diff --git a/README.md b/README.md
index 9cae1d1260f10db240a30eb62e6c43f5792fa2a0..93a68b9975338518a3a3c87e411a1f34a9975186 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,5 @@
# YOLO-DYNI
-
-
Ce git a été créé avec comme objectif une prise en main de YOLOV5 plus facile.
Il contient notamment un script permettant d'extraire les spectrogrammes de plusieurs enregistrements ([get_spectrogram.py](https://gitlab.lis-lab.fr/stephane.chavin/yolo-dyni/-/blob/main/get_spectrogram.py)), un script nécessaire à la conversion des annotations LabelMe vers YOLO ([labelme2yolo.py](https://gitlab.lis-lab.fr/stephane.chavin/yolo-dyni/-/blob/main/labelme2yolo.py)), un script pour convertir des annotations d'un dataframe vers YOLO ([get_train_annot_YOLO.py](https://gitlab.lis-lab.fr/stephane.chavin/yolo-dyni/-/blob/main/get_train_annot_YOLO.py/)), un script permettant de séparer le train et la validation de manière équilibré ([get_train_val_YOLO.py](https://gitlab.lis-lab.fr/stephane.chavin/yolo-dyni/-/blob/main/get_train_val_YOLO.py)) et un script qui permet de compiler les détections, d'un modèle entrainé, dans un dataframe ([get_yolo_detection.py](https://gitlab.lis-lab.fr/stephane.chavin/yolo-dyni/-/blob/main/get_yolo_detection.py)).
diff --git a/get_json_file_YOLO.py b/get_json_file_YOLO.py
index d95216bebb1cbc7d28fceb9b37c260b2ea66409d..1862034b91ca326e2fad214151b87334775b8ca3 100644
--- a/get_json_file_YOLO.py
+++ b/get_json_file_YOLO.py
@@ -11,41 +11,43 @@ def arg_directory(path):
else:
raise argparse.ArgumentTypeError(f'`{path}` is not a valid path')
-parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter, description='TODO')
-parser.add_argument('-p','--path_to_json', type=arg_directory, help = 'Path of the folder that contain the .json',required=True)
-parser.add_argument('-i','--path_to_img', type=arg_directory, help = 'Path of the folder that contain the .jpg',required=True)
-parser.add_argument('-d','--directory', type=arg_directory, help = 'Directory to wich modified .json files will be stored',required=True)
-args = parser.parse_args()
-
-filename = args.path_to_json
-out_file = args.directory
-img_path = args.path_to_img
-
-liste_file = os.listdir(filename)
-liste_file = pd.DataFrame(liste_file, columns =['fn'])
-liste_file['type'] = liste_file.fn.str.split('.').str[-1]
-liste_file = liste_file[liste_file.type == 'json']
-liste_file.reset_index(inplace = True)
-
-for i in range (len(liste_file)):
- if liste_file.fn[i][0] == '.':
- liste_file = liste_file.drop(i)
-
-liste_file = liste_file.reset_index()
-
-for i, row in liste_file.iterrows():
- if len(row.fn) > 30:
- data = labelme.LabelFile.load_image_file(os.path.join(img_path,str(row.fn[:-4]+'jpg')))
- image_data = base64.b64encode(data).decode('utf-8')
- else:
- continue
- try :
- len(data)
- except TypeError:
- continue
- f = open(filename+row.fn,)
- get_data = json.load(f)
- get_data['imageData'] = image_data
- get_data['imagePath'] = img_path+row.fn[:-4]+'jpg'
- with open(out_file+row.fn, 'w') as f:
- json.dump(get_data, f, indent=4)
+def process_json_files(json_dir, img_dir, output_dir):
+ json_files = [f for f in os.listdir(json_dir) if f.endswith('.json')]
+
+ for json_file in json_files:
+ if json_file.startswith('.'):
+ continue
+
+ json_path = os.path.join(json_dir, json_file)
+ img_path = os.path.join(img_dir, json_file.replace('.json', '.jpg'))
+
+ if not os.path.exists(img_path):
+ continue
+
+ try:
+ with open(img_path, 'rb') as img_file:
+ image_data = base64.b64encode(img_file.read()).decode('utf-8')
+ except FileNotFoundError:
+ continue
+
+ with open(json_path, 'r') as f:
+ json_data = json.load(f)
+
+ json_data['imageData'] = image_data
+ json_data['imagePath'] = img_path
+
+ output_path = os.path.join(output_dir, json_file)
+ with open(output_path, 'w') as f:
+ json.dump(json_data, f, indent=4)
+
+def main():
+ parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter, description='TODO')
+ parser.add_argument('-p', '--path_to_json', type=arg_directory, help='Path to the folder containing the .json files', required=True)
+ parser.add_argument('-i', '--path_to_img', type=arg_directory, help='Path to the folder containing the .jpg images', required=True)
+ parser.add_argument('-d', '--directory', type=arg_directory, help='Directory to which modified .json files will be stored', required=True)
+ args = parser.parse_args()
+
+ process_json_files(args.path_to_json, args.path_to_img, args.directory)
+
+if __name__ == "__main__":
+ main()
diff --git a/get_spectrogram.py b/get_spectrogram.py
index 92dfed345e4757b4a2c7d08302998dff745fdee8..be398af994d16372a2fabe028b262de1b3209bc2 100644
--- a/get_spectrogram.py
+++ b/get_spectrogram.py
@@ -1,13 +1,11 @@
import os
import librosa
-import ipdb
import glob
import argparse
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from p_tqdm import p_map
-from tqdm import tqdm
import warnings
warnings.filterwarnings('ignore')
@@ -18,69 +16,65 @@ def arg_directory(path):
else:
raise argparse.ArgumentTypeError(f'`{path}` is not a valid path')
-parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter, description='TODO')
-parser.add_argument('-f','--file', type=str,help = 'Name of the file that contain the recording to print')
-parser.add_argument('-p','--path_to_data', type=arg_directory, help = 'Path of the folder that contain the recordings')
-parser.add_argument('-d','--directory', type=arg_directory, help = 'Directory to wich spectrogram will be stored')
-parser.add_argument('-m','--mode', type=str,choices=['unique','multiple'], help = 'Direction of the saved spectrogram')
