Added the ClassifyForVideo API

mediapipe/tasks/python/test/vision/image_classifier_test.py

@@ -15,6 +15,7 @@
 
 import enum
 
+import numpy as np
 from absl.testing import absltest
 from absl.testing import parameterized
 
@@ -121,6 +122,18 @@ class ImageClassifierTest(parameterized.TestCase):
     # a context.
     classifier.close()
 
+  def test_classify_for_video(self):
+    classifier_options = _ClassifierOptions(max_results=4)
+    options = _ImageClassifierOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.VIDEO,
+        classifier_options=classifier_options)
+    with _ImageClassifier.create_from_options(options) as classifier:
+      for timestamp in range(0, 300, 30):
+        classification_result = classifier.classify_for_video(
+            self.test_image, timestamp)
+        self.assertEqual(classification_result, _EXPECTED_CLASSIFICATION_RESULT)
+
 
 if __name__ == '__main__':
   absltest.main()

mediapipe/tasks/python/vision/image_classifier.py

@@ -14,7 +14,7 @@
 """MediaPipe image classifier task."""
 
 import dataclasses
-from typing import Callable, List, Mapping, Optional
+from typing import Callable, Mapping, Optional
 
 from mediapipe.python import packet_creator
 from mediapipe.python import packet_getter
@@ -40,6 +40,7 @@ _TaskRunner = task_runner_module.TaskRunner
 _CLASSIFICATION_RESULT_OUT_STREAM_NAME = 'classification_result_out'
 _CLASSIFICATION_RESULT_TAG = 'CLASSIFICATION_RESULT'
 _IMAGE_IN_STREAM_NAME = 'image_in'
+_IMAGE_OUT_STREAM_NAME = 'image_out'
 _IMAGE_TAG = 'IMAGE'
 _TASK_GRAPH_NAME = 'mediapipe.tasks.vision.image_classifier.ImageClassifierGraph'
 
@@ -66,8 +67,8 @@ class ImageClassifierOptions:
   running_mode: _RunningMode = _RunningMode.IMAGE
   classifier_options: _ClassifierOptions = _ClassifierOptions()
   result_callback: Optional[
-      Callable[[classifications_module.ClassificationResult],
+      Callable[[classifications_module.ClassificationResult, image_module.Image,
-               None]] = None
+                int], None]] = None
 
   @doc_controls.do_not_generate_docs
   def to_pb2(self) -> _ImageClassifierGraphOptionsProto:
@@ -134,7 +135,9 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
           classifications_module.Classifications.create_from_pb2(classification)
           for classification in classification_result_proto.classifications
       ])
-      options.result_callback(classification_result)
+      image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
+      timestamp = output_packets[_IMAGE_OUT_STREAM_NAME].timestamp
+      options.result_callback(classification_result, image, timestamp)
 
     task_info = _TaskInfo(
         task_graph=_TASK_GRAPH_NAME,
@@ -143,7 +146,8 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
         ],
         output_streams=[
             ':'.join([_CLASSIFICATION_RESULT_TAG,
-                      _CLASSIFICATION_RESULT_OUT_STREAM_NAME])
+                      _CLASSIFICATION_RESULT_OUT_STREAM_NAME]),
+            ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME])
         ],
         task_options=options)
     return cls(
@@ -175,6 +179,40 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
         output_packets[_CLASSIFICATION_RESULT_OUT_STREAM_NAME])
 
     return classifications_module.ClassificationResult([
-      classifications_module.Classifications.create_from_pb2(classification)
+        classifications_module.Classifications.create_from_pb2(classification)
-      for classification in classification_result_proto.classifications
+        for classification in classification_result_proto.classifications
+    ])
+
+  def classify_for_video(
+      self, image: image_module.Image,
+      timestamp_ms: int
+  ) -> classifications_module.ClassificationResult:
+    """Performs image classification on the provided video frames.
+
+    Only use this method when the ImageClassifier 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 classification result object that contains a list of classifications.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If image classification failed to run.
+    """
+    output_packets = self._process_video_data({
+      _IMAGE_IN_STREAM_NAME:
+        packet_creator.create_image(image).at(timestamp_ms)
+    })
+    classification_result_proto = packet_getter.get_proto(
+      output_packets[_CLASSIFICATION_RESULT_OUT_STREAM_NAME])
+
+    return classifications_module.ClassificationResult([
+        classifications_module.Classifications.create_from_pb2(classification)
+        for classification in classification_result_proto.classifications
     ])