def main():
# Construct the db
db_config = SpartanSupvervisedKeypointDBConfig()
db_config.keypoint_yaml_name = 'shoe_6_keypoint_image.yaml'
db_config.pdc_data_root = '/home/wei/data/pdc'
db_config.config_file_path = '/home/wei/Coding/mankey/config/shoe_logs.txt'
database = SpartanSupervisedKeypointDatabase(db_config)
# Construct torch dataset
config = SupervisedKeypointDatasetConfig()
config.network_in_patch_height = 256
config.network_in_patch_width = 256
config.network_out_map_width = 64
config.network_out_map_height = 64
config.image_database_list.append(database)
config.is_train = False
dataset = SupervisedKeypointDataset(config)
# The counter
min_keypoint_depth: float = 10000
max_keypoint_depth: float = 0
avg_keypoint_depth: float = 0
counter = 0
# Iterate
for i in range(len(dataset)):
processed_entry = dataset.get_processed_entry(dataset.entry_list[i])
n_keypoint = processed_entry.keypoint_xy_depth.shape[1]
for j in range(n_keypoint):
min_keypoint_depth = min(min_keypoint_depth,
processed_entry.keypoint_xy_depth[2, j])
max_keypoint_depth = max(max_keypoint_depth,
processed_entry.keypoint_xy_depth[2, j])
avg_keypoint_depth += processed_entry.keypoint_xy_depth[2, j]
counter += 1
# Some output
print('The min value is ', min_keypoint_depth)
print('The max value is ', max_keypoint_depth)
print('The average value is ', avg_keypoint_depth / float(counter))
def visualize_entry_nostage(entry_idx: int, network: torch.nn.Module,
dataset: SupervisedKeypointDataset,
config: SupervisedKeypointDatasetConfig,
save_dir: str):
# The raw input
processed_entry = dataset.get_processed_entry(
dataset.entry_list[entry_idx])
# The processed input
stacked_rgbd = dataset[entry_idx][parameter.rgbd_image_key]
normalized_xy_depth = dataset[entry_idx][parameter.keypoint_xyd_key]
stacked_rgbd = torch.from_numpy(stacked_rgbd)
stacked_rgbd = torch.unsqueeze(stacked_rgbd, dim=0)
stacked_rgbd = stacked_rgbd.cuda()
# Do forward
raw_pred = network(stacked_rgbd)
prob_pred = raw_pred[:, 0:dataset.num_keypoints, :, :]
depthmap_pred = raw_pred[:, dataset.num_keypoints:, :, :]
heatmap = predict.heatmap_from_predict(prob_pred, dataset.num_keypoints)
coord_x, coord_y = predict.heatmap2d_to_normalized_imgcoord_gpu(
heatmap, dataset.num_keypoints)
depth_pred = predict.depth_integration(heatmap, depthmap_pred)
# To actual image coord
coord_x = coord_x.cpu().detach().numpy()
coord_y = coord_y.cpu().detach().numpy()
coord_x = (coord_x + 0.5) * config.network_in_patch_width
coord_y = (coord_y + 0.5) * config.network_in_patch_height
# To actual depth value
depth_pred = depth_pred.cpu().detach().numpy()
depth_pred = (depth_pred *
config.depth_image_scale) + config.depth_image_mean
# Combine them
keypointxy_depth_pred = np.zeros((3, dataset.num_keypoints), dtype=np.int)
keypointxy_depth_pred[0, :] = coord_x[0, :, 0].astype(np.int)
keypointxy_depth_pred[1, :] = coord_y[0, :, 0].astype(np.int)
keypointxy_depth_pred[2, :] = depth_pred[0, :, 0].astype(np.int)
# Get the image
from mankey.utils.imgproc import draw_image_keypoint, draw_visible_heatmap
keypoint_rgb_cv = draw_image_keypoint(processed_entry.cropped_rgb,
keypointxy_depth_pred,
processed_entry.keypoint_validity)
rgb_save_path = os.path.join(save_dir, 'image_%d_rgb.png' % entry_idx)
cv2.imwrite(rgb_save_path, keypoint_rgb_cv)
# The depth error
depth_error_mm = np.abs(processed_entry.keypoint_xy_depth[2, :] -
keypointxy_depth_pred[2, :])
max_depth_error = np.max(depth_error_mm)
print('Entry %d' % entry_idx)
print('The max depth error (mm) is ', max_depth_error)
# The pixel error
pixel_error = np.sum(np.sqrt((processed_entry.keypoint_xy_depth[0:2, :] -
keypointxy_depth_pred[0:2, :])**2),
axis=0)
max_pixel_error = np.max(pixel_error)
print('The max pixel error (pixel in 256x256 image) is ', max_pixel_error)