-parser.add_argument('-n','--columns_name', type=str, help = 'Name of the columns that contain the path of the .wav')
-parser.add_argument('-i','--input', type=str, choices=['file','folder'], help = 'Choose "file" if you have a .csv file or "folder" to export spectrogram from all the .wav of a folder')
-args = parser.parse_args()
+def create_spectrogram(y, directory, filename, offset, duration):
+ window_size = 1024
+ window = np.hanning(window_size)
+ stft = librosa.core.spectrum.stft(y, n_fft=window_size, hop_length=512, window=window)
-path_to_data = args.path_to_data
-direction = args.directory
-folder = 'Spectrogram/'
+ plt.close()
+ plt.figure()
-#PARAMETERS
-DURATION = 8
-OVERLAP = 2
+ log_stft = np.log10(np.abs(stft))
+ vmin, vmax = log_stft.min(), log_stft.max()
-if args.mode == 'multiple':
- NB_IMG_PER_REC = 30
-elif args.mode == 'unique':
- NB_IMG_PER_REC = 1
+ plt.imshow(log_stft[::-1], aspect="auto", interpolation=None, cmap='jet', vmin=vmin, vmax=vmax)
+ plt.subplots_adjust(top=1, bottom=0, left=0, right=1)
-if args.input == 'file':
- df = pd.read_csv(args.file,low_memory=False)
- df['Path'] = df[args.columns_name]
-elif args.input == 'folder':
- df = pd.DataFrame(glob.glob(os.path.join(path_to_data,'*')),columns = ['Path'])
-
-def process(x):
- _, (i) = x
-
- for count, j in enumerate(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
+ name = os.path.join(directory, 'Spectrogram', f"{filename.replace('/', '_').split('.')[0]}_{offset}")
+ try:
+ plt.savefig(name + '.jpg')
+ except FileNotFoundError:
+ os.makedirs(os.path.join(directory, 'Spectrogram'), exist_ok=True)
+ plt.savefig(name + '.jpg')
+
+def process_recordings(args):
+ _, (i) = args
+ duration = 8
+ overlap = 2
+
+ for count in range(args.img_per_rec):
+ offset = count * (duration - overlap)
filename = str(i[0])
- offset = count * (DURATION - OVERLAP)
+
try:
- y, sr = librosa.load(filename, offset = offset, duration = DURATION, sr = None)
+ y, _ = librosa.load(filename, offset=offset, duration=duration, sr=None)
+ create_spectrogram(y, args.directory, filename, offset, duration)
except Exception:
print(filename)
- continue
- window_size = 1024
- window = np.hanning(window_size)
- stft = librosa.core.spectrum.stft(y, n_fft=window_size, hop_length=512, window=window)
-
- plt.close()
- plt.figure()
-
- vmin = np.flipud(np.log10(np.abs(stft))).mean()
- vmax = np.flipud(np.log10(np.abs(stft))).max()
-
- plt.imshow(np.flipud(np.log10(np.abs(stft))),aspect = "auto",interpolation = None,cmap = 'jet',vmin = vmin,vmax = vmax)
-
- plt.subplots_adjust(top=1, bottom=0, left=0, right=1)
-
- name = str(i[0].replace('/','_').split('.')[0]+'_'+str(offset)) #count
- try :
- plt.savefig(os.path.join(direction,folder, str(name+'.jpg')))
-
- except FileNotFoundError:
- print('creating the directory : ',os.path.join(direction,folder))
- os.mkdir(os.path.join(direction,folder))
- plt.savefig(os.path.join(direction,folder, str(name+'.jpg')))
-
-p_map(process, enumerate(df.groupby('Path')), num_cpus=1, total = len(df.groupby('Path')))
-print('saved to ',str(directory+folder))
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter, description='TODO')
+ parser.add_argument('-f', '--file', type=str, help='Name of the file that contains the recording to print')
+ parser.add_argument('-p', '--path_to_data', type=arg_directory, help='Path of the folder that contains the recordings', required=True)
+ parser.add_argument('-d', '--directory', type=arg_directory, help='Directory to which spectrograms will be stored', required=True)
+ parser.add_argument('-m', '--mode', type=str, choices=['unique', 'multiple'], help='Direction of the saved spectrogram')
+ parser.add_argument('-n', '--columns_name', type=str, help='Name of the columns that contain the path of the .wav')
+ parser.add_argument('-i', '--input', type=str, choices=['file', 'folder'], help='Choose "file" if you have a .csv file or "folder" to export spectrogram from all the .wav of a folder')
+ args = parser.parse_args()
+
+ if args.mode == 'multiple':
+ img_per_rec = 30
+ elif args.mode == 'unique':
+ img_per_rec = 1
+
+ path_to_data = args.path_to_data
+
+ if args.input == 'file':
+ df = pd.read_csv(args.file, low_memory=False)
+ df['Path'] = df[args.columns_name]
+ elif args.input == 'folder':
+ df = pd.DataFrame(glob.glob(os.path.join(path_to_data, '*'), recursive=True), columns=['Path'])
+
+ p_map(process_recordings, enumerate(df.groupby('Path'), img_per_rec=img_per_rec), num_cpus=1, total=len(df.groupby('Path')))
+ print(f'Saved to {args.directory}/Spectrogram')
diff --git a/get_time_freq_detection.py b/get_time_freq_detection.py
index c71d873612243dbefaa2d4255e78f35e69a4e891..ea709d078ba7f593f5fc4e84df1c077286312d7a 100644
--- a/get_time_freq_detection.py
+++ b/get_time_freq_detection.py
@@ -1,9 +1,8 @@
import pandas as pd
import os
-import ipdb
-from tqdm import tqdm
import argparse
from datetime import date
+from tqdm import tqdm
def arg_directory(path):
if os.path.isdir(path):
@@ -11,47 +10,44 @@ def arg_directory(path):
else:
raise argparse.ArgumentTypeError(f'`{path}` is not a valid path')
-parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter, description='TODO')
-parser.add_argument('-p','--path_to_data', type=arg_directory, help = 'Path of the folder that contain the .txt files',required=True)
-parser.add_argument('-d','--directory', type=arg_directory, help = 'Directory to wich the dataframe will be stored',required=True)
-parser.add_argument('-t','--duration', type = int, help = 'Duration of the spectrogram', required = True)
-parser.add_argument('-s','--SR', type = int, help = 'Sampling Rate of the spectrogram')
-args = parser.parse_args()
