From 279b542a5b77698ba94f80706e5c0cd594626949 Mon Sep 17 00:00:00 2001
From: Stephane Chavin <stephane.chavin@lis-lab.fr>
Date: Fri, 13 Dec 2024 15:34:20 +0100
Subject: [PATCH] correct
---
get_train_val.py | 13 +++++++------
1 file changed, 7 insertions(+), 6 deletions(-)
diff --git a/get_train_val.py b/get_train_val.py
index 988d96e..3393dc1 100755
--- a/get_train_val.py
+++ b/get_train_val.py
@@ -4,6 +4,7 @@ import argparse
import os
import pandas as pd
import utils
+import glob
from tqdm import tqdm
@@ -24,7 +25,7 @@ def export_split(argument, entry, path, directory):
if argument.test:
test_set = entry[2]
- test_set.file = ['.'.join(x.split('.')[:-1])
+ test_set.file = ['.'.join(x.split('/')[-1].split('.')[:-1])
for num, x in enumerate(test.file)]
utils.copy_files_to_directory(test_set.file, path, os.path.join(
@@ -32,9 +33,9 @@ def export_split(argument, entry, path, directory):
utils.copy_files_to_directory(test_set.file, os.path.join(
path, '../images/all'), os.path.join(directory, 'images/test'), 'jpg')
- val_set.file = ['.'.join(x.split('.')[:-1])
+ val_set.file = ['.'.join(x.split('/')[-1].split('.')[:-1])
for _, x in enumerate(val.file)]
- train_set.file = ['.'.join(x.split('.')[:-1])
+ train_set.file = ['.'.join(x.split('/')[-1].split('.')[:-1])
for _, x in enumerate(train_set.file)]
# Copy the validation set into the folder
@@ -71,7 +72,7 @@ def prepare_data(arguments):
"""
annotations = []
background = []
- for f in tqdm(arguments.path_to_data, desc="Processing",
+ for f in tqdm(glob.glob(os.path.join(arguments.path_to_data)), desc="Processing",
ascii='░▒▓█'):
file_annotation = pd.read_csv(f, sep=' ', names=['species', 'x', 'y', 'w', 'h'])
if len(file_annotation) == 0:
@@ -120,7 +121,7 @@ if __name__ == '__main__':
print(f'Train saved in {saved_directory}\n')
print('To train your model, use the following command : \n')
- current_path = os.getcwd()
+ current_path = os.path.abspath(os.path.dirname( __file__ ))
directory_path = os.path.join(current_path, saved_directory)
@@ -131,7 +132,7 @@ if __name__ == '__main__':
command = f'python {yolo_path} --data {data_path} --imgsz 640 --epochs 100 --weights {weights_path} --hyp {hyp_path} --cache'
print(command,'\n')
- if len(background == 0):
+ if len(background) == 0:
print('\u26A0\uFE0F Be aware that it is recommended to have background images that',
'represents 10% of your dataset. If you do not have background, use the script "get_spectrogram.py"',
'with --background arguments. Comptue on recordings that contains multiple type of noise...')
--
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