in object-detection/visualization_utils.py [0:0]
def draw_densepose_visualizations(eval_dict,
max_boxes_to_draw=20,
min_score_thresh=0.2,
num_parts=24,
dp_coord_to_visualize=0):
"""Draws DensePose visualizations.
Args:
eval_dict: The evaluation dictionary returned by
eval_util.result_dict_for_batched_example().
max_boxes_to_draw: The maximum number of boxes to draw for detections.
min_score_thresh: The minimum score threshold for showing detections.
num_parts: The number of different densepose parts.
dp_coord_to_visualize: Whether to visualize v-coordinates (0) or
u-coordinates (0) overlaid on the person masks.
Returns:
A list of [1, H, W, C] uint8 tensor, each element corresponding to an image
in the batch.
Raises:
ValueError: If `dp_coord_to_visualize` is not 0 or 1.
"""
if dp_coord_to_visualize not in (0, 1):
raise ValueError('`dp_coord_to_visualize` must be either 0 for v '
'coordinates), or 1 for u coordinates, but instead got '
'{}'.format(dp_coord_to_visualize))
detection_fields = fields.DetectionResultFields()
input_data_fields = fields.InputDataFields()
if detection_fields.detection_masks not in eval_dict:
raise ValueError('Expected `detection_masks` in `eval_dict`.')
if detection_fields.detection_surface_coords not in eval_dict:
raise ValueError('Expected `detection_surface_coords` in `eval_dict`.')
images_with_detections_list = []
for indx in range(eval_dict[input_data_fields.original_image].shape[0]):
# Note that detection masks have already been resized to the original image
# shapes, but `original_image` has not.
# TODO(ronnyvotel): Consider resizing `original_image` in
# eval_util.result_dict_for_batched_example().
true_shape = eval_dict[input_data_fields.true_image_shape][indx]
original_shape = eval_dict[
input_data_fields.original_image_spatial_shape][indx]
image = eval_dict[input_data_fields.original_image][indx]
image = shape_utils.pad_or_clip_nd(image, [true_shape[0], true_shape[1], 3])
image = _resize_original_image(image, original_shape)
scores = eval_dict[detection_fields.detection_scores][indx]
detection_masks = eval_dict[detection_fields.detection_masks][indx]
surface_coords = eval_dict[detection_fields.detection_surface_coords][indx]
def draw_densepose_py_func(image, detection_masks, surface_coords, scores):
"""Overlays part masks and surface coords on original images."""
surface_coord_image = np.copy(image)
for i, (score, surface_coord, mask) in enumerate(
zip(scores, surface_coords, detection_masks)):
if i == max_boxes_to_draw:
break
if score > min_score_thresh:
draw_part_mask_on_image_array(image, mask, num_parts=num_parts)
draw_float_channel_on_image_array(
surface_coord_image, surface_coord[:, :, dp_coord_to_visualize],
mask)
return np.concatenate([image, surface_coord_image], axis=1)
image_with_densepose = tf.py_func(
draw_densepose_py_func,
[image, detection_masks, surface_coords, scores],
tf.uint8)
images_with_detections_list.append(
image_with_densepose[tf.newaxis, :, :, :])
return images_with_detections_list