Implemented AI stuff

src/preprocessor/abcpreprocessor.py

@@ -16,7 +16,7 @@ class AbcPreProcessor(ABC):
     Abstract base class PreProcessor. Responsible for manipulating input data from a sensor.
     """
     @abstractmethod
-    def preprocesssignal(self, signal):
+    def preprocesssignal(self, file_path) -> bool:
         """
         Preprocess a signal.
         :return:

src/preprocessor/soundpreprocessor.py

@@ -1,9 +1,15 @@
 #!/usr/bin/env python3
 import requests
-from pyAudioAnalysis import audioTrainTest as aT
-from pyAudioAnalysis.audioTrainTest import load_model, load_model_knn
+from pyAudioAnalysis.audioTrainTest import load_model, load_model_knn, classifier_wrapper
 from utils import config
 from .abcpreprocessor import AbcPreProcessor
+import tempfile
+import os
+import logging
+
+from pyAudioAnalysis import audioBasicIO
+from pyAudioAnalysis import MidTermFeatures
+import numpy
 
 """
 Abstract base class for Sender
@@ -20,27 +26,91 @@ class SoundPreProcessor(AbcPreProcessor):
     SoundPreProcessor class, responsible for detecting birb chirps in sound sample.
     """
 
-    def preprocesssignal(self, signal: str) -> bool:
-        """
-        Classify a sound sample.
-        :param signal: Access path of the sound sample up for processing.
-        :return:
-        """
-        # TODO: Dynamic model injection?
-        r = requests.get(f"http://model-service/model/{config.MODEL_ID}/details")
+    def __init__(self):
+        logging.info("Downloading current model...")
+        _, self._temp_model_name = tempfile.mkstemp()
+        self._temp_means_name = self._temp_model_name + "MEANS"
+
+        logging.debug("Fetching model info...")
+        r = requests.get(f"{config.API_URL}/model/$default/details")
         r.raise_for_status()
 
-        model_details = r.json()
+        self._model_details = r.json()
 
-        if model_details['type'] == 'knn':
-            classifier, mean, std, classes, mid_window, mid_step, short_window, short_step, compute_beat \
-                = load_model_knn(config.MODEL_ID + "MEANS")
+        logging.debug("Downloading model...")
+        r = requests.get(f"{config.API_URL}/model/{self._model_details['id']}")
+        r.raise_for_status()
+
+        with open(self._temp_model_name, 'wb') as f:
+            f.write(r.content)
+
+        logging.debug("Downloading MEANS...")
+        r = requests.get(f"{config.API_URL}/model/{self._model_details['id']}?means")
+        r.raise_for_status()
+
+        with open(self._temp_means_name, 'wb') as f:
+            f.write(r.content)
+
+        logging.info("Loading current model...")
+
+        if self._model_details['type'] == 'knn':
+            self._classifier, self._mean, self._std, self._classes, \
+            self._mid_window, self._mid_step, self._short_window, \
+            self._short_step, self._compute_beat = load_model_knn(self._temp_model_name)
 
         else:
-            classifier, mean, std, classes, mid_window, mid_step, short_window, short_step, compute_beat \
-                = load_model(config.MODEL_ID + "MEANS")
+            self._classifier, self._mean, self._std, self._classes, \
+            self._mid_window, self._mid_step, self._short_window, \
+            self._short_step, self._compute_beat = load_model(self._temp_model_name)
 
-        target_id = classes.index(config.TARGET_NAME)
+    def preprocesssignal(self, file_path: str) -> bool:
+        """
+        Classify a sound sample.
+        :param file_path: Access path of the sound sample up for processing.
+        :return:
+        """
+        logging.info("Running extraction...")
 
-        class_id, probability = aT.file_classification(signal, config.MODEL_ID, "svm")
-        return class_id == target_id
+        sampling_rate, signal = audioBasicIO.read_audio_file(file_path)
+        signal = audioBasicIO.stereo_to_mono(signal)
+
+        if sampling_rate == 0:
+            raise Exception("Could not read the file properly: Sampling rate zero")
+
+        if signal.shape[0] / float(sampling_rate) <= self._mid_window:
+            raise Exception("Could not read the file properly: Signal shape is not good")
+
+        # feature extraction:
+        mid_features, s, _ = \
+            MidTermFeatures.mid_feature_extraction(signal, sampling_rate,
+                                                   self._mid_window * sampling_rate,
+                                                   self._mid_step * sampling_rate,
+                                                   round(sampling_rate * self._short_window),
+                                                   round(sampling_rate * self._short_step))
+
+        # long term averaging of mid-term statistics
+        mid_features = mid_features.mean(axis=1)
+        if self._compute_beat:
+            beat, beat_conf = MidTermFeatures.beat_extraction(s, self._short_step)
+            mid_features = numpy.append(mid_features, beat)
+            mid_features = numpy.append(mid_features, beat_conf)
+
+        logging.info("Running classification...")
+
+        target_id = self._classes.index('chirp')  # Might raise ValueError
+
+        feature_vector = (mid_features - self._mean) / self._std
+        class_id, probability = classifier_wrapper(self._classifier, self._model_details['type'], feature_vector)
+
+        return bool(class_id == target_id)
+
+    def __del__(self):
+        try:
+            os.remove(self._temp_model_name)
+        except FileNotFoundError:
+            pass
+
+        try:
+            os.remove(self._temp_means_name)
+        except FileNotFoundError:
+            pass