diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..97807ae9ce3bfedcd712d1e67c64ac16cae8b4fc Binary files /dev/null and b/.DS_Store differ diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..ac23ed037d3fcdeff1faa6396c2bfe880d9f8506 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,17 @@ +# Utiliser une image de base Python légère +FROM python:3.9-slim + +# Définir le répertoire de travail +WORKDIR /app + +# Copier les fichiers nécessaires dans le conteneur +COPY . /app + +# Installer les dépendances +RUN pip install --no-cache-dir -r requirements.txt + +# Exposer le port 7860 pour le serveur Flask +EXPOSE 7860 + +# Commande pour démarrer Flask +CMD ["python", "app.py"] \ No newline at end of file diff --git a/README.md b/README.md index efa77c4b9869785e08be9d67c1887302fc7145e9..c247b19030f898e97dc3f4458673b608ee240804 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,8 @@ --- title: Segmentation Project -emoji: ⚡ -colorFrom: blue -colorTo: yellow +emoji: 😻 +colorFrom: red +colorTo: purple sdk: docker pinned: false --- diff --git a/app.log b/app.log new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..bdd4318ba4d6e3c135d34d167940dcb959c8eb7d --- /dev/null +++ b/app.py @@ -0,0 +1,797 @@ +from flask import Flask, render_template, request, jsonify +from flask_socketio import SocketIO +import os +import shutil +import numpy as np +from PIL import Image +from utils.predictor import Predictor +from utils.helpers import ( + blend_mask_with_image, + save_mask_as_png, + convert_mask_to_yolo, +) +import torch +from ultralytics import YOLO +import threading +from threading import Lock +import subprocess +import time +import logging +import multiprocessing + + +# Initialize Flask app and SocketIO +app = Flask(__name__) +socketio = SocketIO(app) + +# Define Base Directory +BASE_DIR = os.path.abspath(os.path.dirname(__file__)) + +# Folder structure with absolute paths +UPLOAD_FOLDERS = { + 'input': os.path.join(BASE_DIR, 'static/uploads/input'), + 'segmented_voids': os.path.join(BASE_DIR, 'static/uploads/segmented/voids'), + 'segmented_chips': os.path.join(BASE_DIR, 'static/uploads/segmented/chips'), + 'mask_voids': os.path.join(BASE_DIR, 'static/uploads/mask/voids'), + 'mask_chips': os.path.join(BASE_DIR, 'static/uploads/mask/chips'), + 'automatic_segmented': os.path.join(BASE_DIR, 'static/uploads/segmented/automatic'), +} + +HISTORY_FOLDERS = { + 'images': os.path.join(BASE_DIR, 'static/history/images'), + 'masks_chip': os.path.join(BASE_DIR, 'static/history/masks/chip'), + 'masks_void': os.path.join(BASE_DIR, 'static/history/masks/void'), +} + +DATASET_FOLDERS = { + 'train_images': os.path.join(BASE_DIR, 'dataset/train/images'), + 'train_labels': os.path.join(BASE_DIR, 'dataset/train/labels'), + 'val_images': os.path.join(BASE_DIR, 'dataset/val/images'), + 'val_labels': os.path.join(BASE_DIR, 'dataset/val/labels'), + 'temp_backup': os.path.join(BASE_DIR, 'temp_backup'), + 'models': os.path.join(BASE_DIR, 'models'), + 'models_old': os.path.join(BASE_DIR, 'models/old'), +} + +# Ensure all folders exist +for folder_name, folder_path in {**UPLOAD_FOLDERS, **HISTORY_FOLDERS, **DATASET_FOLDERS}.items(): + os.makedirs(folder_path, exist_ok=True) + logging.info(f"Ensured folder exists: {folder_name} -> {folder_path}") + +training_process = None + + +def initialize_training_status(): + """Initialize global training status.""" + global training_status + training_status = {'running': False, 'cancelled': False} + +def persist_training_status(): + """Save training status to a file.""" + with open(os.path.join(BASE_DIR, 'training_status.json'), 'w') as status_file: + json.dump(training_status, status_file) + +def load_training_status(): + """Load training status from a file.""" + global training_status + status_path = os.path.join(BASE_DIR, 'training_status.json') + if os.path.exists(status_path): + with open(status_path, 'r') as status_file: + training_status = json.load(status_file) + else: + training_status = {'running': False, 'cancelled': False} + +load_training_status() + +os.environ["TORCH_CUDNN_SDPA_ENABLED"] = "0" + +# Initialize SAM Predictor +MODEL_CFG = r"C:\codes\sam2\segment-anything-2\sam2\configs\sam2.1\sam2.1_hiera_l.yaml" +CHECKPOINT = r"C:\codes\sam2\segment-anything-2\checkpoints\sam2.1_hiera_large.pt" +DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") +predictor = Predictor(MODEL_CFG, CHECKPOINT, DEVICE) + +# Initialize YOLO-seg +YOLO_CFG = os.path.join(DATASET_FOLDERS['models'], "best.pt") +yolo_model = YOLO(YOLO_CFG) + +# Configure logging +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s [%(levelname)s] %(message)s', + handlers=[ + logging.StreamHandler(), + logging.FileHandler(os.path.join(BASE_DIR, "app.log")) # Log to a file + ] +) + + +@app.route('/') +def index(): + """Serve the main UI.""" + return render_template('index.html') + +@app.route('/upload', methods=['POST']) +def upload_image(): + """Handle image uploads.""" + if 'file' not in request.files: + return jsonify({'error': 'No file uploaded'}), 400 + file = request.files['file'] + if file.filename == '': + return jsonify({'error': 'No file selected'}), 400 + + # Save the uploaded file to the input folder + input_path = os.path.join(UPLOAD_FOLDERS['input'], file.filename) + file.save(input_path) + + # Set the uploaded image in the predictor + image = np.array(Image.open(input_path).convert("RGB")) + predictor.set_image(image) + + # Return a web-accessible URL instead of the file system path + web_accessible_url = f"/static/uploads/input/{file.filename}" + print(f"Image uploaded and set for prediction: {input_path}") + return jsonify({'image_url': web_accessible_url}) + +@app.route('/segment', methods=['POST']) +def segment(): + """ + Perform segmentation and return the blended image URL. + """ + try: + # Extract data from request + data = request.json + points = np.array(data.get('points', [])) + labels = np.array(data.get('labels', [])) + current_class = data.get('class', 'voids') # Default to 'voids' if class not provided + + # Ensure predictor has an image set + if not predictor.image_set: + raise ValueError("No image set for prediction.") + + # Perform SAM prediction + masks, _, _ = predictor.predict( + point_coords=points, + point_labels=labels, + multimask_output=False + ) + + # Check if masks exist and have non-zero elements + if masks is None or masks.size == 0: + raise RuntimeError("No masks were generated by the predictor.") + + # Define output paths based on class + mask_folder = UPLOAD_FOLDERS.get(f'mask_{current_class}') + segmented_folder = UPLOAD_FOLDERS.get(f'segmented_{current_class}') + + if not mask_folder or not segmented_folder: + raise ValueError(f"Invalid class '{current_class}' provided.") + + os.makedirs(mask_folder, exist_ok=True) + os.makedirs(segmented_folder, exist_ok=True) + + # Save the raw mask + mask_path = os.path.join(mask_folder, 