def main(args):
weight_file = args.weight
process_speed = args.process_speed
resize_fac = args.resize_factor
print('start processing...')
# Video input & output
data_dir = args.dir
json_path = args.coco
# load model
print('[*]Loading model...')
model = get_model('vgg19')
model.load_state_dict(torch.load(weight_file))
model = torch.nn.DataParallel(model).cuda()
model.float()
model.eval()
print('Model Ready!')
# Video reader
t0 = time.time()
acc_count = 0
data = get_data(data_dir, json_path)
data_count = len(data)
for i, (input_path, keypoints) in enumerate(data):
input_image = cv2.imread(input_path)
t1 = time.time()
# generate image with body parts
resized_image = cv2.resize(input_image, (0, 0),
fx=1 * resize_fac,
fy=1 * resize_fac,
interpolation=cv2.INTER_CUBIC)
to_plot, canvas, joint_list, person_to_joint_assoc = process(
model, resized_image, process_speed)
kp_count = 0
for c, kps in keypoints:
kp_count += c
if len(person_to_joint_assoc) == len(keypoints): # human count equal
acc_count += 1
if args.verb:
cv2.imshow('preview', to_plot)
cv2.waitKey(1)
t2 = time.time()
processBar(
i,
data_count,
'[{}/{}]find {} keypoints in {} humans, groundtruth is {} kps in {} humans. acc:{} process time:{:.3f}, total time:{:.3f}'
.format(i, data_count,
len(joint_list), len(person_to_joint_assoc), kp_count,
len(keypoints), acc_count / (i + 1), (t2 - t1), (t2 - t0)),
length=20,
end="\n")
cv2.destroyAllWindows()
processBar(data_count,
data_count,
'{}/{}, acc:{} total time:{:.3f}'.format(
data_count, data_count, acc_count / data_count,
(time.time() - t0)),
length=20)
def load_openpose_model(weights=os.path.join(dir_name, '../PoseEstimation/network/weight/pose_model.pth'),
model_type='vgg19'):
model = get_model(model_type)
model.load_state_dict(torch.load(weights))
model = torch.nn.DataParallel(model).to(device)
model.float()
model.eval()
return model
def __init__(self, max_size=600):
cur_dir = os.path.dirname(os.path.abspath(__file__))
weight_name = os.path.join(cur_dir, 'pose_model_scratch.pth')
assert os.path.exists(
weight_name), 'open pose model not found at {}'.format(weight_name)
self.model = get_model('vgg19')
state_dict = torch.load(weight_name)
# remove 'module.' prefix
state_dict = {k[7:]: v for k, v in state_dict.items()}
self.model.load_state_dict(state_dict)
self.model = torch.nn.DataParallel(self.model).cuda()
self.model.float()
self.model.eval()
self.max_size = max_size
def create_pose_estimation_model(pretrained,
dataset,
arch,
load_vgg19=None,
parallel=True,
device_ids=None):
# noinspection PyGlobalUndefined
global msglogger
model = None
dataset = dataset.lower()
if dataset == 'coco':
if arch == 'shufflenetv2':
model = rtpose_shufflenetV2.Network(width_multiplier=1.0)
if pretrained:
msglogger.info(
'No pretrained ShuffleNetV2 model available. Init randomly.'
)
elif arch == 'vgg19':
model = rtpose_vgg.get_model(trunk='vgg19')
if pretrained:
model_dir = Path('./pretrained')
model_dir.mkdir(exist_ok=True)
rtpose_vgg.use_vgg(model, model_path, 'vgg19')
if load_vgg19:
model.load_state_dict(torch.load(load_vgg19))
elif arch == 'hourglass':
model = rtpose_hourglass.hg(num_stacks=8,
num_blocks=1,
paf_classes=38,
ht_classes=19)
if pretrained:
msglogger.info(
'No pretrained Hourglass model available. Init randomly.')
