def convert_world_to_cam(data_3d, center, focus):
data_2d = np.zeros((data_3d.shape[0], data_3d.shape[1], 2))
for i in tqdm(range(data_3d.shape[0])):
viz = Visualize(1)
viz.place_camera_circular(0, 2000, data_3d[0, 0, :])
data_2d[i, :, :] = viz.get_projection(data_3d[i, :, :], 0, focus,
center)
return data_2d
import constants as jnt
from utils import parse_metadata, read_h5, read_npz
from conversion import convert_json_to_npz, convert_h5_directory_to_augmented, convert_h5_to_projected
from visualize import Visualize
from tqdm import tqdm
import numpy as np
if __name__ == "__main__":
viz = Visualize(50)
data = read_h5("annot.h5")
viz.place_random_cameras(50, [3000, 3500], data['pose/3d-univ'][()][0,
0, :])
data_2d = np.zeros((0, 32, 2))
data_3d = np.zeros((0, 32, 3))
point_2d = viz.get_projection(data['pose/3d-univ'][()][0, :, :], 32,
jnt.CAMERAS[0]['focal_length'],
jnt.CAMERAS[0]['center'])
#viz.plot_3d(data['pose/3d-univ'][()][0, :, :], True)
#viz.plot_2d(point_2d)
#convert_h5_directory_to_augmented("../H36M_H5_Annotations/**/**/*.h5", 15)
convert_h5_to_projected('annot.h5', 'h36m_s1_reprojected')
def convert_h5_directory_to_augmented(directory_path, cam_count):
files = glob.glob(directory_path)
data = {}
viz = Visualize(cam_count)
prev_subject = ""
for file in files:
meta = file.split("/")
subject = meta[2]
"""
if subject != prev_subject:
if data is not None:
np.savez_compressed("output/h36m_augment_" + subject + "_cam_count_" + str(cam_count), data=data, cameras=jnt.CAMERAS)
print("Dumped data for", subject)
data = {}
prev_subject = subject
"""
action_name = meta[3].split("-")[0]
if subject not in data:
data[subject] = {}
if action_name not in data[subject]:
data[subject][action_name] = {
'2d': np.empty((0, 32, 2), dtype=np.float32),
'3d': np.empty((0, 32, 3), dtype=np.float32)
}
h5_data = read_h5(file)
print("Processing", subject, "for", action_name)
if action_name != "SittingDown":
continue
for i in tqdm(range(h5_data['pose/3d'][()].shape[0])):
viz.place_random_cameras(cam_count, [3000, 3500],
h5_data['pose/3d'][i, 0, :])
for j in range(4):
for k in range(cam_count):
point_2d = viz.get_projection(
h5_data['pose/3d-univ'][()][i, :, :], k,
jnt.CAMERAS[j]['focal_length'],
jnt.CAMERAS[j]['center']).reshape(1, 32, 2)
point_3d = viz.get_camspace_coord(
h5_data['pose/3d-univ'][()][i, :, :],
k).reshape(1, 32, 3)
R, t = viz.get_rotation_and_translation(k)
data[subject][action_name]['2d'] = np.vstack(
(data[subject][action_name]['2d'], point_2d))
data[subject][action_name]['3d'] = np.vstack(
(data[subject][action_name]['3d'], point_3d))
data[subject][action_name]['cam_id'] = jnt.CAMERAS[j]['id']
data[subject][action_name]['R'] = R
data[subject][action_name]['t'] = t
np.savez_compressed("output/h36m_augment_sitting_down_cam_count" +
str(cam_count),
data=data,
cameras=jnt.CAMERAS)