def __getitem__(self, idx):
subpath, file_num = self.get_subpath_and_file_num(idx)
depth_filepath = self.root_folder + self.video_folder + subpath + \
self.depth_folder + 'depth_' + \
file_num + '.' + self.depth_fileext
depth_image = fpa_io.read_depth_img(depth_filepath)
joints_filepath = self.root_folder + self.hand_pose_folder + \
subpath + 'skeleton.txt'
hand_joints = fpa_io.read_action_joints_sequence(joints_filepath)[int(
file_num)]
_, crop_coords = self.get_cropped_depth_img(depth_image, hand_joints)
crop_coords_numpy = np.zeros((2, 2))
crop_coords_numpy[0, 0] = crop_coords[0]
crop_coords_numpy[0, 1] = crop_coords[1]
crop_coords_numpy[1, 0] = crop_coords[2]
crop_coords_numpy[1, 1] = crop_coords[3]
corner_heatmap1 = conv.color_space_label_to_heatmap(
crop_coords_numpy[0, :],
heatmap_res=self.orig_img_res,
orig_img_res=self.orig_img_res)
#print(np.unravel_index(np.argmax(corner_heatmap1), corner_heatmap1.shape))
corner_heatmap2 = conv.color_space_label_to_heatmap(
crop_coords_numpy[1, :],
heatmap_res=self.orig_img_res,
orig_img_res=self.orig_img_res)
corner_heatmaps = np.stack((corner_heatmap1, corner_heatmap2))
corner_heatmaps = torch.from_numpy(corner_heatmaps).float()
return data_image, corner_heatmaps
def read_depth_img(self, subpath, file_num):
depth_filepath = self.root_folder + self.video_folder + subpath + \
self.depth_folder + 'depth_' + \
file_num + '.' + self.depth_fileext
return fpa_io.read_depth_img(depth_filepath)
gt_folder = 'Hand_pose_annotation_v1'
data_folder = 'video_files'
subject = 'Subject_1'
action = 'put_tea_bag'
sequence = '4'
gt_filepath = '/'.join([dataset_root_folder, gt_folder, subject, action, sequence, 'skeleton.txt'])
curr_data_folder = '/'.join([dataset_root_folder, data_folder, subject, action, sequence])
gt_skeletons = fpa_io.read_action_joints_sequence(gt_filepath)
fig = visualize.create_fig()
for i in range(99):
if i < 10:
frame_num = '000' + str(i)
else:
frame_num = '00' + str(i)
joints = gt_skeletons[i, :].reshape((21, 3))
color_filepath = '/'.join([curr_data_folder, 'color', 'color_' + frame_num + '.jpeg'])
depth_filepath = '/'.join([curr_data_folder, 'depth', 'depth_' + frame_num + '.png'])
depth_img = fpa_io.read_depth_img(depth_filepath)
joints_uv = cam.joints_depth2color(joints, cam.fpa_depth_intrinsics)
visualize.plot_joints_from_colorspace(joints_colorspace=joints_uv, data=depth_img,
fig=fig, title='/'.join([subject, action, sequence]))
visualize.pause(0.001)
visualize.clear_plot()
visualize.show()