else:
raise ValueError('Could not recognize dataset {}'.format(dataset))
msglogger.info("=> creating a %s%s model with the %s dataset" %
('pretrained ' if pretrained else '', arch, dataset))
if torch.cuda.is_available() and device_ids != -1:
device = 'cuda'
if parallel:
print('Data parallel: device_ids =', device_ids)
net = torch.nn.DataParallel(model, device_ids=device_ids)
else:
device = 'cpu'
return model.to(device)
def pose_model():
# Pose estimation (OpenPose)
openpose_dir = Path('../src/pytorch_Realtime_Multi-Person_Pose_Estimation/')
sys.path.append(str(openpose_dir))
# get_ipython().run_line_magic('load_ext', 'autoreload')
# get_ipython().run_line_magic('autoreload', '2')
# openpose
from network.rtpose_vgg import get_model
weight_name = openpose_dir.joinpath('network/weight/pose_model.pth')
# weight_name.mkdir(exist_ok=True)
model = get_model('vgg19')
model.load_state_dict(torch.load(str(weight_name)))
model = torch.nn.DataParallel(model).cuda()
# model.float()
# model.eval()
return model
# validation data
valid_data = get_loader(args.json_path,
args.data_dir,
args.mask_dir,
368,
8,
preprocess='vgg',
training=False,
batch_size=args.batch_size,
params_transform=params_transform,
shuffle=False,
num_workers=4)
print('val dataset len: {}'.format(len(valid_data.dataset)))
# model
model = get_model(trunk='vgg19')
#model = encoding.nn.DataParallelModel(model, device_ids=args.gpu_ids)
model = torch.nn.DataParallel(model).cuda()
# load pretrained
use_vgg(model, args.model_path, 'vgg19')
# Fix the VGG weights first, and then the weights will be released
for i in range(20):
for param in model.module.model0[i].parameters():
param.requires_grad = False
trainable_vars = [param for param in model.parameters() if param.requires_grad]
optimizer = torch.optim.SGD(trainable_vars,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay,
def _train(class_name, path_to_data_dir, path_to_logs_dir, batch_size, epochs,
restore):
# create tensorboard
writer = SummaryWriter(path_to_logs_dir)
# dataloader
train_dataset = Dataset(class_name=class_name,
path_to_data=path_to_data_dir)
train_dataloader = DataLoader(train_dataset,
batch_size,
shuffle=True,
num_workers=0,
drop_last=True)
val_dataset = Dataset(path_to_data=path_to_data_dir,
class_name=class_name,
split='val')
val_dataloader = DataLoader(val_dataset,
batch_size,
shuffle=False,
num_workers=0,
drop_last=True)
# load model
model = get_model(trunk='vgg19')
model = model.cuda()
use_vgg(model, './model', 'vgg19')
# restore model
if restore:
model.load_state_dict(torch.load(restore))
model.train()
# freeze low-level layer
for i in range(20):
for param in model.model0[i].parameters():
param.requires_grad = False
trainable_vars = [
param for param in model.parameters() if param.requires_grad
]
optimizer = torch.optim.Adam(trainable_vars, lr=0.0001)
epoch = 0
step = 1
best_mse = 1.0
while epoch != epochs:
for batch_index, (images, heatmaps_target, pafs_target, _,
_) in enumerate(train_dataloader):
images = images.cuda()
_, saved_for_loss = model(images)
loss, heatmaps_losses, pafs_losses = _loss(saved_for_loss,
heatmaps_target.cuda(),
pafs_target.cuda())
optimizer.zero_grad()
loss.backward()
optimizer.step()
if step % 10 == 0:
print('Epoch: {}, Step: {}, Loss: {}'.format(
epoch, step, loss.data.item()))
writer.add_scalar('train_total_loss/loss', loss, step)
for stage, (heatmaps_loss, pafs_loss) in enumerate(
zip(heatmaps_losses, pafs_losses)):
writer.add_scalar(
'train_heatmaps_loss/stage_{}'.format(str(stage)),
heatmaps_loss, step)
writer.add_scalar(
'train_pafs_loss/stage_{}'.format(str(stage)), pafs_loss,
step)
if step % 1000 == 0:
pafs_loss, heatmaps_loss = _validate(model, val_dataloader)
total_loss = pafs_loss + heatmaps_loss
print('Validation Paf MSE: {} Heatmap MSE: {} Total MSE: {}'.
format(pafs_loss, heatmaps_loss, total_loss))
writer.add_scalar('val/heatmaps_loss', heatmaps_loss, step)
writer.add_scalar('val/pafs_loss', pafs_loss, step)
writer.add_scalar('val/total_loss', total_loss, step)
if total_loss < best_mse:
print('Save checkpoint')
torch.save(
model.state_dict(),
os.path.join(
path_to_logs_dir,
'{}-checkpoint-best.pth'.format(class_name)))
best_mse = total_loss
print('Best MSE: {}'.format(total_loss))
model.train()
step += 1
epoch += 1
print('Save checkpoint')
torch.save(
model.state_dict(),
os.path.join(path_to_logs_dir,
'{}-checkpoint-last.pth'.format(class_name)))
def main(args):
input_data = args.input
weight_file = args.weight
frame_rate_ratio = args.frame_ratio
process_speed = args.process_speed
resize_fac = args.resize_factor
output_dir = args.output
output_format = '.h5'
save_demo = args.verb
print('start processing...')
# Video input & output
io_paths = organize_1to1_io_paths(input_data, VIDEO_EXT, output_dir,
output_format)
# load model
print('[*]Loading model...')
model = get_model('vgg19')
model.load_state_dict(torch.load(weight_file))
model = torch.nn.DataParallel(model).cuda()
model.float()
model.eval()
# Video reader
for input_path, output_path in zip(io_paths["input"], io_paths["output"]):
print('[*]Process video {} into {}'.format(input_path, output_path))
os.makedirs(os.path.dirname(output_path), exist_ok=True)
# input video info
cap = cv2.VideoCapture(input_path)
input_fps = cap.get(cv2.CAP_PROP_FPS)
height = int(resize_fac * cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
width = int(resize_fac * cap.get(cv2.CAP_PROP_FRAME_WIDTH))
video_length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
ending_frame = args.out_length
if ending_frame is None:
ending_frame = video_length
out_h5 = h5py.File(output_path, mode="w")
out_h5["height"] = height
out_h5["width"] = width
if save_demo: # Video writer
demo_path = os.path.splitext(output_path)[0] + ".mp4"
output_fps = input_fps / frame_rate_ratio
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out_demo = cv2.VideoWriter(demo_path, fourcc, output_fps,
(width, height))
i = 0 # default is 0
t0 = time.time()
while (cap.isOpened()) and i < ending_frame:
ret_val, input_image = cap.read()
if not ret_val:
break
if i % frame_rate_ratio == 0:
t1 = time.time()
# generate image with body parts
resized_image = cv2.resize(input_image, (0, 0),
fx=1 * resize_fac,
fy=1 * resize_fac,
interpolation=cv2.INTER_CUBIC)
to_plot, canvas, joint_list, person_to_joint_assoc = process(
model, resized_image, process_speed)
frame_h5 = out_h5.create_group("frame%d" % i)
frame_h5.create_dataset("joint_list", data=joint_list)
frame_h5.create_dataset("person_to_joint_assoc",
data=person_to_joint_assoc)
if save_demo:
out_demo.write(canvas)
t2 = time.time()
processBar(
i,
ending_frame,
'{}/{}, process time:{:.3f}, total time:{:.3f}'.format(
i, ending_frame, (t2 - t1), (t2 - t0)),
length=20)
i += 1
if save_demo:
out_demo.release()
out_h5.close()
processBar(ending_frame,
ending_frame,
'{}/{}, total time:{:.3f}'.format(i, ending_frame,
(time.time() - t0)),
length=45)
13-'left_ankle' 14-'right_eye' 15-'left_eye' 16-'right_ear'
17-'left_ear' )
'''
orderCOCO = [0, 15, 14, 17, 16, 5, 2, 6, 3, 7, 4, 11, 8, 12, 9, 13, 10]
mid_1 = [1, 8, 9, 1, 11, 12, 1, 2, 3, 2, 1, 5, 6, 5, 1, 0, 0, 14, 15]
mid_2 = [8, 9, 10, 11, 12, 13, 2, 3, 4, 16, 5, 6, 7, 17, 0, 14, 15, 16, 17]
# This txt file is get at the caffe_rtpose repository:
# https://github.com/CMU-Perceptual-Computing-Lab/caffe_rtpose/blob/master/image_info_val2014_1k.txt
image_dir = '/data/coco/val2014/'
save_dir = '/data/coco/val2014_features/'
model = get_model('vgg19')
model = torch.nn.DataParallel(model).cuda()
# model = get_ying_model(stages=5, have_bn=True, have_bias=False)
# Load our model
weight_name = './network/weight/pose_model_scratch.pth'
model.load_state_dict(torch.load(weight_name))
model = model.module
# model.load_state_dict(torch.load('pose_model.pth'))
# model.load_state_dict(torch.load('../caffe_model/dilated3_5stage_merged.pth'))
# model.load_state_dict(torch.load('../caffe_model/dilated3_remove_stage1_test.pth'))
model.eval()
model.float()
model.cuda()
feature_extractor = FeatureExtractor(model.model0)
def main(args):
output_dir = args.output_dir
if output_dir:
os.makedirs(output_dir, exist_ok=True)
path_to_data_dir = args.path_to_data_dir
if not os.path.exists(path_to_data_dir):
raise FileNotFoundError(path_to_data_dir)
path_to_checkpoint = args.checkpoint
if not os.path.exists(path_to_checkpoint):
raise FileNotFoundError(path_to_data_dir)
class_name = args.class_name
fps = args.fps
img_prefix = args.img_prefix
# load pre-trained model
model = get_model(trunk='vgg19')
model = model.cuda()
use_vgg(model, './model', 'vgg19')
print("=> Load pre-trained model from {}".format(path_to_checkpoint))
model.load_state_dict(torch.load(path_to_checkpoint))
model.eval()
# parameter of object size for pnp solver
print("=> Load {} object size".format(class_name))
path_to_object_seetings = os.path.join(path_to_data_dir,
'_object_settings.json')
if not os.path.exists(path_to_object_seetings):
raise FileNotFoundError(path_to_object_seetings)
object_list = json.load(open(path_to_object_seetings))['exported_objects']
object_size = None
