add script

get_yolo_detection.py

+import pandas as pd
+import os
+import ipdb
+from tqdm import tqdm
+import argparse
+from datetime import date
+
+parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter, description='TODO')
+parser.add_argument('-p','--path_to_data', type=str, help = 'Path of the folder that contain the .txt files',required=True)
+parser.add_argument('-d','--direction', type=str, help = 'Directory to wich the dataframe will be stored',required=True)
+args = parser.parse_args()
+
+annots = str(args.path_to_data)
+
+today = date.today()
+out_file = str('YOLO_detection_'+str('_'+today.day+'_'+today.month))
+
+outdir = str(args.direction)
+
+df = pd.DataFrame(columns = ['file','idx','espece','x','y','w','h','conf'])
+for i in tqdm(os.listdir(annots)):
+	if i == 'list_results_YOLO.csv':
+		continue
+	else:
+		table = pd.read_csv(str(annots+i), sep=' ')
+		l=[]
+		tab= pd.DataFrame(columns = ['file','idx','espece','x','y','w','h','conf'])
+		tab2 = pd.DataFrame(columns = ['file','idx','espece','x','y','w','h','conf'])
+
+		if len (table) == 0:
+			for j in table.columns:
+				l.append(j)
+
+			name = i.split('.')[0]
+
+			if len(name.split('_')[-1]) == 2:
+				name = name.split('.')[0][0:-3]
+			elif len(name.split('_')[-1]) == 3:
+				name = name.split('.')[0][0:-4]
+			else :
+				name= name.split('.')[0][0:-2]
+
+			name = str(name+'.wav')
+			idx = i.split('_')[-1]
+			idx = idx.split('.')[0]
+
+			table = pd.DataFrame([[name, idx, l[0],l[1],l[2],l[3],l[4],l[5]]], columns = ['file', 'idx','espece','x','y','w','h','conf'])
+			df = pd.concat([df,table])
+		else:
+			nb = len(table)
+
+			name = i.split('.')[0]
+
+			if len(name.split('_')[-1]) == 2:
+				name = name.split('.')[0][0:-3]
+			elif len(name.split('_')[-1]) == 3:
+				name = name.split('.')[0][0:-4]
+			else :
+				name= name.split('.')[0][0:-2]
+
+			name = str(name+'.wav' )
+			idx = i.split('_')[-1]
+			idx = idx.split('.')[0]
+
+			for j in table.columns:
+				l.append(j)
+			try :
+				tab2 = pd.DataFrame([[name, idx, l[0],l[1],l[2],l[3],l[4],l[5]]],columns = ['file', 'idx','espece','x','y','w','h','conf'])
+			except:
+				ipdb.set_trace()
+			for v in range(nb):
+				new = pd.DataFrame([[name, idx, table.iloc[v][0],table.iloc[v][1], table.iloc[v][2],table.iloc[v][3],table.iloc[v][4],table.iloc[v][5]]], columns = ['file', 'idx','espece','x','y','w','h','conf'])
+				tab = pd.concat([tab , new])
+
+			table = pd.concat([tab2, tab])
+			df = pd.concat([df, table])
+
+#put the classes here
+names = ['0:Homo sapiens',
+  '1:Vulpes vulpes',
+  '2:Meles meles',
+  '3:Canis familiaris',
+  '4:Sus scrofa',
+  '5:voiture',
+  '6:vélo',
+  '7:Equus caballus',
+  '8:Capreolus capreolus',
+  '9:Rupicapra rupicapra',
+  '10:Felis catus',
+  '11:Lynx lynx',
+  '12:Lepus europaeus',
+  '13:camion',
+  '14:Genetta genetta',
+  '15:moto',
+  '16:Felis silvestris',
+  '17:Oryctolagus cuniculus',
+  '18:Myocastor coypus',
+  '19:Bos taurus',
+  '20:Cervus elaphus',
+  '21:Cinclus cinclus',
+  '22:Capra hircus',
+  '23:Martes martes',
+  '24:Erinaceus europaeus',
+  '25:Ardea cinerea',
+  '26:train',
+  '27:Sciurus vulgaris',
+  '28:avion',
+  '29:bus',
+  '30:Eliomys quercinus',
+  '31:Garrulus glandarius',
+  '32:Ovis aries',
+  '33:Natrix helvetica',
+  '34:Salamandra salamandra',
+  '35:Lutra lutra',
+  '36:Motacilla cinerea',
+  '37:Streptopelia turtur',
+  '38:Phasianus colchicus',
+  '39:Lacerta bilineata',
+  '40:Testudo hermanni',
+  '41:Equus asinus',
+  '42:Ardea alba',
+  '43:Buteo buteo',
+  '44:Erithacus rubecula',
+  '45:Natrix maura',
+  '46:Rattus norvegicus']
+
+df['annot'] = 'None'
+for j in range (len(df)):
+	df['annot'].iloc[j] = names[int(df.espece.iloc[j])]
+
+df.to_csv(str(outdir+out_file+'.csv'), index= False)