def postprocess(self, y):
if self.labeled_segments:
y, coordinates = y
else:
coordinates = []
if self.type == "auto":
if isinstance(y, np.ndarray):
dtype = "numpy"
elif isinstance(y, PIL.Image.Image):
dtype = "pil"
elif isinstance(y, str):
dtype = "file"
elif isinstance(y, ModuleType):
dtype = "plot"
else:
raise ValueError("Unknown type. Please choose from: 'numpy', 'pil', 'file', 'plot'.")
else:
dtype = self.type
if dtype in ["numpy", "pil"]:
if dtype == "pil":
y = np.array(y)
out_y = processing_utils.encode_array_to_base64(y)
elif dtype == "file":
out_y = processing_utils.encode_file_to_base64(y)
elif dtype == "plot":
out_y = processing_utils.encode_plot_to_base64(y)
else:
raise ValueError("Unknown type: " + dtype + ". Please choose from: 'numpy', 'pil', 'file', 'plot'.")
return out_y, coordinates
def postprocess(self, y):
if self.type in ["numpy", "file", "auto"]:
if self.type == "numpy" or (self.type == "auto" and isinstance(y, tuple)):
file = tempfile.NamedTemporaryFile()
scipy.io.wavfile.write(file, y[0], y[1])
y = file.name
return processing_utils.encode_file_to_base64(y, type="audio", ext="wav")
else:
raise ValueError("Unknown type: " + self.type + ". Please choose from: 'numpy', 'file'.")
def postprocess(self, y):
if self.type in ["numpy", "pil"]:
if self.type == "pil":
y = np.array(y)
return processing_utils.encode_array_to_base64(y)
elif self.type == "file":
return processing_utils.encode_file_to_base64(y)
elif self.type == "plot":
return processing_utils.encode_plot_to_base64(y)
else:
raise ValueError("Unknown type: " + self.type + ". Please choose from: 'numpy', 'pil', 'file', 'plot'.")
def postprocess(self, y):
"""
Parameters:
y (str): file path
Returns:
(Dict[name: str, size: number, data: str]): JSON object with key 'name' for filename, 'data' for base64 url, and 'size' for filesize in bytes
"""
return {
"name": os.path.basename(y),
"size": os.path.getsize(y),
"data": processing_utils.encode_file_to_base64(y)
}
def postprocess(self, y):
"""
Parameters:
y (str): path to video
Returns:
(str): base64 url data
"""
returned_format = y.split(".")[-1].lower()
if self.type is not None and returned_format != self.type:
output_file_name = y[0:y.rindex(".") + 1] + self.type
ff = FFmpeg(inputs={y: None}, outputs={output_file_name: None})
ff.run()
y = output_file_name
return {
"name": os.path.basename(y),
"data": processing_utils.encode_file_to_base64(y)
}
def get_interpretation_neighbors(self, x):
file_obj = processing_utils.decode_base64_to_file(x)
x = scipy.io.wavfile.read(file_obj.name)
sample_rate, data = x
leave_one_out_sets = []
duration = data.shape[0]
boundaries = np.linspace(0, duration, self.interpretation_segments + 1).tolist()
boundaries = [round(boundary) for boundary in boundaries]
for index in range(len(boundaries) - 1):
leave_one_out_data = np.copy(data)
start, stop = boundaries[index], boundaries[index + 1]
leave_one_out_data[start:stop] = 0
file = tempfile.NamedTemporaryFile()
scipy.io.wavfile.write(file, sample_rate, leave_one_out_data)
out_data = processing_utils.encode_file_to_base64(file.name, type="audio", ext="wav")
leave_one_out_sets.append(out_data)
return leave_one_out_sets, {}, True
def postprocess(self, y):
return {
"name": os.path.basename(y),
"size": os.path.getsize(y),
"data": processing_utils.encode_file_to_base64(y, header=False)
}
def preprocess_example(self, x):
return processing_utils.encode_file_to_base64(x, type="audio")
def process_example(self, example):
return processing_utils.encode_file_to_base64(example)
def postprocess(self, y):
return processing_utils.encode_file_to_base64(y, type="video")
def process_example(self, example):
if os.path.exists(example):
return processing_utils.encode_file_to_base64(example)
else:
return example
def restore_flagged_file(self, dir, file, encryption_key):
data = processing_utils.encode_file_to_base64(
os.path.join(dir, file), encryption_key=encryption_key)
return {"name": file, "data": data}