'raw_mask.png') + save_mask_as_png(masks[0], mask_path) + + # Generate blended image + blend_color = [34, 139, 34] if current_class == 'voids' else [30, 144, 255] # Green for voids, blue for chips + blended_image = blend_mask_with_image(predictor.image, masks[0], blend_color) + + # Save blended image + blended_filename = f"blended_{current_class}.png" + blended_path = os.path.join(segmented_folder, blended_filename) + Image.fromarray(blended_image).save(blended_path) + + # Return URL for frontend access + segmented_url = f"/static/uploads/segmented/{current_class}/{blended_filename}" + logging.info(f"Segmentation completed for {current_class}. Points: {points}, Labels: {labels}") + return jsonify({'segmented_url': segmented_url}) + + except ValueError as ve: + logging.error(f"Value error during segmentation: {ve}") + return jsonify({'error': str(ve)}), 400 + + except Exception as e: + logging.error(f"Unexpected error during segmentation: {e}") + return jsonify({'error': 'Segmentation failed', 'details': str(e)}), 500 + +@app.route('/automatic_segment', methods=['POST']) +def automatic_segment(): + """Perform automatic segmentation using YOLO.""" + if 'file' not in request.files: + return jsonify({'error': 'No file uploaded'}), 400 + file = request.files['file'] + if file.filename == '': + return jsonify({'error': 'No file selected'}), 400 + + input_path = os.path.join(UPLOAD_FOLDERS['input'], file.filename) + file.save(input_path) + + try: + # Perform YOLO segmentation + results = yolo_model.predict(input_path, save=False, save_txt=False) + output_folder = UPLOAD_FOLDERS['automatic_segmented'] + os.makedirs(output_folder, exist_ok=True) + + chips_data = [] + chips = [] + voids = [] + + # Process results and save segmented images + for result in results: + annotated_image = result.plot() + result_filename = f"{file.filename.rsplit('.', 1)[0]}_pred.jpg" + result_path = os.path.join(output_folder, result_filename) + Image.fromarray(annotated_image).save(result_path) + + # Separate chips and voids + for i, label in enumerate(result.boxes.cls): # YOLO labels + label_name = result.names[int(label)] # Get label name (e.g., 'chip' or 'void') + box = result.boxes.xyxy[i].cpu().numpy() # Bounding box (x1, y1, x2, y2) + area = float((box[2] - box[0]) * (box[3] - box[1])) # Calculate area + + if label_name == 'chip': + chips.append({'box': box, 'area': area, 'voids': []}) + elif label_name == 'void': + voids.append({'box': box, 'area': area}) + + # Assign voids to chips based on proximity + for void in voids: + void_centroid = [ + (void['box'][0] + void['box'][2]) / 2, # x centroid + (void['box'][1] + void['box'][3]) / 2 # y centroid + ] + for chip in chips: + # Check if void centroid is within chip bounding box + if (chip['box'][0] <= void_centroid[0] <= chip['box'][2] and + chip['box'][1] <= void_centroid[1] <= chip['box'][3]): + chip['voids'].append(void) + break + + # Calculate metrics for each chip + for idx, chip in enumerate(chips): + chip_area = chip['area'] + total_void_area = sum([float(void['area']) for void in chip['voids']]) + max_void_area = max([float(void['area']) for void in chip['voids']], default=0) + + void_percentage = (total_void_area / chip_area) * 100 if chip_area > 0 else 0 + max_void_percentage = (max_void_area / chip_area) * 100 if chip_area > 0 else 0 + + chips_data.append({ + "chip_number": int(idx + 1), + "chip_area": round(chip_area, 2), + "void_percentage": round(void_percentage, 2), + "max_void_percentage": round(max_void_percentage, 2) + }) + + # Return the segmented image URL and table data + segmented_url = f"/static/uploads/segmented/automatic/{result_filename}" + return jsonify({ + "segmented_url": segmented_url, # Use the URL for frontend access + "table_data": { + "image_name": file.filename, + "chips": chips_data + } + }) + + except Exception as e: + print(f"Error in automatic segmentation: {e}") + return jsonify({'error': 'Segmentation failed.'}), 500 + +@app.route('/save_both', methods=['POST']) +def save_both(): + """Save both the image and masks into the history folders.""" + data = request.json + image_name = data.get('image_name') + + if not image_name: + return jsonify({'error': 'Image name not provided'}), 400 + + try: + # Ensure image_name is a pure file name + image_name = os.path.basename(image_name) # Strip any directory path + print(f"Sanitized Image Name: {image_name}") + + # Correctly resolve the input image path + input_image_path = os.path.join(UPLOAD_FOLDERS['input'], image_name) + if not os.path.exists(input_image_path): + print(f"Input image does not exist: {input_image_path}") + return jsonify({'error': f'Input image not found: {input_image_path}'}), 404 + + # Copy the image to history/images + image_history_path = os.path.join(HISTORY_FOLDERS['images'], image_name) + os.makedirs(os.path.dirname(image_history_path), exist_ok=True) + shutil.copy(input_image_path, image_history_path) + print(f"Image saved to history: {image_history_path}") + + # Backup void mask + void_mask_path = os.path.join(UPLOAD_FOLDERS['mask_voids'], 'raw_mask.png') + if os.path.exists(void_mask_path): + void_mask_history_path = os.path.join(HISTORY_FOLDERS['masks_void'], f"{os.path.splitext(image_name)[0]}.png") + os.makedirs(os.path.dirname(void_mask_history_path), exist_ok=True) + shutil.copy(void_mask_path, void_mask_history_path) + print(f"Voids mask saved to history: {void_mask_history_path}") + else: + print(f"Voids mask not found: {void_mask_path}") + + # Backup chip mask + chip_mask_path = os.path.join(UPLOAD_FOLDERS['mask_chips'], 'raw_mask.png') + if os.path.exists(chip_mask_path): + chip_mask_history_path = os.path.join(HISTORY_FOLDERS['masks_chip'], f"{os.path.splitext(image_name)[0]}.png") + os.makedirs(os.path.dirname(chip_mask_history_path), exist_ok=True) + shutil.copy(chip_mask_path, chip_mask_history_path) + print(f"Chips mask saved to history: {chip_mask_history_path}") + else: + print(f"Chips mask not found: {chip_mask_path}") + + return jsonify({'message': 'Image and masks saved successfully!'}), 200 + + except Exception as e: + print(f"Error saving files: {e}") + return jsonify({'error': 'Failed to save files.', 'details': str(e)}), 500 + +@app.route('/get_history', methods=['GET']) +def get_history(): + try: + saved_images = os.listdir(HISTORY_FOLDERS['images']) + return jsonify({'status': 'success', 'images': saved_images}), 200 + except Exception as e: + return jsonify({'status': 'error', 'message': f'Failed to fetch history: {e}'}), 500 + + +@app.route('/delete_history_item', methods=['POST']) +def delete_history_item(): + data = request.json + image_name = data.get('image_name') + + if not image_name: + return jsonify({'error': 'Image name not provided'}), 400 + + try: + image_path = os.path.join(HISTORY_FOLDERS['images'], image_name) + if os.path.exists(image_path): + os.remove(image_path) + + void_mask_path = os.path.join(HISTORY_FOLDERS['masks_void'], f"{os.path.splitext(image_name)[0]}.png") + if os.path.exists(void_mask_path): + os.remove(void_mask_path) + + chip_mask_path = os.path.join(HISTORY_FOLDERS['masks_chip'], f"{os.path.splitext(image_name)[0]}.png") + if os.path.exists(chip_mask_path): + os.remove(chip_mask_path) + + return jsonify({'message': f'{image_name} and associated masks deleted successfully.'}), 200 + except Exception as e: + return jsonify({'error': f'Failed to delete files: {e}'}), 500 + +# Lock for training status updates +status_lock = Lock() + +def update_training_status(key, value): + """Thread-safe update for training status.""" + with status_lock: + training_status[key] = value + +@app.route('/retrain_model', methods=['POST']) +def retrain_model(): + """Handle retrain model workflow.""" + global training_status + + if training_status.get('running', False): + return jsonify({'error': 'Training is already in progress'}), 400 + + try: + # Update training status + update_training_status('running', True) + update_training_status('cancelled', False) + logging.info("Training status updated. Starting training workflow.") + + # Backup masks and images + backup_masks_and_images() + logging.info("Backup completed successfully.") + + # Prepare YOLO labels + prepare_yolo_labels() + logging.info("YOLO labels prepared successfully.") + + # Start YOLO training in a separate thread + threading.Thread(target=run_yolo_training).start() + return jsonify({'message': 'Training started successfully!'}), 200 + + except Exception as e: + logging.error(f"Error during training preparation: {e}") + update_training_status('running', False) + return jsonify({'error': f"Failed to start training: {e}"}), 500 + +def prepare_yolo_labels(): + """Convert all masks into YOLO-compatible labels and copy images to the dataset folder.""" + images_folder = HISTORY_FOLDERS['images'] # Use history images as the source + train_labels_folder = DATASET_FOLDERS['train_labels'] + train_images_folder = DATASET_FOLDERS['train_images'] + val_labels_folder = DATASET_FOLDERS['val_labels'] + val_images_folder = DATASET_FOLDERS['val_images'] + + # Ensure destination directories exist + os.makedirs(train_labels_folder, exist_ok=True) + os.makedirs(train_images_folder, exist_ok=True) + os.makedirs(val_labels_folder, exist_ok=True) + os.makedirs(val_images_folder, exist_ok=True) + + try: + all_images = [img for img in os.listdir(images_folder) if img.endswith(('.jpg', '.png'))] + random.shuffle(all_images) # Shuffle the images for randomness + + # Determine split index + split_idx = int(len(all_images) * 0.8) # 80% for training, 20% for validation + + # Split images into train and validation sets + train_images = all_images[:split_idx] + val_images = all_images[split_idx:] + + # Process training images + for image_name in train_images: + process_image_and_mask( + image_name, + source_images_folder=images_folder, + dest_images_folder=train_images_folder, + dest_labels_folder=train_labels_folder + ) + + # Process validation images + for image_name in val_images: + process_image_and_mask( + image_name, + source_images_folder=images_folder, + dest_images_folder=val_images_folder, + dest_labels_folder=val_labels_folder + ) + + logging.info("YOLO labels prepared, and images split into train and validation successfully.") + + except Exception as e: + logging.error(f"Error in preparing YOLO labels: {e}") + raise + +import random + +def prepare_yolo_labels(): + """Convert all masks into YOLO-compatible labels and copy images to the dataset folder.""" + images_folder = HISTORY_FOLDERS['images'] # Use history images as the source + train_labels_folder = DATASET_FOLDERS['train_labels'] + train_images_folder = DATASET_FOLDERS['train_images'] + val_labels_folder = DATASET_FOLDERS['val_labels'] + val_images_folder = DATASET_FOLDERS['val_images'] + + # Ensure destination directories exist + os.makedirs(train_labels_folder, exist_ok=True) + os.makedirs(train_images_folder, exist_ok=True) + os.makedirs(val_labels_folder, exist_ok=True) + os.makedirs(val_images_folder, exist_ok=True) + + try: + all_images = [img for img in os.listdir(images_folder) if img.endswith(('.jpg', '.png'))] + random.shuffle(all_images) # Shuffle the images for randomness + + # Determine split index + split_idx = int(len(all_images) * 0.8) # 80% for training, 20% for validation + + # Split images into train and validation sets + train_images = all_images[:split_idx] + val_images = all_images[split_idx:] + + # Process training images + for image_name in train_images: + process_image_and_mask( + image_name, + source_images_folder=images_folder, + dest_images_folder=train_images_folder, + dest_labels_folder=train_labels_folder + ) + + # Process validation images + for image_name in val_images: + process_image_and_mask( + image_name, + source_images_folder=images_folder, + dest_images_folder=val_images_folder, + dest_labels_folder=val_labels_folder + ) + + logging.info("YOLO labels prepared, and images split into train and validation successfully.") + + except Exception as e: + logging.error(f"Error in preparing YOLO labels: {e}") + raise + + +def process_image_and_mask(image_name, source_images_folder, dest_images_folder, dest_labels_folder): + """ + Process a single image and its masks, saving them in the appropriate YOLO format. + """ + try: + image_path = os.path.join(source_images_folder, image_name) + label_file_path = os.path.join(dest_labels_folder, f"{os.path.splitext(image_name)[0]}.txt") + + # Copy image to the destination images folder + shutil.copy(image_path, os.path.join(dest_images_folder, image_name)) + + # Clear the label file if it exists + if os.path.exists(label_file_path): + os.remove(label_file_path) + + # Process void mask + void_mask_path = os.path.join(HISTORY_FOLDERS['masks_void'], f"{os.path.splitext(image_name)[0]}.png") + if os.path.exists(void_mask_path): + convert_mask_to_yolo( + mask_path=void_mask_path, + image_path=image_path, + class_id=0, # Void class + output_path=label_file_path + ) + + # Process chip mask + chip_mask_path = os.path.join(HISTORY_FOLDERS['masks_chip'], f"{os.path.splitext(image_name)[0]}.png") + if os.path.exists(chip_mask_path): + convert_mask_to_yolo( + mask_path=chip_mask_path, + image_path=image_path, + class_id=1, # Chip class + output_path=label_file_path, + append=True # Append chip annotations + ) + + logging.info(f"Processed {image_name} into YOLO format.") + except Exception as e: + logging.error(f"Error processing {image_name}: {e}") + raise + +def backup_masks_and_images(): + """Backup current masks and images from history folders.""" + temp_backup_paths = { + 'voids': os.path.join(DATASET_FOLDERS['temp_backup'], 'masks/voids'), + 'chips': os.path.join(DATASET_FOLDERS['temp_backup'], 'masks/chips'), + 'images': os.path.join(DATASET_FOLDERS['temp_backup'], 'images') + } + + # Prepare all backup directories + for path in temp_backup_paths.values(): + if os.path.exists(path): + shutil.rmtree(path) + os.makedirs(path, exist_ok=True) + + try: + # Backup images from history + for file in os.listdir(HISTORY_FOLDERS['images']): + src_image_path = os.path.join(HISTORY_FOLDERS['images'], file) + dst_image_path = os.path.join(temp_backup_paths['images'], file) + shutil.copy(src_image_path, dst_image_path) + + # Backup void masks from history + for file in os.listdir(HISTORY_FOLDERS['masks_void']): + src_void_path = os.path.join(HISTORY_FOLDERS['masks_void'], file) + dst_void_path = os.path.join(temp_backup_paths['voids'], file) + shutil.copy(src_void_path, dst_void_path) + + # Backup chip masks from history + for file in os.listdir(HISTORY_FOLDERS['masks_chip']): + src_chip_path = os.path.join(HISTORY_FOLDERS['masks_chip'], file) + dst_chip_path = os.path.join(temp_backup_paths['chips'], file) + shutil.copy(src_chip_path, dst_chip_path) + + logging.info("Masks and images backed up successfully from history.") + except Exception as e: + logging.error(f"Error during backup: {e}") + raise RuntimeError("Backup process failed.") + +def run_yolo_training(num_epochs=10): + """Run YOLO training process.""" + global training_process + + try: + device = "cuda" if torch.cuda.is_available() else "cpu" + data_cfg_path = os.path.join(BASE_DIR, "models/data.yaml") # Ensure correct YAML path + + logging.info(f"Starting YOLO training on {device} with {num_epochs} epochs.") + logging.info(f"Using dataset configuration: {data_cfg_path}") + + training_command = [ + "yolo", + "train", + f"data={data_cfg_path}", + f"model={os.path.join(DATASET_FOLDERS['models'], 'best.pt')}", + f"device={device}", + f"epochs={num_epochs}", + "project=runs", + "name=train" + ] + + training_process = subprocess.Popen( + training_command, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + text=True, + env=os.environ.copy(), + ) + + # Display and log output in real time + for line in iter(training_process.stdout.readline, ''): + print(line.strip()) + logging.info(line.strip()) + socketio.emit('training_update', {'message': line.strip()}) # Send updates to the frontend + + training_process.wait() + + if training_process.returncode == 0: + finalize_training() # Finalize successfully completed training + else: + raise RuntimeError("YOLO training process failed. Check logs for details.") + except Exception as e: + logging.error(f"Training error: {e}") + restore_backup() # Restore the dataset and masks + + # Emit training error event to the frontend + socketio.emit('training_status', {'status': 'error', 'message': f"Training failed: {str(e)}"}) + finally: + update_training_status('running', False) + training_process = None # Reset the process + + +@socketio.on('cancel_training') +def handle_cancel_training(): + """Cancel the YOLO training process.""" + global training_process, training_status + + if not training_status.get('running', False): + socketio.emit('button_update', {'action': 'retrain'}) # Update button to retrain + return + + try: + training_process.terminate() + training_process.wait() + training_status['running'] = False + training_status['cancelled'] = True + + restore_backup() + cleanup_train_val_directories() + + # Emit button state change + socketio.emit('button_update', {'action': 'retrain'}) + socketio.emit('training_status', {'status': 'cancelled', 'message': 'Training was canceled by the user.'}) + except Exception as e: + logging.error(f"Error cancelling training: {e}") + socketio.emit('training_status', {'status': 'error', 'message': str(e)}) + +def finalize_training(): + """Finalize training by promoting the new model and cleaning up.""" + try: + # Locate the most recent training directory + runs_dir = os.path.join(BASE_DIR, 'runs') + if not os.path.exists(runs_dir): + raise FileNotFoundError("Training runs directory does not exist.") + + # Get the latest training run folder + latest_run = max( + [os.path.join(runs_dir, d) for d in os.listdir(runs_dir)], + key=os.path.getmtime + ) + weights_dir = os.path.join(latest_run, 'weights') + best_model_path = os.path.join(weights_dir, 'best.pt') + + if not os.path.exists(best_model_path): + raise FileNotFoundError(f"'best.pt' not found in {weights_dir}.") + + # Backup the old model + old_model_folder = DATASET_FOLDERS['models_old'] + os.makedirs(old_model_folder, exist_ok=True) + existing_best_model = os.path.join(DATASET_FOLDERS['models'], 'best.pt') + + if os.path.exists(existing_best_model): + timestamp = time.strftime("%Y%m%d_%H%M%S") + shutil.move(existing_best_model, os.path.join(old_model_folder, f"old_{timestamp}.pt")) + logging.info(f"Old model backed up to {old_model_folder}.") + + # Move the new model to the models directory + new_model_dest = os.path.join(DATASET_FOLDERS['models'], 'best.pt') + shutil.move(best_model_path, new_model_dest) + logging.info(f"New model saved to {new_model_dest}.") + + # Notify frontend that training is completed + socketio.emit('training_status', { + 'status': 'completed', + 'message': 'Training completed successfully! Model saved as best.pt.' + }) + + # Clean up train/val directories + cleanup_train_val_directories() + logging.info("Train and validation directories cleaned up successfully.") + + except Exception as e: + logging.error(f"Error finalizing training: {e}") + # Emit error status to the frontend + socketio.emit('training_status', {'status': 'error', 'message': f"Error finalizing training: {str(e)}"}) + +def restore_backup(): + """Restore the dataset and masks from the backup.""" + try: + temp_backup = DATASET_FOLDERS['temp_backup'] + shutil.copytree(os.path.join(temp_backup, 'masks/voids'), UPLOAD_FOLDERS['mask_voids'], dirs_exist_ok=True) + shutil.copytree(os.path.join(temp_backup, 'masks/chips'), UPLOAD_FOLDERS['mask_chips'], dirs_exist_ok=True) + shutil.copytree(os.path.join(temp_backup, 'images'), UPLOAD_FOLDERS['input'], dirs_exist_ok=True) + logging.info("Backup restored successfully.") + except Exception as e: + logging.error(f"Error restoring backup: {e}") + +@app.route('/cancel_training', methods=['POST']) +def cancel_training(): + global training_process + + if training_process is None: + logging.error("No active training process to terminate.") + return jsonify({'error': 'No active training process to cancel.'}), 400 + + try: + training_process.terminate() + training_process.wait() + training_process = None # Reset the process after termination + + # Update training status + update_training_status('running', False) + update_training_status('cancelled', True) + + # Check if the model is already saved as best.pt + best_model_path = os.path.join(DATASET_FOLDERS['models'], 'best.pt') + if os.path.exists(best_model_path): + logging.info(f"Model already saved as best.pt at {best_model_path}.") + socketio.emit('button_update', {'action': 'revert'}) # Notify frontend to revert button state + else: + logging.info("Training canceled, but no new model was saved.") + + # Restore backup if needed + restore_backup() + cleanup_train_val_directories() + + # Emit status update to frontend + socketio.emit('training_status', {'status': 'cancelled', 'message': 'Training was canceled by the user.'