def _load_data_set(self):
clk = Clock()
print('start loading mosh data.')
anno_file_path = os.path.join(self.data_folder, 'mosh_annot.h5')
with h5py.File(anno_file_path) as fp:
self.shapes = np.array(fp['shape'])
self.poses = np.array(fp['pose'])
print('finished load mosh data, total {} samples'.format(
len(self.poses)))
clk.stop()
def _load_data_set(self):
clk = Clock()
self.images = []
self.kp2ds = []
self.boxs = []
self.kp3ds = []
self.shapes = []
self.poses = []
print('start loading hum3.6m data.')
anno_file_path = os.path.join(self.data_folder, 'annot.h5')
with h5py.File(anno_file_path) as fp:
total_kp2d = np.array(fp['gt2d'])
total_kp3d = np.array(fp['gt3d'])
total_shap = np.array(fp['shape'])
total_pose = np.array(fp['pose'])
total_image_names = np.array(fp['imagename'])
assert len(total_kp2d) == len(total_kp3d) and len(total_kp2d) == len(total_image_names) and \
len(total_kp2d) == len(total_shap) and len(total_kp2d) == len(total_pose)
l = len(total_kp2d)
def _collect_valid_pts(pts):
r = []
for pt in pts:
if pt[2] != 0:
r.append(pt)
return r
for index in range(l):
kp2d = total_kp2d[index].reshape((-1, 3))
if np.sum(kp2d[:, 2]) < self.min_pts_required:
continue
lt, rb, v = calc_aabb(_collect_valid_pts(kp2d))
self.kp2ds.append(np.array(kp2d.copy(), dtype=np.float))
self.boxs.append((lt, rb))
self.kp3ds.append(total_kp3d[index].copy().reshape(-1, 3))
self.shapes.append(total_shap[index].copy())
self.poses.append(total_pose[index].copy())
self.images.append(
os.path.join(self.data_folder, 'image') +
total_image_names[index].decode())
print('finished load hum3.6m data, total {} samples'.format(
len(self.kp3ds)))
clk.stop()
def _load_data_set(self):
clk = Clock()
print('loading LSP ext data.')
self.images = []
self.kp2ds = []
self.boxs = []
anno_file_path = os.path.join(self.data_folder, 'joints.mat')
anno = scio.loadmat(anno_file_path)
kp2d = anno['joints'].transpose(2, 0, 1) # N x k x 3
image_folder = os.path.join(self.data_folder, 'images')
images = sorted(glob.glob(image_folder + '/im*.jpg'))
for _ in range(len(images)):
self._handle_image(images[_], kp2d[_])
print('finished load LSP ext data.')
clk.stop()
def _load_data_set(self):
self.images = []
self.kp2ds = []
self.boxs = []
clk = Clock()
print('start loading coco 2017 dataset.')
#anno_file_path = os.path.join(self.data_folder, 'annotations', 'person_keypoints_train2017.json')
anno_file_path = os.path.join(self.data_folder, 'annotations',
'person_keypoints_val2017.json')
with open(anno_file_path, 'r') as reader:
anno = json.load(reader)
def _hash_image_id_(image_id_to_info, coco_images_info):
for image_info in coco_images_info:
image_id = image_info['id']
image_name = image_info['file_name']
_anno = {}
#_anno['image_path'] = os.path.join(self.data_folder, 'images', 'train-valid2017', image_name)
#_anno['image_path'] = os.path.join(self.data_folder, 'images', 'train2017', image_name)
_anno['image_path'] = os.path.join(self.data_folder, 'images',
'val2017', image_name)
_anno['kps'] = []
_anno['box'] = []
assert not (image_id in image_id_to_info)
image_id_to_info[image_id] = _anno
images = anno['images']
image_id_to_info = {}
_hash_image_id_(image_id_to_info, images)
annos = anno['annotations']
for anno_info in annos:
self._handle_anno_info(anno_info, image_id_to_info)
for k, v in image_id_to_info.items():
self._handle_image_info_(v)
print('finished load coco 2017 dataset, total {} samples.'.format(
len(self.images)))
clk.stop()
def _load_data_set(self):
clk = Clock()
print('loading LSP data.')
self.images = []
self.kp2ds = []
self.boxs = []
anno_file_path = os.path.join(self.data_folder, 'joints.mat')
anno = scio.loadmat(anno_file_path)
kp2d = anno['joints'].transpose(2, 1, 0) # N x k x 3
visible = np.logical_not(kp2d[:, :, 2])
kp2d[:, :, 2] = visible.astype(kp2d.dtype)
image_folder = os.path.join(self.data_folder, 'images')
images = sorted(glob.glob(image_folder + '/im*.jpg'))
for _ in range(len(images)):
self._handle_image(images[_], kp2d[_])
print('finished load LSP data.')
clk.stop()
def _load_data_set(self):
clk = Clock()
self.images = []
self.kp2ds = []
self.boxs = []
print('start loading AI CH keypoint data.')
anno_file_path = os.path.join(self.data_folder, 'keypoint_train_annotations_20170902.json')
with open(anno_file_path, 'r') as reader:
anno = json.load(reader)
for record in anno:
image_name = record['image_id'] + self.img_ext
image_path = os.path.join(self.data_folder, 'keypoint_train_images_20170902', image_name)
kp_set = record['keypoint_annotations']
box_set = record['human_annotations']
self._handle_image(image_path, kp_set, box_set)
print('finished load Ai CH keypoint data, total {} samples'.format(len(self)))
clk.stop()