for obj in object_list:
if obj['class'].find(class_name) != -1:
object_size = obj['cuboid_dimensions']
if not object_size:
raise ValueError("Object size is none")
_cuboid3d = Cuboid3d(object_size)
cuboid3d_points = np.array(_cuboid3d.get_vertices())
# parameter of camera for pnp solver
path_to_camera_seetings = os.path.join(path_to_data_dir,
'_camera_settings.json')
if not os.path.exists(path_to_camera_seetings):
raise FileNotFoundError(path_to_camera_seetings)
intrinsic_settings = json.load(open(
path_to_camera_seetings))['camera_settings'][0]['intrinsic_settings']
matrix_camera = np.zeros((3, 3))
matrix_camera[0, 0] = intrinsic_settings['fx']
matrix_camera[1, 1] = intrinsic_settings['fy']
matrix_camera[0, 2] = max(intrinsic_settings['cx'],
intrinsic_settings['cy'])
matrix_camera[1, 2] = max(intrinsic_settings['cx'],
intrinsic_settings['cy'])
matrix_camera[2, 2] = 1
try:
dist_coeffs = np.array(
json.load(open(path_to_camera_seetings))['camera_settings'][0]
["distortion_coefficients"])
except KeyError:
dist_coeffs = np.zeros((4, 1))
path_to_sequences = sorted(
glob.glob(os.path.join(path_to_data_dir, '*.{}'.format(img_prefix))))
for img_path in path_to_sequences:
original_img = crop(cv2.imread(img_path))
ratio = max(original_img.shape[:2]) / Config.crop_size
img = cv2.resize(original_img, (Config.crop_size, Config.crop_size))
img = preprocess(img).float()
img = torch.unsqueeze(img, 0)
out, _ = model(img.cuda())
line, vertex = out[0].squeeze(), out[1].squeeze()
objects, peaks = find_objects(vertex, line)
original_img = cv2.putText(original_img,
"Class name: {}".format(class_name),
(50, 50), cv2.FONT_HERSHEY_COMPLEX, 1,
(255, 255, 255), 2)
if len(objects) > 0:
for object in objects:
cuboid2d_points = object[1] + [
(object[0][0] * 8, object[0][1] * 8)
]
cuboid3d_points = np.array(cuboid3d_points)
location = None
quaternion = None
obj_2d_points = []
obj_3d_points = []
for i in range(8):
check_point_2d = cuboid2d_points[i]
# Ignore invalid points
if check_point_2d is None:
continue
elif check_point_2d[0] < 0 or check_point_2d[
1] < 0 or check_point_2d[
0] >= Config.crop_size / Config.stride or check_point_2d[
1] >= Config.crop_size / Config.stride:
continue
else:
check_point_2d = (check_point_2d[0] * Config.stride *
ratio, check_point_2d[1] *
Config.stride * ratio)
obj_2d_points.append(check_point_2d)
obj_3d_points.append(cuboid3d_points[i])
centroid = tuple([
int(point * Config.stride * ratio) for point in object[0]
])
original_img = cv2.circle(original_img, centroid, 5, -1)
obj_2d_points = np.array(obj_2d_points, dtype=float)
obj_3d_points = np.array(obj_3d_points, dtype=float)
valid_point_count = len(obj_2d_points)
if valid_point_count >= 5:
ret, rvec, tvec = cv2.solvePnP(
obj_3d_points,
obj_2d_points,
matrix_camera,
dist_coeffs,
flags=cv2.SOLVEPNP_ITERATIVE)
if ret:
location = list(x[0] for x in tvec)
quaternion = convert_rvec_to_quaternion(rvec)
projected_points, _ = cv2.projectPoints(
cuboid3d_points, rvec, tvec, matrix_camera,
dist_coeffs)
projected_points = np.squeeze(projected_points)
# If the location.Z is negative or object is behind the camera then flip both location and rotation
x, y, z = location
original_img = cv2.putText(
original_img,
"Location Prediction: x: {:.2f} y: {:.2f} z: {:.2f}"
.format(x / 10, y / 10, z / 10), (50, 150),
cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
print(
"Location Prediction: x: {:.2f} y: {:.2f} z: {:.2f}"
.format(x / 10, y / 10, z / 10))
if z < 0:
# Get the opposite location
location = [-x, -y, -z]
# Change the rotation by 180 degree
rotate_angle = np.pi
rotate_quaternion = Quaternion.from_axis_rotation(
location, rotate_angle)
quaternion = rotate_quaternion.cross(quaternion)
vertexes = [tuple(p) for p in projected_points]
plot(original_img, vertexes)
if args.save:
if not os.path.exists(output_dir):
os.makedirs(output_dir, exist_ok=True)
output_path = os.path.join(output_dir, img_path.split('/')[-1])
print('=> Save {}'.format(output_path))
cv2.imwrite(output_path, original_img)
if args.plot:
original_img = cv2.resize(original_img, (600, 600))
cv2.imshow('prediction', original_img)
cv2.waitKey(int(1000 / fps))
def main(args):
input_data = args.input
weight_file = args.weight
process_speed = args.process_speed
resize_fac = args.resize_factor
output_dir = args.output
output_format = '.h5'
save_demo = args.verb
print('start processing...')
