Patrick McGuire
291 lines
11 KiB
Python
291 lines
11 KiB
Python
import os
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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os.environ['CUDA_VISIBLE_DEVICES'] = ''
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try:
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import tflite_runtime.interpreter as tflite
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except:
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from tensorflow import lite as tflite
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import argparse
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import operator
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import librosa
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import numpy as np
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import math
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import time
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###############################################################################
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import mysql.connector
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###############################################################################
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from datetime import date, datetime
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def loadModel():
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global INPUT_LAYER_INDEX
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global OUTPUT_LAYER_INDEX
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global MDATA_INPUT_INDEX
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global CLASSES
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print('LOADING TF LITE MODEL...', end=' ')
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# Load TFLite model and allocate tensors.
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interpreter = tflite.Interpreter(model_path='model/BirdNET_6K_GLOBAL_MODEL.tflite',num_threads=2)
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interpreter.allocate_tensors()
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# Get input and output tensors.
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input_details = interpreter.get_input_details()
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output_details = interpreter.get_output_details()
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# Get input tensor index
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INPUT_LAYER_INDEX = input_details[0]['index']
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MDATA_INPUT_INDEX = input_details[1]['index']
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OUTPUT_LAYER_INDEX = output_details[0]['index']
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# Load labels
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CLASSES = []
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with open('model/labels.txt', 'r') as lfile:
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for line in lfile.readlines():
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CLASSES.append(line.replace('\n', ''))
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print('DONE!')
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return interpreter
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def loadCustomSpeciesList(path):
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slist = []
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if os.path.isfile(path):
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with open(path, 'r') as csfile:
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for line in csfile.readlines():
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slist.append(line.replace('\r', '').replace('\n', ''))
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return slist
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def splitSignal(sig, rate, overlap, seconds=3.0, minlen=1.5):
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# Split signal with overlap
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sig_splits = []
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for i in range(0, len(sig), int((seconds - overlap) * rate)):
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split = sig[i:i + int(seconds * rate)]
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# End of signal?
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if len(split) < int(minlen * rate):
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break
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# Signal chunk too short? Fill with zeros.
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if len(split) < int(rate * seconds):
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temp = np.zeros((int(rate * seconds)))
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temp[:len(split)] = split
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split = temp
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sig_splits.append(split)
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return sig_splits
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def readAudioData(path, overlap, sample_rate=48000):
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print('READING AUDIO DATA...', end=' ', flush=True)
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# Open file with librosa (uses ffmpeg or libav)
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sig, rate = librosa.load(path, sr=sample_rate, mono=True, res_type='kaiser_fast')
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# Split audio into 3-second chunks
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chunks = splitSignal(sig, rate, overlap)
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print('DONE! READ', str(len(chunks)), 'CHUNKS.')
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return chunks
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def convertMetadata(m):
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# Convert week to cosine
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if m[2] >= 1 and m[2] <= 48:
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m[2] = math.cos(math.radians(m[2] * 7.5)) + 1
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else:
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m[2] = -1
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# Add binary mask
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mask = np.ones((3,))
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if m[0] == -1 or m[1] == -1:
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mask = np.zeros((3,))
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if m[2] == -1:
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mask[2] = 0.0
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return np.concatenate([m, mask])
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def custom_sigmoid(x, sensitivity=1.0):
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return 1 / (1.0 + np.exp(-sensitivity * x))
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def predict(sample, interpreter, sensitivity):
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# Make a prediction
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interpreter.set_tensor(INPUT_LAYER_INDEX, np.array(sample[0], dtype='float32'))
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interpreter.set_tensor(MDATA_INPUT_INDEX, np.array(sample[1], dtype='float32'))
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interpreter.invoke()
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prediction = interpreter.get_tensor(OUTPUT_LAYER_INDEX)[0]
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# Apply custom sigmoid
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p_sigmoid = custom_sigmoid(prediction, sensitivity)
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# Get label and scores for pooled predictions
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p_labels = dict(zip(CLASSES, p_sigmoid))
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# Sort by score
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p_sorted = sorted(p_labels.items(), key=operator.itemgetter(1), reverse=True)
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# Remove species that are on blacklist
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for i in range(min(10, len(p_sorted))):
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if p_sorted[i][0] in ['Human_Human', 'Non-bird_Non-bird', 'Noise_Noise', 'Quiscalus major_Boat-tailed Grackle', 'Megascops asio_Eastern Screech-Owl', 'Cygnus columbianus_Tundra Swan', 'Cathartes aura_Turkey Vulture', 'Cygnus buccinator_Trumpeter Swan', 'Scolopax minor_American Woodcock', 'Bubo virginianus_Great Horned Owl', 'Strix varia_Barred Owl', 'Quiscalus quiscula_Common Grackle']:
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p_sorted[i] = (p_sorted[i][0], 0.0)
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# Only return first the top ten results
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return p_sorted[:10]
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def analyzeAudioData(chunks, lat, lon, week, sensitivity, overlap, interpreter):
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detections = {}
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start = time.time()
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print('ANALYZING AUDIO...', end=' ', flush=True)
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# Convert and prepare metadata
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mdata = convertMetadata(np.array([lat, lon, week]))
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mdata = np.expand_dims(mdata, 0)
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# Parse every chunk
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pred_start = 0.0
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for c in chunks:
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# Prepare as input signal
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sig = np.expand_dims(c, 0)
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# Make prediction
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p = predict([sig, mdata], interpreter, sensitivity)
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# Save result and timestamp
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pred_end = pred_start + 3.0
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detections[str(pred_start) + ';' + str(pred_end)] = p
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pred_start = pred_end - overlap
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print('DONE! Time', int((time.time() - start) * 10) / 10.0, 'SECONDS')
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return detections
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def writeResultsToFile(detections, min_conf, path):
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print('WRITING RESULTS TO', path, '...', end=' ')
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rcnt = 0
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with open(path, 'w') as rfile:
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rfile.write('Start (s);End (s);Scientific name;Common name;Confidence\n')
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for d in detections:
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for entry in detections[d]:
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if entry[1] >= min_conf and (entry[0] in WHITE_LIST or len(WHITE_LIST) == 0):
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rfile.write(d + ';' + entry[0].replace('_', ';') + ';' + str(entry[1]) + '\n')
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rcnt += 1
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print('DONE! WROTE', rcnt, 'RESULTS.')