}) + return jsonify({'message': 'Training canceled and data restored successfully.'}), 200 + + except Exception as e: + logging.error(f"Error cancelling training: {e}") + return jsonify({'error': f"Failed to cancel training: {e}"}), 500 + +@app.route('/clear_history', methods=['POST']) +def clear_history(): + try: + for folder in [HISTORY_FOLDERS['images'], HISTORY_FOLDERS['masks_chip'], HISTORY_FOLDERS['masks_void']]: + shutil.rmtree(folder, ignore_errors=True) + os.makedirs(folder, exist_ok=True) # Recreate the empty folder + return jsonify({'message': 'History cleared successfully!'}), 200 + except Exception as e: + return jsonify({'error': f'Failed to clear history: {e}'}), 500 + +@app.route('/training_status', methods=['GET']) +def get_training_status(): + """Return the current training status.""" + if training_status.get('running', False): + return jsonify({'status': 'running', 'message': 'Training in progress.'}), 200 + elif training_status.get('cancelled', False): + return jsonify({'status': 'cancelled', 'message': 'Training was cancelled.'}), 200 + return jsonify({'status': 'idle', 'message': 'No training is currently running.'}), 200 + +def cleanup_train_val_directories(): + """Clear the train and validation directories.""" + try: + for folder in [DATASET_FOLDERS['train_images'], DATASET_FOLDERS['train_labels'], + DATASET_FOLDERS['val_images'], DATASET_FOLDERS['val_labels']]: + shutil.rmtree(folder, ignore_errors=True) # Remove folder contents + os.makedirs(folder, exist_ok=True) # Recreate empty folders + logging.info("Train and validation directories cleaned up successfully.") + except Exception as e: + logging.error(f"Error cleaning up train/val directories: {e}") + + +if __name__ == '__main__': + multiprocessing.set_start_method('spawn') # Required for multiprocessing on Windows + app.run(debug=True, 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testing + +nc: 2 # Number of classes +names: ['void', 'chip'] # Class names diff --git a/models/old/old.pt b/models/old/old.pt new file mode 100644 index 0000000000000000000000000000000000000000..a41ca2fa9d12eef772f44654d92e21b37a7594e7 --- /dev/null +++ b/models/old/old.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6c1a69f8b7ebf89dff6c8512342a91782165b0536aafdc8ffa40ded5bd647843 +size 6012957 diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..d0509b46c0d1c65bd315e66a54f6b93f8e55f32a --- /dev/null +++ b/requirements.txt @@ -0,0 +1,9 @@ +Flask +Pillow +numpy<2 +Flask-SocketIO +Pillow +torch +torchaudio +torchvision +ultralytics diff --git a/runs/.DS_Store b/runs/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..42f24cd135e6f096fa9d63a97e722c7a39da6257 Binary files /dev/null and b/runs/.DS_Store differ diff --git a/runtime.txt b/runtime.txt new file mode 100644 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*/ +body { + font-family: 'Inter', sans-serif; + background-color: #f4f6f8; + margin: 0; + padding: 0; + min-height: 100vh; +} + +/* Container Styling */ +.container { + max-width: 1400px; /* Widened to better fit the boxes */ + background: white; + padding: 30px; + border-radius: 16px; + box-shadow: 0 4px 20px rgba(0, 0, 0, 0.1); + margin: 20px auto; +} + +/* Section Headers */ +h1, h2 { + font-size: 2rem; + font-weight: 700; + color: #333; + text-align: center; + margin-bottom: 20px; +} + +/* Tool Sections */ +.tool-section { + background: #fff; + border: 1px solid #ddd; + border-radius: 16px; + padding: 20px; + box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1); + margin-bottom: 30px; + width: 100%; + max-width: 1200px; /* Increased to align with SAM boxes */ + margin: 20px auto; +} + +/* Buttons Styling */ +.btn-group { + display: flex; + justify-content: center; + margin-bottom: 20px; + gap: 10px; +} + +.btn-group button { + font-size: 16px; + padding: 10px 20px; + border-radius: 8px; + cursor: pointer; + transition: all 0.3s ease-in-out; + width: 50%; /* Equal button width */ +} + +.btn-group button.active { + background-color: #007bff; + color: white; + border: 1px solid #0056b3; +} + +.btn-group button:hover { + background-color: #0056b3; + color: white; +} + +/* Tool Layout */ +.tool-container { + display: flex; + flex-direction: column; + gap: 40px; + align-items: center; +} + +/* Canvas Containers */ +.canvas-container { + width: 100%; + max-width: 600px; /* Adjusted for better alignment */ + aspect-ratio: 1 / 1; + border: 2px solid #ddd; + border-radius: 16px; + background-color: #fff; + box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1); + display: flex; + justify-content: center; + align-items: center; + padding: 10px; + margin: 0 auto; +} + +/* File Upload Fields */ +input[type="file"] { + margin-top: 20px; + padding: 10px; + font-size: 16px; + border-radius: 8px; + border: 1px solid #ddd; + box-shadow: inset 0px 1px 3px rgba(0, 0, 0, 0.1); + width: 100%; + max-width: 600px; +} + +input[type="file"]:focus { + border: 1px solid #007bff; + outline: none; +} + +/* Processed Image Styling */ +#automaticProcessedImage { + border: 2px solid #ddd; + border-radius: 16px; + background-color: #fff; + box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1); + width: 100%; + height: auto; + margin-top: 20px; +} + +/* Clear Points Button */ +#clearPoints { + margin-top: 20px; + font-size: 14px; + padding: 10px 15px; + border-radius: 5px; + color: white; + background-color: #dc3545; + border: none; + cursor: pointer; +} + +#clearPoints:hover { + background-color: #b52b37; +} + +/* Table Styling */ +.table { + border: 1px solid #ddd; + border-radius: 10px; + overflow: hidden; + box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1); + width: 100%; +} + +.table th { + background-color: #007bff; /* Blue column headers */ + color: white; + font-weight: bold; + text-align: center; + padding: 10px; +} + +.table td { + padding: 10px; + vertical-align: middle; + background-color: #f9f9f9; + text-align: center; +} + +.table-responsive { + border-radius: 10px; + overflow: hidden; +} + +/* Buttons Styling */ +#clearTableButton, #exportTableButton { + font-size: 16px; + padding: 10px 20px; + border-radius: 8px; + color: white; + background-color: #007bff; + border: none; + transition: all 0.3s ease-in-out; +} + +#clearTableButton:hover, #exportTableButton:hover { + background-color: #0056b3; + cursor: pointer; +} + +/* Responsive Design */ +@media screen and (max-width: 768px) { + .tool-section { + flex-direction: column; + align-items: center; + } + + .canvas-container { + margin-bottom: 20px; + } + + .btn-group button { + width: 100%; + } + + .table { + font-size: 14px; + } +} + +/* Additional Adjustments */ +.tool-section img, canvas { + max-width: 100%; + height: auto; + border-radius: 8px; +} + +#historyButton { + font-size: 16px; + padding: 10px 20px; + border-radius: 8px; + color: white; + background-color: #6c757d; /* Bootstrap secondary color */ + border: none; +} + 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elements +const inputCanvas = document.getElementById('inputCanvas'); +const segmentedCanvas = document.getElementById('segmentedCanvas'); +const imageUpload = document.getElementById('imageUpload'); +const clearPointsButton = document.getElementById('clearPoints'); +const voidsButton = document.getElementById('voidsButton'); +const chipsButton = document.getElementById('chipsButton'); +const retrainModelButton = document.getElementById('retrainModelButton'); +const etaDisplay = document.getElementById('etaDisplay'); + +// Automatic Segmentation DOM elements +const automaticImageUpload = document.getElementById('automaticImageUpload'); +const automaticProcessedImage = document.getElementById('automaticProcessedImage'); +const resultsTableBody = document.getElementById('resultsTableBody'); +const clearTableButton = document.getElementById('clearTableButton'); +const exportTableButton = document.getElementById('exportTableButton'); + +// Constants for consistent canvas and SAM model dimensions +const CANVAS_SIZE = 512; +inputCanvas.width = CANVAS_SIZE; +inputCanvas.height = CANVAS_SIZE; +segmentedCanvas.width = CANVAS_SIZE; +segmentedCanvas.height = CANVAS_SIZE; + +// Interactive segmentation variables +let points = { Voids: [], Chips: [] }; +let labels = { Voids: [], Chips: [] }; +let currentClass = 'Voids'; +let imageUrl = ''; +let originalImageWidth = 0; +let originalImageHeight = 0; +let trainingInProgress = false; + +// Disable right-click menu on canvas +inputCanvas.addEventListener('contextmenu', (event) => event.preventDefault()); + +// Switch between classes +voidsButton.addEventListener('click', () => { + currentClass = 'Voids'; + voidsButton.classList.add('active'); + chipsButton.classList.remove('active'); + clearAndRestorePoints(); +}); + +chipsButton.addEventListener('click', () => { + currentClass = 'Chips'; + chipsButton.classList.add('active'); + voidsButton.classList.remove('active'); + clearAndRestorePoints(); +}); + +// Handle image upload for interactive tool +imageUpload.addEventListener('change', async (event) => { + const file = event.target.files[0]; + const formData = new FormData(); + formData.append('file', file); + + try { + const response = await fetch('/upload', { method: 'POST', body: formData }); + const data = await response.json(); + if (data.error) { + console.error('Error uploading image:', data.error); + return; + } + + imageUrl = data.image_url; + console.log('Uploaded image URL:', imageUrl); + + const img = new Image(); + img.src = imageUrl; + img.onload = () => { + console.log('Image loaded:', img.width, img.height); + originalImageWidth = img.width; + originalImageHeight = img.height; + resizeAndDrawImage(inputCanvas, img); + resizeAndDrawImage(segmentedCanvas, img); + }; + img.onerror = () => { + console.error('Failed to load image from URL:', imageUrl); + }; + } catch (error) { + console.error('Failed to upload image:', error); + } +}); + + +// Handle input canvas clicks +inputCanvas.addEventListener('mousedown', async (event) => { + const rect = inputCanvas.getBoundingClientRect(); + const x = (event.clientX - rect.left) * (originalImageWidth / CANVAS_SIZE); + const y = (event.clientY - rect.top) * (originalImageHeight / CANVAS_SIZE); + + if (event.button === 2) { + points[currentClass].push([x, y]); + labels[currentClass].push(0); // Exclude point (red) + } else if (event.button === 0) { + points[currentClass].push([x, y]); + labels[currentClass].push(1); // Include point (green) + } + + drawPoints(); + await updateSegmentation(); +}); + +// Clear points for current class +clearPointsButton.addEventListener('click', () => { + points[currentClass] = []; + labels[currentClass] = []; + drawPoints(); + resetSegmentation(); +}); + +function resizeAndDrawImage(canvas, img) { + const ctx = canvas.getContext('2d'); + ctx.clearRect(0, 0, canvas.width, canvas.height); // Clear the canvas + + // Scale the image to fit within the canvas + const scale = Math.min(canvas.width / img.width, canvas.height / img.height); + const x = (canvas.width - img.width * scale) / 2; + const y = (canvas.height - img.height * scale) / 2; + + ctx.drawImage(img, x, y, img.width * scale, img.height * scale); +} + + +// Draw points on canvases +function drawPoints() { + [inputCanvas, segmentedCanvas].forEach((canvas) => { + const ctx = canvas.getContext('2d'); + ctx.clearRect(0, 0, CANVAS_SIZE, CANVAS_SIZE); + + const img = new Image(); + img.src = imageUrl; + img.onload = () => { + resizeAndDrawImage(canvas, img); + + points[currentClass].forEach(([x, y], i) => { + const scaledX = x * (CANVAS_SIZE / originalImageWidth); + const scaledY = y * (CANVAS_SIZE / originalImageHeight); + ctx.beginPath(); + ctx.arc(scaledX, scaledY, 5, 0, 2 * Math.PI); + ctx.fillStyle = labels[currentClass][i] === 1 ? 'green' : 'red'; + ctx.fill(); + }); + }; + img.onerror = () => { + console.error('Error loading image for canvas:', img.src); + }; + }); +} + +async function updateSegmentation() { + try { + const response = await fetch('/segment', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ points: points[currentClass], labels: labels[currentClass], class: currentClass.toLowerCase() }) + }); + + const data = await response.json(); + + if (data.error) { + console.error('Error during segmentation:', data.error); + alert(`Segmentation error: ${data.error}`); + return; + } + + console.log('Segmentation result:', data); + + const img = new Image(); + img.src = `${data.segmented_url}?t=${new Date().getTime()}`; // Add timestamp to prevent caching + img.onload = () => { + console.log('Segmented image loaded successfully:', img.src); + resizeAndDrawImage(segmentedCanvas, img); // Render the segmented image + }; + img.onerror = () => { + console.error('Failed to load segmented image:', img.src); + alert('Failed to load the segmented image.'); + }; + } catch (error) { + console.error('Error updating segmentation:', error); + alert('Failed to process segmentation.'); + } +} + +// Reset segmented canvas +function resetSegmentation() { + const ctx = segmentedCanvas.getContext('2d'); + ctx.clearRect(0, 0, CANVAS_SIZE, CANVAS_SIZE); + const img = new Image(); + img.src = imageUrl; + img.onload = () => resizeAndDrawImage(segmentedCanvas, img); +} + +// Handle automatic segmentation +automaticImageUpload.addEventListener('change', async (event) => { + const file = event.target.files[0]; + const formData = new FormData(); + formData.append('file', file); + + try { + const response = await fetch('/automatic_segment', { method: 'POST', body: formData }); + const data = await