correction dataloader and get time freq offset

get_spectrogram.py

@@ -53,8 +53,8 @@ def main(data, arguments):
                 name = os.path.join(arguments.directory, folder, f'{file}_{offset}')
             
             utils.create_spectrogram(
-                sig, arguments.directory, name, arguments.cmap, window_size=int(arguments.window),
-                overlap=arguments.hop, vmin=arguments.vmin)
+                sig, arguments.directory, name, arguments.cmap, arguments.vmin, window_size=int(arguments.window),
+                overlap=arguments.hop)
             
         except Exception as error:
             folder = 'spectrograms'

utils.py

@@ -87,15 +87,17 @@ def signal_processing(sig, rf, fs, high=None, low=None):
     return sig
 
 
-def create_spectrogram(sig, directory, names, cmap, window_size=1024, overlap=.5, minimum=True):
+def create_spectrogram(sig, directory, names, cmap, minimum, window_size=1024, overlap=.5):
     """
     Create a spectrogram STFT with hanning window and save it into a directory
 
     :param sig (array): Signal to process.
+    :param directory (str): Path to save the spectrogram.
+    :param names (str): Name of the final spectrogram.
+    :param cmap (str): Name of the colormap for matplotlib.
+    :param minimum (str): If True minimum of spectrogram imshow will be stft.mean(), else stft.min()
     :param window_size (int): Number of sample / STFT window.
     :param overlap (float): Ratio of overlapping samples between each window (default 50%).
-    :param directory (str): Path to save the spectrogram.
-    :param filename (str): Name of the final spectrogram.
     """
     if overlap >= 1:
         print(f'You put a hop value over 1. This has been corrected to have {overlap} as overlap size between window')
@@ -107,7 +109,7 @@ def create_spectrogram(sig, directory, names, cmap, window_size=1024, overlap=.5
                         hop_length=int(overlap_size), window='hann')  # Compute the STFT
     stft = np.log10(np.abs(stft))  # Adapt the Complex-valued matrix
     fig = plt.figure()
-    if minimum:
+    if minimum == 'True':
         vmin = stft.mean()
     else:
         vmin = stft.min()
@@ -395,7 +397,7 @@ def detection2time_freq(annotations_folder, duration, outdir, rf, names, wav, ra
         names = np.arange(0, total+1).tolist()
     df['species'] = df['class'].apply(lambda x: names[int(x)])
 
-    df['pos'] = (df['x'] * duration) + df['class'].astype(int)
+    df['pos'] = (df['x'] * duration) + df['offset'].astype(int)
     df['Low Freq (Hz)'] = (1 - df['y']) * (rf / 2) - (df['h'] * (rf / 2)) / 2
     df['High Freq (Hz)'] = (1 - df['y']) * (rf / 2) + (df['h'] * (rf / 2)) / 2
     df['Begin Time (s)'] = df['pos'] - (df['w'] * duration) / 2

yolov5/utils/dataloaders.py

@@ -239,8 +239,8 @@ class LoadScreenshots:
         return str(self.screen), im, im0, None, s  # screen, img, original img, im0s, s
 
 class LoadSpectros:
-    def __init__(self, folder, sampleDur, rf, window, hop, low, high, cmap, img_size, stride=32, auto=True, minimum=True):
-        self.folder, self.sampleDur, self.rf, self.window, self.hop, self.low, self.high, self.cmap, self.img_size, self.stride, self.auto, self.minimum = folder, sampleDur, rf, window, hop, low, high, cmap, img_size, stride, auto, minimum
+    def __init__(self, folder, sampleDur, rf, window, hop, low, high, cmap, minimum, img_size, stride=32, auto=True):
+        self.folder, self.sampleDur, self.rf, self.window, self.hop, self.low, self.high, self.cmap, self.minimum, self.img_size, self.stride, self.auto = folder, sampleDur, rf, window, hop, low, high, cmap, minimum, img_size, stride, auto
         self.files = os.listdir(folder)
         self.mode = 'image'
         self.samples = []
@@ -290,7 +290,7 @@ class LoadSpectros:
         stft = librosa.stft(sig, n_fft=self.window,
                 hop_length=hop, window='hann')  # Compute the STFT
         stft = np.log10(np.abs(stft))
-        if minimum:
+        if self.minimum == 'True':
             vmin = stft.mean()
         else:
             vmin = stft.min()