.shape[0] * inputs_3d.shape[1]
inputs = inputs_3d.cpu().numpy().reshape(-1, inputs_3d.shape[-2], inputs_3d.shape[-1])
predicted_3d_pos = predicted_3d_pos.cpu().numpy().reshape(-1, inputs_3d.shape[-2], inputs_3d.shape[-1])
epoch_loss_3d_pos_procrustes += inputs_3d.shape[0]*inputs_3d.shape[1] * p_mpjpe(predicted_3d_pos, inputs)
# Compute velocity error
epoch_loss_3d_vel += inputs_3d.shape[0]*inputs_3d.shape[1] * mean_velocity_error(predicted_3d_pos, inputs)
if action is None:
print('----------')
else:
print('----'+action+'----')
e1 = (epoch_loss_3d_pos / N)*1000
e2 = (epoch_loss_3d_pos_procrustes / N)*1000
e3 = (epoch_loss_3d_pos_scale / N)*1000
ev = (epoch_loss_3d_vel / N)*1000
print('Test time augmentation:', eval_generator.augment_enabled())
print('Protocol #1 Error (MPJPE):', e1, 'mm')
print('Protocol #2 Error (P-MPJPE):', e2, 'mm')
print('Protocol #3 Error (N-MPJPE):', e3, 'mm')
print('Velocity Error (MPJVE):', ev, 'mm')
print('----------')
return e1, e2, e3, ev
image_keypoints2d = kp
gen = UnchunkedGenerator(None, None, [[image_keypoints2d]],
pad=pad, causal_shift=causal_shift, augment=False,
kps_left=kps_left, kps_right=kps_right, joints_left=joints_left, joints_right=joints_right)
prediction = evaluate(gen, return_predictions=True)
# here is the data format
# public enum VideoPose3dJointOrder
# {
# HIP = 0,
# R_HIP = 1,
# R_KNEE = 2,
# R_FOOT = 3,
# L_HIP = 4,
# L_KNEE = 5,
# L_FOOT = 6,
# SPINE = 7,
# THORAX = 8,
# NOSE = 9,
# HEAD = 10,
# L_SHOULDER = 11,
# L_ELBOW = 12,
# L_WRIST = 13,
# R_SHOULDER = 14,
# R_ELBOW = 15,
# R_WRIST = 16
# }
# this bugs out. dunno what the hell they were trying to do.
# anyway we can fix it by just getting width/height some other way.
# Invert camera transformation
cam = dataset.cameras()
width = cam['frame'][0]['res_w']
height = cam['frame'][0]['res_h']
image_keypoints2d = image_coordinates(image_keypoints2d[..., :2], w=width, h=height)
viz_camera = 0
# If the ground truth is not available, take the camera extrinsic params from a random subject.
# They are almost the same, and anyway, we only need this for visualization purposes.
for subject in dataset.cameras():
if 'orientation' in dataset.cameras()[subject][viz_camera]:
rot = dataset.cameras()[subject][viz_camera]['orientation']
break
prediction = camera_to_world(prediction, R=rot, t=0)
# We don't have the trajectory, but at least we can rebase the height
prediction[:, :, 2] -= np.min(prediction[:, :, 2])
# because algo was meant for a list of frames, we take the first frame (our only frame)
prediction3d = prediction[0]
return prediction3d, image_keypoints2d
# do we want to visualize? this code used to write to json and create a video for visualization
#if args.viz_output is not None:
if True:
anim_output = {'Reconstruction': prediction}
# format the data in the same format as mediapipe, so we can load it in unity with the same script
# we need a list (frames) of lists of 3d landmarks.
unity_landmarks = prediction.tolist()
