cleaned up code, removed unused imports, etc

Author: stevenenriquez

snaw-backend/acousticIndices.py

@@ -6,7 +6,9 @@
 from scipy.stats import itemfreq
 import os
 import traceback
-DEBUG_FLAG = True
+
+DEBUG_FLAG = False
+PREDICTION_VERBOSE = False
 
 class AcousticIndices(object):
 
@@ -80,12 +82,6 @@ def _get_segments_above_noise(self):
         non_zero = envelope[np.nonzero(envelope)]
         data_log = 20*np.log10(non_zero)
 
-        # print(data_log.shape)
-        # kernel = 1/256.0*np.ones(156)
-        #
-        # data_log = np.convolve(data_log,kernel)
-        # print(data_log.shape)
-
         ind = np.where(data_log > threshold)
 
         check_array = np.zeros(self.envelope.size)
@@ -183,14 +179,6 @@ def _get_spectral_entropy(self):
 
         self.spectral_entropy = AudioProcessing.get_entropy(pmf)/np.log2(N)
 
-
-        # stft = self.stft[np.nonzero(self.stft)]
-        # stft = AudioProcessing.rescale(stft,(0,1))
-        # pdf,bins = AudioProcessing.get_histogram(stft,bins = np.arange(0,1 + 1.0/1000,1.0/1000))
-        # pdf = pdf[np.nonzero(pdf)]
-        # spectral_entropy = AudioProcessing.get_entropy(pdf)/np.log2(self.data.size)
-        # self.spectral_entropy = spectral_entropy
-
     def get_spectral_entropy(self):
         return self.spectral_entropy
 
@@ -248,8 +236,6 @@ def get_acoustic_events_average_duration(self):
         average_segments_duration = np.mean(segments_duration)
         average_segments_duration_ms = (average_segments_duration/float(self.fs))*1000.0
 
-        # print(average_segments_duration_ms)
-
         average_segments_duration_ms = AcousticIndices.get_normalized_value(average_segments_duration_ms,(0,3000))
 
         return average_segments_duration_ms
@@ -297,8 +283,6 @@ def get_spectral_average_variance_entropy(self):
         N = 2**10
 
         stft = np.copy(self.stft)
-        # stft = AudioProcessing.rescale(self.stft,(0,N))
-        # stft = stft.astype(np.uint16)
 
         for segment in self.segments:
             start = int(float(segment[0])/self.n_fft)
@@ -353,17 +337,6 @@ def get_soundscape_indices(self,total_bins = 7):
         for i in range(0,max_bin,frequency_interval):
             bin = psd[i:i + frequency_interval, :]
             biophony_levels.append(np.sum(bin) / psd_sum)
-        # # while i < total_bins*frequency_interval :
-        #
-        #     if i + frequency_interval > total_bins*frequency_interval:
-        #         bin = psd[i:, :]
-        #     else:
-        #
-        #         bin = psd[i:i + frequency_interval, :]
-
-
-
-            # i = i + frequency_interval
 
         a = biophony_levels[0]
         b = np.max(biophony_levels[1:])
@@ -701,7 +674,8 @@ def getAcousticIndices(audiofile):
         acoustic_indices = acousticIndices.get_acoustic_indices()
 
         acoustic_indices = list(map(lambda x: round(x, 4), acoustic_indices))
-        print(acoustic_indices)
+        if( PREDICTION_VERBOSE ):
+            print(acoustic_indices)
 
         acoustic_headers = acousticIndices.get_acoustic_indices_headers()
         acoustic_descs = acousticIndices.get_acoustic_indices_descs()

snaw-backend/analysis_driver.py

@@ -1,10 +1,4 @@
 from keras.models import load_model
-import keras
-from keras.models import Sequential
-from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D, LSTM, Activation
-from keras.utils import to_categorical
-import wandb
-from wandb.keras import WandbCallback
 import matplotlib.pyplot as plt
 import sklearn.metrics as metrics
 import librosa
@@ -16,7 +10,12 @@
 from get_spectrogram import runScript as run_spectrogram
 from acousticIndices import getAcousticIndices as run_indices
 
