File size: 3,921 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 | from __future__ import print_function, unicode_literals
import matplotlib.pyplot as plt
import argparse
from utils.fh_utils import *
def show_training_samples(base_path, version, num2show=None, render_mano=False):
if render_mano:
from utils.model import HandModel, recover_root, get_focal_pp, split_theta
if num2show == -1:
num2show = db_size('training') # show all
# load annotations
db_data_anno = load_db_annotation(base_path, 'training')
# iterate over all samples
for idx in range(db_size('training')):
if idx >= num2show:
break
# load image and mask
img = read_img(idx, base_path, 'training', version)
msk = read_msk(idx, base_path)
# annotation for this frame
K, mano, xyz = db_data_anno[idx]
K, mano, xyz = [np.array(x) for x in [K, mano, xyz]]
uv = projectPoints(xyz, K)
# render an image of the shape
msk_rendered = None
if render_mano:
# split mano parameters
poses, shapes, uv_root, scale = split_theta(mano)
focal, pp = get_focal_pp(K)
xyz_root = recover_root(uv_root, scale, focal, pp)
# set up the hand model and feed hand parameters
renderer = HandModel(use_mean_pca=False, use_mean_pose=True)
renderer.pose_by_root(xyz_root[0], poses[0], shapes[0])
msk_rendered = renderer.render(K, img_shape=img.shape[:2])
# show
fig = plt.figure()
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
ax1.imshow(img)
ax2.imshow(msk if msk_rendered is None else msk_rendered)
plot_hand(ax1, uv, order='uv')
plot_hand(ax2, uv, order='uv')
ax1.axis('off')
ax2.axis('off')
plt.show()
def show_eval_samples(base_path, num2show=None):
if num2show == -1:
num2show = db_size('evaluation') # show all
for idx in range(db_size('evaluation')):
if idx >= num2show:
break
# load image only, because for the evaluation set there is no mask
img = read_img(idx, base_path, 'evaluation')
# show
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.imshow(img)
ax1.axis('off')
plt.show()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Show some samples from the dataset.')
parser.add_argument('base_path', type=str,
help='Path to where the FreiHAND dataset is located.')
parser.add_argument('--show_eval', action='store_true',
help='Shows samples from the evaluation split if flag is set, shows training split otherwise.')
parser.add_argument('--mano', action='store_true',
help='Enables rendering of the hand if mano is available. See README for details.')
parser.add_argument('--num2show', type=int, default=-1,
help='Number of samples to show. ''-1'' defaults to show all.')
parser.add_argument('--sample_version', type=str, default=sample_version.gs,
help='Which sample version to use when showing the training set.'
' Valid choices are %s' % sample_version.valid_options())
args = parser.parse_args()
# check inputs
msg = 'Invalid choice: ''%s''. Must be in %s' % (args.sample_version, sample_version.valid_options())
assert args.sample_version in sample_version.valid_options(), msg
if args.show_eval:
""" Show some evaluation samples. """
show_eval_samples(args.base_path,
num2show=args.num2show)
else:
""" Show some training samples. """
show_training_samples(
args.base_path,
args.sample_version,
num2show=args.num2show,
render_mano=args.mano
)
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