# how to send data? or display it?
# maybe draw it on the webcam feed....?!?!?!
#with open(args.output_json, "w") as json_file:
# json.dump(unity_landmarks, json_file)
#if args.rendervideo == "yes":
# from common.visualization import render_animation
# render_animation(input_keypoints, keypoints_metadata, anim_output,
# dataset.skeleton(), dataset.fps(), args.viz_bitrate, cam['azimuth'], args.viz_output,
# limit=args.viz_limit, downsample=args.viz_downsample, size=args.viz_size,
# input_video_path=args.viz_video, viewport=(cam['res_w'], cam['res_h']),
# input_video_skip=args.viz_skip)
we_re_done_here = 1
class Predictor:
def __init__(self,
dataset_path,
checkpoint_path,
input_video_path=None,
export_path=None,
output_path=None,
with_cude=False):
self.with_cuda = with_cude
self.dataset_path = dataset_path
self.export_path = export_path
self.output_path = output_path
self.input_video_path = input_video_path
self.dataset = CustomDataset(self.dataset_path)
self.keypoints = None
self.keypoints_left = None
self.keypoints_right = None
self.joints_left = None
self.joints_right = None
self.checkpoint = torch.load(checkpoint_path,
map_location=lambda storage, loc: storage)
self.model = None
self.init_keypoints()
self.valid_poses = self.keypoints["detectron2"]["custom"]
self.init_model()
self.test_generator = None
self.init_generator()
self.prediction = None
self.make_prediction()
def export_prediction(self):
if self.export_path is not None:
np.save(self.export_path, self.prediction)
def init_model(self):
self.model = TemporalModel(self.valid_poses[0].shape[-2],
self.valid_poses[0].shape[-1],
self.dataset.skeleton().num_joints(),
filter_widths=[3, 3, 3, 3, 3],
causal=False,
dropout=0.25,
channels=1024,
dense=False)
self.model.load_state_dict(self.checkpoint['model_pos'])
def init_keypoints(self):
self.keypoints = np.load(self.dataset_path, allow_pickle=True)
keypoints_metadata = self.keypoints['metadata'].item()
keypoints_symmetry = keypoints_metadata['keypoints_symmetry']
self.keypoints_left, self.keypoints_right = list(
keypoints_symmetry[0]), list(keypoints_symmetry[1])
self.joints_left, self.joints_right = list(
self.dataset.skeleton().joints_left()), list(
self.dataset.skeleton().joints_right())
self.keypoints = self.keypoints['positions_2d'].item()
for subject in self.keypoints.keys():
for action in self.keypoints[subject]:
for cam_idx, kps in enumerate(self.keypoints[subject][action]):
# Normalize camera frame
cam = self.dataset.cameras()[subject][cam_idx]
kps[..., :2] = normalize_screen_coordinates(kps[..., :2],
w=cam['res_w'],
h=cam['res_h'])
self.keypoints[subject][action][cam_idx] = kps
def init_generator(self):
receptive_field = self.model.receptive_field()
pad = (receptive_field - 1) // 2
causal_shift = 0
self.test_generator = UnchunkedGenerator(
None,
None,
self.valid_poses,
pad=pad,
causal_shift=causal_shift,
augment=False,
kps_left=self.keypoints_left,
kps_right=self.keypoints_right,
joints_left=self.joints_left,
joints_right=self.joints_right)
def make_prediction(self):
if self.with_cuda:
self.model = self.model.cuda()
with torch.no_grad():
self.model.eval()
for _, batch, batch_2d in self.test_generator.next_epoch():
inputs_2d = torch.from_numpy(batch_2d.astype('float32'))
if self.with_cuda:
inputs_2d = inputs_2d.cuda()
predicted_3d_pos = self.model(inputs_2d)
if self.test_generator.augment_enabled():
predicted_3d_pos[1, :, :, 0] *= -1
predicted_3d_pos[1, :, self.joints_left +
self.joints_right] = predicted_3d_pos[
1, :,
self.joints_right + self.joints_left]
predicted_3d_pos = torch.mean(predicted_3d_pos,
dim=0,
keepdim=True)
predicted_3d_pos = predicted_3d_pos.squeeze(0).cpu().numpy()
