tracing fixes

cnn_classification_service/cnn_classifier.py

@@ -29,28 +29,32 @@ class Classifier(object):
 
     @staticmethod
     def create_spectrogram(wav_filename: str) -> Tuple[str, str]:
-        matplotlib.pyplot.interactive(False)
-        clip, sample_rate = librosa.load(wav_filename, sr=None)
-        fig = matplotlib.pyplot.figure(figsize=[0.72, 0.72])
-        ax = fig.add_subplot(111)
-        ax.axes.get_xaxis().set_visible(False)
-        ax.axes.get_yaxis().set_visible(False)
-        ax.set_frame_on(False)
-        spectogram = librosa.feature.melspectrogram(y=clip, sr=sample_rate)
-        librosa.display.specshow(librosa.power_to_db(spectogram, ref=numpy.max))
 
-        target_dir = tempfile.mkdtemp(dir="/dev/shm")
-        wav_basename = os.path.basename(wav_filename)
+        with opentracing.tracer.start_active_span('classifier.librosa.load'):
+            clip, sample_rate = librosa.load(wav_filename, sr=None)
 
-        # Change extension to jpg... mert 110% biztos vagyok benne hogy a keras nem bírná beolvasni máshogy
-        file_name = os.path.join(target_dir, "unknown", f"{wav_basename[:-4]}.jpg")
-        os.mkdir(os.path.join(target_dir, "unknown"))
+        with opentracing.tracer.start_active_span('classifier.Plot'):
+            matplotlib.pyplot.interactive(False)
+            fig = matplotlib.pyplot.figure(figsize=[0.72, 0.72])
+            ax = fig.add_subplot(111)
+            ax.axes.get_xaxis().set_visible(False)
+            ax.axes.get_yaxis().set_visible(False)
+            ax.set_frame_on(False)
+            spectogram = librosa.feature.melspectrogram(y=clip, sr=sample_rate)
+            librosa.display.specshow(librosa.power_to_db(spectogram, ref=numpy.max))
 
-        matplotlib.pyplot.savefig(file_name, dpi=400, bbox_inches='tight', pad_inches=0)
-        matplotlib.pyplot.close()
-        fig.clf()
-        matplotlib.pyplot.close(fig)
-        matplotlib.pyplot.close('all')
+            target_dir = tempfile.mkdtemp(dir="/dev/shm")
+            wav_basename = os.path.basename(wav_filename)
+
+            # Change extension to jpg... mert 110% biztos vagyok benne hogy a keras nem bírná beolvasni máshogy
+            file_name = os.path.join(target_dir, "unknown", f"{wav_basename[:-4]}.jpg")
+            os.mkdir(os.path.join(target_dir, "unknown"))
+
+            matplotlib.pyplot.savefig(file_name, dpi=400, bbox_inches='tight', pad_inches=0)
+            matplotlib.pyplot.close()
+            fig.clf()
+            matplotlib.pyplot.close(fig)
+            matplotlib.pyplot.close('all')
 
         return target_dir, file_name  # Az unknown nélkülivel kell visszatérni
 
@@ -85,12 +89,10 @@ class Classifier(object):
         return predicted_class_name, labeled_predictions
 
     def predict(self, wav_filename: str) -> Tuple[str, dict]:
-        span = opentracing.tracer.scope_manager.active().span
-
-        with opentracing.tracer.start_active_span('createSpectrogram', child_of=span):
+        with opentracing.tracer.start_active_span('classifier.createSpectrogram'):
             directory, _ = self.create_spectrogram(wav_filename)
 
-        with opentracing.tracer.start_active_span('runPredictor', child_of=span):
+        with opentracing.tracer.start_active_span('classifier.runPredictor'):
             result = self._run_predictor(directory)
 
         shutil.rmtree(directory)  # The image is no longer needed

cnn_classification_service/magic_doer.py

@@ -19,15 +19,16 @@ class MagicDoer:
 
     @classmethod
     def run_everything(cls, parameters: dict) -> dict:
-        span = opentracing.tracer.scope_manager.active().span
+
 
         tag = parameters['tag']
         sample_file_handle, sample_file_path = tempfile.mkstemp(prefix=f"{tag}_", suffix=".wav", dir="/dev/shm")
+        span = opentracing.tracer.scope_manager.active().span
         span.log_kv({'event': 'sampleFileOpened', 'sampleTag': tag})
         response = None
         try:
 
-            with opentracing.tracer.start_active_span('downloadSample', child_of=span):
+            with opentracing.tracer.start_active_span('magicDoer.downloadSample'):
                 # Download Sample
                 object_path = urljoin(Config.STORAGE_SERVICE_URL, f"object/{tag}")
 
@@ -38,11 +39,11 @@ class MagicDoer:
 
                 logging.debug(f"Downloaded sample to {sample_file_path}")
 
-            with opentracing.tracer.start_active_span('loadClassifier', child_of=span):
+            with opentracing.tracer.start_active_span('magicDoer.getClassifier'):
                 # Get a classifier that uses the default model
                 model_details, classifier = cls.classifier_cache.get_default_classifier()
 
-            with opentracing.tracer.start_active_span('runClassifier', child_of=span) as child_span:
+            with opentracing.tracer.start_active_span('magicDoer.runClassifier'):
                 # do the majic
                 classification_start_time = time.time()
                 predicted_class_name, labeled_predictions = classifier.predict(sample_file_path)
@@ -63,6 +64,7 @@ class MagicDoer:
             except FileNotFoundError:
                 pass
 
+        span = opentracing.tracer.scope_manager.active().span
         span.log_kv({'event': 'sampleFileDeleted', 'sampleTag': tag})
 
         if not response:

cnn_classification_service/main.py

@@ -19,16 +19,16 @@ from magic_doer import MagicDoer
 
 
 def message_callback(channel, method, properties, body):
-    with opentracing.tracer.start_active_span('messageHandling', finish_on_close=True) as span:
+    with opentracing.tracer.start_active_span('main.handleMessage', finish_on_close=True) as scope:
         try:
             msg = json.loads(body.decode('utf-8'))
         except (UnicodeDecodeError, json.JSONDecodeError) as e:
             logging.warning(f"Invalid message recieved: {e}")
             return
 
-        span.log_kv({'event': 'messageParsed', 'sampleTag': msg['tag']})
+        scope.span.log_kv({'event': 'messageParsed', 'sampleTag': msg['tag']})
 
-        with opentracing.tracer.start_active_span('runAlgorithm', child_of=span) as child_span:
+        with opentracing.tracer.start_active_span('magicDoer.run_everything'):
             results = MagicDoer.run_everything(msg)  # <- This is where the magic happens
 
         if results: