SF_THRESH config

2023-01-15 13:22:13 -05:00
parent bb01cd0cfb
commit 797fef4802
5 changed files with 152 additions and 0 deletions
@@ -0,0 +1,139 @@
+from pathlib import Path
+from tzlocal import get_localzone
+import datetime
+import sqlite3
+import requests
+import json
+import time
+import math
+import numpy as np
+import librosa
+import operator
+import socket
+import threading
+import os
+import sys
+import argparse
+import datetime
+
+try:
+    import tflite_runtime.interpreter as tflite
+except BaseException:
+    from tensorflow import lite as tflite
+
+
+
+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_3K_V2.2_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):
+
+    global M_INTERPRETER
+
+    # Does interpreter exist?
+    try:
+        if M_INTERPRETER == None:
+            loadMetaModel()
+    except Exception as e:
+        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):
+
+    # Make filter prediction
+    l_filter = predictFilter(lat, lon, week)
+
+    # Apply threshold
+    l_filter = np.where(l_filter >= 0.03, 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)
+
+    # 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'
+with open(userDir + '/BirdNET-Pi/scripts/thisrun.txt', 'r') as f:
+
+    this_run = f.readlines()
+    lat = str(str(str([i for i in this_run if i.startswith('LATITUDE')]).split('=')[1]).split('\\')[0])
+    lon = str(str(str([i for i in this_run if i.startswith('LONGITUDE')]).split('=')[1]).split('\\')[0])
+
+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('--o', default='/home/pi/BirdNET-Pi/include_species_list.txt', help='Path to output file or folder. If this is a folder, file will be named \'species_list.txt\'.')
+    #parser.add_argument('--lat', type=float, default=##, help='Recording location latitude. Set -1 to ignore.')
+    #parser.add_argument('--lon', type=float, default=##, help='Recording location longitude. Set -1 to ignore.')
+    #parser.add_argument('--week', type=int, default=dayofweek, help='Week of the year when the recording was made. Values in [1, 48] (4 weeks per month). Set -1 for year-round species list.')
+    parser.add_argument('--threshold', type=float, default=0.05, help='Occurrence frequency threshold. Defaults to 0.05.')
+    #parser.add_argument('--sortby', default='freq', help='Sort species by occurrence frequency or alphabetically. Values in [\'freq\', \'alpha\']. Defaults to \'freq\'.')
+    
+    args = parser.parse_args()
+
+    LOCATION_FILTER_THRESHOLD = args.threshold
+
+    # 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("\nThe above species list describes all of the species that have been historically observed at the specified lat/long ("+lat+", "+lon+") for this week of the year. The frequency threshold is the percentage of submitted eBird checklists that the species appeared on, meaning a higher threshold means that the species is more common.")
+    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)")
+
+
+