Merge pull request #150 from UCSD-E4E/ctrl_c_exception_handling

Ctrl c exception handling

.gitignore

@@ -1,31 +1,30 @@
 .ipynb_checkpoints
+**/__pycache__/
 PyHa/__pycache__
 PyHa/microfaune_package/microfaune/__pycache__
+PyHa/microfaune_package/microfaune/__pycache__/*
 PyHa/tweetynet_package/tweetynet/__pycache__
-TEST/*
-/mixed_bird/*
-TEST/ScreamingPiha1.wav
-TEST/ScreamingPiha1.wav
 PyHa/tweetynet_package/tweetynet/__pycache__/*
-PyHa/tweetynet_package/tweetynet/__pycache__/
-outputs/result.csv
+PyHa/birdnet_lite/__pycache__/*
 PyHa/birdnet_lite/__pycache__/analyze.cpython-37.pyc
-*.wav
+
+
+outputs/*
+/mixed_bird/*
+TEST/*
+
+*.csv
+*.flac
+*.ipynb
+*.mp3
 *.pyc
+*.wav
+
 PyHa_Model_Comparison.ipynb
 PyHa_Testing-Copy1.ipynb
 PyHa_Testing.ipynb
+mixed_bird_manual.csv
 
 
-PyHa/microfaune_package/microfaune/__pycache__/*
-PyHa/tweetynet_package/tweetynet/__pycache__/*
-PyHa/birdnet_lite/__pycache__/*
-mixed_bird_manual.csv
-outputs/result.csv
-*.mp3
-*.flac
-outputs/*
-*.ipynb
 !PyHa_Tutorial.ipynb
-*.csv
-!ScreamingPiha_Manual_Labels.csv
+!ScreamingPiha_Manual_Labels.csv

PyHa/IsoAutio.py

@@ -11,6 +11,7 @@
 import numpy as np
 from math import ceil
 from copy import deepcopy
+from sys import exit
 
 
 def checkVerbose(
@@ -931,6 +932,8 @@ def generate_automated_labels_microfaune(
         try:
             SIGNAL, SAMPLE_RATE = librosa.load(audio_dir + audio_file, sr=None, mono=True)
             SIGNAL = SIGNAL * 32768
+        except KeyboardInterrupt:
+            exit("Keyboard interrupt")
         except BaseException:
             checkVerbose("Failed to load" + audio_file, isolation_parameters)
             continue
@@ -944,11 +947,13 @@ def generate_automated_labels_microfaune(
                 SIGNAL = scipy_signal.resample(
                     SIGNAL, int(len(SIGNAL) * rate_ratio))
                 SAMPLE_RATE = normalized_sample_rate
+        except KeyboardInterrupt:
+            exit("Keyboard interrupt")
         except:
             checkVerbose("Failed to Downsample" + audio_file, isolation_parameters)
             # resample produces unreadable float32 array so convert back
             # SIGNAL = np.asarray(SIGNAL, dtype=np.int16)
-
+            
         # print(SIGNAL.shape)
         # convert stereo to mono if needed
         # Might want to compare to just taking the first set of data.
@@ -958,10 +963,13 @@ def generate_automated_labels_microfaune(
         try:
             microfaune_features = detector.compute_features([SIGNAL])
             global_score, local_scores = detector.predict(microfaune_features)
+        except KeyboardInterrupt:
+            exit("Keyboard interrupt")
         except BaseException as e:
             checkVerbose("Error in detection, skipping" + audio_file, isolation_parameters)
             continue
-
+
+
         # get duration of clip
         duration = len(SIGNAL) / SAMPLE_RATE
 
@@ -982,6 +990,8 @@ def generate_automated_labels_microfaune(
                 annotations = new_entry
             else:
                 annotations = annotations.append(new_entry)
+        except KeyboardInterrupt:
+            exit("Keyboard interrupt")
         except BaseException as e:
 
             checkVerbose("Error in isolating bird calls from" + audio_file, isolation_parameters)
@@ -1058,22 +1068,24 @@ def generate_automated_labels_tweetynet(
         try:
             SIGNAL, SAMPLE_RATE = librosa.load(audio_dir + audio_file, sr=None, mono=True)
             SIGNAL = SIGNAL * 32768
+        except KeyboardInterrupt:
+            exit("Keyboard interrupt")
         except BaseException:
             checkVerbose("Failed to load" + audio_file, isolation_parameters)
             continue
-
-        # downsample the audio if the sample rate isn't 44.1 kHz
-        # Force everything into the human hearing range.
-        # May consider reworking this function so that it upsamples as well
+
+        # Resample the audio if it isn't the normalized sample rate
         try:
             if SAMPLE_RATE != normalized_sample_rate:
                 rate_ratio = normalized_sample_rate / SAMPLE_RATE
                 SIGNAL = scipy_signal.resample(
                     SIGNAL, int(len(SIGNAL) * rate_ratio))
                 SAMPLE_RATE = normalized_sample_rate
+        except KeyboardInterrupt:
+            exit("Keyboard interrupt")
         except:
             checkVerbose("Failed to Downsample" + audio_file, isolation_parameters)
-
+            
         # convert stereo to mono if needed
         # Might want to compare to just taking the first set of data.
         if len(SIGNAL.shape) == 2:
@@ -1082,10 +1094,12 @@ def generate_automated_labels_tweetynet(
         try:
             tweetynet_features = compute_features([SIGNAL])
             predictions, local_scores = detector.predict(tweetynet_features, model_weights=weight_path, norm=normalize_local_scores)
+        except KeyboardInterrupt:
+            exit("Keyboard interrupt")
         except BaseException as e:
             checkVerbose("Error in detection, skipping" + audio_file, isolation_parameters)
             continue
-
+           
         try:
             # Running moment to moment algorithm and appending to a master
             # dataframe. 
@@ -1112,6 +1126,8 @@ def generate_automated_labels_tweetynet(
                 annotations = new_entry
             else:
                 annotations = annotations.append(new_entry)
+        except KeyboardInterrupt:
+            exit("Keyboard interrupt")
         except BaseException as e:
 
             checkVerbose("Error in isolating bird calls from" + audio_file, isolation_parameters)

PyHa/birdnet_lite/analyze.py

@@ -249,6 +249,8 @@ def analyze(audio_path, output_path = None, lat=-1, lon=-1, week=-1, overlap=0.0
                     os.makedirs(output_directory)
                 output_file = os.path.join(output_directory, 'result.csv')
             df = writeResultsToDf(df, detections, min_conf, output_metadata)
+        except KeyboardInterrupt:
+            exit("Keyboard interrupt")
         except:
              print("Error processing file: {}".format(datafile))
     elif len(dataset) > 0:
@@ -263,6 +265,8 @@ def analyze(audio_path, output_path = None, lat=-1, lon=-1, week=-1, overlap=0.0
                 output_metadata['IN FILE'] = os.path.split(datafile)[1]
                 output_metadata['CLIP LENGTH'] = clip_length
                 df = writeResultsToDf(df, detections, min_conf, output_metadata)
+            except KeyboardInterrupt:
+                exit("Keyboard interrupt")
             except:
                 print("Error in processing file: {}".format(datafile)) 
         if output_path is None: