cleanup add training bell

backend/services/generate_json_yolox.py

@@ -7,12 +7,30 @@ from models.Images import Image
 from models.Annotation import Annotation
 
 def generate_training_json(training_id):
-    """Generate COCO JSON for training, validation, and test sets"""
-    # training_id is now project_details_id
-    training_project_details = TrainingProjectDetails.query.get(training_id)
+    """Generate COCO JSON for training, validation, and test sets
+    
+    Args:
+        training_id: Can be either a Training.id or TrainingProjectDetails.id
+                     Function will automatically detect which one and find the correct details_id
+    """
+    from models.training import Training
+    
+    # First, try to get as a Training record
+    training_record = Training.query.get(training_id)
+    
+    if training_record:
+        # It's a Training.id - use its project_details_id
+        details_id = training_record.project_details_id
+        print(f'[generate_training_json] Using training_id={training_id}, mapped to project_details_id={details_id}')
+    else:
+        # Try as TrainingProjectDetails.id directly
+        details_id = training_id
+        print(f'[generate_training_json] Using training_id={training_id} as project_details_id directly')
+    
+    training_project_details = TrainingProjectDetails.query.get(details_id)
     
     if not training_project_details:
-        raise Exception(f'No TrainingProjectDetails found for project_details_id {training_id}')
+        raise Exception(f'No TrainingProjectDetails found for id {training_id} (details_id: {details_id})')
     
     details_obj = training_project_details.to_dict()
     
@@ -110,22 +128,35 @@ def generate_training_json(training_id):
                     break
             
             # Construct ABSOLUTE path using data_dir
-            # Normalize data_dir - ensure it uses backslashes for Windows
-            normalized_data_dir = data_dir.rstrip('/\\').replace('/', '\\')
+            # Detect platform for proper path handling
+            import platform
+            is_windows = platform.system() == 'Windows'
+            
+            # Normalize data_dir and file_name based on platform
+            if is_windows:
+                # Windows: use backslashes
+                normalized_data_dir = data_dir.rstrip('/\\').replace('/', '\\')
+                file_name = file_name.replace('/', '\\')
+            else:
+                # Linux/Mac: use forward slashes
+                normalized_data_dir = data_dir.rstrip('/\\').replace('\\', '/')
+                file_name = file_name.replace('\\', '/')
             
             # Check if already absolute path
-            if not (file_name.startswith('\\\\') or (len(file_name) > 1 and file_name[1] == ':')):
-                # It's a relative path, combine with data_dir
-                # For UNC paths, we need to manually concatenate to preserve \\
-                if normalized_data_dir.startswith('\\\\'):
-                    # UNC path
-                    file_name = normalized_data_dir + '\\' + file_name.replace('/', '\\')
-                else:
-                    # Regular path
-                    file_name = os.path.join(normalized_data_dir, file_name.replace('/', '\\'))
+            is_absolute = False
+            if is_windows:
+                is_absolute = file_name.startswith('\\\\') or (len(file_name) > 1 and file_name[1] == ':')
             else:
-                # Already absolute, just normalize separators
-                file_name = file_name.replace('/', '\\')
+                is_absolute = file_name.startswith('/')
+            
+            if not is_absolute:
+                # It's a relative path, combine with data_dir
+                if is_windows and normalized_data_dir.startswith('\\\\'):
+                    # Windows UNC path
+                    file_name = normalized_data_dir + '\\' + file_name
+                else:
+                    # Regular path (Windows or Linux)
+                    file_name = os.path.join(normalized_data_dir, file_name)
             
             # Get annotations for this image
             annotations = Annotation.query.filter_by(image_id=image.image_id).all()
@@ -218,13 +249,19 @@ def generate_training_json(training_id):
     
     project_name = training_project.title.replace(' ', '_') if training_project and training_project.title else f'project_{details_obj["project_id"]}'
     
-    # Get training record to use its name for folder
-    training_record = Training.query.filter_by(project_details_id=training_id).first()
-    training_folder_name = f"{training_record.exp_name or training_record.training_name or 'training'}_{training_record.id}" if training_record else str(training_id)
-    training_folder_name = training_folder_name.replace(' ', '_')
+    # Get training record to use its name and ID for folder and file names
+    # Use the same training_id that was passed in (if it was a Training.id)
+    # or find the first training for this details_id
+    if not training_record:
+        training_record = Training.query.filter_by(project_details_id=details_id).first()
     
-    # Use training_record.id for file names to match what generate_yolox_exp expects
-    training_file_id = training_record.id if training_record else training_id
+    if training_record:
+        training_folder_name = f"{training_record.exp_name or training_record.training_name or 'training'}_{training_record.id}"
+        training_folder_name = training_folder_name.replace(' ', '_')
+        training_file_id = training_record.id
+    else:
+        training_folder_name = str(details_id)
+        training_file_id = details_id
     
     # Save annotations to the configured output folder
     annotations_dir = os.path.join(output_base_path, project_name, training_folder_name, 'annotations')
@@ -242,7 +279,7 @@ def generate_training_json(training_id):
     with open(test_path, 'w') as f:
         json.dump(test_json, f, indent=2)
     
-    print(f'COCO JSON splits written to {annotations_dir} for trainingId {training_id}')
+    print(f'COCO JSON splits written to {annotations_dir} for training_id={training_file_id} (details_id={details_id})')
     
     # Also generate inference exp.py
     from services.generate_yolox_exp import generate_yolox_inference_exp