From d8d0d5aa0993ef6ec59c43e8676de93f396f39f8 Mon Sep 17 00:00:00 2001
From: frederik <frederik.slos@gmail.com>
Date: Sat, 8 Nov 2025 14:59:49 +0100
Subject: [PATCH] use models.py, add support for data model V2

---
 scripts/species.py | 137 +++++++++------------------------------------
 1 file changed, 26 insertions(+), 111 deletions(-)

diff --git a/scripts/species.py b/scripts/species.py
index 56be8be..477e698 100644
--- a/scripts/species.py
+++ b/scripts/species.py
@@ -2,123 +2,38 @@ import argparse
 import datetime
 import os
 
-import numpy as np
+from utils.helpers import get_settings, MODEL_PATH
+from utils.models import MDataModel1, MDataModel2
 
-from utils.helpers import get_settings
-
-try:
-    import tflite_runtime.interpreter as tflite
-except BaseException:
-    from tensorflow import lite as tflite
-
-M_INTERPRETER, M_INPUT_LAYER_INDEX, M_OUTPUT_LAYER_INDEX, CLASSES = (None, None, None, None)
-
-
-def loadMetaModel():
-    global M_INTERPRETER
-    global M_INPUT_LAYER_INDEX
-    global M_OUTPUT_LAYER_INDEX
-    global CLASSES
-
-    # Load TFLite model and allocate tensors.
-    M_INTERPRETER = tflite.Interpreter(model_path=userDir + '/BirdNET-Pi/model/BirdNET_GLOBAL_6K_V2.4_MData_Model_FP16.tflite')
-    M_INTERPRETER.allocate_tensors()
-
-    # Get input and output tensors.
-    input_details = M_INTERPRETER.get_input_details()
-    output_details = M_INTERPRETER.get_output_details()
-
-    # Get input tensor index
-    M_INPUT_LAYER_INDEX = input_details[0]['index']
-    M_OUTPUT_LAYER_INDEX = output_details[0]['index']
-
-    # Load labels
-    CLASSES = []
-    labelspath = userDir + '/BirdNET-Pi/model/labels.txt'
-    with open(labelspath, 'r') as lfile:
-        for line in lfile.readlines():
-            CLASSES.append(line.replace('\n', ''))
-
-    print("loaded META model")
-
-
-def predictFilter(lat, lon, week):
-    # Does interpreter exist?
-    try:
-        if M_INTERPRETER is None:
-            loadMetaModel()
-    except Exception:
-        loadMetaModel()
-
-    # Prepare mdata as sample
-    sample = np.expand_dims(np.array([lat, lon, week], dtype='float32'), 0)
-
-    # Run inference
-    M_INTERPRETER.set_tensor(M_INPUT_LAYER_INDEX, sample)
-    M_INTERPRETER.invoke()
-
-    return M_INTERPRETER.get_tensor(M_OUTPUT_LAYER_INDEX)[0]
-
-
-def explore(lat, lon, week, threshold):
-
-    # Make filter prediction
-    l_filter = predictFilter(lat, lon, week)
-
-    # Apply threshold
-    l_filter = np.where(l_filter >= threshold, l_filter, 0)
-
-    # Zip with labels
-    l_filter = list(zip(l_filter, CLASSES))
-
-    # Sort by filter value
-    l_filter = sorted(l_filter, key=lambda x: x[0], reverse=True)
-
-    return l_filter
-
-
-def getSpeciesList(lat, lon, week, threshold=0.05, sort=False):
-
-    print('Getting species list for {}/{}, Week {}...'.format(lat, lon, week), end='', flush=True)
-
-    # Extract species from model
-    pred = explore(lat, lon, week, threshold)
-
-    # Make species list
-    slist = []
-    for p in pred:
-        if p[0] >= threshold:
-            slist.append([p[1], p[0]])
-
-    return slist
-
-
-userDir = os.path.expanduser('~')
-DB_PATH = userDir + '/BirdNET-Pi/scripts/birds.db'
-
-conf = get_settings()
-lat = conf.getfloat('LATITUDE')
-lon = conf.getfloat('LONGITUDE')
-
-weekofyear = datetime.datetime.today().isocalendar()[1]
 if __name__ == '__main__':
-
-    # Parse arguments
     parser = argparse.ArgumentParser(
         description='Get list of species for a given location with BirdNET. Sorted by occurrence frequency.'
     )
-    parser.add_argument('--threshold', type=float, default=0.05, help='Occurrence frequency threshold. Defaults to 0.05.')
-
+    parser.add_argument('--threshold', type=float, default=0.05,
+                        help='Occurrence frequency threshold. Defaults to 0.05.')
     args = parser.parse_args()
 
-    LOCATION_FILTER_THRESHOLD = args.threshold
+    conf = get_settings()
+    lat = conf.getfloat('LATITUDE')
+    lon = conf.getfloat('LONGITUDE')
+    week = datetime.datetime.today().isocalendar()[1]
 
-    # Get species list
-    species_list = getSpeciesList(lat, lon, weekofyear, LOCATION_FILTER_THRESHOLD, False)
-    for x in range(len(species_list)):
-        print(species_list[x][0] + " - " + str(species_list[x][1]))
+    print(f'Getting species list for {lat}/{lon}, Week {week}...', flush=True)
+    labels_path = os.path.join(MODEL_PATH, 'labels.txt')
+    with open(labels_path, 'r') as lfile:
+        labels = [line.strip() for line in lfile]
 
-    print("\nThe above species list describes all the species that the model will attempt to detect. \
-          If you don't see a species you want detected on this list, decrease your threshold.")
-    print("\nNOTE: no actual changes to your BirdNET-Pi species list were made by running this command. \
-          To set your desired frequency threshold, do it through the BirdNET-Pi web interface (Tools -> Settings -> Model)")
+    model = MDataModel1(args.threshold) if conf.getint('DATA_MODEL_VERSION') == 1 else MDataModel2(args.threshold)
+    model.set_meta_data(lat, lon, week)
+    species_list = model.get_species_list_details(labels)
+
+    for species in species_list:
+        print(f'{species[1]} - {species[0]:.4f}')
+
+    print("""
+The above species list describes all the species that the model will attempt to detect.
+If you don't see a species you want detected on this list, decrease your threshold.
+
+NOTE: no actual changes to your BirdNET-Pi species list were made by running this command.
+To set your desired frequency threshold, do it through the BirdNET-Pi web interface (Tools -> Settings -> Model)
+""")