Update Analyzer and add additional test for multiple detections of sa…

…me sample (#56)

* Add additional test for multiple detections of same sample

* Return all detections in predict (pre-conf/species filtering)

* Bump for 0.5.1 release

pyproject.toml

@@ -10,7 +10,7 @@ exclude = [
 
 [project]
 name = "birdnetlib"
-version = "0.5.0"
+version = "0.5.1"
 authors = [
   { name="Joe Weiss", email="joe.weiss@gmail.com" },
 ]

src/birdnetlib/analyzer.py

@@ -192,8 +192,8 @@ def analyze_recording(self, recording):
                 p_labels.items(), key=operator.itemgetter(1), reverse=True
             )
 
-            # Store top 5 results and advance indicies
-            results[str(start) + "-" + str(end)] = p_sorted[:5]
+            # Store results
+            results[str(start) + "-" + str(end)] = p_sorted
 
             # Increment start and end
             start += recording.sample_secs - recording.overlap

tests/test_overlap.py

@@ -0,0 +1,163 @@
+from birdnetlib import Recording
+from birdnetlib.analyzer import Analyzer, MODEL_PATH, LABEL_PATH
+
+from pprint import pprint
+import pytest
+import os
+import tempfile
+import csv
+from collections import Counter
+
+
+# Overlapping detections are when the library returns more than one detection for the same (3 sec) piece of audio.
+# We want to ensure that birdnetlib isn't filtering out these overlapping detections, or prematurely
+# filtering or ranking them.
+
+
+def test_without_species_list():
+
+    # Process file with command line utility, then process with python library and ensure equal commandline_results.
+
+    min_conf = 0.0  # Irresponsibly low, just for testing.
+    input_path = os.path.join(os.path.dirname(__file__), "test_files/soundscape.wav")
+
+    tf = tempfile.NamedTemporaryFile(suffix=".csv")
+    output_path = tf.name
+
+    # Process using python script as is.
+    birdnet_analyzer_path = os.path.join(os.path.dirname(__file__), "BirdNET-Analyzer")
+
+    cmd = f"python analyze.py --i '{input_path}' --o={output_path} --min_conf {min_conf} --rtype=csv"
+
+    print(cmd)
+    os.system(f"cd {birdnet_analyzer_path}; {cmd}")
+
+    with open(tf.name) as f:
+        for line in f:
+            print(line)
+
+    with open(tf.name, newline="") as csvfile:
+        # reader = csv.reader(csvfile, delimiter=";", quotechar="|")
+        reader = csv.DictReader(csvfile)
+        commandline_results = []
+        for row in reader:
+            commandline_results.append(
+                {
+                    "start_time": float(row["Start (s)"]),
+                    "end_time": float(row["End (s)"]),
+                    "common_name": row["Common name"],
+                    "scientific_name": row["Scientific name"],
+                    "confidence": float(row["Confidence"]),
+                }
+            )
+
+    # pprint(commandline_results)
+    assert len(commandline_results) > 0
+
+    analyzer = Analyzer()
+    recording = Recording(
+        analyzer,
+        input_path,
+        min_conf=min_conf,
+    )
+    recording.analyze()
+
+    # Check that birdnetlib results match command line results.
+    assert len(recording.detections) == len(commandline_results)
+
+    # Check that detection confidence is float.
+    assert type(recording.detections[0]["confidence"]) is float
+
+    # groups
+    start_times = [i["start_time"] for i in recording.detections]
+    item_counts = Counter(start_times)
+    assert item_counts[48.0] == 15
+
+    # Ensure that multiple detections exist for 57-60 seconds.
+    overlapping = [i for i in recording.detections if i["start_time"] == 57.0]
+    assert len(overlapping) == 15
+
+
+def test_with_species_list():
+
+    # Process file with command line utility, then process with python library and ensure equal commandline_results.
+
+    min_conf = 0.1
+    input_path = os.path.join(os.path.dirname(__file__), "test_files/soundscape.wav")
+    custom_list_path = os.path.join(
+        os.path.dirname(__file__), "test_files/species_list.txt"
+    )
+
+    tf = tempfile.NamedTemporaryFile(suffix=".csv")
+    output_path = tf.name
+
+    # Process using python script as is.
+    birdnet_analyzer_path = os.path.join(os.path.dirname(__file__), "BirdNET-Analyzer")
+
+    cmd = f"python analyze.py --i '{input_path}' --o={output_path} --min_conf {min_conf} --slist {custom_list_path} --rtype=csv"
+    os.system(f"cd {birdnet_analyzer_path}; {cmd}")
+
+    with open(tf.name) as f:
+        for line in f:
+            print(line)
+
+    with open(tf.name, newline="") as csvfile:
+        # reader = csv.reader(csvfile, delimiter=";", quotechar="|")
+        reader = csv.DictReader(csvfile)
+        commandline_results = []
+        for row in reader:
+            commandline_results.append(
+                {
+                    "start_time": float(row["Start (s)"]),
+                    "end_time": float(row["End (s)"]),
+                    "common_name": row["Common name"],
+                    "scientific_name": row["Scientific name"],
+                    "confidence": float(row["Confidence"]),
+                }
+            )
+
+    pprint(commandline_results)
+    assert len(commandline_results) > 0
+
+    analyzer = Analyzer(custom_species_list_path=custom_list_path)
+    recording = Recording(
+        analyzer,
+        input_path,
+        min_conf=min_conf,
+    )
+    recording.analyze()
+
+    assert recording.duration == 120
+    assert (
+        commandline_results[0]["common_name"] == recording.detections[0]["common_name"]
+    )
+
+    commandline_birds = [i["common_name"] for i in commandline_results]
+    detected_birds = [i["common_name"] for i in recording.detections]
+    assert commandline_birds == detected_birds
+
+    assert len(recording.detections) == len(commandline_results)
+    assert (
+        len(analyzer.custom_species_list) == 41
+    )  # Check that this matches the number printed by the cli version.
+
+    # Ensure that multiple detections exist for 57-60 seconds.
+    overlapping = [i for i in recording.detections if i["start_time"] == 57.0]
+    assert len(overlapping) == 2
+
+    # Run a recording with lat/lon and throw an error when used with custom species list.
+
+    lon = -120.7463
+    lat = 35.4244
+    week_48 = 18
+
+    with pytest.raises(ValueError):
+        recording = Recording(
+            analyzer,
+            input_path,
+            lon=lon,
+            lat=lat,
+            week_48=week_48,
+            min_conf=min_conf,
+        )
+        recording.analyze()