response.json(); + if (data.error) return console.error('Error during automatic segmentation:', data.error); + + // Display the processed image + const processedImage = document.getElementById('automaticProcessedImage'); + processedImage.src = `${data.segmented_url}?t=${new Date().getTime()}`; + processedImage.style.display = 'block'; + + // Optionally append the table data + appendRowToTable(data.table_data); + } catch (error) { + console.error('Failed to process image automatically:', error); + } +}); + +function appendRowToTable(tableData) { + // Remove duplicates based on the image name and chip number + const existingRows = Array.from(resultsTableBody.querySelectorAll('tr')); + const existingIdentifiers = existingRows.map(row => { + const cells = row.querySelectorAll('td'); + return `${cells[0]?.textContent}_${cells[1]?.textContent}`; // Combine Image Name and Chip # + }); + + tableData.chips.forEach((chip, index) => { + const uniqueId = `${tableData.image_name}_${chip.chip_number}`; + if (existingIdentifiers.includes(uniqueId)) return; // Skip if already present + + const row = document.createElement('tr'); + + // Image Name (unchanged for each chip) + const imageNameCell = document.createElement('td'); + imageNameCell.textContent = tableData.image_name; + row.appendChild(imageNameCell); + + // Chip # (1, 2, etc.) + const chipNumberCell = document.createElement('td'); + chipNumberCell.textContent = chip.chip_number; + row.appendChild(chipNumberCell); + + // Chip Area + const chipAreaCell = document.createElement('td'); + chipAreaCell.textContent = chip.chip_area.toFixed(2); + row.appendChild(chipAreaCell); + + // Void % (Total void area / Chip area * 100) + const voidPercentageCell = document.createElement('td'); + voidPercentageCell.textContent = chip.void_percentage.toFixed(2); + row.appendChild(voidPercentageCell); + + // Max Void % (Largest void area / Chip area * 100) + const maxVoidPercentageCell = document.createElement('td'); + maxVoidPercentageCell.textContent = chip.max_void_percentage.toFixed(2); + row.appendChild(maxVoidPercentageCell); + + resultsTableBody.appendChild(row); + }); +} + +// Handle automatic segmentation +automaticImageUpload.addEventListener('change', async (event) => { + const file = event.target.files[0]; + const formData = new FormData(); + formData.append('file', file); + + try { + const response = await fetch('/automatic_segment', { method: 'POST', body: formData }); + const data = await response.json(); + if (data.error) return console.error('Error during automatic segmentation:', data.error); + + automaticProcessedImage.src = `${data.segmented_url}?t=${new Date().getTime()}`; + automaticProcessedImage.style.display = 'block'; + appendRowToTable(data.table_data); // Append new data to the table + } catch (error) { + console.error('Failed to process image automatically:', error); + } +}); + +// Clear table +clearTableButton.addEventListener('click', () => { + resultsTableBody.innerHTML = ''; +}); + +// Export table to CSV +exportTableButton.addEventListener('click', () => { + const rows = Array.from(resultsTableBody.querySelectorAll('tr')); + const csvContent = [ + ['Image Name', 'Chip #', 'Chip Area', 'Void %', 'Max Void %'], + ...rows.map(row => + Array.from(row.children).map(cell => cell.textContent) + ), + ] + .map(row => row.join(',')) + .join('\n'); + + const blob = new Blob([csvContent], { type: 'text/csv' }); + const url = URL.createObjectURL(blob); + const link = document.createElement('a'); + link.href = url; + link.download = 'segmentation_results.csv'; + link.click(); + URL.revokeObjectURL(url); +}); +saveBothButton.addEventListener('click', async () => { + const imageName = imageUrl.split('/').pop(); // Extract the image name from the URL + if (!imageName) { + alert("No image to save."); + return; + } + + const confirmSave = confirm("Are you sure you want to save both voids and chips segmentations?"); + if (!confirmSave) return; + + try { + const response = await fetch('/save_both', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ image_name: imageName }) + }); + const result = await response.json(); + if (response.ok) { + alert(result.message); + } else { + alert("Failed to save segmentations."); + } + } catch (error) { + console.error("Error saving segmentations:", error); + alert("Failed to save segmentations."); + } +}); +// Update the "historyButton" click listener to populate the list correctly +document.getElementById('historyButton').addEventListener('click', async () => { + try { + const response = await fetch('/get_history'); // Fetch the saved history + const result = await response.json(); + + if (response.ok) { + const historyList = document.getElementById('historyList'); + historyList.innerHTML = ''; // Clear the list + + if (result.images.length === 0) { + historyList.innerHTML = '
  • No images found in history.
    • '; + return; + } + + result.images.forEach(image => { + const listItem = document.createElement('li'); + listItem.className = 'list-group-item'; + + const imageName = document.createElement('span'); + imageName.textContent = image; + + const deleteButton = document.createElement('button'); + deleteButton.className = 'btn btn-danger btn-sm'; + deleteButton.textContent = 'Delete'; + deleteButton.addEventListener('click', async () => { + if (confirm(`Are you sure you want to delete ${image}?`)) { + await deleteHistoryItem(image, listItem); + } + }); + + listItem.appendChild(imageName); + listItem.appendChild(deleteButton); + historyList.appendChild(listItem); + }); + + new bootstrap.Modal(document.getElementById('historyModal')).show(); + } else { + alert("Failed to fetch history."); + } + } catch (error) { + console.error("Error fetching history:", error); + alert("Failed to fetch history."); + } +}); + +// Function to delete history item +async function deleteHistoryItem(imageName, listItem) { + try { + const response = await fetch('/delete_history_item', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ image_name: imageName }) + }); + const result = await response.json(); + + if (response.ok) { + alert(result.message); + listItem.remove(); // Remove the item from the list + } else { + alert("Failed to delete image."); + } + } catch (error) { + console.error("Error deleting image:", error); + alert("Failed to delete image."); + } +} + +historyButton.addEventListener('click', async () => { + try { + const response = await fetch('/get_history'); + const result = await response.json(); + + if (response.ok) { + const historyList = document.getElementById('historyList'); + historyList.innerHTML = ''; // Clear the list + + if (result.images.length === 0) { + historyList.innerHTML = '
  • No images found in history.