rot = self.dataset.cameras()['detectron2'][0]['orientation']
predicted_3d_pos = camera_to_world(predicted_3d_pos, R=rot, t=0)
predicted_3d_pos[:, :, 2] -= np.min(predicted_3d_pos[:, :, 2])
self.prediction = predicted_3d_pos
def plot_pose(self, pose_index=0):
pose = make_pose(self.prediction.tolist()[pose_index])
pose.prepare_plot()
pose.plot()
def videpose_infer(args):
from common.camera import normalize_screen_coordinates, camera_to_world, image_coordinates
from common.generators import UnchunkedGenerator
from common.model import TemporalModel
from common.utils import Timer, evaluate, add_path
from videopose import get_detector_2d, ckpt_time, metadata, time0
import gene_npz
gene_npz.args.outputpath = str(args.viz_output / "alpha_pose_kunkun_cut")
print(gene_npz.args)
# detector_2d = get_detector_2d(args.detector_2d)
detector_2d = gene_npz.generate_kpts(args.detector_2d)
assert detector_2d, 'detector_2d should be in ({alpha, hr, open}_pose)'
# 2D kpts loads or generate
if not args.input_npz:
video_name = args.viz_video
keypoints = detector_2d(video_name)
else:
npz = np.load(args.input_npz)
keypoints = npz['kpts'] # (N, 17, 2)
keypoints_symmetry = metadata['keypoints_symmetry']
kps_left, kps_right = list(
keypoints_symmetry[0]), list(keypoints_symmetry[1])
joints_left, joints_right = list(
[4, 5, 6, 11, 12, 13]), list([1, 2, 3, 14, 15, 16])
# normlization keypoints Suppose using the camera parameter
keypoints = normalize_screen_coordinates(
keypoints[..., :2], w=1000, h=1002)
model_pos = TemporalModel(17, 2, 17, filter_widths=[3, 3, 3, 3, 3], causal=args.causal, dropout=args.dropout, channels=args.channels,
dense=args.dense)
if torch.cuda.is_available():
model_pos = model_pos.cuda()
ckpt, time1 = ckpt_time(time0)
print('-------------- load data spends {:.2f} seconds'.format(ckpt))
# load trained model
chk_filename = os.path.join(
args.checkpoint, args.resume if args.resume else args.evaluate)
print('Loading checkpoint', chk_filename)
checkpoint = torch.load(
chk_filename, map_location=lambda storage, loc: storage) # 把loc映射到storage
model_pos.load_state_dict(checkpoint['model_pos'])
ckpt, time2 = ckpt_time(time1)
print('-------------- load 3D model spends {:.2f} seconds'.format(ckpt))
# Receptive field: 243 frames for args.arc [3, 3, 3, 3, 3]
receptive_field = model_pos.receptive_field()
pad = (receptive_field - 1) // 2 # Padding on each side
causal_shift = 0
print('Rendering...')
input_keypoints = keypoints.copy()
gen = UnchunkedGenerator(None, None, [input_keypoints],
pad=pad, causal_shift=causal_shift, augment=args.test_time_augmentation,
kps_left=kps_left, kps_right=kps_right, joints_left=joints_left, joints_right=joints_right)
prediction = evaluate(gen, model_pos, return_predictions=True)
# save 3D joint points
np.save(args.viz_output / "test_3d_output.npy",
prediction, allow_pickle=True)
rot = np.array([0.14070565, -0.15007018, -0.7552408,
0.62232804], dtype=np.float32)
prediction = camera_to_world(prediction, R=rot, t=0)
# We don't have the trajectory, but at least we can rebase the height
prediction[:, :, 2] -= np.min(prediction[:, :, 2])
anim_output = {'Reconstruction': prediction}
input_keypoints = image_coordinates(
input_keypoints[..., :2], w=1000, h=1002)
ckpt, time3 = ckpt_time(time2)
print(
'-------------- generate reconstruction 3D data spends {:.2f} seconds'.format(ckpt))
ckpt, time4 = ckpt_time(time3)
print('total spend {:2f} second'.format(ckpt))
def main(input_args):
vp3d_dir = input_args.vp3d_dir
sys.path.append(vp3d_dir)
from common.camera import normalize_screen_coordinates
from common.model import TemporalModel
from common.generators import UnchunkedGenerator