-
-annots = args.path_to_data
+def process_annotations(annotations_folder, duration, sr):
+ today = date.today()
+ out_file = f'YOLO_detection_{today.day}_{today.month}'
-today = date.today()
-out_file = str('YOLO_detection'+str('_'+str(today.day)+'_'+str(today.month)))
+ df_list = []
+ names = [] # Add your class names here
-outdir = args.directory
+ for file_name in tqdm(os.listdir(annotations_folder)):
+ if file_name.endswith('.txt'):
+ file_path = os.path.join(annotations_folder, file_name)
+ annotation_df = pd.read_csv(file_path, sep=' ', names=['espece', 'x', 'y', 'w', 'h'])
-df = pd.concat({f:pd.read_csv(os.path.join(annots, f), sep=' ', names=['espece', 'x', 'y', 'w', 'h'])
- for f in tqdm(os.listdir(annots))}, names=['file'])
+ annotation_df['file'] = file_name
+ annotation_df['idx'] = annotation_df['file'].str.split('_').str[-1].str.split('.').str[0]
+ annotation_df['file'] = annotation_df['file'].str.rsplit('.', 1).str[0] + '.wav'
-df = df.reset_index(level=[0])
-df = df.reset_index()
-del df['index']
-df['idx'] = df.file.str.split('_').str[-1].str.split('.').str[0]
-df.file = df.file.str.rsplit('.',1).str[0]+'.wav'
+ annotation_df['annot'] = annotation_df['espece'].apply(lambda x: names[x])
-DUREE_SPECTRO = args.duration
-SR = args.SR
+ annotation_df['midl'] = (annotation_df['x'] * duration) + annotation_df['idx'].astype(int)
+ annotation_df['freq_center'] = (1 - annotation_df['y']) * (sr / 2)
+ annotation_df['freq_min'] = annotation_df['freq_center'] - (annotation_df['h'] * (sr / 2)) / 2
+ annotation_df['freq_max'] = annotation_df['freq_center'] + (annotation_df['h'] * (sr / 2)) / 2
+ annotation_df['start'] = annotation_df['midl'] - (annotation_df['w'] * duration) / 2
+ annotation_df['stop'] = annotation_df['midl'] + (annotation_df['w'] * duration) / 2
+ annotation_df['duration'] = annotation_df['stop'] - annotation_df['start']
-#put the classes here
-names = []
+ df_list.append(annotation_df)
-df['annot'] = 'None'
-for j in range (len(df)):
- df.loc[j,('annot')] = names[int(df.espece.iloc[j])]
+ result_df = pd.concat(df_list, ignore_index=True)
+ result_df.to_csv(os.path.join(outdir, f'{out_file}.csv'), index=False)
+ print(f'Saved as {os.path.join(outdir, f"{out_file}.csv")}')
-print('Calculating the positions','\n')
-df['midl'] = (df.x*DUREE_SPECTRO)+(df.idx.astype(int))
-df['freq_center'] = (1-df.y)*(SR/2)
-df['freq_min'] = df.freq_center - (df.h*(SR/2))/2
-df['freq_max'] = df.freq_center + (df.h*(SR/2))/2
-df['start'] = df.midl - (df.w * DUREE_SPECTRO)/2
-df['stop'] = df.midl + (df.w * DUREE_SPECTRO)/2
-df['duration'] = df.stop - df.start
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter, description='TODO')
+ parser.add_argument('-p', '--path_to_data', type=arg_directory, help='Path of the folder that contains the .txt files', required=True)
+ parser.add_argument('-d', '--directory', type=arg_directory, help='Directory where the dataframe will be stored', required=True)
+ parser.add_argument('-t', '--duration', type=int, help='Duration of the spectrogram', required=True)
+ parser.add_argument('-s', '--SR', type=int, help='Sampling Rate of the spectrogram')
+ args = parser.parse_args()
-df.to_csv(os.path.join(outdir,str(out_file+'.csv')), index= False)
-print('saved as ',os.path.join(outdir,str(out_file+'.csv')))
+ process_annotations(args.path_to_data, args.duration, args.SR)
diff --git a/get_train_annot_YOLO.py b/get_train_annot_YOLO.py
index 19d4b1c2fac73c020f8df0173a11cdc75d728793..55d63ee599e4f478c6aa562615f60d6958bd0a84 100644
--- a/get_train_annot_YOLO.py
+++ b/get_train_annot_YOLO.py
@@ -3,223 +3,167 @@ import pandas as pd
import librosa
import numpy as np
import matplotlib.pyplot as plt
-from p_tqdm import p_map
-import ipdb
import random
from datetime import date
import argparse
+from p_tqdm import p_map
import cv2
-import matplotlib.patches as patches
-from matplotlib.patches import Rectangle
-from random import randrange
-from PIL import Image
from mycolorpy import colorlist as mcp
-today = date.today()
-
def arg_directory(path):
if os.path.isdir(path):
return path
else:
raise argparse.ArgumentTypeError(f'`{path}` is not a valid path')
-parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter, description='TODO')
-parser.add_argument('-f','--filename_path', type= str, help = 'Path and name of the file containing the annotations',required=True)
-parser.add_argument('-p','--path_to_data', type=arg_directory, help = 'Path of the folder that contain the recordings',required=True)
-parser.add_argument('-d','--directory', type=arg_directory, help = 'Directory to wich spectrograms and .txt files will be stored',required=True)
-parser.add_argument('-m','--mode',type=str,choices=['uniform','personalized'],help = 'Choose the mode to calculate the y and height value',required=True)
-parser.add_argument('-u','--unique',type=str, choices=['unique','multiple'], help = 'unique for only one spectrogram per file, multple for multiple spectrogram',required=True)
-parser.add_argument('-c','--columns_name',type=str,help = 'Name of the column that contain the path',required=True)
-parser.add_argument('--export',type=str, default=None, help='To export the position of the bounding box on the spectrogram',required=False)
-args = parser.parse_args()
-
-directory = args.directory
-
-DURATION = 8
-NB_CLASS = 5
-
-df = pd.read_csv(args.filename_path, low_memory=False)
-
-df.rename(columns={'label':'Code'},inplace=True)
-df.rename(columns={'annotation_initial_time':'start'},inplace=True)
-df.rename(columns={'annotation_final_time':'stop'},inplace=True)
-df.rename(columns={'duree':'d_annot'},inplace=True)
