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 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_flip_job(images_def: oft.ListListNumpy.Placeholder(
shape=image_shape, dtype=flow.float)):
images_buffer = flow.tensor_list_to_tensor_buffer(images_def)
flip_images = flow.image_flip(images_buffer, flip_code)
return flow.tensor_buffer_to_tensor_list(flip_images,
shape=image_shape[1:],
dtype=flow.float)
def image_resize_keep_aspect_ratio(
image_list: otp.ListListNumpy.Placeholder(shape=image_static_shape,
dtype=dtype),
) -> tp.Tuple[otp.ListListNumpy, otp.ListNumpy, otp.ListNumpy]:
image_buffer = flow.tensor_list_to_tensor_buffer(image_list)
res_image, scale, new_size = flow.image.resize(
image_buffer,
target_size=target_size,
min_size=min_size,
max_size=max_size,
keep_aspect_ratio=True,
resize_side=resize_side,
interpolation_type=interpolation_type,
)
out_shape = _infer_resized_image_static_shape(
target_size,
origin_image_size_range,
resize_side,
channels,
)
if print_debug_info:
print("resized image_static_shape: {}".format(out_shape))
res_image = flow.tensor_buffer_to_tensor_list(
res_image,
shape=out_shape,
dtype=dtype,
)
return res_image, scale, new_size
def image_normalize_job(images_def: oft.ListListNumpy.Placeholder(
shape=image_shape, dtype=flow.float)):
images_buffer = flow.tensor_list_to_tensor_buffer(images_def)
norm_images = flow.image_normalize(images_buffer, std, mean)
return flow.tensor_buffer_to_tensor_list(norm_images,
shape=image_shape[1:],
dtype=flow.float)
def image_decode_job(images_def: oft.ListListNumpy.Placeholder(
shape=static_shape, dtype=flow.int8)):
images_buffer = flow.tensor_list_to_tensor_buffer(images_def)
decoded_images_buffer = flow.image_decode(images_buffer)
return flow.tensor_buffer_to_tensor_list(decoded_images_buffer,
shape=(640, 640, 3),
dtype=flow.uint8)
def foo_job(
input_def: oft.ListListNumpy.Placeholder(shape=(2, 5, 4), dtype=flow.float)
):
tensor_buffer = flow.tensor_list_to_tensor_buffer(input_def)
return flow.tensor_buffer_to_tensor_list(
tensor_buffer, shape=(5, 4), dtype=flow.float
)
def image_batch_align_job(images_def: oft.ListListNumpy.Placeholder(
shape=input_shape, dtype=flow.float)):
images_buffer = flow.tensor_list_to_tensor_buffer(images_def)
image = flow.image_batch_align(images_buffer,
shape=output_shape[1:],
dtype=flow.float,
alignment=alignment)
return image
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 object_segm_poly_flip_job(
poly_def: oft.ListListNumpy.Placeholder(shape=tuple(poly_shape),
dtype=flow.float),
image_size_def: oft.ListNumpy.Placeholder(shape=image_size.shape,
dtype=flow.int32),
):
poly_buffer = flow.tensor_list_to_tensor_buffer(poly_def)
flip_poly = flow.object_segmentation_polygon_flip(
poly_buffer, image_size_def, flip_code)
return flow.tensor_buffer_to_tensor_list(flip_poly,
shape=poly_shape[1:],
dtype=flow.float)
def object_bbox_flip_job(
bbox_def: oft.ListListNumpy.Placeholder(shape=tuple(bbox_shape),
dtype=flow.float),
image_size_def: oft.ListNumpy.Placeholder(shape=image_size.shape,
dtype=flow.int32),
):
bbox_buffer = flow.tensor_list_to_tensor_buffer(bbox_def)
flip_bbox = flow.object_bbox_flip(bbox_buffer, image_size_def,
flip_code)
return flow.tensor_buffer_to_tensor_list(flip_bbox,
shape=bbox_shape[1:],
dtype=flow.float)
def image_resize_to_fixed(image_list: otp.ListListNumpy.Placeholder(
shape=image_static_shape,
dtype=origin_dtype)) -> tp.Tuple[otp.ListNumpy, otp.ListNumpy]:
image_buffer = flow.tensor_list_to_tensor_buffer(image_list)
res_image, scale, _ = flow.image.resize(
image_buffer,
target_size=target_size,
keep_aspect_ratio=False,
channels=channels,
dtype=dtype,
interpolation_type=interpolation_type,
)
return res_image, scale
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
def job_fn(x_def: oft.ListListNumpy.Placeholder(shape=(2, 5, 4), dtype=flow.float)):
x = flow.tensor_list_to_tensor_buffer(x_def)
return flow.tensor_buffer_to_tensor_list(x, shape=(5, 4), dtype=flow.float)