from common.arguments import parse_args
args = parse_args()
print(args)
kps_left = [4, 5, 6, 11, 12, 13]
kps_right = [1, 2, 3, 14, 15, 16]
joints_left = [4, 5, 6, 11, 12, 13]
joints_right = [1, 2, 3, 14, 15, 16]
filter_widths = [int(x) for x in args.architecture.split(',')]
num_joints_in = 17
in_features = 2
num_joints_out = 17
model_pos = TemporalModel(num_joints_in, in_features, num_joints_out,
filter_widths=filter_widths, causal=args.causal, dropout=args.dropout, channels=args.channels,
dense=args.dense)
receptive_field = model_pos.receptive_field()
print('INFO: Receptive field: {} frames'.format(receptive_field))
pad = (receptive_field - 1) // 2 # Padding on each side
if args.causal:
print('INFO: Using causal convolutions')
causal_shift = pad
else:
causal_shift = 0
model_params = 0
for parameter in model_pos.parameters():
model_params += parameter.numel()
print('INFO: Trainable parameter count:', model_params)
if torch.cuda.is_available():
model_pos = model_pos.cuda()
if args.resume or args.evaluate:
chk_filename = os.path.join(vp3d_dir, args.checkpoint, args.resume if args.resume else args.evaluate)
print('Loading checkpoint', chk_filename)
checkpoint = torch.load(chk_filename, map_location=lambda storage, loc: storage)
print('This model was trained for {} epochs'.format(checkpoint['epoch']))
model_pos.load_state_dict(checkpoint['model_pos'])
# Evaluate
def evaluate(test_generator, action=None, return_predictions=False):
epoch_loss_3d_pos = 0
epoch_loss_3d_pos_procrustes = 0
epoch_loss_3d_pos_scale = 0
epoch_loss_3d_vel = 0
with torch.no_grad():
model_pos.eval()
N = 0
for _, batch, batch_2d in test_generator.next_epoch():
inputs_2d = torch.from_numpy(batch_2d.astype('float32'))
if torch.cuda.is_available():
inputs_2d = inputs_2d.cuda()
# Positional model
predicted_3d_pos = model_pos(inputs_2d)
# Test-time augmentation (if enabled)
if test_generator.augment_enabled():
# Undo flipping and take average with non-flipped version
predicted_3d_pos[1, :, :, 0] *= -1
predicted_3d_pos[1, :, joints_left + joints_right] = predicted_3d_pos[1, :, joints_right + joints_left]
predicted_3d_pos = torch.mean(predicted_3d_pos, dim=0, keepdim=True)
if return_predictions:
return predicted_3d_pos.squeeze(0).cpu().numpy()
inputs_3d = torch.from_numpy(batch.astype('float32'))
if torch.cuda.is_available():
inputs_3d = inputs_3d.cuda()
inputs_3d[:, :, 0] = 0
if test_generator.augment_enabled():
inputs_3d = inputs_3d[:1]
error = mpjpe(predicted_3d_pos, inputs_3d)
epoch_loss_3d_pos_scale += inputs_3d.shape[0]*inputs_3d.shape[1] * n_mpjpe(predicted_3d_pos, inputs_3d).item()
epoch_loss_3d_pos += inputs_3d.shape[0]*inputs_3d.shape[1] * error.item()
N += inputs_3d.shape[0] * inputs_3d.shape[1]
inputs = inputs_3d.cpu().numpy().reshape(-1, inputs_3d.shape[-2], inputs_3d.shape[-1])
predicted_3d_pos = predicted_3d_pos.cpu().numpy().reshape(-1, inputs_3d.shape[-2], inputs_3d.shape[-1])
epoch_loss_3d_pos_procrustes += inputs_3d.shape[0]*inputs_3d.shape[1] * p_mpjpe(predicted_3d_pos, inputs)
# Compute velocity error
epoch_loss_3d_vel += inputs_3d.shape[0]*inputs_3d.shape[1] * mean_velocity_error(predicted_3d_pos, inputs)
if action is None:
print('----------')
else:
print('----'+action+'----')
e1 = (epoch_loss_3d_pos / N)*1000
e2 = (epoch_loss_3d_pos_procrustes / N)*1000
e3 = (epoch_loss_3d_pos_scale / N)*1000
ev = (epoch_loss_3d_vel / N)*1000
print('Test time augmentation:', test_generator.augment_enabled())
print('Protocol #1 Error (MPJPE):', e1, 'mm')
print('Protocol #2 Error (P-MPJPE):', e2, 'mm')
print('Protocol #3 Error (N-MPJPE):', e3, 'mm')
print('Velocity Error (MPJVE):', ev, 'mm')
print('----------')
