mirror of
https://github.com/macaodha/batdetect2.git
synced 2025-06-29 14:41:58 +02:00
109 lines
3.7 KiB
Python
109 lines
3.7 KiB
Python
import gradio as gr
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import os
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import matplotlib.pyplot as plt
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import pandas as pd
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import numpy as np
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import bat_detect.utils.detector_utils as du
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import bat_detect.utils.audio_utils as au
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import bat_detect.utils.plot_utils as viz
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# setup the arguments
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args = {}
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args = du.get_default_bd_args()
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args['detection_threshold'] = 0.3
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args['time_expansion_factor'] = 1
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args['model_path'] = 'models/Net2DFast_UK_same.pth.tar'
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max_duration = 2.0
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# load the model
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model, params = du.load_model(args['model_path'])
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df = gr.Dataframe(
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headers=["species", "time", "detection_prob", "species_prob"],
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datatype=["str", "str", "str", "str"],
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row_count=1,
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col_count=(4, "fixed"),
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label='Predictions'
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)
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examples = [['example_data/audio/20170701_213954-MYOMYS-LR_0_0.5.wav', 0.3],
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['example_data/audio/20180530_213516-EPTSER-LR_0_0.5.wav', 0.3],
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['example_data/audio/20180627_215323-RHIFER-LR_0_0.5.wav', 0.3]]
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def make_prediction(file_name=None, detection_threshold=0.3):
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if file_name is not None:
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audio_file = file_name
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else:
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return "You must provide an input audio file."
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if detection_threshold is not None and detection_threshold != '':
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args['detection_threshold'] = float(detection_threshold)
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# process the file to generate predictions
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results = du.process_file(audio_file, model, params, args, max_duration=max_duration)
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anns = [ann for ann in results['pred_dict']['annotation']]
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clss = [aa['class'] for aa in anns]
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st_time = [aa['start_time'] for aa in anns]
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cls_prob = [aa['class_prob'] for aa in anns]
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det_prob = [aa['det_prob'] for aa in anns]
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data = {'species': clss, 'time': st_time, 'detection_prob': det_prob, 'species_prob': cls_prob}
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df = pd.DataFrame(data=data)
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im = generate_results_image(audio_file, anns)
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return [df, im]
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def generate_results_image(audio_file, anns):
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# load audio
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sampling_rate, audio = au.load_audio_file(audio_file, args['time_expansion_factor'],
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params['target_samp_rate'], params['scale_raw_audio'], max_duration=max_duration)
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duration = audio.shape[0] / sampling_rate
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# generate spec
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spec, spec_viz = au.generate_spectrogram(audio, sampling_rate, params, True, False)
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# create fig
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plt.close('all')
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fig = plt.figure(1, figsize=(spec.shape[1]/100, spec.shape[0]/100), dpi=100, frameon=False)
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spec_duration = au.x_coords_to_time(spec.shape[1], sampling_rate, params['fft_win_length'], params['fft_overlap'])
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viz.create_box_image(spec, fig, anns, 0, spec_duration, spec_duration, params, spec.max()*1.1, False, True)
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plt.ylabel('Freq - kHz')
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plt.xlabel('Time - secs')
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plt.tight_layout()
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# convert fig to image
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fig.canvas.draw()
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data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
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w, h = fig.canvas.get_width_height()
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im = data.reshape((int(h), int(w), -1))
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return im
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descr_txt = "Demo of BatDetect2 deep learning-based bat echolocation call detection. " \
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"<br>This model is only trained on bat species from the UK. If the input " \
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"file is longer than 2 seconds, only the first 2 seconds will be processed." \
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"<br>Check out the paper [here](https://www.biorxiv.org/content/10.1101/2022.12.14.520490v1)."
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gr.Interface(
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fn = make_prediction,
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inputs = [gr.Audio(source="upload", type="filepath", optional=True),
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gr.Dropdown([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])],
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outputs = [df, gr.Image(label="Visualisation")],
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theme = "huggingface",
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title = "BatDetect2 Demo",
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description = descr_txt,
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examples = examples,
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allow_flagging = 'never',
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).launch()
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