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# ColorHandPose3DNetwork - Network for estimating 3D Hand Pose from a single RGB Image
# Copyright (C) 2017 Christian Zimmermann
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# 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
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
"""
Script to convert Rendered Handpose Dataset into binary files,
which allows for much faster reading than plain image files.
Set "path_to_db" and "set" accordingly.
In order to use this file you need to download and unzip the dataset first.
"""
from __future__ import print_function, unicode_literals
import pickle
import os
import scipy.misc
import struct
# SET THIS to where RHD is located on your machine
path_to_db = './RHD_published_v2/'
# chose if you want to create a binary for training or evaluation set
# set = 'training'
set = 'evaluation'
### No more changes below this line ###
# function to write the binary file
def write_to_binary(file_handle, image, mask, kp_coord_xyz, kp_coord_uv, kp_visible, K_mat):
"""" Writes records to an open binary file. """
bytes_written = 0
# 1. write kp_coord_xyz
for coord in kp_coord_xyz:
file_handle.write(struct.pack('f', coord[0]))
file_handle.write(struct.pack('f', coord[1]))
file_handle.write(struct.pack('f', coord[2]))
bytes_written += 4*kp_coord_xyz.shape[0]*kp_coord_xyz.shape[1]
# 2. write kp_coord_uv
for coord in kp_coord_uv:
file_handle.write(struct.pack('f', coord[0]))
file_handle.write(struct.pack('f', coord[1]))
bytes_written += 4*kp_coord_uv.shape[0]*kp_coord_uv.shape[1]
# 3. write camera intrinsic matrix
for K_row in K_mat:
for K_element in K_row:
file_handle.write(struct.pack('f', K_element))
bytes_written += 4*9
file_handle.write(struct.pack('B', 255))
file_handle.write(struct.pack('B', 255))
bytes_written += 2
# 4. write image
for x in range(image.shape[0]):
for y in range(image.shape[1]):
file_handle.write(struct.pack('B', image[x, y, 0]))
file_handle.write(struct.pack('B', image[x, y, 1]))
file_handle.write(struct.pack('B', image[x, y, 2]))
bytes_written += 4*image.shape[0]*image.shape[1]*image.shape[2]
# 5. write mask
for x in range(mask.shape[0]):
for y in range(mask.shape[1]):
file_handle.write(struct.pack('B', mask[x, y]))
bytes_written += 4*mask.shape[0]*mask.shape[1]
# 6. write visibility
for x in range(kp_visible.shape[0]):
file_handle.write(struct.pack('B', kp_visible[x]))
bytes_written += kp_visible.shape[0]
# print('bytes_written', bytes_written)
# binary file we will write
file_name_out = './data/bin/rhd_%s.bin' % set
if not os.path.exists('./data/bin'):
os.mkdir('./data/bin')
# load annotations of this set
with open(os.path.join(path_to_db, set, 'anno_%s.pickle' % set), 'rb') as fi:
anno_all = pickle.load(fi)
# iterate samples of the set and write to binary file
with open(file_name_out, 'wb') as fo:
num_samples = len(anno_all.items())
for sample_id, anno in anno_all.items():
# load data
image = scipy.misc.imread(os.path.join(path_to_db, set, 'color', '%.5d.png' % sample_id))
mask = scipy.misc.imread(os.path.join(path_to_db, set, 'mask', '%.5d.png' % sample_id))
# get info from annotation dictionary
kp_coord_uv = anno['uv_vis'][:, :2] # u, v coordinates of 42 hand keypoints, pixel
kp_visible = anno['uv_vis'][:, 2] == 1 # visibility of the keypoints, boolean
kp_coord_xyz = anno['xyz'] # x, y, z coordinates of the keypoints, in meters
camera_intrinsic_matrix = anno['K'] # matrix containing intrinsic parameters
write_to_binary(fo, image, mask, kp_coord_xyz, kp_coord_uv, kp_visible, camera_intrinsic_matrix)
if (sample_id % 100) == 0:
print('%d / %d images done: %.3f percent' % (sample_id, num_samples, sample_id*100.0/num_samples))
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