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Updated gradio app to new api
This commit is contained in:
Santiago Martinez
parent
b8bbfe8ad4
commit
0a7ad18193
136
app.py
136
app.py
@ -3,21 +3,10 @@ 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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import batdetect2.utils.audio_utils as au
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import batdetect2.utils.detector_utils as du
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import batdetect2.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_run_config()
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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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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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@ -28,97 +17,71 @@ df = gr.Dataframe(
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)
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examples = [
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["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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[
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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=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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run_config = {
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**params,
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**args,
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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 = du.process_file(
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audio_file,
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model,
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run_config,
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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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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 = {
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"species": clss,
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"time": st_time,
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"detection_prob": det_prob,
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"species_prob": cls_prob,
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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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df = pd.DataFrame(data=data)
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im = generate_results_image(audio_file, anns)
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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(audio_file, anns):
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# load audio
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sampling_rate, audio = au.load_audio(
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audio_file,
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args["time_expansion_factor"],
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params["target_samp_rate"],
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params["scale_raw_audio"],
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max_duration=max_duration,
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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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duration = audio.shape[0] / sampling_rate
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# generate spec
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spec, spec_viz = au.generate_spectrogram(
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audio, sampling_rate, params, True, False
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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=(spec.shape[1] / 100, spec.shape[0] / 100),
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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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spec_duration = au.x_coords_to_time(
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spec.shape[1],
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sampling_rate,
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params["fft_win_length"],
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params["fft_overlap"],
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)
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viz.create_box_image(
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spec,
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fig,
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anns,
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0,
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spec_duration,
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spec_duration,
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params,
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spec.max() * 1.1,
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False,
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True,
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)
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plt.ylabel("Freq - kHz")
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plt.xlabel("Time - secs")
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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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@ -126,7 +89,6 @@ def generate_results_image(audio_file, anns):
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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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@ -140,7 +102,7 @@ descr_txt = (
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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", optional=True),
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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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