correction bug

get_spectrogram.py

@@ -14,6 +14,8 @@ args = parser.parse_args()
 
 path_to_data = args.path_to_data
 folder = 'Spectrogramme/'
+DURATION = 8
+NB_IMG_PER_REC = 30
 
 count = 0
 
@@ -22,14 +24,14 @@ df = pd.read_csv(str(args.file+'.csv'))
 for i in tqdm(df.groupby('dir')):  #la colonne dir correspond au path
    count = 0
 
-   for j in range (30): #30*8 secondes - 30*2 secondes (overlap) = 180 secondes affichées sur 30 images : n'affiche que les 3 premières minutes d'un enregistrement
+   for j in range (NB_IMG_PER_REC): #30*8 secondes - 30*2 secondes (overlap) = 180 secondes affichées sur 30 images : n'affiche que les 3 premières minutes d'un enregistrement
 
         filename = str(path_to_data+i[0])
         if count ==0:
-            duration = 8
+            duration = DURATION
             offset = 0 
         else :
-            duration = 8
+            duration = DURATION
             ffset = offset + 6
         try:
             y, sr = librosa.load(filename, offset = offset, duration = duration)
@@ -62,7 +64,3 @@ for i in tqdm(df.groupby('dir')):  #la colonne dir correspond au path
             plt.savefig(str(folder+name+'.jpg'))
 
         count+=1
-
-      
-
-

get_train_annot_YOLO.py

@@ -37,6 +37,9 @@ df.rename(columns={'min_frequency':'min_freq'},inplace=True)
 df.rename(columns={'max_frequency':'max_freq'},inplace=True)
 df.rename(columns={'avg_frequency':'midl_y'},inplace=True)
 
+df['max_freq'].fillna(9000,inplace = True)
+df['min_freq'].fillna(1000,inplace = True)
+
 df['d_annot'] = df.stop - df.start
 df['midl'] = (df.stop + df.start)/2
 df['Path'] = ''

get_train_val_YOLO.py

@@ -10,7 +10,7 @@ import ipdb
 parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter, description='RATIO')
 parser.add_argument('-r','-ratio', type=float, default=0.7,help = 'Train Ratio (val = 1 - ratio)')
 parser.add_argument('-p','--path_to_data', type=str, help = 'Path of the folder that contain the recodings',required=True)
-parser.add_argument('-d','--direction', type=str, help = 'Direction to export the spectrogram and the .txt files',required=True)
+parser.add_argument('-d','--direction', type=str, help = 'Direction of the folder to export the spectrogram and the .txt files',required=True)
 args = parser.parse_args()
 
 path = str(args.path_to_txt)
@@ -31,6 +31,9 @@ for i in tqdm(os.listdir(path)):
 	tab= pd.DataFrame(columns = ['file','espece','x','y','w','h'])
 	tab2 = pd.DataFrame(columns = ['file','espece','x','y','w','h'])
 
+	if table.columns[1] == 'Unnamed: 1': #remove the annotation file that contains error
+		continue
+
 	if len (table) == 0:
 		for j in table.columns:
 			l.append(j)
@@ -104,7 +107,7 @@ for i,row in train.iterrows():
 	shutil.copy2(str(path+'labels/'+row.file+'.txt'),str(direction+'labels/train/'+row.file+'.txt'))
 	shutil.copy2(str(path+'../images/all/'+row.file+'.jpg'),str(direction+'images/train/'+row.file+'.jpg'))
 
-with open(str(direction+'custom_data.yaml','w') as f:
+with open(str(direction+'custom_data.yaml'),'w') as f:
 	f.write('train : '+direction+'images/train\n')
 	f.write('test : '+direction+'images/val\n')
 	f.write('nc : ' + len(tab)+'\n')