def image_boxes(
asset_path: str,
tensor_image,
tensor_boxes,
rescale=1,
dataformats="CHW",
asset_rel_path: str = None,
):
if not np:
logger.warning(NUMPY_ERROR_MESSAGE)
return UNKNOWN
tensor_image = to_np(tensor_image)
tensor_image = convert_to_HWC(tensor_image, dataformats)
tensor_boxes = to_np(tensor_boxes)
tensor_image = tensor_image.astype(np.float32) * calculate_scale_factor(
tensor_image
)
return make_image(
asset_path,
tensor_image.astype(np.uint8),
rescale=rescale,
rois=tensor_boxes,
asset_rel_path=asset_rel_path,
)
def video(asset_path, tensor, fps=4, content_type="gif"):
if not np:
logger.warning(NUMPY_ERROR_MESSAGE)
return UNKNOWN
tensor = to_np(tensor)
tensor = prepare_video(tensor)
# If user passes in uint8, then we don't need to rescale by 255
scale_factor = calculate_scale_factor(tensor)
tensor = tensor.astype(np.float32)
tensor = (tensor * scale_factor).astype(np.uint8)
return make_video(asset_path, tensor, fps, content_type)
def image(asset_path, data, rescale=1, dataformats="CHW"):
if not np:
logger.warning(NUMPY_ERROR_MESSAGE)
return UNKNOWN
tensor = to_np(data)
tensor = convert_to_HWC(tensor, dataformats)
# Do not assume that user passes in values in [0, 255], use data type to detect
scale_factor = calculate_scale_factor(tensor)
tensor = tensor.astype(np.float32)
tensor = (tensor * scale_factor).astype(np.uint8)
return make_image(asset_path, tensor, rescale=rescale)