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def main():
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global WHITE_LIST
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# Parse passed arguments
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parser = argparse.ArgumentParser()
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parser.add_argument('--i', help='Path to input file.')
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parser.add_argument('--o', default='result.csv', help='Path to output file. Defaults to result.csv.')
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parser.add_argument('--lat', type=float, default=-1, help='Recording location latitude. Set -1 to ignore.')
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parser.add_argument('--lon', type=float, default=-1, help='Recording location longitude. Set -1 to ignore.')
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parser.add_argument('--week', type=int, default=-1, help='Week of the year when the recording was made. Values in [1, 48] (4 weeks per month). Set -1 to ignore.')
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parser.add_argument('--overlap', type=float, default=0.0, help='Overlap in seconds between extracted spectrograms. Values in [0.0, 2.9]. Defaults tp 0.0.')
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parser.add_argument('--sensitivity', type=float, default=1.0, help='Detection sensitivity; Higher values result in higher sensitivity. Values in [0.5, 1.5]. Defaults to 1.0.')
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parser.add_argument('--min_conf', type=float, default=0.1, help='Minimum confidence threshold. Values in [0.01, 0.99]. Defaults to 0.1.')
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parser.add_argument('--custom_list', default='', help='Path to text file containing a list of species. Not used if not provided.')
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args = parser.parse_args()
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# Load model
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interpreter = loadModel()
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# Load custom species list
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if not args.custom_list == '':
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WHITE_LIST = loadCustomSpeciesList(args.custom_list)
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else:
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WHITE_LIST = []
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# Read audio data
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audioData = readAudioData(args.i, args.overlap)
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# Process audio data and get detections
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week = max(1, min(args.week, 48))
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sensitivity = max(0.5, min(1.0 - (args.sensitivity - 1.0), 1.5))
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detections = analyzeAudioData(audioData, args.lat, args.lon, week, sensitivity, args.overlap, interpreter)
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# Write detections to output file
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min_conf = max(0.01, min(args.min_conf, 0.99))
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writeResultsToFile(detections, min_conf, args.o)
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now = datetime.now()
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###############################################################################
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###############################################################################
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# Write detections to Database
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for i in detections:
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print("\n", detections[i][0],"\n")
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with open('BirdDB.txt', 'a') as rfile:
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for d in detections:
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print("\n", "Database Entry", "\n")
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for entry in detections[d]:
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if entry[1] >= min_conf and (entry[0] in WHITE_LIST or len(WHITE_LIST) == 0):
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current_date = now.strftime("%Y/%m/%d")
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current_time = now.strftime("%H:%M:%S")
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rfile.write(str(current_date) + ';' + str(current_time) + ';' + entry[0].replace('_', ';') + ';' \
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+ str(entry[1]) +";" + str(args.lat) + ';' + str(args.lon) + ';' + str(min_conf) + ';' + str(week) + ';' \
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+ str(sensitivity) +';' + str(args.overlap) + '\n')
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def insert_variables_into_table(Date, Time, Sci_Name, Com_Name, Confidence, Lat, Lon, Cutoff, Week, Sens, Overlap):
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try:
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connection = mysql.connector.connect(host='localhost',
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database='birds',
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user='birder',
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password='databasepassword')
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cursor = connection.cursor()
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mySql_insert_query = """INSERT INTO detections (Date, Time, Sci_Name, Com_Name, Confidence, Lat, Lon, Cutoff, Week, Sens, Overlap)
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VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s) """
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record = (Date, Time, Sci_Name, Com_Name, Confidence, Lat, Lon, Cutoff, Week, Sens, Overlap)
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cursor.execute(mySql_insert_query, record)
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connection.commit()
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print("Record inserted successfully into detections table")
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except mysql.connector.Error as error:
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print("Failed to insert record into detections table {}".format(error))
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finally:
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if connection.is_connected():
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connection.close()
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print("MySQL connection is closed")
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species = entry[0]
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sci_name,com_name = species.split('_')
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insert_variables_into_table(str(current_date), str(current_time), sci_name, com_name, \
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str(entry[1]), str(args.lat), str(args.lon), str(min_conf), str(week), \
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str(sensitivity), str(args.overlap))
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print(str(current_date) + ';' + str(current_time) + ';' + entry[0].replace('_', ';') + ';' + str(entry[1]) +";" + str(args.lat) + ';' + str(args.lon) + ';' + str(min_conf) + ';' + str(week) + ';' + str(sensitivity) +';' + str(args.overlap) + '\n')
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time.sleep(3)
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now = datetime.now()
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###############################################################################
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###############################################################################
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if __name__ == '__main__':
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main()
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# Example calls
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# python3 analyze.py --i 'example/XC558716 - Soundscape.mp3' --lat 35.4244 --lon -120.7463 --week 18
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# python3 analyze.py --i 'example/XC563936 - Soundscape.mp3' --lat 47.6766 --lon -122.294 --week 11 --overlap 1.5 --min_conf 0.25 --sensitivity 1.25 --custom_list 'example/custom_species_list.txt'
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