-DEBUG_FLAG = True
+DEBUG_FLAG = False
+
+# Path to each CNN model (Example format: 'model\\anthro\\model.h5)
+ANTHRO_CNN_MODEL = 'model\\anthro\\ant_cnn_model.h5'
+BIO_CNN_MODEL = 'model\\bio\\bio_cnn_model.h5'
+GEO_CNN_MODEL = 'model\\geo\\geo_cnn_model.h5'
 
 def run_driver(personalID) :
     if DEBUG_FLAG : print("[WORKING] Attempting to run analysis driver - analysis_driver.py")
@@ -27,9 +26,9 @@ def run_driver(personalID) :
     fileCount = 0
 
     if DEBUG_FLAG : print("[WORKING] Loading CNN Models..")
-    all_models = [ load_model('model\\anthro\\ant_cnn_model.h5'),
-                   load_model('model\\bio\\bio_cnn_model.h5'),
-                   load_model('model\\geo\\geo_cnn_model.h5') ]
+    all_models = [ load_model(ANTHRO_CNN_MODEL),
+                   load_model(BIO_CNN_MODEL),
+                   load_model(GEO_CNN_MODEL) ]
     if DEBUG_FLAG : print("[SUCCESS] Loaded CNN Models..")
 
     try:

snaw-backend/api.py

@@ -22,7 +22,7 @@
 app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
 app.secret_key = 'secret key'
 app.config['SESSION_TYPE'] = 'filesystem'
-DEBUG_FLAG = True
+DEBUG_FLAG = False
 '''
 ###------------------------------------------------------###
 App Routing: '/'
@@ -176,7 +176,6 @@ def closeUserFolder():
 
 
 
-#print('Starting Flask!')
 app.run(debug=True)
 
 

snaw-backend/classification.py

@@ -16,22 +16,11 @@ def classify_file( audiofile, model, model_type, model_color ):
         frames = f.getnframes()
         rate = f.getframerate()
         duration = frames / float(rate)
-        print(duration)
-
-    # only used for console output, can be removed to speed up runtime
-    #   function will throw error because of true flag at end,
-    #   console log is still displayed in spite of
-    #try:
-    #    aS.mtFileClassification(audiofile, model,"svm", True)
-    #except TypeError:
-    #    print("TypeError")
+        if( DEBUG_FLAG ):
+            print(duration)
 
     # pulls all the data given from the classification function
     [flagsInd, classesAll, acc, CM] = aS.mtFileClassification(audiofile, model,"svm")
-    # print( flagsInd )
-    # print( classesAll )
-    # print( acc )
-    # print( CM )
 
     flag_len = len(flagsInd) # amount of segments made
     segment = duration / flag_len # length of each time segment
@@ -56,17 +45,14 @@ def classify_file( audiofile, model, model_type, model_color ):
     return classify_dict
 
 def anthro_model():
-    # for filename in os.listdir('model/anthro'):
     modelfile = 'model/anthro/svmAnthroClassModel'
     return modelfile
 
 def bio_model():
-   # for filename in os.listdir('model/bio'):
    modelfile = 'model/bio/svmBioClassModel'
    return modelfile
 
 def geo_model():
-    #for filename in os.listdir('model/geo'):
     modelfile = 'model/geo/svmGeoClassModel'
     return modelfile
 
@@ -75,8 +61,8 @@ def runScript():
     if( DEBUG_FLAG ):
         print("[WORKING] Attempting to run SVM classification calculator - classification_svm.py")
     # Create dictionary for storing return information
-    # Create a counter for files
     finalResult = {}
+    # Create a counter for files
     fileCount = 0
 
     try:

snaw-backend/classification_cnn.py

@@ -1,10 +1,3 @@
-from keras.models import load_model
-import keras
-from keras.models import Sequential
-from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D, LSTM, Activation
-from keras.utils import to_categorical
-import wandb
-from wandb.keras import WandbCallback
 import matplotlib.pyplot as plt
 import sklearn.metrics as metrics
 import librosa
@@ -13,12 +6,9 @@
 import os
 import json
 