-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)
-
-tab = df.groupby('Code').count()
-tab = tab.sort_values(tab.columns[0],ascending = False)[:NB_CLASS]
-df = df[df.Code.isin(tab.index)]
-
-try :
- df['max_freq'].fillna(9000,inplace = True)
- df['min_freq'].fillna(1000,inplace = True)
-except Exception:
- df['max_freq'] = 9000
- df['min_freq'] = 1000
- df['midl_y'] = 5000
-
-df['d_annot'] = df.stop - df.start
-df['midl'] = (df.stop + df.start)/2
-df['Path'] = df[args.columns_name]
-
-df = df[df.d_annot<8]
-df = df.reset_index()
-
-list_espece = df.groupby('Code').count().sort_values(df.columns[0],ascending = False)
-data = pd.DataFrame(columns = ['espece','ind'])
-
-for i in range (len(list_espece)):
- esp = list_espece.index[i]
- new_col = pd.DataFrame([[esp,i]],columns = ['espece','ind'])
- data = pd.concat([data,new_col])
-
-liste_espece = data.espece
-liste_espece.to_csv(str(directory+'liste_especes.csv'),index = False)
-
-print('\n',data)
-
-#color = mcp.gen_color(cmap = "Wistia", n= len(list_espece))
-
-colors = pd.DataFrame(columns = ['color', 'species'])
-
-for i in range (30):
- r = randrange(255)
- g = randrange(255)
- b = randrange(255)
- rand_color = (r, g, b)
- new = pd.DataFrame([[rand_color, i]], columns = ['color', 'species'])
- colors = pd.concat([colors, new])
-
-
-def process(x):
- count, (f, grp) = x
- filename = str(f)
- duration = DURATION
-
- while len(grp) != 0:
-
- tab = grp[grp.midl <= grp.start.iloc[0]+7]
- fin = pd.DataFrame(columns = ['id','x', 'y', 'width', 'height'])
- duree = tab.d_annot
-
- if len(tab)==0:
- tab = grp
- print(tab)
- rd = round(random.uniform(-1.5,1.5),2)
-
- if args.unique == 'multiple':
- if tab.start.iloc[0] <= 3:
+def process_annotations(file_path, duration, mode, unique, columns_name, export):
+ today = date.today()
+
+ df = pd.read_csv(file_path, low_memory=False)
+ df.rename(columns={'label': 'Code', 'annotation_initial_time': 'start', 'annotation_final_time': 'stop', 'duree': 'd_annot', 'min_frequency': 'min_freq', 'max_frequency': 'max_freq', 'avg_frequency': 'midl_y'}, inplace=True)
+ df['max_freq'].fillna(9000, inplace=True)
+ df['min_freq'].fillna(1000, inplace=True)
+ df['midl_y'].fillna(5000, inplace=True)
+
+ NB_CLASS = 5
+ tab = df.groupby('Code').count()
+ tab = tab.sort_values(tab.columns[0], ascending=False)[:NB_CLASS]
+ df = df[df.Code.isin(tab.index)]
+ df['d_annot'] = df.stop - df.start
+ df['midl'] = (df.stop + df.start) / 2
+ df['Path'] = df[columns_name]
+
+ df = df[df.d_annot < 8]
+ df = df.reset_index()
+
+ # Add your class names to the 'colors' list
+ colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)) for _ in range(30)]
+
+ def process(x):
+ count, (f, grp) = x
+ filename = str(f)
+ duration = DURATION
+
+ while len(grp) != 0:
+ tab = grp[grp.midl <= grp.start.iloc[0] + 7]
+ fin = pd.DataFrame(columns=['id', 'x', 'y', 'width', 'height'])
+ duree = tab.d_annot
+
+ if len(tab) == 0:
+ tab = grp
+
+ rd = round(random.uniform(-1.5, 1.5), 2)
+
+ if unique == 'multiple':
+ if tab.start.iloc[0] <= 3:
+ offset = 0
+ elif tab.start.iloc[0] == 3:
+ offset = 1
+ elif tab.start.iloc[0] >= 3:
+ offset = tab.midl.iloc[0] - 3.5 + rd
+ elif unique == 'unique':
offset = 0
- if tab.start.iloc[0] == 3:
- offset = 1
- if tab.start.iloc[0] >= 3:
- offset = tab.midl.iloc[0] - 3.5 + rd
- elif args.unique == 'unique':
- offset= 0
- window_size = 1024
- window = np.hanning(window_size)
+ window_size = 1024
+ window = np.hanning(window_size)
+ y, sr = librosa.load(filename, offset=offset, duration=duration, sr=None)
+ stft = librosa.core.spectrum.stft(y, n_fft=window_size, hop_length=512, window=window)
+ spectrum, freq, time, im = plt.specgram(y, Fs=sr, NFFT=window_size, noverlap=512, cmap='jet')
- y, sr = librosa.load(filename, offset = offset, duration = duration, sr = None)
- stft = librosa.core.spectrum.stft(y, n_fft=window_size, hop_length=512, window=window)
- spectrum, freq, time, im = plt.specgram(y, Fs=sr, NFFT=window_size, noverlap=512, cmap='jet')
+ vmin = np.flipud(np.log10(np.abs(stft))).mean()
+ vmax = np.flipud(np.log10(np.abs(stft))).max()
- vmin = np.flipud(np.log10(np.abs(stft))).mean()
- vmax = np.flipud(np.log10(np.abs(stft))).max()
-
- plt.close()
- plt.imshow(np.flipud(np.log10(np.abs(stft))),aspect = "auto", interpolation = None, cmap = 'jet', vmin = vmin, vmax = vmax)
- plt.subplots_adjust(top=1, bottom=0, left=0, right=1)
-
- for idxs, row in tab.iterrows():
- species = row.Code
- x_pxl = (row.midl - offset) / duration
- width_pxl = (row.stop-row.start)/duration
-
- if args.mode == 'uniform':
- height_pxl = 0.8
- y_pxl = 0.5
-
- else:
- y_pxl = 1 - (row.midl_y / (sr/2))
-
- height_pxl = (row.max_freq - row.min_freq)/(sr/2)
- if height_pxl > 1:
- height_pxl = 1
- elif height_pxl > y_pxl*2:
- y_pxl=y_pxl+0.5*(height_pxl-y_pxl*2)
-
- annotation = pd.DataFrame([[str(data.loc[data.espece == row.Code,'ind'][0]),x_pxl,y_pxl,width_pxl,height_pxl]],columns = ['id','x', 'y', 'width', 'height'])
-
- fin = pd.concat([fin,annotation])
- grp = grp.drop(tab.index)
-
- name = str(row.Path.replace('/','_').replace('.','_')+'_'+str(count))
- name_file = os.path.join(directory,str('labels_'+str(today.day)+'_'+str(today.month)),str(name+'.txt'))
-
- try :
- plt.savefig(os.path.join(directory,str('images_'+str(today.day)+'_'+str(today.month)),row.Code,str(name+'.jpg')))
- fin.to_csv(name_file,sep =' ',header=False,index=False)
- plt.savefig(os.path.join(directory,str('images_'+str(today.day)+'_'+str(today.month)),'all',str(name+'.jpg')))
-
- except :
- os.mkdir(os.path.join(directory,str('images_'+str(today.day)+'_'+str(today.month))))
- for especes in list_espece.index:
-
- os.mkdir(os.path.join(directory,str('images_'+str(today.day)+'_'+str(today.month)),especes))
- os.mkdir(os.path.join(directory,str('images_'+str(today.day)+'_'+str(today.month)),'all'))
- os.mkdir(os.path.join(directory,str('labels_'+str(today.day)+'_'+str(today.month))))
-
- fin.to_csv(name_file,sep =' ',header=False,index=False)
- plt.savefig(os.path.join(directory,str('images_'+str(today.day)+'_'+str(today.month)),row.Code,str(name+'.jpg')))
- plt.savefig(os.path.join(directory,str('images_'+str(today.day)+'_'+str(today.month)),'all',str(name+'.jpg')))
-
- plt.close()
-
- if args.export != None:
-
- im = cv2.imread(os.path.join(directory,str('images_'+str(today.day)+'_'+str(today.month)),'all',str(name+'.jpg')))
- im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
- H,W = im.shape[0], im.shape[1]
- for l in range(len(fin)):
-
- x, y, w, h = fin.x.iloc[l]*W , fin.y.iloc[l]*H , fin.width.iloc[l]*W , fin.height.iloc[l]*H
-
- shape1 = (int(x-(0.5*w)), int(y+(0.5*h)))
- shape2 = (int(x+(0.5*w)), int(y+(0.5*h)))
- shape3 = (int(x+-(0.5*w)), int(y-(0.5*h)))
- shape4 = (int(x+(0.5*w)), int(y-(0.5*h)))
+ plt.close()
+ plt.imshow(np.flipud(np.log10(np.abs(stft)), aspect='auto', interpolation=None, cmap='jet', vmin=vmin, vmax=vmax))
+ plt.subplots_adjust(top=1, bottom=0, left=0, right=1)
- #rectangle text shape
+ for idxs, row in tab.iterrows():
+ species = row.Code
+ x_pxl = (row.midl - offset) / duration
+ width_pxl = (row.stop - row.start) / duration
- shp1 = shape4[0]-10, shape4[1]+20
- shp2 = shape4[0], shape4[1]+20
- shp3 = shape4[0]-10, shape4[1]
- shp4 = shape4[0], shape4[1]
+ if mode == 'uniform':
+ height_pxl = 0.8
+ y_pxl = 0.5
+ else:
+ y_pxl = 1 - (row.midl_y / (sr / 2))
+ height_pxl = (row.max_freq - row.min_freq) / (sr / 2)
+ if height_pxl > 1:
+ height_pxl = 1
+ elif height_pxl > y_pxl * 2:
+ y_pxl = y_pxl + 0.5 * (height_pxl - y_pxl * 2)
- #text placement
+ annotation = pd.DataFrame([[str(data.loc[data.espece == row.Code, 'ind'][0]), x_pxl, y_pxl, width_pxl, height_pxl]],
+ columns=['id', 'x', 'y', 'width', 'height'])
+ fin = pd.concat([fin, annotation])
- text_shape = shp1[0], shp1[1]-5
+ grp = grp.drop(tab.index)
- label = str(fin.id.iloc[l])
- cv2.rectangle(im, pt1=shape1, pt2=shape4, color= colors[colors.species == label].color, thickness=1)
+ name = str(row.Path.replace('/', '_').replace('.', '_') + '_' + str(count))
+ name_file = os.path.join(directory, str('labels_' + str(today.day) + '_' + str(today.month)), str(name + '.txt'))
- cv2.rectangle(im, pt1=shp1 , pt2= shp4, color= colors[colors.species == label].color, thickness= -1)
- cv2.putText(im, label, text_shape, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,255,255), 1)
-
- plt.imshow(im)
- plt.subplots_adjust(top=1, bottom=0, left=0, right=1)
try:
- plt.savefig(os.path.join(directory, str('images_annotes_'+str(today.day)+'_'+str(today.month)),str(name+'.jpg')))
-
- except Exception:
- os.mkdir(os.path.join(directory,str('images_annotes_'+str(today.day)+'_'+str(today.month))))
- plt.savefig(os.path.join(directory, str('images_annotes_'+str(today.day)+'_'+str(today.month)),str(name+'.jpg')))
-
- plt.close()
-
-p_map(process, enumerate(df.groupby('Path')), num_cpus=2, total = len(df.groupby('Path')))
-
-print('saved to ',directory)
+ plt.savefig(os.path.join(directory, str('images_' + str(today.day) + '_' + str(today.month)), row.Code, str(name + '.jpg')))
+ fin.to_csv(name_file, sep=' ', header=False, index=False)
+ plt.savefig(os.path.join(directory, str('images_' + str(today.day) + '_' + str(today.month)), 'all',
+ str(name + '.jpg')))
+ except:
+ os.mkdir(os.path.join(directory, str('images_' + str(today.day) + '_' + str(today.month))))
+ for especes in list_espece.index:
+ os.mkdir(os.path.join(directory, str('images_' + str(today.day) + '_' + str(today.month), especes)))
+ os.mkdir(os.path.join(directory, str('images_' + str(today.day) + '_' + str(today.month), 'all')))
+ os.mkdir(os.path.join(directory, str('labels_' + str(today.day) + '_' + str(today.month))))
+ fin.to_csv(name_file, sep=' ', header=False, index=False)
+ plt.savefig(os.path.join(directory, str('images_' + str(today.day) + '_' + str(today.month)), row.Code,
+ str(name + '.jpg')))
+ plt.savefig(os.path.join(directory, str('images_' + str(today.day) + '_' + str(today.month)), 'all',
+ str(name + '.jpg')))
+
+ plt.close()
+
+ if export:
+ im = cv2.imread(
+ os.path.join(directory, str('images_' + str(today.day) + '_' + str(today.month)), 'all', str(name + '.jpg')))
+ im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
+ H, W = im.shape[0], im.shape[1]
+ for l in range(len(fin)):
+ x, y, w, h = fin.x.iloc[l] * W, fin.y.iloc[l] * H, fin.width.iloc[l] * W, fin.height.iloc[l] * H
+ shape1 = (int(x - 0.5 * w), int(y + 0.5 * h))
+ shape2 = (int(x + 0.5 * w), int(y + 0.5 * h))
+ shape3 = (int(x - 0.5 * w), int(y - 0.5 * h))
+ shape4 = (int(x + 0.5 * w), int(y - 0.5 * h)
+ )
+ shp1 = shape4[0] - 10, shape4[1] + 20
+ shp2 = shape4[0], shape4[1] + 20
+ shp3 = shape4[0] - 10, shape4[1]
+ shp4 = shape4[0], shape4[1]
+ text_shape = shp1[0], shp1[1] - 5
+ label = str(fin.id.iloc[l])
+ cv2.rectangle(im, pt1=shape1, pt2=shape4, color=colors[colors.species == label].color, thickness=1)
+ cv2.rectangle(im, pt1=shp1, pt2=shp4, color=colors[colors.species == label].color, thickness=-1)
+ cv2.putText(im, label, text_shape, cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
+ plt.imshow(im)
+ plt.subplots_adjust(top=1, bottom=0, left=0, right=1)
+ try:
+ plt.savefig(
+ os.path.join(directory, str('images_annotes_' + str(today.day) + '_' + str(today.month), str(name + '.jpg'))))