# Video input & output
if args.input_type == 'serial':
io_paths = organize_Nto1_io_paths(input_data, IMAGE_EXT, output_dir,
output_format)
else:
io_paths = organize_1to1_io_paths(input_data, IMAGE_EXT, output_dir,
output_format)
data_count = len(io_paths["input"])
# load model
print('[*]Loading model...')
model = get_model('vgg19')
model.load_state_dict(torch.load(weight_file))
model = torch.nn.DataParallel(model).cuda()
model.float()
model.eval()
print('Model Ready!')
# Video reader
t0 = time.time()
for i, (input_path, output_path) in enumerate(
zip(io_paths["input"], io_paths["output"])):
if io_paths["type"] == "1to1":
print('[*]Process {} into {}'.format(input_path, output_path))
os.makedirs(os.path.dirname(output_path), exist_ok=True)
input_image = cv2.imread(input_path)
out_h5 = h5py.File(output_path, mode="w")
out_h5["height"] = input_image.shape[0]
out_h5["width"] = input_image.shape[1]
t1 = time.time()
# generate image with body parts
resized_image = cv2.resize(input_image, (0, 0),
fx=1 * resize_fac,
fy=1 * resize_fac,
interpolation=cv2.INTER_CUBIC)
to_plot, canvas, joint_list, person_to_joint_assoc = process(
model, resized_image, process_speed)
frame_h5 = out_h5.create_group("frame0")
frame_h5.create_dataset("joint_list", data=joint_list)
frame_h5.create_dataset("person_to_joint_assoc",
data=person_to_joint_assoc)
if save_demo:
demo_path = os.path.splitext(output_path)[0] + ".jpg"
cv2.imwrite(demo_path, canvas)
t2 = time.time()
processBar(i,
data_count,
'{}/{}, process time:{:.3f}, total time:{:.3f}'.format(
i, data_count, (t2 - t1), (t2 - t0)),
length=20)
elif len(input_path[0]) > 0:
print('[*]Process {} into {}'.format(
os.path.dirname(input_path[0]), output_path))
os.makedirs(os.path.dirname(output_path), exist_ok=True)
input_image = cv2.imread(input_path[0])
height = input_image.shape[0]
width = input_image.shape[1]
out_h5 = h5py.File(output_path, mode="w")
out_h5["height"] = height
out_h5["width"] = width
if save_demo: # Video writer
demo_path = os.path.splitext(output_path)[0] + ".mp4"
output_fps = 15
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out_demo = cv2.VideoWriter(demo_path, fourcc, output_fps,
(width, height))
i = 0 # default is 0
t0 = time.time()
for j, path in enumerate(input_path):
input_image = cv2.imread(path)
t1 = time.time()
# generate image with body parts
resized_image = cv2.resize(input_image, (0, 0),
fx=1 * resize_fac,
fy=1 * resize_fac,
interpolation=cv2.INTER_CUBIC)
to_plot, canvas, joint_list, person_to_joint_assoc = process(
model, resized_image, process_speed)
frame_h5 = out_h5.create_group("frame%d" % j)
frame_h5.create_dataset("joint_list", data=joint_list)
frame_h5.create_dataset("person_to_joint_assoc",
data=person_to_joint_assoc)
if save_demo:
out_demo.write(canvas)
t2 = time.time()
processBar(
j,
len(input_path),
'{}/{}, process time:{:.3f}, total time:{:.3f}'.format(
j, len(input_path), (t2 - t1), (t2 - t0)),
length=20)
if save_demo:
out_demo.release()
out_h5.close()
processBar(len(input_path),
len(input_path),
'{}/{}, total time:{:.3f}'.format(
j, len(input_path), (time.time() - t0)),
length=45)
cv2.destroyAllWindows()
processBar(data_count,
data_count,
'{}/{}, total time:{:.3f}'.format(i, data_count,
(time.time() - t0)),
length=45)
vis_dir = './result_vis'
# ------------------------------------------------------------------------------
tic = datetime.datetime.now()
if vis_dir is not None:
if not os.path.exists(vis_dir):
os.mkdir(vis_dir)
print('mkdir:', vis_dir)
print('save vis images to:', vis_dir)
with torch.autograd.no_grad():
if model_name == 'vgg19': # --- VGG19
model = get_model(trunk='vgg19',
numkeypoints=CF.NUM_KEYPOINTS,
numlims=CF.NUM_LIMBS)
preprocess = 'vgg'
elif model_name == 'shufflenet': # --- ShuffleNet
model = rtpose_shufflenetV2.Network(width_multiplier=1.0,
numkeypoints=CF.NUM_KEYPOINTS,
numlims=CF.NUM_LIMBS,
multistage=multistage)
preprocess = 'rtpose'
else:
print('Please check the model name.')
