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output.py
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output.py
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import pandas as pd
import os
import numpy as np
from math import floor
from math import ceil
import soundfile as sf
import ML.Utils.InvalidParameterError as InvalidParameterError
def convert_to_letters(num):
"""Converts number to string of capital letters in Excel column name
fashion, i.e. 1 -> A, 26 -> Z, 27 -> AA ...."""
string_equiv = ""
while num > 0:
currNum = (num - 1) % 26
num = (num - 1) // 26
string_equiv = chr(currNum + ord("A")) + string_equiv
return string_equiv
def get_file_name(file_path, timestamp, outDir):
file_name_without_ext = os.path.split(file_path)[-1] + "_Timestamp_{}".format(timestamp)
file_name_without_ext = os.path.join(outDir, file_name_without_ext)
file_extension = os.path.splitext(file_path)[-1]
outpath = file_name_without_ext + file_extension
i = 0
while os.path.exists(outpath):
i += 1
outpath = file_name_without_ext + "({})".format(i) + file_extension
with sf.SoundFile(file_path, "r") as f:
sr = f.samplerate
channels = f.channels
start = int(max((timestamp - 2.5) * sr, 0))
end = int(min((timestamp + 2.5) * sr, len(f)))
f.seek(start)
data = f.read(frames=(end - start), always_2d=True)
with sf.SoundFile(outpath, "w", samplerate=sr, channels=channels) as f:
f.write(data)
return outpath
def create_flutter_output(outFrame, outDir):
"""
Auxiliary Function to create output for Flutter Frontend.
"""
map_from_type_to_files_with_secs = {}
for i in outFrame.index:
if outFrame["CallType"][i] not in map_from_type_to_files_with_secs.keys():
map_from_type_to_files_with_secs[outFrame["CallType"][i]] = []
map_from_type_to_files_with_secs[outFrame["CallType"][i]].append(
(outFrame["FullPath"][i], outFrame["SecondDetected"][i])
)
final_output = {
"CallType": [],
"Number": [],
"File1": [],
"File2": [],
"File3": [],
"File4": [],
"File5": [],
}
FileKeys = ["File1", "File2", "File3", "File4", "File5"]
for callType in map_from_type_to_files_with_secs.keys():
final_output["CallType"].append(callType)
final_output["Number"].append(str(len(map_from_type_to_files_with_secs[callType])))
if len(map_from_type_to_files_with_secs[callType]) < 5:
for i in range(len(map_from_type_to_files_with_secs[callType])):
file_name = get_file_name(
*(map_from_type_to_files_with_secs[callType][i]), outDir
)
final_output[FileKeys[i]].append(file_name)
for i in range(len(map_from_type_to_files_with_secs[callType]), 5):
final_output[FileKeys[i]].append(" ")
else:
idx = np.random.choice(
len(map_from_type_to_files_with_secs[callType]), 5, replace=False
)
for i in range(5):
file_name = get_file_name(
*(map_from_type_to_files_with_secs[callType][idx[i]]), outDir
)
final_output[FileKeys[i]].append(file_name)
final_output = pd.DataFrame.from_dict(final_output)
final_output.sort_values(by=["CallType"], ascending=True, inplace=True)
output_path = os.path.join(outDir, "flutter_aux_output.csv")
final_output.to_csv(output_path, encoding="UTF-8", index=False)
def create_output(filenames, clusters, timestamps, output_path, detection_map, outDir):
"""
Saves information about bird clusters and when their audio can be heard into a csv file, with headers Filename, CallType, and SecondDetected.
Parameters
----------
filenames: list of str
Contains the filenames where each cluster was extracted from.
timestamps: list of tuples of int
Contains timestamps described as a pair (start, end) in seconds where bird calls were detected.
clusters: list of int
Contains cluster_ID assigned to each pair of timestamps. It is assumed that timestamps[i] corresponds to clusters[i]
output_path : str
The path where we wish to save the output. Must contain the CSV file name.
detection_map : dict from str to set of int
For each filename, a set of integers contains elements n (with unit seconds)
such that [n, n + 1) has a bird call detected in it.
outDir : str
Path to the output directory where the Audio Samples will be stored.
Returns
-------
None
Raises
------
InvalidParameterError
Raised when one-to-one correspondences between filenames, clusters, and timestamps
cannot be made, or when output_path is a directory or already exists.
"""
if not (len(filenames) == len(clusters) and len(clusters) == len(timestamps)):
raise InvalidParameterError(
"Number of files, clusters, and timestamps are not equal. Cannot match correctly. Program terminating."
)
if os.path.exists(output_path) and os.path.isdir(output_path):
raise InvalidParameterError(
"Please indicate a filename rather than a directory. Program terminating."
)
calls = {"Filename": [], "CallType": [], "SecondDetected": [], "FullPath": []}
filenames_relative = [os.path.split(x)[-1] for x in filenames]
for idx in range(len(timestamps)):
currFilename = filenames[idx]
currFilename_relative = filenames_relative[idx]
currCluster = clusters[idx] + 1
start_time, end_time = floor(timestamps[idx][0]), ceil(timestamps[idx][1])
for currTime in range(start_time, end_time):
if currTime in detection_map[currFilename]:
calls["Filename"].append(currFilename_relative)
calls["SecondDetected"].append(currTime)
calls["CallType"].append(currCluster)
calls["FullPath"].append(currFilename)
output = pd.DataFrame.from_dict(calls)
output.sort_values(
by=["Filename", "CallType", "SecondDetected"], ascending=True, inplace=True
)
output["CallType"] = output["CallType"].apply(lambda num: convert_to_letters(num))
output["CallType"] = "Type " + output["CallType"]
output.drop_duplicates(inplace=True)
create_flutter_output(output, outDir)
del output["FullPath"]
output.to_csv(output_path, encoding="UTF-8", index=False)