+ except Exception:
+ os.mkdir(os.path.join(directory, str('images_annotes_' + str(today.day) + '_' + str(today.month))))
+ plt.savefig(os.path.join(directory, str('images_annotes_' + str(today.day) + '_' + str(today.month), str(name + '.jpg'))))
+ plt.close()
+
+ p_map(process, enumerate(df.groupby('Path')), num_cpus=2, total=len(df.groupby('Path')))
+ print('saved to', directory)
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter, description='TODO')
+ parser.add_argument('-f', '--filename_path', type=str, help='Path and name of the file containing the annotations', required=True)
+ parser.add_argument('-p', '--path_to_data', type=arg_directory, help='Path of the folder that contains the recordings', required=True)
+ parser.add_argument('-d', '--directory', type=arg_directory, help='Directory to which spectrograms and .txt files will be stored', required=True)
+ parser.add_argument('-m', '--mode', type=str, choices=['uniform', 'personalized'], help='Choose the mode to calculate the y and height value', required=True)
+ parser.add_argument('-u', '--unique', type=str, choices=['unique', 'multiple'], help='unique for only one spectrogram per file, multiple for multiple spectrograms', required=True)
+ parser.add_argument('-c', '--columns_name', type=str, help='Name of the column that contains the path', required=True)
+ parser.add_argument('--export', type=str, default=None, help='To export the position of the bounding box on the spectrogram', required=False)
+ args = parser.parse_args()
+
+ process_annotations(args.filename_path, duration=8, mode=args.mode, unique=args.unique, columns_name=args.columns_name, export=args.export)
diff --git a/get_train_val_YOLO.py b/get_train_val_YOLO.py
index c0c8ec00a3e5251fd310cadd37f654db1241ad96..7539eed7cc23f58ff36adfcb08fdebd57f55e671 100644
--- a/get_train_val_YOLO.py
+++ b/get_train_val_YOLO.py
@@ -5,7 +5,6 @@ from tqdm import tqdm
import numpy as np
import shutil
import argparse
-import ipdb
def arg_directory(path):
if os.path.isdir(path):
@@ -13,83 +12,87 @@ def arg_directory(path):
else:
raise argparse.ArgumentTypeError(f'`{path}` is not a valid path')
-parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter, description='TODO')
-parser.add_argument('-r','--ratio', type=float, default = 0.7,help = 'Train Ratio (val = 1 - ratio)')
-parser.add_argument('-p','--path_to_data', type=arg_directory, help = 'Path of the folder that contain the .txt (ending with labels/)',required=True)
-parser.add_argument('-d','--direction', type=arg_directory, help = 'Directory to wich spectrogram and .txt files will be stored (different from -p)',required=True)
-args = parser.parse_args()
-
-path = args.path_to_data
-direction = args.direction
-
-NB_CLASS = 2
-
-df = pd.concat({f:pd.read_csv(os.path.join(path, f), sep=' ', names=['espece', 'x', 'y', 'w', 'h'])
- for f in tqdm(os.listdir(path))}, names=['file'])
-
-df = df.reset_index(level=[0])
-df = df.reset_index()
-del df['index']
-
-df = df[df.espece!='y']
-df.espece = df.espece.astype(float)
-tab = df.groupby('espece').count()
-tab = tab.sort_values(tab.columns[0],ascending = False)[:NB_CLASS]
-compte = pd.DataFrame(np.zeros((len(tab)+1,1)),columns = ['nombre'])
-
-nb_val = int(tab.file.min()*(1-args.ratio))
-esp_min = tab[tab.file == tab.file.min()].index[0]
-
-val = df[df.file.isin(df[df.espece == esp_min].sample(int(nb_val)).file)]
-df.drop(val.index,inplace=True)
-
-for n in tab.iterrows():
- if n[0] == esp_min:
- continue
- else:
- nb_val = len(df[df.espece == n[0]])*(1-args.ratio)
- new_row = df[df.file.isin(df[df.espece == n[0]].sample(int(nb_val)).file)]
- val = pd.concat((val, new_row))
- df = df.drop(new_row.index)
-
-compte = val.groupby('espece').count()
-
-val = pd.DataFrame(val.groupby('file').count().index,columns = ['file'])
-train = pd.DataFrame(df.groupby('file').count().index,columns = ['file'])
-
-val.file = val.file.str.rsplit('.',1).str[0]
-train.file = train.file.str.rsplit('.',1).str[0]
-
-isExist = os.path.exists(os.path.join(direction,'images'))
-
-if not isExist:
- os.mkdir(os.path.join(direction,'images'))
- os.mkdir(os.path.join(direction,'images/train'))
- os.mkdir(os.path.join(direction,'images/val'))
- os.mkdir(os.path.join(direction,'labels'))
- os.mkdir(os.path.join(direction,'labels/train'))
- os.mkdir(os.path.join(direction,'labels/val'))
-
-for i,row in tqdm(val.iterrows(), total=val.shape[0]):
-
- shutil.copy2(os.path.join(path, str(row.file+'.txt')), os.path.join(direction, str('labels/val/'+row.file+'.txt')))
- shutil.copy2(os.path.join(path, str('../images/all/'+row.file+'.jpg')), os.path.join(direction, str('images/val/'+row.file+'.jpg')))
-
-for i,row in tqdm(train.iterrows(), total=train.shape[0]):
-
- shutil.copy2(os.path.join(path, str(row.file+'.txt')), os.path.join(direction, str('labels/train/'+row.file+'.txt')))
- shutil.copy2(os.path.join(path, str('../images/all/'+row.file+'.jpg')), os.path.join(direction, str('images/train/'+row.file+'.jpg')))
-
-try:
- liste_espece = pd.read_csv(str(path+'../liste_especes.csv'))
-except Exception:
- print('No species list detected, please add to ',str(direction+'custom_data.yaml'))
-
-with open(str(direction+'custom_data.yaml'),'w') as f:
- f.write('train : '+direction+'images/train\n')
- f.write('val : '+direction+'images/val\n')
- f.write('nc : ' + str(len(liste_espece))+'\n')
- try:
- f.write('names : ' + str(liste_espece.espece.tolist()))
- except Exception:
- print('.yaml saved to ',str(direction+'custom_data.yaml'))
\ No newline at end of file
+def create_directory_if_not_exists(directory):
+ if not os.path.exists(directory):
+ os.mkdir(directory)
+