exit(0)
print('Network backbone:{}'.format(model_name))
# this path is with respect to the root of the project
state_dict = torch.load(weight_name)
# validation data
valid_data = None
if params.val_nbatch > 0:
valid_data = get_loader(json_path,
data_dir,
mask_dir,
inp_size,
feat_stride,
preprocess='vgg',
training=False,
batch_size=params.batch_size,
shuffle=True)
print('val dataset len: {}'.format(len(valid_data.dataset)))
# model
model = get_model(trunk=trunk)
# load pretrained
if params.ckpt is None:
use_vgg(model, model_path, trunk)
# Fix the VGG weights first, and then the weights will be released
for i in range(20):
for param in model.model0[i].parameters():
param.requires_grad = False
trainable_vars = [param for param in model.parameters() if param.requires_grad]
params.optimizer = torch.optim.SGD(trainable_vars,
lr=params.init_lr,
momentum=momentum,
weight_decay=weight_decay,
def extract_pose_main(video_path, source_path):
#if __name__ == "__main__":
'''
:param video_path: viedo's img size size is 256*256
:param source_path: Image size must be 256*176 and .jpg
:return:
'''
os.environ["CUDA_VISIBLE_DEVICES"] = '7'
model = get_model('vgg19')
model.load_state_dict(torch.load(weight_name))
model.cuda()
model.float()
model.eval()
video = video_path
print("video path is ", video_path)
source_img = cv2.imread(source_path)
video_capture = cv2.VideoCapture(video)
pairLst = "./demo_data/demo-resize-pairs-test.csv"
fps = video_capture.get(cv2.CAP_PROP_FPS)
print("fps is ", fps)
a = 0
while video_capture.isOpened():
if a == 0:
result_file = open("./demo_data/demo-resize-annotation-test.csv",
'w')
print("name:keypoints_y:keypoints_x", file=result_file)
result_file1 = open("./demo_data/demo-resize-pairs-test.csv", 'w')
writer = csv.writer(result_file1)
writer.writerow(["from", "to"])
extract_pose(
video_path.split("/")[-1][:-4], source_img,
source_path.split("/")[-1][:-4], result_file, model)
ret, oriImg = video_capture.read()
print(oriImg.shape)
#oriImg = oriImg[27:710,430:900,:]
oriImg = oriImg[40:808, 8:536, :]
oriImg = cv2.copyMakeBorder(oriImg,
0,
0,
120,
120,
cv2.BORDER_CONSTANT,
value=[255, 255, 255])
# cv2.imwrite('../demo_data/test/{}.jpg'.format(a), oriImg)
# break
oriImg = cv2.resize(oriImg, (256, 256), interpolation=cv2.INTER_LINEAR)
shape_dst = np.min(oriImg.shape[0:2])
extract_pose(
video_path.split("/")[-1][:-4], oriImg, a, result_file, model)
# pairLst
writer.writerow([source_path.split("/")[-1], str(a) + ".jpg"])
print("finished {} pics".format(a))
# cv2.imshow('Video', to_plot)
a = a + 1
if a > 100:
break
if cv2.waitKey(1) & 0xFF == ord('q'):
break
video_capture.release()
cv2.destroyAllWindows()
torch.cuda.empty_cache()
def _eval(class_name, path_to_data_dir, path_to_checkpoint, img_prefix):
# load pre-trained model
model = get_model(trunk='vgg19')
model = model.cuda()
use_vgg(model, './model', 'vgg19')
print("=> Load pre-trained model from {}".format(path_to_checkpoint))
model.load_state_dict(torch.load(path_to_checkpoint))
model.eval()
# parameter of object size for pnp solver
print("=> Load {} object size".format(class_name))
path_to_object_seetings = os.path.join(path_to_data_dir,
'_object_settings.json')
if not os.path.exists(path_to_object_seetings):
raise FileNotFoundError(path_to_object_seetings)
object_list = json.load(open(path_to_object_seetings))['exported_objects']
object_size = None
for obj in object_list:
if obj['class'].find(class_name) != -1:
object_size = obj['cuboid_dimensions']
if not object_size:
raise ValueError("Object size is none")
_cuboid3d = Cuboid3d(object_size)
cuboid3d_points = np.array(_cuboid3d.get_vertices())
# parameter of camera for pnp solver
path_to_camera_seetings = os.path.join(path_to_data_dir,
