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c0f2554 2839cb6 cb6da6a c0f2554 cb6da6a 2839cb6 c0f2554 cb6da6a c0f2554 2839cb6 c0f2554 2839cb6 cb6da6a 2839cb6 cb6da6a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | import glob
import os
import random
import shutil
# Training Yolo for Object Detection in PyTorch with Your Custom Dataset — The Simple Way
# https://medium.com/data-science/training-yolo-for-object-detection-in-pytorch-with-your-custom-dataset-the-simple-way-1aa6f56cf7d9
images_dir = "./images"
labels_dir = "./labels"
output_dir = "./dataset"
val_pct = 10 # 10% validation
test_pct = 10 # 10% test
# Récupère toutes les images (en gérant .jpg et .JPG)
images = glob.glob(os.path.join(images_dir, "*.jpg")) + \
glob.glob(os.path.join(images_dir, "*.JPG"))
random.seed(42) # pour un split reproductible
random.shuffle(images)
n_total = len(images)
n_val = round(n_total * val_pct / 100)
n_test = round(n_total * test_pct / 100)
val_images = images[:n_val]
test_images = images[n_val:n_val + n_test]
train_images = images[n_val + n_test:]
def copy_split(split_name, image_list):
split_images_dir = os.path.join(output_dir, split_name, "images")
split_labels_dir = os.path.join(output_dir, split_name, "labels")
os.makedirs(split_images_dir, exist_ok=True)
os.makedirs(split_labels_dir, exist_ok=True)
copied = 0
missing_labels = 0
for image_path in image_list:
filename = os.path.basename(image_path)
identifier, ext = os.path.splitext(filename)
label_path = os.path.join(labels_dir, identifier + ".txt")
# Copie l'image
shutil.copy2(image_path, os.path.join(split_images_dir, filename))
# Copie le label correspondant, s'il existe
if os.path.isfile(label_path):
shutil.copy2(label_path, os.path.join(split_labels_dir, identifier + ".txt"))
copied += 1
else:
print(f"Label manquant pour {filename}")
missing_labels += 1
return copied, missing_labels
train_copied, train_missing = copy_split("train", train_images)
val_copied, val_missing = copy_split("val", val_images)
test_copied, test_missing = copy_split("test", test_images)
print(f"\nTotal images : {n_total}")
print(f"Train : {len(train_images)} images ({len(train_images)/n_total*100:.1f}%), {train_missing} labels manquants")
print(f"Val : {len(val_images)} images ({len(val_images)/n_total*100:.1f}%), {val_missing} labels manquants")
print(f"Test : {len(test_images)} images ({len(test_images)/n_total*100:.1f}%), {test_missing} labels manquants")
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