+def copy_files_to_directory(file_list, source_dir, destination_dir):
+ for file_name in file_list:
+ source_path = os.path.join(source_dir, f'{file_name}.txt')
+ destination_path = os.path.join(destination_dir, f'{file_name}.txt')
+ shutil.copy2(source_path, destination_path)
+
+def process_data(args):
+ path = args.path_to_data
+ direction = args.direction
+
+ NB_CLASS = 2
+
+ df = pd.concat({f: pd.read_csv(os.path.join(path, f), sep=' ', names=['espece', 'x', 'y', 'w', 'h'])
+ for f in tqdm(os.listdir(path))}, names=['file'])
+
+ df = df.reset_index(level=[0])
+ df = df.reset_index()
+ del df['index']
+
+ df = df[df.espece != 'y']
+ df.espece = df.espece.astype(float)
+ tab = df.groupby('espece').count()
+ tab = tab.sort_values(tab.columns[0], ascending=False)[:NB_CLASS]
+ compte = pd.DataFrame(np.zeros((len(tab) + 1, 1)), columns=['nombre'])
+
+ nb_val = int(tab.file.min() * (1 - args.ratio))
+ esp_min = tab[tab.file == tab.file.min()].index[0]
+
+ val = df[df.file.isin(df[df.espece == esp_min].sample(int(nb_val)).file)]
+ df.drop(val.index, inplace=True)
+
+ for n in tab.iterrows():
+ if n[0] == esp_min:
+ continue
+ else:
+ nb_val = len(df[df.espece == n[0]]) * (1 - args.ratio)
+ new_row = df[df.file.isin(df[df.espece == n[0]].sample(int(nb_val)).file)]
+ val = pd.concat((val, new_row))
+ df = df.drop(new_row.index)
+
+ compte = val.groupby('espece').count()
+
+ val = pd.DataFrame(val.groupby('file').count().index, columns=['file'])
+ train = pd.DataFrame(df.groupby('file').count().index, columns=['file'])
+
+ val.file = val.file.str.rsplit('.', 1).str[0]
+ train.file = train.file.str.rsplit('.', 1).str[0]
+
+ create_directory_if_not_exists(os.path.join(direction, 'images'))
+ create_directory_if_not_exists(os.path.join(direction, 'images/train'))
+ create_directory_if_not_exists(os.path.join(direction, 'images/val'))
+ create_directory_if_not_exists(os.path.join(direction, 'labels'))
+ create_directory_if_not_exists(os.path.join(direction, 'labels/train'))
+ create_directory_if_not_exists(os.path.join(direction, 'labels/val'))
+
+ copy_files_to_directory(val.file, path, os.path.join(direction, 'labels/val'))
+ copy_files_to_directory(val.file, os.path.join(path, '../images/all'), os.path.join(direction, 'images/val'))
+
+ copy_files_to_directory(train.file, path, os.path.join(direction, 'labels/train'))
+ copy_files_to_directory(train.file, os.path.join(path, '../images/all'), os.path.join(direction, 'images/train'))
+
+ try:
+ liste_espece = pd.read_csv(os.path.join(path, '../liste_especes.csv'))
+ except Exception:
+ print('No species list detected, please add it to', os.path.join(direction, 'custom_data.yaml'))
+
+ with open(os.path.join(direction, 'custom_data.yaml'), 'w') as f:
+ f.write(f'train: {os.path.join(direction, "images/train")}\n')
+ f.write(f'val: {os.path.join(direction, "images/val")}\n')
+ f.write(f'nc: {len(liste_espece)}\n')
+ f.write(f'names: {liste_espece.espece.tolist()}')
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter, description='TODO')
+ parser.add_argument('-r', '--ratio', type=float, default=0.7, help='Train Ratio (val = 1 - ratio)')
+ parser.add_argument('-p', '--path_to_data', type=arg_directory, help='Path of the folder that contains the .txt (ending with labels/)', required=True)
+ parser.add_argument('-d', '--direction', type=arg_directory, help='Directory to which spectrogram and .txt files will be stored (different from -p)', required=True)
+ args = parser.parse_args()
+
+ process_data(args)
diff --git a/get_yolo_detection.py b/get_yolo_detection.py
index 180da13fe0418c3f295dbd7e81684bfe462cc6c9..6146e95c6e806b82298821ba21fd1ad848bf46f5 100644
--- a/get_yolo_detection.py
+++ b/get_yolo_detection.py
@@ -1,6 +1,5 @@
import pandas as pd
import os
-import ipdb
from tqdm import tqdm
import argparse
from datetime import date
@@ -11,36 +10,39 @@ def arg_directory(path):
else:
raise argparse.ArgumentTypeError(f'`{path}` is not a valid path')
-parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter, description='TODO')
-parser.add_argument('-p','--path_to_data', type=arg_directory, help = 'Path of the folder that contain the .txt files',required=True)
-parser.add_argument('-d','--direction', type=arg_directory, help = 'Directory to wich the dataframe will be stored',required=True)
-args = parser.parse_args()
+def process_data(args):
+ annots = args.path_to_data
+ today = date.today()
+ out_file = f'YOLO_detection_{today.day}_{today.month}'
+ outdir = args.direction
-annots = args.path_to_data
+ # Define constants
+ DUREE_SPECTRO = 8
+ OVERLAP = 2
-today = date.today()
-out_file = str('YOLO_detection_'+str('_'+str(today.day)+'_'+str(today.month)))
+ # Load and process data
+ df = pd.concat({f: pd.read_csv(os.path.join(annots, f), sep=' ', names=['espece', 'x', 'y', 'w', 'h', 'conf'])
+ for f in tqdm(os.listdir(annots))}, names=['file'])
-outdir = args.direction
+ df = df.reset_index(level=[0])
+ df = df.reset_index()
+ del df['index']
+ df['idx'] = df.file.str.split('_').str[-1].str.split('.').str[0]
+ df.file = df.file.str.rsplit('.', 1).str[0] + '.wav'
-df = pd.concat({f:pd.read_csv(os.path.join(annots, f), sep=' ', names=['espece', 'x', 'y', 'w', 'h','conf'])
- for f in tqdm(os.listdir(annots))}, names=['file'])
+ # Define class names
+ names = []
-df = df.reset_index(level=[0])
-df = df.reset_index()
-del df['index']
-df['idx'] = df.file.str.split('_').str[-1].str.split('.').str[0]
-df.file = df.file.str.rsplit('.',1).str[0]+'.wav'
+ df['annot'] = df['espece'].apply(lambda x: names[int(x)])
-DUREE_SPECTRO = 8
-OVERLAP = 2
+ # Save the processed DataFrame to a CSV file
