cleanup filter_humans a bit

scripts/utils/analysis.py

@@ -87,52 +87,42 @@ def analyzeAudioData(chunks, overlap, lat, lon, week):
     return labeled, predicted_species_list
 
 
-def filter_humans(detections):
+def filter_humans(predictions):
     conf = get_settings()
     priv_thresh = conf.getfloat('PRIVACY_THRESHOLD')
     human_cutoff = max(10, int(6000 * priv_thresh / 100.0))
     log.debug("HUMAN-CUTOFF AT: %d", human_cutoff)
-
-    censored_detections = []
-    for detection in detections:
-        p = detection[:human_cutoff]
-        human_detected = False
-        # Catch if Human is recognized in any of the predictions
-        for x in p:
-            if 'Human' in x[0]:
-                human_detected = True
-
-        # If human detected set detection to human to make sure voices are not saved
-        if human_detected is True:
-            p = [('Human_Human', 0.0)]
-        else:
-            p = p[:10]
-
-        censored_detections.append(p)
-
-    # now overwrite detections that have a human neighbour too
     try:
-        extraction_length = conf.getint('EXTRACTION_LENGTH')
+        if conf.getint('EXTRACTION_LENGTH') > 9:
+            log.warning("EXTRACTION_LENGTH is set to %d. Privacy filter might miss human sound, "
+                        "if you care about privacy, set EXTRACTION_LENGTH to below 9 or leave empty.", conf.getint('EXTRACTION_LENGTH'))
     except ValueError:
-        extraction_length = 6
+        pass
-    if extraction_length > 9:
+
-        log.warning("EXTRACTION_LENGTH is set to %d. Privacy filter might miss human sound, "
+    # mask for humans
-                    "if you care about privacy, set EXTRACTION_LENGTH to below 9 or leave empty.", extraction_length)
+    human_mask = [False] * len(predictions)
-    human_neighbour_mask = [False] * len(censored_detections)
+    for i, prediction in enumerate(predictions):
-    for i, detection in enumerate(censored_detections):
+        for p in prediction[:human_cutoff]:
-        if i != 0:
+            if 'Human' in p[0]:
-            if censored_detections[i - 1][0][0] == 'Human_Human':
+                human_mask[i] = True
-                human_neighbour_mask[i] = True
+                break
-        if i != len(censored_detections) - 1:
+
-            if censored_detections[i + 1][0][0] == 'Human_Human':
+    # mask for predictions that have a human neighbour
-                human_neighbour_mask[i] = True
+    human_neighbour_mask = [False] * len(predictions)
+    for i, _ in enumerate(human_mask):
+        if i != 0 and human_mask[i - 1]:
+            human_neighbour_mask[i] = True
+        if i != len(human_mask) - 1 and human_mask[i + 1]:
+            human_neighbour_mask[i] = True
 
     clean_detections = []
-    for i, (has_human_neighbour, detection) in enumerate(zip(human_neighbour_mask, censored_detections)):
+    for prediction, human, has_human_neighbour in zip(predictions, human_mask, human_neighbour_mask):
-        if has_human_neighbour and detection[0][0] != 'Human_Human':
+        if human or has_human_neighbour:
-            log.debug('Overwriting detection %d %s - Has Human neighbour', i + 1, detection[0])
+            log.debug('Overwriting prediction %s', prediction[0])
-            detection = [('Human_Human', 0.0)]
+            prediction = [('Human_Human', 0.0)]
-        clean_detections.append(detection)
+        else:
+            prediction = prediction[:10]
+        clean_detections.append(prediction)
 
     return clean_detections
 

scripts/utils/notifications.py

@@ -77,10 +77,10 @@ def sendAppriseNotifications(sci_name, com_name, confidence, confidencepct, path
     if websiteurl is None or len(websiteurl) == 0:
         websiteurl = f"http://{socket.gethostname()}.local"
 
-    listenurl = websiteurl+"?filename="+path
+    listenurl = f"{websiteurl}?filename= {path}"
-    friendlyurl = "[Listen here]("+listenurl+")"
+    friendlyurl = f"[Listen here]({listenurl})"
-    image_url = ""
 
+    image_url = ""
     if "$flickrimage" in body or "$image" in body:
         if com_name not in images:
             try:
@@ -88,7 +88,7 @@ def sendAppriseNotifications(sci_name, com_name, confidence, confidencepct, path
                 resp = requests.get(url=url, timeout=10).json()
                 images[com_name] = resp['data']['image_url']
             except Exception as e:
-                print("IMAGE API ERROR: " + str(e))
+                print("IMAGE API ERROR:", e)
         image_url = images.get(com_name, "")
 
     if settings_dict.get('APPRISE_NOTIFY_EACH_DETECTION') == "1":

tests/test_analysis.py

@@ -5,6 +5,7 @@ from unittest.mock import patch
 from scripts.utils.analysis import run_analysis
 from scripts.utils.classes import ParseFileName
 from tests.helpers import TESTDATA, Settings
+from scripts.utils.analysis import filter_humans
 
 
 class TestRunAnalysis(unittest.TestCase):
@@ -36,5 +37,151 @@ class TestRunAnalysis(unittest.TestCase):
             self.assertEqual(det.scientific_name, expected['sci_name'])
 
 
+class TestFilterHumans(unittest.TestCase):
+
+    @patch('scripts.utils.helpers._load_settings')
+    def test_filter_humans_no_human(self, mock_load_settings):
+        mock_load_settings.return_value = Settings.with_defaults()
+
+        # Input detections without humans
+        detections = [
+            [('Bird_A', 0.9), ('Bird_B', 0.8)],
+            [('Bird_C', 0.7), ('Bird_D', 0.6)]
+        ]
+
+        # Expected output
+        expected = [
+            [('Bird_A', 0.9), ('Bird_B', 0.8)],
+            [('Bird_C', 0.7), ('Bird_D', 0.6)]
+        ]
+
+        # Run filter_humans
+        result = filter_humans(detections)
+
+        # Assertions
+        self.assertEqual(result, expected)
+
+    @patch('scripts.utils.helpers._load_settings')
+    def test_filter_empty(self, mock_load_settings):
+        mock_load_settings.return_value = Settings.with_defaults()
+
+        # Input detections without humans
+        detections = []
+
+        # Expected output
+        expected = []
+
+        # Run filter_humans
+        result = filter_humans(detections)
+
+        # Assertions
+        self.assertEqual(result, expected)
+
+    @patch('scripts.utils.helpers._load_settings')
+    def test_filter_humans_with_human(self, mock_load_settings):
+        mock_load_settings.return_value = Settings.with_defaults()
+
+        # Input detections with humans
+        detections = [
+            [('Human_Human', 0.95), ('Bird_A', 0.8)],
+            [('Bird_A', 0.9), ('Bird_B', 0.8)],
+            [('Bird_C', 0.9), ('Bird_D', 0.8)],
+            [('Bird_B', 0.7), ('Human vocal_Human vocal', 0.9)]
+        ]
+
+        # Expected output
+        expected = [
+            [('Human_Human', 0.0)],
+            [('Human_Human', 0.0)],
+            [('Human_Human', 0.0)],
+            [('Human_Human', 0.0)]
+        ]
+
+        # Run filter_humans
+        result = filter_humans(detections)
+
+        # Assertions
+        self.assertEqual(result, expected)
+
+    @patch('scripts.utils.helpers._load_settings')
+    def test_filter_humans_with_human_neighbour(self, mock_load_settings):
+        mock_load_settings.return_value = Settings.with_defaults()
+
+        # Input detections with human neighbours
+        detections = [
+            [('Bird_A', 0.9), ('Bird_B', 0.8)],
+            [('Bird_D', 0.9), ('Bird_E', 0.8)],
+            [('Human_Human', 0.95), ('Bird_C', 0.7)],
+            [('Bird_F', 0.6), ('Bird_G', 0.5)]
+        ]
+
+        # Expected output
+        expected = [
+            [('Bird_A', 0.9), ('Bird_B', 0.8)],
+            [('Human_Human', 0.0)],
+            [('Human_Human', 0.0)],
+            [('Human_Human', 0.0)]
+        ]
+
+        # Run filter_humans
+        result = filter_humans(detections)
+
+        # Assertions
+        self.assertEqual(result, expected)
+
+    @patch('scripts.utils.helpers._load_settings')
+    def test_filter_humans_with_deep_human(self, mock_load_settings):
+        mock_load_settings.return_value = Settings.with_defaults()
+
+        # Input detections with human neighbours
+        detections = [
+            [('Bird_A', 0.9), ('Bird_B', 0.8)],
+            [('Bird_D', 0.9), ('Bird_E', 0.8)],
+            [('Bird_C', 0.7), ('Bird_C', 0.7), ('Bird_C', 0.7), ('Bird_C', 0.7), ('Bird_C', 0.7), ('Bird_C', 0.7), ('Bird_C', 0.7), ('Bird_C', 0.7), ('Bird_C', 0.7), ('Bird_C', 0.5), ('Bird_C', 0.7), ('Human_Human', 0.95)],
+            [('Bird_F', 0.6), ('Bird_G', 0.5)]
+        ]
+
+        # Expected output
+        expected = [
+            [('Bird_A', 0.9), ('Bird_B', 0.8)],
+            [('Bird_D', 0.9), ('Bird_E', 0.8)],
+            [('Bird_C', 0.7), ('Bird_C', 0.7), ('Bird_C', 0.7), ('Bird_C', 0.7), ('Bird_C', 0.7), ('Bird_C', 0.7), ('Bird_C', 0.7), ('Bird_C', 0.7), ('Bird_C', 0.7), ('Bird_C', 0.5)],
+            [('Bird_F', 0.6), ('Bird_G', 0.5)]
+        ]
+
+        # Run filter_humans
+        result = filter_humans(detections)
+
+        # Assertions
+        self.assertEqual(result, expected)
+
+    @patch('scripts.utils.helpers._load_settings')
+    def test_filter_humans_with_human_deep(self, mock_load_settings):
+        settings = Settings.with_defaults()
+        settings['PRIVACY_THRESHOLD'] = 1
+        mock_load_settings.return_value = settings
+
+        # Input detections with human neighbours
+        detections = [
+            [('Bird_A', 0.9), ('Bird_B', 0.8)],
+            [('Bird_D', 0.9), ('Bird_E', 0.8)],
+            [('Bird_C', 0.7), ('Bird_C', 0.7), ('Bird_C', 0.7), ('Bird_C', 0.7), ('Bird_C', 0.7), ('Bird_C', 0.7), ('Bird_C', 0.7), ('Bird_C', 0.7), ('Bird_C', 0.7), ('Bird_C', 0.5), ('Bird_C', 0.7), ('Human_Human', 0.95)],
+            [('Bird_F', 0.6), ('Bird_G', 0.5)]
+        ]
+
+        # Expected output
+        expected = [
+            [('Bird_A', 0.9), ('Bird_B', 0.8)],
+            [('Human_Human', 0.0)],
+            [('Human_Human', 0.0)],
+            [('Human_Human', 0.0)]
+        ]
+
+        # Run filter_humans
+        result = filter_humans(detections)
+
+        # Assertions
+        self.assertEqual(result, expected)
+
 if __name__ == '__main__':
     unittest.main()