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Revised API implementation and added more tests for segment_for_video and segment_async

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kinaryml
2022-10-18 04:24:12 -07:00
parent 36ac0689d7
commit f84e0bc1c6
3 changed files with 277 additions and 40 deletions
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mediapipe/tasks/python/vision/image_segmenter.py
+74 -8
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@@ -42,6 +42,7 @@ _IMAGE_IN_STREAM_NAME = 'image_in'
_IMAGE_OUT_STREAM_NAME = 'image_out'
_IMAGE_TAG = 'IMAGE'
_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.ImageSegmenterGraph'
_MICRO_SECONDS_PER_MILLISECOND = 1000
@dataclasses.dataclass
@@ -52,9 +53,9 @@ class ImageSegmenterOptions:
base_options: Base options for the image segmenter task.
running_mode: The running mode of the task. Default to the image mode.
Image segmenter task has three running modes:
1) The image mode for detecting objects on single image inputs.
2) The video mode for detecting objects on the decoded frames of a video.
3) The live stream mode for detecting objects on a live stream of input
1) The image mode for segmenting objects on single image inputs.
2) The video mode for segmenting objects on the decoded frames of a video.
3) The live stream mode for segmenting objects on a live stream of input
data, such as from camera.
segmenter_options: Options for the image segmenter task.
result_callback: The user-defined result callback for processing live stream
@@ -86,7 +87,8 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
@classmethod
def create_from_model_path(cls, model_path: str) -> 'ImageSegmenter':
"""Creates an `ImageSegmenter` object from a TensorFlow Lite model and the default `ImageSegmenterOptions`.
"""Creates an `ImageSegmenter` object from a TensorFlow Lite model and the
default `ImageSegmenterOptions`.
Note that the created `ImageSegmenter` instance is in image mode, for
performing image segmentation on single image inputs.
@@ -131,8 +133,9 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
segmentation_result = packet_getter.get_image_list(
output_packets[_SEGMENTATION_OUT_STREAM_NAME])
image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
timestamp = output_packets[_IMAGE_OUT_STREAM_NAME].timestamp
options.result_callback(segmentation_result, image, timestamp)
timestamp = output_packets[_SEGMENTATION_OUT_STREAM_NAME].timestamp
options.result_callback(segmentation_result, image,
timestamp.value // _MICRO_SECONDS_PER_MILLISECOND)
task_info = _TaskInfo(
task_graph=_TASK_GRAPH_NAME,
@@ -148,7 +151,6 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
_RunningMode.LIVE_STREAM), options.running_mode,
packets_callback if options.result_callback else None)
# TODO: Create an Image class for MediaPipe Tasks.
def segment(self,
image: image_module.Image) -> List[image_module.Image]:
"""Performs the actual segmentation task on the provided MediaPipe Image.
@@ -162,10 +164,74 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
Raises:
ValueError: If any of the input arguments is invalid.
RuntimeError: If object detection failed to run.
RuntimeError: If image segmentation failed to run.
"""
output_packets = self._process_image_data(
{_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image)})
segmentation_result = packet_getter.get_image_list(
output_packets[_SEGMENTATION_OUT_STREAM_NAME])
return segmentation_result
def segment_for_video(self, image: image_module.Image,
timestamp_ms: int) -> List[image_module.Image]:
"""Performs segmentation on the provided video frames.
Only use this method when the ImageSegmenter is created with the video
running mode. It's required to provide the video frame's timestamp (in
milliseconds) along with the video frame. The input timestamps should be
monotonically increasing for adjacent calls of this method.
Args:
image: MediaPipe Image.
timestamp_ms: The timestamp of the input video frame in milliseconds.
Returns:
A segmentation result object that contains a list of segmentation masks
as images.
Raises:
ValueError: If any of the input arguments is invalid.
RuntimeError: If image segmentation failed to run.
"""
output_packets = self._process_video_data({
_IMAGE_IN_STREAM_NAME:
packet_creator.create_image(image).at(
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
})
segmentation_result = packet_getter.get_image_list(
output_packets[_SEGMENTATION_OUT_STREAM_NAME])
return segmentation_result
def segment_async(self, image: image_module.Image, timestamp_ms: int) -> None:
"""Sends live image data (an Image with a unique timestamp) to perform
image segmentation.
Only use this method when the ImageSegmenter is created with the live stream
running mode. The input timestamps should be monotonically increasing for
adjacent calls of this method. This method will return immediately after the
input image is accepted. The results will be available via the
`result_callback` provided in the `ImageSegmenterOptions`. The
`segment_async` method is designed to process live stream data such as
camera input. To lower the overall latency, image segmenter may drop the
input images if needed. In other words, it's not guaranteed to have output
per input image.
The `result_callback` prvoides:
- A segmentation result object that contains a list of segmentation masks
as images.
- The input image that the image segmenter runs on.
- The input timestamp in milliseconds.
Args:
image: MediaPipe Image.
timestamp_ms: The timestamp of the input image in milliseconds.
Raises:
ValueError: If the current input timestamp is smaller than what the image
segmenter has already processed.
"""
self._send_live_stream_data({
_IMAGE_IN_STREAM_NAME:
packet_creator.create_image(image).at(
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
})