Update training

.gitignore

@@ -31,3 +31,4 @@ src/server/venv/*
 __pycache__/
 
 my_database.db
+latest_detect

src/server/routes/detect.py

@@ -111,7 +111,7 @@ def detect_image():
     file.save(image_path)
 
     # Run with model
-    results = model(image_path, conf=0.6)  
+    results = model(image_path, conf=0.5)  
     points = []
 
     # Result predict

src/server/train.py

@@ -13,6 +13,8 @@ import sqlite3
 DATA_SPLIT_FOLDER = "model_datasets"
 IMAGE_EXTENSION = ".png"  
 LOG_FILE = "training_logs.log"
+LATEST_DETECT_IMAGES = "latest_detect/images"
+LATEST_DETECT_LABELS = "latest_detect/labels"
 
 def get_label_names():
     conn = sqlite3.connect("my_database.db")
@@ -84,6 +86,17 @@ split_idx = int(len(image_files) * train_ratio)
 train_files = image_files[:split_idx]
 val_files = image_files[split_idx:]
 
+latest_img_folder = [f for f in os.listdir(LATEST_DETECT_IMAGES) if f.endswith(IMAGE_EXTENSION)]
+train_files += latest_img_folder
+# Copy latest detect images to train folder
+for img_file in latest_img_folder:
+    img_path = os.path.join(LATEST_DETECT_IMAGES, img_file)
+    label_file = os.path.splitext(img_file)[0] + ".txt"
+    label_path = os.path.join(LATEST_DETECT_LABELS, label_file)
+    
+    if os.path.exists(img_path) and os.path.exists(label_path) and not os.path.exists(os.path.join(image_folder, img_file)):
+        shutil.copy(img_path, os.path.join(image_folder, img_file))
+        shutil.copy(label_path, os.path.join(label_folder, label_file))
 
 
 def log_message(message: str):
@@ -146,17 +159,43 @@ def call_reload_model_api(base_url="http://localhost:5000"):
         log_message(f" Error calling reload model API: {e} \n")
         return {"error": str(e)}
 
-def clear_images_source():
-    paths = [image_folder, label_folder]
-    # paths = [image_folder, label_folder, train_img_folder, train_lbl_folder, val_img_folder, val_lbl_folder]
+def copy_files(src_folder, dest_folder, num_files=20):
+    if not os.path.exists(dest_folder):
+        os.makedirs(dest_folder)
+
+    files = sorted(
+        glob.glob(os.path.join(src_folder, "*")),
+        key=os.path.getmtime,  # Sort by modification time (latest first)
+        reverse=True
+    )[:num_files]  # Get top N files
+
+    for file in files:
+        try:
+            shutil.copy(file, os.path.join(dest_folder, os.path.basename(file)))
+            # log_message(f"Copied: {file} -> {dest_folder}")
+        except Exception as e:
+            log_message(f"Error copying {file}: {e}")
+
+def clear_images_source():
+    # Copy top 20 images and labels
+    copy_files(image_folder, LATEST_DETECT_IMAGES, 20)
+    copy_files(label_folder, LATEST_DETECT_LABELS, 20)
+
+    # Delete all files in source folders
+    for path in [image_folder, label_folder]:
+        if not os.path.exists(path):
+            log_message(f"Path does not exist: {path}")
+            continue  
 
-    for path in paths:
         for file in glob.glob(os.path.join(path, "*")):
             if os.path.isfile(file):
-                os.remove(file)
+                try:
+                    os.remove(file)
+                    # log_message(f"Deleted: {file}")
+                except Exception as e:
+                    log_message(f"Error deleting {file}: {e}")
 
-    
-    log_message(f"Delete source image success \n")
+    log_message("Delete source image success \n")
     log_message("END " + ("=" * 20) + "\n")
 
 
@@ -185,7 +224,15 @@ def move_files(file_list, dest_img_folder, dest_lbl_folder):
     return copied_images, copied_labels
 
 
-def train_yolo_model(pretrained_model: str, dataset_folder: str,project_name: str, name: str, epochs: int = 50, batch_size: int = 16, img_size: int = 640, lr: float = 0.001):
+def train_yolo_model(
+    pretrained_model: str,
+    dataset_folder: str,
+    project_name: str,
+    name: str,
+    epochs: int = 50,
+    batch_size: int = 16,
+    img_size: int = 640,
+    lr: float = 0.001):
     dataset_yaml = os.path.join(dataset_folder, "data.yaml")
 
     if not os.path.exists(dataset_yaml):
@@ -207,7 +254,7 @@ def train_yolo_model(pretrained_model: str, dataset_folder: str,project_name: st
         optimizer="AdamW",
         lr0=lr,
         weight_decay=0.0005,
-        patience=10, 
+        patience=0, 
         verbose=True,
         project=project_name,  
         name=name
@@ -219,9 +266,6 @@ def train_yolo_model(pretrained_model: str, dataset_folder: str,project_name: st
     call_reload_model_api()
     clear_images_source()   
 
-
-
-
 # 🚀 Copy files
 train_copied_imgs, train_copied_lbls = move_files(train_files, train_img_folder, train_lbl_folder)
 val_copied_imgs, val_copied_lbls = move_files(val_files, val_img_folder, val_lbl_folder)
@@ -248,6 +292,9 @@ else:
     train_yolo_model(pretrained_model = get_latest_model(
         trained_model_folder=TRAINED_MODEL_FOLDER, 
         default_model=PRETRAINED_MODEL), 
-        dataset_folder = dataset_folder, epochs = 50, name=today_str, project_name=model_folder_name
+        dataset_folder = dataset_folder,
+        epochs = 50,
+        name=today_str,
+        project_name=model_folder_name,
         )