'_camera_settings.json')
if not os.path.exists(path_to_camera_seetings):
raise FileNotFoundError(path_to_camera_seetings)
intrinsic_settings = json.load(open(
path_to_camera_seetings))['camera_settings'][0]['intrinsic_settings']
matrix_camera = np.zeros((3, 3))
matrix_camera[0, 0] = intrinsic_settings['fx']
matrix_camera[1, 1] = intrinsic_settings['fy']
matrix_camera[0, 2] = max(intrinsic_settings['cx'],
intrinsic_settings['cy'])
matrix_camera[1, 2] = max(intrinsic_settings['cx'],
intrinsic_settings['cy'])
matrix_camera[2, 2] = 1
try:
dist_coeffs = np.array(
json.load(open(path_to_camera_seetings))['camera_settings'][0]
["distortion_coefficients"])
except KeyError:
dist_coeffs = np.zeros((4, 1))
# dataloader
val_dataset = Dataset(path_to_data=path_to_data_dir,
class_name=class_name,
split='val',
img_prefix=img_prefix)
val_dataloader = DataLoader(val_dataset,
batch_size=1,
shuffle=False,
num_workers=0,
drop_last=False)
correct = 0
wrong = 0
# set threshold (cm)
threshold = 3.0
for batch_index, (images, _, _, location_targets,
ratio) in tqdm(enumerate(val_dataloader)):
images = images.cuda()
output, _ = model(images)
line, vertex = output[0], output[1]
line, vertex = line.squeeze(), vertex.squeeze()
objects, peaks = find_objects(vertex, line)
location_predictions = []
if len(objects) > 0:
for object in objects:
cuboid2d_points = object[1] + [
(object[0][0] * 8, object[0][1] * 8)
]
cuboid3d_points = np.array(cuboid3d_points)
location = None
quaternion = None
obj_2d_points = []
obj_3d_points = []
for i in range(8):
check_point_2d = cuboid2d_points[i]
# Ignore invalid points
if (check_point_2d is None):
continue
elif check_point_2d[0] < 0 or check_point_2d[
1] < 0 or check_point_2d[
0] >= Config.crop_size / Config.stride or check_point_2d[
1] >= Config.crop_size / Config.stride:
continue
else:
check_point_2d = (check_point_2d[0] * Config.stride *
ratio, check_point_2d[1] *
Config.stride * ratio)
obj_2d_points.append(check_point_2d)
obj_3d_points.append(cuboid3d_points[i])
projected_points = object[1]
vertexes = projected_points.copy()
centroid = tuple([
int(point * Config.stride * ratio) for point in object[0]
])
obj_2d_points = np.array(obj_2d_points, dtype=np.float32)
obj_3d_points = np.array(obj_3d_points, dtype=np.float32)
valid_point_count = len(obj_2d_points)
if valid_point_count >= 4:
ret, rvec, tvec = cv2.solvePnP(
obj_3d_points,
obj_2d_points,
matrix_camera,
dist_coeffs,
flags=cv2.SOLVEPNP_ITERATIVE)
if ret:
location = list(x[0] for x in tvec)
quaternion = convert_rvec_to_quaternion(rvec)
projected_points, _ = cv2.projectPoints(
cuboid3d_points, rvec, tvec, matrix_camera,
dist_coeffs)
projected_points = np.squeeze(projected_points)
# If the location.Z is negative or object is behind the camera then flip both location and rotation
x, y, z = location
if z < 0:
# Get the opposite location
location = [-x, -y, -z]
# Change the rotation by 180 degree
rotate_angle = np.pi
rotate_quaternion = Quaternion.from_axis_rotation(
location, rotate_angle)
quaternion = rotate_quaternion.cross(quaternion)
vertexes = [tuple(p) for p in projected_points]
location_predictions.append(location)
location_predictions = np.array(location_predictions)
if len(location_targets) == 0:
wrong += len(location_predictions)
else:
location_targets = location_targets.cpu().data.numpy()[0]
for location_target in location_targets:
distances = [
np.sqrt(
np.sum(
np.square(location_target -
location_prediction / 10.0)))
for location_prediction in location_predictions
]
if len(distances) == 0:
pass
wrong += 1
elif min(distances) > threshold:
wrong += 1
else:
correct += 1
print('Object: {} Accuracy: {}%'.format(
