Added a check to see if the output packet is empty in the API and updated tests

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

@@ -44,24 +44,27 @@ _RUNNING_MODE = running_mode_module.VisionTaskRunningMode
 
 _MODEL_FILE = 'mobilenet_v2_1.0_224.tflite'
 _IMAGE_FILE = 'burger.jpg'
-_EMPTY_CLASSIFICATION_RESULT = _ClassificationResult(
-  classifications=[
-    _Classifications(
-      entries=[
-        _ClassificationEntry(
-          categories=[],
-          timestamp_ms=0
-        )
-      ],
-      head_index=0,
-      head_name='probability')
-  ])
 _ALLOW_LIST = ['cheeseburger', 'guacamole']
 _DENY_LIST = ['cheeseburger']
 _SCORE_THRESHOLD = 0.5
 _MAX_RESULTS = 3
 
 
+def _generate_empty_results(timestamp_ms: int) -> _ClassificationResult:
+  return _ClassificationResult(
+    classifications=[
+      _Classifications(
+        entries=[
+          _ClassificationEntry(
+            categories=[],
+            timestamp_ms=timestamp_ms
+          )
+        ],
+        head_index=0,
+        head_name='probability')
+    ])
+
+
 def _generate_burger_results(timestamp_ms: int) -> _ClassificationResult:
   return _ClassificationResult(
     classifications=[
@@ -447,16 +450,14 @@ class ImageClassifierTest(parameterized.TestCase):
           ValueError, r'Input timestamp must be monotonically increasing'):
         classifier.classify_async(self.test_image, 0)
 
-  # TODO: Fix the packet is empty issue.
-  """
-  @parameterized.parameters((0, _EXPECTED_CLASSIFICATION_RESULT),
-                            (1, _EMPTY_CLASSIFICATION_RESULT))
-  def test_classify_async_calls(self, threshold, expected_result):
+  @parameterized.parameters((0, _generate_burger_results),
+                            (1, _generate_empty_results))
+  def test_classify_async_calls(self, threshold, expected_result_fn):
     observed_timestamp_ms = -1
 
     def check_result(result: _ClassificationResult, output_image: _Image,
                      timestamp_ms: int):
-      self.assertEqual(result, expected_result)
+      self.assertEqual(result, expected_result_fn(timestamp_ms))
       self.assertTrue(
         np.array_equal(output_image.numpy_view(),
                        self.test_image.numpy_view()))
@@ -474,7 +475,6 @@ class ImageClassifierTest(parameterized.TestCase):
     for timestamp in range(0, 300, 30):
       classifier.classify_async(self.test_image, timestamp)
     classifier.close()
-  """
 
 
 if __name__ == '__main__':

mediapipe/tasks/python/vision/image_classifier.py

@@ -138,6 +138,8 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
     """
 
     def packets_callback(output_packets: Mapping[str, packet_module.Packet]):
+      if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
+        return
       classification_result_proto = packet_getter.get_proto(
           output_packets[_CLASSIFICATION_RESULT_OUT_STREAM_NAME])
 
@@ -148,7 +150,7 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
       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.value / _MICRO_SECONDS_PER_MILLISECOND)
+                              timestamp.value // _MICRO_SECONDS_PER_MILLISECOND)
 
     task_info = _TaskInfo(
         task_graph=_TASK_GRAPH_NAME,