-DEBUG_FLAG = True
+DEBUG_FLAG = False
 PREDICTION_VERBOSE = False
 
-def get_category( label ) :
-    switch
-
 def get_category( label ):
     return {
         "AAT" : "Air  Traffic",
@@ -44,8 +34,6 @@ def get_category( label ):
     }.get(label, "Label Missing")
 
 def classify_file( audio_file, all_models ) :
-    # load the models
-
 
     all_labels = [ ["AAT", "AHV", "AMA", "ART", "ASI", "AVH", "AVT"],
                    ["BRA", "BAM", "BBI", "BMA", "BIN"],
@@ -64,9 +52,9 @@ def classify_file( audio_file, all_models ) :
 
     ## Running the models
 
-    n_mfcc = 128 # bucket size !!SUBJECT TO CHANGE!!
-    max_len = 32 # max_len size !!SUBJECT TO CHANGE!!
-    channels = 1 # channels !!SUBJECT TO CHANGE!!
+    n_mfcc = 128 # bucket size
+    max_len = 32 # max_len size
+    channels = 1 # channels
 
     # convert file to wav2mfcc
     # Mel-frequency cepstral coefficients

snaw-backend/get_spectrogram.py

@@ -119,8 +119,9 @@ def plotstft(data, audiopath, binsize=2**10, plotpath=None, colormap="jet"):
             # get prediction value from label, divide by 100 to get percent,
             #   multiply by negative and add .33 to get reasonable opacity level
             alpha_val = int(cat[-3:-1]) / 100 * -1 + .5
-            print(alpha_val)
-            print(int(cat[-3:-1]))
+            if( DEBUG_FLAG ):
+                print(alpha_val)
+                print(int(cat[-3:-1]))
             plt.fill([x,x+x_size,x+x_size,x], [0,0,freqbins,freqbins], 'r', alpha=alpha_val)
             plt.text(x+5, 5, cat[-3:], rotation=90, color="white")
             plt.text(x+5, 40, cat[:-4], rotation=90, color="white")

snaw-backend/output.py

@@ -38,6 +38,7 @@ const customtheme = createMuiTheme({
     }
 })
 
+// Configure toast (notifications on top-right corner of web application)
 toast.configure()
 
 class App extends React.Component {
@@ -162,7 +163,6 @@ class App extends React.Component {
      })
   }
 
-
   getFileByteSize(size){
       let result = 0;
       if(size >= 1024 && size < 1048576){
@@ -172,21 +172,17 @@ class App extends React.Component {
       else if(size >= 1048576 && size < 1073741824){
           result = Number.parseFloat(String(size/1048576)).toFixed(2);
           return String(result + " MB")
-
       }
       else if(size >= 1073741824){
           result = Number.parseFloat(String(size/1073741824)).toFixed(2);
           return String(result + " GB")
-
       }
       else{
           return 0;
       }
   }
 
   removeFile(file, filename){
-
-      console.log(file);
       let currentFiles = this.state.selectedFile;
       let newFileList = [];
       console.log("REMOVING FILE....");

snaw-frontend/src/App.js

@@ -121,12 +121,12 @@ function Information() {
                 </MuiThemeProvider>
             </Container>
             <footer>
-                <Container>
-                    <br/><br/>
-                    <Typography variant='subtitle1' style={{marginLeft: 'auto', marginRight: 'auto', marginTop: '50px'}}>Created by NAU Capstone Team IntelliChirp · <a href="https://www.ceias.nau.edu/capstone/projects/CS/2020/IntelliChirp-S20/">Visit project website</a> · <a href="https://soundscapes2landscapes.org/">Visit our sponsor</a></Typography>
-                    <br/>
-                </Container>
-            </footer>
+              <Container>
+                <br/><br/>
+                <Typography variant='subtitle1' style={{marginLeft: 'auto', marginRight: 'auto'}}>Created by NAU Capstone Team IntelliChirp · <a href="https://www.ceias.nau.edu/capstone/projects/CS/2020/IntelliChirp-S20/">Visit project website</a> · <a href="https://soundscapes2landscapes.org/">Visit our sponsor</a></Typography>
+                <br/>
+              </Container>
+          </footer>
         </div>
     )
 }