def __getitem__(self, idx):
hand_joints = self.get_hand_joints(idx)
hand_root, hand_joints_rel = self.conv_hand_joints_to_rel(hand_joints)
if self.fpa_subj_split:
hand_obj_pose = hand_joints_rel
else:
obj_pose_rel = self.get_obj_pose(idx)
obj_pose_rel[0:3] = obj_pose_rel[0:3] - hand_root
hand_obj_pose = np.concatenate((hand_joints_rel, obj_pose_rel), 0)
hand_obj_pose = torch.from_numpy(hand_obj_pose).float()
subpath, file_num = self.get_subpath_and_file_num(idx)
depth_img_numpy = self.read_depth_img(subpath, file_num)
cropped_depth_img, crop_coords = self.get_cropped_depth_img(
depth_img_numpy, hand_joints)
cropped_depth_img = io_image.change_res_image(cropped_depth_img,
new_res=(200, 200))
depth_img_torch = self.conv_depth_img_with_torch_transform(
cropped_depth_img, self.transform_depth)
if self.type == "train":
return depth_img_torch, hand_obj_pose
else:
return depth_img_torch, hand_obj_pose, hand_root
def __getitem__(self, idx):
pose = genfromtxt(self.pose_filepaths[self.file_idxs[idx]],
delimiter=',')[1:]
pose = torch.from_numpy(pose).float()
colour = io_image.read_RGB_image(
self.colour_filepaths[self.file_idxs[idx]])
mask = io_image.read_RGB_image(
self.mask_filepaths[self.file_idxs[idx]])
cropped_img = self.crop_image(colour, mask)
colour = io_image.change_res_image(colour, self.img_res)
mask = io_image.change_res_image(mask, self.img_res)
cropped_img = io_image.change_res_image(cropped_img, self.img_res)
with_imagenet = io_image.read_RGB_image(
self.with_imagenet_filepaths[self.file_idxs[idx]],
new_res=self.img_res)
data_image = with_imagenet
if (not self.noise_channel) and self.num_channels > 3:
depth = io_image.read_RGB_image(
self.depth_filepaths[self.file_idxs[idx]],
new_res=self.img_res)
depth = np.reshape(depth, (depth.shape[0], depth.shape[1], 1))
data_image = np.concatenate((data_image, depth),
axis=-1).astype(float)
#cropped_img_non_noisy = np.copy(cropped_img)
#cropped_img_noisy, noise_idxs = util.add_noise(cropped_img, 0.2)
data_image_noisy, noise_idxs = util.add_noise(data_image,
self.noise_level)
#colour = np.concatenate((colour, noise_idxs), axis=-1).astype(float)
if self.transform:
data_image_noisy = self.transform(data_image_noisy).float()
noise_idxs = self.transform(noise_idxs).float()
#cropped_img_noisy = self.transform(cropped_img_noisy)
#cropped_img_non_noisy = self.transform(cropped_img_non_noisy)
colour = self.transform(colour).float()
data_image_noisy = torch.cat((data_image_noisy, noise_idxs), 0)
#vis.plot_image((data_image_noisy.numpy()[0:3, :, :] + 0.5) * 255)
#vis.show()
#vis.plot_image((colour.numpy() + 0.5) * 255)
#vis.show()
return data_image_noisy, colour
def __getitem__(self, idx):
#idx = 16
# Read images
subpath, file_num = self.get_subpath_and_file_num(idx)
rgb_img = self.read_rgb_img(subpath, file_num)
#gen_obj_rgb_img = self.read_gen_obj_rgb_img(subpath, file_num)
# Transform images for pytorch
rgb_img = io_image.change_res_image(rgb_img,
self.params_dict['img_res'])
rgb_img = self.transform(rgb_img)
#gen_obj_rgb_img = io_image.change_res_image(gen_obj_rgb_img,
# self.params_dict['img_res'])
#gen_obj_rgb_img = self.transform(gen_obj_rgb_img)
return (rgb_img, rgb_img)
def __getitem__(self, idx):
hand_joints = self.get_hand_joints(idx)
hand_root, hand_joints_rel = self.conv_hand_joints_to_rel(hand_joints)
if self.fpa_subj_split:
hand_obj_pose = hand_joints_rel
else:
obj_pose_rel = self.get_obj_pose(idx)
obj_pose_rel[0:3] = obj_pose_rel[0:3] - hand_root
hand_obj_pose = np.concatenate((hand_joints_rel, obj_pose_rel), 0)
hand_obj_pose = torch.from_numpy(hand_obj_pose).float()
subpath, file_num = self.get_subpath_and_file_num(idx)
depth_img_numpy = self.read_depth_img(subpath, file_num)
cropped_depth_img, crop_coords = self.get_cropped_depth_img(
depth_img_numpy, hand_joints)
cropped_depth_img = io_image.change_res_image(cropped_depth_img,
new_res=(200, 200))
# read reconstructed image
img_recon_path = self.root_folder + self.gen_folder +\
subpath + str(int(file_num)) + '_recon.npy'
depth_img_final = np.zeros((200, 200, 2))
recon = True
try:
depth_img_recon = np.load(img_recon_path)
#vis.plot_image(depth_img_recon)
#vis.show()
#vis.plot_image(cropped_depth_img)
#vis.show()
depth_img_final[:, :, 0] = cropped_depth_img
depth_img_final[:, :, 1] = depth_img_recon
except:
depth_img_final[:, :, 1] = cropped_depth_img
depth_img_torch = self.conv_depth_img_with_torch_transform(
depth_img_final, self.transform_depth, recon=recon)
if self.type == "train":
return depth_img_torch, hand_obj_pose
else:
return depth_img_torch, hand_obj_pose, hand_root
loss = my_losses.calculate_loss_HALNet(
loss_func, output, label_heatmaps, model.joint_ixs,
model.WEIGHT_LOSS_INTERMED1, model.WEIGHT_LOSS_INTERMED2,
model.WEIGHT_LOSS_INTERMED3, model.WEIGHT_LOSS_MAIN,
train_vars['iter_size'])
loss_pixel, label_bbox, out_bbox = calculate_pixel_loss(
output[3].detach().cpu().numpy()[0, :, :, :], label_heatmaps)
losses_pixel.append(loss_pixel)
if batch_idx == 4:
depth_img = data.cpu().numpy()[0, 0, :, :]
depth_img_crop = depth_img[label_bbox[0, 0]:label_bbox[1, 0],
label_bbox[0, 1]:label_bbox[1, 1]]
depth_img_crop = io_image.change_res_image(depth_img_crop,
((200, 200)))
vis.plot_image(depth_img_crop,
title=train_loader.dataset.get_img_title(batch_idx))
vis.show()
fig = vis.plot_image(
depth_img, title=train_loader.dataset.get_img_title(batch_idx))
#vis.plot_bound_box(label_bbox, fig=fig, color='blue')
#vis.plot_bound_box(out_bbox, fig=fig, color='red')
vis.show()
a = 0
train_vars['total_loss'] = loss.item()
print('Batch {} / {}'.format(batch_idx, len_dataset))
print('Loss (pixel): {}'.format(loss_pixel))