mirror of
https://github.com/macaodha/batdetect2.git
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115 lines
3.0 KiB
Python
115 lines
3.0 KiB
Python
import gradio as gr
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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from batdetect2 import api, plot
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MAX_DURATION = 2
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DETECTION_THRESHOLD = 0.3
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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 = [
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[
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"example_data/audio/20170701_213954-MYOMYS-LR_0_0.5.wav",
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DETECTION_THRESHOLD,
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],
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[
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"example_data/audio/20180530_213516-EPTSER-LR_0_0.5.wav",
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DETECTION_THRESHOLD,
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],
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[
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"example_data/audio/20180627_215323-RHIFER-LR_0_0.5.wav",
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DETECTION_THRESHOLD,
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],
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]
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def make_prediction(file_name, detection_threshold=DETECTION_THRESHOLD):
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# configure the model run
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run_config = api.get_config(
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detection_threshold=detection_threshold,
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max_duration=MAX_DURATION,
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)
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# process the file to generate predictions
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results = api.process_file(file_name, config=run_config)
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# extract the detections
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detections = results["pred_dict"]["annotation"]
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# create a dataframe of the predictions
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df = pd.DataFrame(
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[
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{
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"species": pred["class"],
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"time": pred["start_time"],
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"detection_prob": pred["class_prob"],
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"species_prob": pred["class_prob"],
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}
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for pred in detections
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]
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)
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im = generate_results_image(file_name, detections, run_config)
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return [df, im]
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def generate_results_image(file_name, detections, config):
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audio = api.load_audio(
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file_name,
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max_duration=config["max_duration"],
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time_exp_fact=config["time_expansion"],
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target_samp_rate=config["target_samp_rate"],
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)
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spec = api.generate_spectrogram(audio, config=config)
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# create fig
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plt.close("all")
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fig = plt.figure(
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1,
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figsize=(15, 4),
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dpi=100,
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frameon=False,
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)
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ax = fig.add_subplot(111)
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plot.spectrogram_with_detections(spec, detections, ax=ax)
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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 = (
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"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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)
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gr.Interface(
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fn=make_prediction,
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inputs=[
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gr.Audio(source="upload", type="filepath"),
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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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],
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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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