+ df.to_csv(os.path.join(outdir, f'{out_file}.csv'), index=False)
+ print(f'Saved as {os.path.join(outdir, f"{out_file}.csv")}')
-#put the classes here
-names = []
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter, description='TODO')
+ parser.add_argument('-p', '--path_to_data', type=arg_directory, help='Path of the folder that contains the .txt files', required=True)
+ parser.add_argument('-d', '--direction', type=arg_directory, help='Directory to which the dataframe will be stored', required=True)
+ args = parser.parse_args()
-df['annot'] = 'None'
-for j in range (len(df)):
- df['annot'].iloc[j] = names[int(df.espece.iloc[j])]
-
-df.to_csv(os.path.join(outdir,str(out_file+'.csv')), index= False)
-print('saved as ',os.path.join(outdir,str(out_file+'.csv')))
+ process_data(args)
diff --git a/labelme2yolo.py b/labelme2yolo.py
index 6c1a7c0955ee4a659331a45d217bf81beed4719a..5ba5fa268fa120bd78be874bc80e5b2261a3b5d7 100644
--- a/labelme2yolo.py
+++ b/labelme2yolo.py
@@ -1,11 +1,10 @@
import os
import json
-import random
import base64
import shutil
import argparse
-from pathlib import Path
from glob import glob
+from pathlib import Path
def arg_directory(path):
if os.path.isdir(path):
@@ -13,55 +12,61 @@ def arg_directory(path):
else:
raise argparse.ArgumentTypeError(f'`{path}` is not a valid path')
-parser = argparse.ArgumentParser(description='Convert LabelMe annotations to YOLO compatible')
-parser.add_argument('-p','--path_to_data',type=arg_directory,help='Path to LabelMe annotations')
-parser.add_argument('-d','--directory',type=arg_directory,help='Directory to which YOLO annotations will be stored')
-args = parser.parse_args()
+def convert_labelme_to_yolo(labelme_annotation_path, yolo_directory):
+ # Load LabelMe annotation
+ image_id = Path(labelme_annotation_path).stem
+ with open(labelme_annotation_path, 'r') as labelme_annotation_file:
+ labelme_annotation = json.load(labelme_annotation_file)
+
+ # YOLO annotation and image paths
+ yolo_annotation_path = os.path.join(yolo_directory, 'labels', f'{image_id}.txt')
+ yolo_image_path = os.path.join(yolo_directory, 'images', f'{image_id}.jpg')
+
+ with open(yolo_annotation_path, 'w') as yolo_annotation_file:
+ yolo_image_data = base64.b64decode(labelme_annotation['imageData'])
-# YOLO metadata and files
-yolo_names = []
-yolo_names_path = os.path.join(args.directory, 'custom.names')
+ # Write YOLO image
+ with open(yolo_image_path, 'wb') as yolo_image_file:
+ yolo_image_file.write(yolo_image_data)
-# Convert image annotations
+ # Write YOLO image annotation
+ for shape in labelme_annotation['shapes']:
+ if shape['shape_type'] != 'rectangle':
+ print(f'Invalid type `{shape["shape_type"]}` in annotation `{labelme_annotation_path}`')
+ continue
-for index, labelme_annotation_path in enumerate(glob(f'{args.path_to_data}/*.json')):
- image_id = os.path.basename(labelme_annotation_path).rstrip('.json')
+ label = shape['label']
+ x1, y1 = shape['points'][0]
+ x2, y2 = shape['points'][1]
+ width = x2 - x1
+ height = y2 - y1
+ x_center = (x1 + x2) / 2
+ y_center = (y1 + y2) / 2
- labelme_annotation_file = open(labelme_annotation_path, 'r')
- labelme_annotation = json.load(labelme_annotation_file)
+ annotation_line = f'{label} {x_center} {y_center} {width} {height}\n'
+ yolo_annotation_file.write(annotation_line)
- yolo_annotation_path = os.path.join(args.directory,'labels', image_id + '.txt')
- yolo_annotation_file = open(yolo_annotation_path, 'w')
- yolo_image = base64.decodebytes(labelme_annotation['imageData'].encode())
- yolo_image_path = os.path.join(args.directory, 'labels', image_id + '.jpg')
+def main(args):
+ yolo_names = set()
- # Write YOLO image (and it to the list)
- yolo_image_file = open(yolo_image_path, 'wb')
- yolo_image_file.write(yolo_image)
- yolo_image_file.close()
+ for labelme_annotation_path in glob(f'{args.path_to_data}/*.json'):
+ convert_labelme_to_yolo(labelme_annotation_path, args.directory)
- # Write YOLO image annotation
- for shape in labelme_annotation['shapes']:
- if shape['shape_type'] != 'rectangle':
- print(
- f'Invalid type `{shape["shape_type"]}` in annotation `annotation_path`')
- continue
- if shape['label'] not in yolo_names:
- yolo_names.append(shape['label'])
+ with open(labelme_annotation_path, 'r') as labelme_annotation_file:
+ labelme_annotation = json.load(labelme_annotation_file)
- points = shape['points']
- scale_width = 1.0 / labelme_annotation['imageWidth']
- scale_height = 1.0 / labelme_annotation['imageHeight']
- width = abs(points[1][0] - points[0][0]) * scale_width
- height = abs(points[1][1] - points[0][1]) * scale_height
- x = (abs(points[1][0] + points[0][0]) / 2) * scale_width
- y = (abs(points[1][1] + points[0][1]) / 2) * scale_height
+ for shape in labelme_annotation['shapes']:
+ yolo_names.add(shape['label'])
- object_class = shape['label']
- yolo_annotation_file.write(f'{object_class} {x} {y} {width} {height}\n')
+ # Write YOLO names
+ yolo_names_path = os.path.join(args.directory, 'custom.names')
+ with open(yolo_names_path, 'w') as yolo_names_file:
+ yolo_names_file.write('\n'.join(yolo_names))
-# Write YOLO names
-yolo_names_file = open(yolo_names_path, 'w')
-yolo_names_file.write(os.linesep.join(yolo_names))
-yolo_names_file.close()
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(description='Convert LabelMe annotations to YOLO compatible')
+ parser.add_argument('-p', '--path_to_data', type=arg_directory, help='Path to LabelMe annotations', required=True)
+ parser.add_argument('-d', '--directory', type=arg_directory, help='Directory to which YOLO annotations will be stored', required=True)
+ args = parser.parse_args()
+ main(args)