class_name, correct / (wrong + correct) * 100.0))
def main(args):
input_data = args.input_dir
input_type = args.input_type # choose from ["image", "video"]
output_dir = args.output_dir
weight_file = args.weight
input_ext = args.input_ext
output_ext = args.out_ext
frame_rate_ratio = args.frame_ratio # analyze every [n] frames
process_speed = args.process_speed # int, 1 (fastest, lowest quality) to 4 (slowest, highest quality)
resize_fac = args.resize_factor # minification factor
output_length = args.out_length # int, frame count for output, None for input length
show_visualize_process = args.verb # show canvas through matplotlib
rebuild_exist_file = args.rebuild
## Load Model
model = get_model('vgg19')
model.load_state_dict(t.load(weight_file))
model = t.nn.DataParallel(model)
model.cuda()
model.float()
model.eval()
print("Model Ready!")
## Init I/O Paths
_input_ext_ = IMAGE_EXT if input_ext == "image" \
else VIDEO_EXT if input_ext == "video" \
else input_ext if isinstance(input_ext, list) \
else [input_ext]
if input_type == "1to1":
io_paths = organize_1to1_io_paths(input_data, _input_ext_, output_dir,
output_ext)
else:
io_paths = organize_Nto1_io_paths(input_data, _input_ext_, output_dir,
output_ext)
total_item = len(io_paths["input"])
print("Items count: ", total_item)
ignore_item = 0
for i, (input_dir, output_path) in enumerate(
zip(io_paths["input"], io_paths["output"])):
if os.path.isfile(output_path):
if rebuild_exist_file:
title = '[{}/{}]Rebuild {} from {}'
else:
print('[{}/{}]{} already exist, pass'.format(
i, total_item, output_path))
ignore_item += 1
continue
else:
title = '[{}/{}]Build {} from {}'
if isinstance(input_dir, str): # process video
source_position = input_dir
loader = load_video_frames(input_dir, output_length,
frame_rate_ratio)
length, h, w = get_video_size(input_dir, output_length)
elif isinstance(input_dir, list): # process images
source_position = os.path.dirname(input_dir[0])
loader = load_images_list(input_dir, output_length,
frame_rate_ratio)
length, h, w = get_images_size(input_dir, output_length)
else:
raise TypeError("Expected string or list(string), but got %s" %
type(input_dir))
print(title.format(i, total_item, output_path, source_position))
# Video writer
try:
os.makedirs(os.path.dirname(output_path), exist_ok=True)
output_fps = 15
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
height = int(resize_fac * h)
width = int(resize_fac * w)
print("source:{}x{} target:{}x{}".format(h, w, height, width))
out = cv2.VideoWriter(output_path, fourcc, output_fps,
(width, height))
out_h5 = h5py.File(output_path + ".h5", mode="w")
out_h5["height"] = height
out_h5["width"] = width
t0 = time.time()
for i, frame in enumerate(loader):
t1 = time.time()
# generate image with body parts
resized_image = cv2.resize(frame, (0, 0),
fx=1 * resize_fac,
fy=1 * resize_fac,
interpolation=cv2.INTER_CUBIC)
to_plot, canvas, joint_list, person_to_joint_assoc = process(
model, resized_image, process_speed)
# save outputs
out.write(canvas)
frame_h5 = out_h5.create_group("frame%d" % i)
frame_h5.create_dataset("joint_list", data=joint_list)
frame_h5.create_dataset("person_to_joint_assoc",
data=person_to_joint_assoc)
t2 = time.time()
# print messages
print(
'{}[{}/{}] process time:{:.3f}s total time:{:.3f}s'.format(
time.strftime('%H:%M:%S'), i, length, (t2 - t1),
(t2 - t0)))
if show_visualize_process:
cv2.imshow(os.path.basename(output_path), to_plot)
cv2.waitKey(1)
finally:
out.release()
out_h5.close()
cv2.destroyAllWindows()
print("Prosessed {} items, ignore {} existing items. Saved into {}".format(
total_item - ignore_item, ignore_item, output_dir))
print("All work are Finished！")
