Merge branch 'master' of ssh://cloud.tormakristof.eu:2222/birbnetes/cnn-classification-service
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@ -39,9 +39,11 @@ class Classifier(object):
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librosa.display.specshow(librosa.power_to_db(spectogram, ref=numpy.max))
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librosa.display.specshow(librosa.power_to_db(spectogram, ref=numpy.max))
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target_dir = tempfile.mkdtemp()
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target_dir = tempfile.mkdtemp()
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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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# 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_filename[:-4]}.jpg")
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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.savefig(file_name, dpi=400, bbox_inches='tight', pad_inches=0)
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matplotlib.pyplot.close()
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matplotlib.pyplot.close()
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@ -49,7 +51,7 @@ class Classifier(object):
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matplotlib.pyplot.close(fig)
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matplotlib.pyplot.close(fig)
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matplotlib.pyplot.close('all')
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matplotlib.pyplot.close('all')
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return target_dir, file_name
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return target_dir, file_name # Az unknown nélkülivel kell visszatérni
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def _run_predictor(self, directory: str) -> list:
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def _run_predictor(self, directory: str) -> list:
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predict_generator = self.datagen.flow_from_directory(
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predict_generator = self.datagen.flow_from_directory(
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@ -58,7 +58,7 @@ def run_everything(parameters: dict):
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response = {
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response = {
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"tag": tag,
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"tag": tag,
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"probability": 1.0 if results[0] == 'sturnus' else 0.0,
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"probability": 1.0 if results[0] == 'sturnus' else 0.0,
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"model": ...
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"model": "TODO"
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}
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}
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logging.info(f"Classification done!")
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logging.info(f"Classification done!")
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