def poly_to_mask_job(
image_def: oft.ListListNumpy.Placeholder(shape=tuple(image_shape),
dtype=flow.float),
poly_def: oft.ListListNumpy.Placeholder(shape=tuple(poly_shape),
dtype=flow.float),
poly_index_def: oft.ListListNumpy.Placeholder(
shape=tuple(poly_index_shape), dtype=flow.int32),
):
images_buffer = flow.tensor_list_to_tensor_buffer(image_def)
resized_images_buffer, new_size, scale = flow.image_target_resize(
images_buffer, target_size=target_size, max_size=max_size)
poly_buffer = flow.tensor_list_to_tensor_buffer(poly_def)
poly_index_buffer = flow.tensor_list_to_tensor_buffer(poly_index_def)
scaled_poly_buffer = flow.object_segmentation_polygon_scale(
poly_buffer, scale)
mask_buffer = flow.object_segmentation_polygon_to_mask(
scaled_poly_buffer, poly_index_buffer, new_size)
mask_list = flow.tensor_buffer_to_tensor_list(mask_buffer,
shape=(max_num_segms,
target_size,
max_size),
dtype=flow.int8)
scaled_poly_list = flow.tensor_buffer_to_tensor_list(
scaled_poly_buffer, shape=poly_shape[1:], dtype=flow.float)
return mask_list, scaled_poly_list
def image_target_resize_job(images_def: oft.ListListNumpy.Placeholder(
shape=image_static_shape, dtype=flow.float)):
images_buffer = flow.tensor_list_to_tensor_buffer(images_def)
resized_images_buffer, size, scale = flow.image_target_resize(
images_buffer, target_size, max_size)
resized_images = flow.tensor_buffer_to_tensor_list(
resized_images_buffer,
shape=(target_size, max_size, image_static_shape[-1]),
dtype=flow.float,
)
return resized_images, size, scale
def target_resize_bbox_scale_job(
image_def: oft.ListListNumpy.Placeholder(shape=tuple(image_shape),
dtype=flow.float),
bbox_def: oft.ListListNumpy.Placeholder(shape=tuple(bbox_shape),
dtype=flow.float),
):
images_buffer = flow.tensor_list_to_tensor_buffer(image_def)
resized_images_buffer, new_size, scale = flow.image_target_resize(
images_buffer, target_size, max_size)
bbox_buffer = flow.tensor_list_to_tensor_buffer(bbox_def)
scaled_bbox = flow.object_bbox_scale(bbox_buffer, scale)
scaled_bbox_list = flow.tensor_buffer_to_tensor_list(
scaled_bbox, shape=bbox_shape[1:], dtype=flow.float)
return scaled_bbox_list, new_size
def image_target_resize_job(
image: otp.ListListNumpy.Placeholder(shape=image_static_shape, dtype=flow.float)
) -> tp.Tuple[otp.ListListNumpy, otp.ListNumpy, otp.ListNumpy]:
image_buffer = flow.tensor_list_to_tensor_buffer(image)
res_image_buffer, new_size, scale = flow.image_target_resize(
image_buffer,
target_size=target_size,
max_size=max_size,
resize_side="shorter",
)
out_shape = image_test_util.infer_keep_aspect_ratio_resized_images_static_shape(
target_size=target_size,
min_size=None,
max_size=max_size,
aspect_ratio_list=aspect_ratio_list,
resize_side="shorter",
channels=3,
)
res_image = flow.tensor_buffer_to_tensor_list(
res_image_buffer, shape=out_shape, dtype=flow.float,
)
return res_image, new_size, scale