File size: 5,854 Bytes
f7ca549 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 | from __future__ import print_function, unicode_literals
import numpy as np
import json
import os
import time
import skimage.io as io
""" General util functions. """
def _assert_exist(p):
msg = 'File does not exists: %s' % p
assert os.path.exists(p), msg
def json_load(p):
_assert_exist(p)
with open(p, 'r') as fi:
d = json.load(fi)
return d
def projectPoints(xyz, K):
""" Project 3D coordinates into image space. """
xyz = np.array(xyz)
K = np.array(K)
uv = np.matmul(K, xyz.T).T
return uv[:, :2] / uv[:, -1:]
""" Draw functions. """
def plot_hand(axis, coords_hw, vis=None, color_fixed=None, linewidth='1', order='hw', draw_kp=True):
""" Plots a hand stick figure into a matplotlib figure. """
if order == 'uv':
coords_hw = coords_hw[:, ::-1]
colors = np.array([[0.4, 0.4, 0.4],
[0.4, 0.0, 0.0],
[0.6, 0.0, 0.0],
[0.8, 0.0, 0.0],
[1.0, 0.0, 0.0],
[0.4, 0.4, 0.0],
[0.6, 0.6, 0.0],
[0.8, 0.8, 0.0],
[1.0, 1.0, 0.0],
[0.0, 0.4, 0.2],
[0.0, 0.6, 0.3],
[0.0, 0.8, 0.4],
[0.0, 1.0, 0.5],
[0.0, 0.2, 0.4],
[0.0, 0.3, 0.6],
[0.0, 0.4, 0.8],
[0.0, 0.5, 1.0],
[0.4, 0.0, 0.4],
[0.6, 0.0, 0.6],
[0.7, 0.0, 0.8],
[1.0, 0.0, 1.0]])
colors = colors[:, ::-1]
# define connections and colors of the bones
bones = [((0, 1), colors[1, :]),
((1, 2), colors[2, :]),
((2, 3), colors[3, :]),
((3, 4), colors[4, :]),
((0, 5), colors[5, :]),
((5, 6), colors[6, :]),
((6, 7), colors[7, :]),
((7, 8), colors[8, :]),
((0, 9), colors[9, :]),
((9, 10), colors[10, :]),
((10, 11), colors[11, :]),
((11, 12), colors[12, :]),
((0, 13), colors[13, :]),
((13, 14), colors[14, :]),
((14, 15), colors[15, :]),
((15, 16), colors[16, :]),
((0, 17), colors[17, :]),
((17, 18), colors[18, :]),
((18, 19), colors[19, :]),
((19, 20), colors[20, :])]
if vis is None:
vis = np.ones_like(coords_hw[:, 0]) == 1.0
for connection, color in bones:
if (vis[connection[0]] == False) or (vis[connection[1]] == False):
continue
coord1 = coords_hw[connection[0], :]
coord2 = coords_hw[connection[1], :]
coords = np.stack([coord1, coord2])
if color_fixed is None:
axis.plot(coords[:, 1], coords[:, 0], color=color, linewidth=linewidth)
else:
axis.plot(coords[:, 1], coords[:, 0], color_fixed, linewidth=linewidth)
if not draw_kp:
return
for i in range(21):
if vis[i] > 0.5:
axis.plot(coords_hw[i, 1], coords_hw[i, 0], 'o', color=colors[i, :])
""" Dataset related functions. """
def db_size(set_name):
""" Hardcoded size of the datasets. """
if set_name == 'training':
return 32560 # number of unique samples (they exists in multiple 'versions')
elif set_name == 'evaluation':
return 3960
else:
assert 0, 'Invalid choice.'
def load_db_annotation(base_path, set_name=None):
if set_name is None:
# only training set annotations are released so this is a valid default choice
set_name = 'training'
print('Loading FreiHAND dataset index ...')
t = time.time()
# assumed paths to data containers
k_path = os.path.join(base_path, '%s_K.json' % set_name)
mano_path = os.path.join(base_path, '%s_mano.json' % set_name)
xyz_path = os.path.join(base_path, '%s_xyz.json' % set_name)
# load if exist
K_list = json_load(k_path)
mano_list = json_load(mano_path)
xyz_list = json_load(xyz_path)
# should have all the same length
assert len(K_list) == len(mano_list), 'Size mismatch.'
assert len(K_list) == len(xyz_list), 'Size mismatch.'
print('Loading of %d samples done in %.2f seconds' % (len(K_list), time.time()-t))
return zip(K_list, mano_list, xyz_list)
class sample_version:
gs = 'gs' # green screen
hom = 'hom' # homogenized
sample = 'sample' # auto colorization with sample points
auto = 'auto' # auto colorization without sample points: automatic color hallucination
db_size = db_size('training')
@classmethod
def valid_options(cls):
return [cls.gs, cls.hom, cls.sample, cls.auto]
@classmethod
def check_valid(cls, version):
msg = 'Invalid choice: "%s" (must be in %s)' % (version, cls.valid_options())
assert version in cls.valid_options(), msg
@classmethod
def map_id(cls, id, version):
cls.check_valid(version)
return id + cls.db_size*cls.valid_options().index(version)
def read_img(idx, base_path, set_name, version=None):
if version is None:
version = sample_version.gs
if set_name == 'evaluation':
assert version == sample_version.gs, 'This the only valid choice for samples from the evaluation split.'
img_rgb_path = os.path.join(base_path, set_name, 'rgb',
'%08d.jpg' % sample_version.map_id(idx, version))
_assert_exist(img_rgb_path)
return io.imread(img_rgb_path)
def read_msk(idx, base_path):
mask_path = os.path.join(base_path, 'training', 'mask',
'%08d.jpg' % idx)
_assert_exist(mask_path)
return io.imread(mask_path)
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