return e1, e2, e3, ev
def get_gt_dirs(input_path, camera_id='dev3'):
"""Get all directories with ground-truth 2D human pose annotations
"""
gt_path_list = []
category_path_list = get_subdirs(input_path)
for category in category_path_list:
if os.path.basename(category) != 'Calibration':
category_scans = get_subdirs(category)
for category_scan in category_scans:
device_list = get_subdirs(category_scan)
for device_path in device_list:
if camera_id in device_path:
if os.path.exists(os.path.join(device_path, 'pose2d')): # 2D annotations exist
gt_path_list.append(device_path) # eg <root>/Lack_TV_Bench/0007_white_floor_08_04_2019_08_28_10_47/dev3
return gt_path_list
def get_subdirs(input_path):
'''
get a list of subdirectories in input_path directory
:param input_path: parent directory (in which to get the subdirectories)
:return:
subdirs: list of subdirectories in input_path
'''
subdirs = [os.path.join(input_path, dir_i) for dir_i in os.listdir(input_path)
if os.path.isdir(os.path.join(input_path, dir_i))]
subdirs.sort()
return subdirs
fps = 30
frame_width = 1920.0
frame_height = 1080.0
h36m_joint_names = get_h36m_joint_names()
h36m_joint_names_dict = {name: i for i, name in enumerate(h36m_joint_names)}
joint_names = get_body25_joint_names()
joint_names_dict = {name: i for i, name in enumerate(joint_names)}
dataset_dir = input_args.dataset_dir
camera_id = input_args.camera_id
gt_dirs = get_gt_dirs(dataset_dir, camera_id)
for i, gt_dir in enumerate(gt_dirs):
print(f"\nProcessing {i} of {len(gt_dirs)}: {' '.join(gt_dir.split('/')[-3:-1])}")
input_dir = os.path.join(gt_dir, 'predictions', 'pose2d', 'openpose')
output_dir = os.path.join(gt_dir, 'predictions', 'pose3d', 'vp3d')
os.makedirs(output_dir, exist_ok=True)
json_mask = os.path.join(input_dir, 'scan_video_00000000????_keypoints.json')
json_files = sorted(glob(json_mask))
input_keypoints = []
for json_file in json_files:
with open(json_file, 'r') as f:
pose2d = json.load(f)
if len(pose2d["people"]) == 0:
keypoints_op = np.zeros((19, 3))
else:
keypoints_op = np.array(pose2d["people"][0]["pose_keypoints_2d"]).reshape(-1, 3) # Takes first detected person every time...
keypoints = np.zeros((17, 3))
for i, joint_name in enumerate(h36m_joint_names):
if joint_name == 'spine' or joint_name == 'head':
continue
joint_id = joint_names_dict[joint_name]
keypoints[i, :] = keypoints_op[joint_id, :]
keypoints[h36m_joint_names_dict['mid hip'], :] = np.mean((keypoints[h36m_joint_names_dict['left hip'], :], keypoints[h36m_joint_names_dict['right hip'], :]), axis=0) # mid hip = mean(left hip, right hip)
keypoints[h36m_joint_names_dict['spine'], :] = np.mean((keypoints[h36m_joint_names_dict['neck'], :], keypoints[h36m_joint_names_dict['mid hip'], :]), axis=0) # spine = mean(neck, mid hip)
keypoints[h36m_joint_names_dict['head'], :] = np.mean((keypoints_op[joint_names_dict['left ear'], :], keypoints_op[joint_names_dict['right ear'], :]), axis=0) # head = mean(left ear, right ear)
input_keypoints.append(keypoints)
input_keypoints = np.array(input_keypoints)
input_keypoints = input_keypoints[:, :, :2] # For pretrained_h36m_cpn.bin and cpn_ft_h36m_dbb
input_keypoints[..., :2] = normalize_screen_coordinates(input_keypoints[..., :2], w=frame_width, h=frame_height)
args.test_time_augmentation=True
gen = UnchunkedGenerator(None, None, [input_keypoints],
pad=pad, causal_shift=causal_shift, augment=args.test_time_augmentation,
kps_left=kps_left, kps_right=kps_right, joints_left=joints_left, joints_right=joints_right)
prediction = evaluate(gen, return_predictions=True) # Nx17x3
pickle.dump(prediction, open(os.path.join(output_dir, 'vp3d_output.pkl'), "wb"))