108 lines
4.0 KiB
Python
108 lines
4.0 KiB
Python
#!/usr/bin/env python3
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from typing import Tuple
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import tempfile
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import os
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import os.path
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import shutil
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import opentracing
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import librosa
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import librosa.display
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import numpy
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import matplotlib.pyplot
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from tensorflow.keras.models import model_from_json
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from tensorflow.keras import optimizers
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from keras_preprocessing.image import ImageDataGenerator
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class Classifier(object):
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def __init__(self, model_filename: str, weights_filename: str):
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with opentracing.tracer.start_active_span('classifier.loadModel'):
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with open(model_filename, 'r') as f:
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self.loaded_model = model_from_json(f.read())
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with opentracing.tracer.start_active_span('classifier.loadWeights'):
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self.loaded_model.load_weights(weights_filename)
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with opentracing.tracer.start_active_span('classifier.loadDatagen'):
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self.datagen = ImageDataGenerator(rescale=1. / 255., validation_split=0.25)
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with opentracing.tracer.start_active_span('classifier.compile'):
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self.loaded_model.compile(optimizers.RMSprop(lr=0.0005, decay=1e-6), loss="categorical_crossentropy",
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metrics=["accuracy"])
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self.loaded_model.summary() # prints to stdout??
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@staticmethod
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def create_spectrogram(wav_filename: str) -> Tuple[str, str]:
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with opentracing.tracer.start_active_span('classifier.librosa.load'):
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clip, sample_rate = librosa.load(wav_filename, sr=None)
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with opentracing.tracer.start_active_span('classifier.Plot'):
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matplotlib.pyplot.interactive(False)
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fig = matplotlib.pyplot.figure(figsize=[0.72, 0.72])
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ax = fig.add_subplot(111)
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ax.axes.get_xaxis().set_visible(False)
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ax.axes.get_yaxis().set_visible(False)
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ax.set_frame_on(False)
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spectogram = librosa.feature.melspectrogram(y=clip, sr=sample_rate)
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librosa.display.specshow(librosa.power_to_db(spectogram, ref=numpy.max))
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target_dir = tempfile.mkdtemp(dir="/dev/shm")
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wav_basename = os.path.basename(wav_filename)
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# Change extension to jpg... mert 110% biztos vagyok benne hogy a keras nem bírná beolvasni máshogy
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file_name = os.path.join(target_dir, "unknown", f"{wav_basename[:-4]}.jpg")
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os.mkdir(os.path.join(target_dir, "unknown"))
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matplotlib.pyplot.savefig(file_name, dpi=400, bbox_inches='tight', pad_inches=0)
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matplotlib.pyplot.close()
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fig.clf()
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matplotlib.pyplot.close(fig)
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matplotlib.pyplot.close('all')
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return target_dir, file_name # Az unknown nélkülivel kell visszatérni
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def _run_predictor(self, directory: str) -> Tuple[str, dict]:
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predict_generator = self.datagen.flow_from_directory(
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directory=directory,
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batch_size=128,
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seed=42,
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shuffle=False,
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class_mode="categorical",
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target_size=(64, 64))
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prediction = self.loaded_model.predict_generator(predict_generator, steps=1)
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predicted_class_indices = numpy.argmax(prediction, axis=1)
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labels = {
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'anser': 0,
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'columba': 1,
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'hirundo': 2,
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'passer': 3,
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'sturnus': 4,
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'turdus': 5,
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'upupa': 6
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}
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labels = dict((v, k) for k, v in labels.items())
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labeled_predictions = {labels[i]: float(p) for i, p in enumerate(prediction[0])}
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predicted_class_name = [labels[k] for k in predicted_class_indices][0] # eh?
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return predicted_class_name, labeled_predictions
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def predict(self, wav_filename: str) -> Tuple[str, dict]:
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with opentracing.tracer.start_active_span('classifier.createSpectrogram'):
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directory, _ = self.create_spectrogram(wav_filename)
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with opentracing.tracer.start_active_span('classifier.runPredictor'):
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result = self._run_predictor(directory)
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shutil.rmtree(directory) # The image is no longer needed
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return result
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