[update] update on split.py and Readme

.gitignore

@@ -29,4 +29,5 @@ lib64/
 
 #website
 generated_html/
-annotations/
+annotations/
+dataset/

README.md

@@ -34,8 +34,8 @@ GenHisDoc
 ├── labels
 ├── classes.txt 
 ├── origin.csv #Show you the dataset origin of a specific image
-├── split.py # split the dataset between a test, train and val set.
-└── html.py # will create a html page with updated statistic about the dataset and a full annotation display on all images.
+├── split.py # split the dataset between a test, train and val as shown in the .txt files
+└── html.py # will create a html page with updated statistic about the dataset and a full annotation display on all images in the annotations/ folder
 ```
 
 The annotations are in YOLO format using the classes below. 

split.py

@@ -1,18 +1,21 @@
 import glob
 import os
-import numpy as np
 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
 
-current_dir = "./images"
+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(current_dir, "*.jpg")) + \
-         glob.glob(os.path.join(current_dir, "*.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)
@@ -25,16 +28,40 @@ val_images = images[:n_val]
 test_images = images[n_val:n_val + n_test]
 train_images = images[n_val + n_test:]
 
-def write_list(filename, image_list):
-    with open(filename, "w") as f:
-        for path in image_list:
-            f.write(path.replace("\\", "/") + "\n")
 
-write_list("train.txt", train_images)
-write_list("val.txt", val_images)
-write_list("test.txt", test_images)
+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"Total images : {n_total}")
-print(f"Train : {len(train_images)} ({len(train_images)/n_total*100:.1f}%)")
-print(f"Val   : {len(val_images)} ({len(val_images)/n_total*100:.1f}%)")
-print(f"Test  : {len(test_images)} ({len(test_images)/n_total*100:.1f}%)")
+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")