    • '; + return; + } + + result.images.forEach(image => { + const listItem = document.createElement('li'); + listItem.className = 'list-group-item d-flex justify-content-between align-items-center'; + listItem.textContent = image; + + const deleteButton = document.createElement('button'); + deleteButton.className = 'btn btn-danger btn-sm'; + deleteButton.textContent = 'Delete'; + deleteButton.addEventListener('click', async () => { + if (confirm(`Are you sure you want to delete ${image}?`)) { + await deleteHistoryItem(image, listItem); + } + }); + + listItem.appendChild(deleteButton); + historyList.appendChild(listItem); + }); + + new bootstrap.Modal(document.getElementById('historyModal')).show(); + } else { + alert("Failed to fetch history."); + } + } catch (error) { + console.error("Error fetching history:", error); + alert("Failed to fetch history."); + } +}); + +// Function to delete history item +async function deleteHistoryItem(imageName, listItem) { + try { + const response = await fetch('/delete_history_item', { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ image_name: imageName }) + }); + const result = await response.json(); + + if (response.ok) { + alert(result.message); + listItem.remove(); // Remove the item from the list + } else { + alert("Failed to delete image."); + } + } catch (error) { + console.error("Error deleting image:", error); + alert("Failed to delete image."); + } +} + +// Handle Retrain Model button click +retrainModelButton.addEventListener('click', async () => { + if (!trainingInProgress) { + const confirmRetrain = confirm("Are you sure you want to retrain the model?"); + if (!confirmRetrain) return; + + try { + const response = await fetch('/retrain_model', { method: 'POST' }); + const result = await response.json(); + + if (response.ok) { + // Update button to "Cancel Training" + trainingInProgress = true; + retrainModelButton.textContent = "Cancel Training"; + retrainModelButton.classList.replace("btn-primary", "btn-danger"); + startTrainingMonitor(); // Start monitoring the training status + } else { + alert(result.error || "Failed to start retraining."); + } + } catch (error) { + console.error("Error starting training:", error); + alert("An error occurred while starting the training process."); + } + } else { + // Handle cancel training + const confirmCancel = confirm("Are you sure you want to cancel the training?"); + if (!confirmCancel) return; + + try { + const response = await fetch('/cancel_training', { method: 'POST' }); + const result = await response.json(); + + if (response.ok) { + // Reset button to "Retrain Model" + trainingInProgress = false; + retrainModelButton.textContent = "Retrain Model"; + retrainModelButton.classList.replace("btn-danger", "btn-primary"); + alert(result.message || "Training canceled successfully."); + } else { + alert(result.error || "Failed to cancel training."); + } + } catch (error) { + console.error("Error canceling training:", error); + alert("An error occurred while canceling the training process."); + } + } +}); + + +function startTrainingMonitor() { + const monitorInterval = setInterval(async () => { + try { + const response = await fetch('/training_status'); + const result = await response.json(); + + const retrainButton = document.getElementById('retrainModelButton'); + const cancelButton = document.getElementById('cancelTrainingButton'); + const etaDisplay = document.getElementById('etaDisplay'); + + if (result.status === 'running') { + // Show training progress + retrainButton.style.display = 'none'; + cancelButton.style.display = 'inline-block'; + etaDisplay.textContent = `Estimated Time Left: ${result.eta || "Calculating..."}`; + } else if (result.status === 'idle' || result.status === 'cancelled') { + // Revert button to "Retrain Model" (blue) + cancelButton.style.display = 'none'; + retrainButton.style.display = 'inline-block'; + retrainButton.textContent = 'Retrain Model'; + retrainButton.classList.replace('btn-danger', 'btn-primary'); + etaDisplay.textContent = ''; + + // Stop monitoring if training is idle + if (result.status === 'idle') { + clearInterval(monitorInterval); + } + } + } catch (error) { + console.error("Error fetching training status:", error); + } + }, 5000); // Poll every 5 seconds +} + +function resetTrainingUI() { + trainingInProgress = false; + retrainModelButton.textContent = "Retrain Model"; + retrainModelButton.classList.replace("btn-danger", "btn-primary"); + etaDisplay.textContent = ""; +} + +clearHistoryButton.addEventListener('click', async () => { + const confirmClear = confirm("Are you sure you want to clear the history? This will delete all images and masks."); + if (!confirmClear) return; + + try { + const response = await fetch('/clear_history', { method: 'POST' }); + const result = await response.json(); + if (response.ok) { + alert(result.message); + // Optionally update UI to reflect the cleared history + const historyList = document.getElementById('historyList'); + if (historyList) historyList.innerHTML = '
  • No images found in history.
    • '; + } else { + alert("Failed to clear history."); + } + } catch (error) { + console.error("Error clearing history:", error); + alert("Failed to clear history."); + } +}); + +// Toggle training progress display +function showTrainingProgress(message = "Initializing...", timeLeft = "Calculating...") { + document.getElementById("trainingProgress").style.display = "block"; + document.getElementById("progressMessage").textContent = message; + document.getElementById("estimatedTimeLeft").textContent = `Estimated Time Left: ${timeLeft}`; +} + +function hideTrainingProgress() { + document.getElementById("trainingProgress").style.display = "none"; +} + +// Toggle Cancel Training Button +function showCancelTrainingButton() { + document.getElementById("cancelTrainingButton").style.display = "inline-block"; + document.getElementById("retrainModelButton").style.display = "none"; +} + +function hideCancelTrainingButton() { + document.getElementById("cancelTrainingButton").style.display = "none"; + document.getElementById("retrainModelButton").style.display = "inline-block"; +} + +// Add event listener to Cancel Training button +document.getElementById("cancelTrainingButton").addEventListener("click", async () => { + const confirmCancel = confirm("Are you sure you want to cancel training?"); + if (!confirmCancel) return; + + try { + const response = await fetch("/cancel_training", { method: "POST" }); + const result = await response.json(); + + if (result.message) { + alert(result.message); + hideTrainingProgress(); + hideCancelTrainingButton(); + } + } catch (error) { + console.error("Error canceling training:", error); + alert("Failed to cancel training."); + } +}); +// Handle training status updates +socket.on('training_status', (data) => { + const trainingButton = document.getElementById('retrainModelButton'); + const cancelButton = document.getElementById('cancelTrainingButton'); + + if (data.status === 'completed') { + // Update UI: change "Cancel Training" to "Retrain Model" + trainingButton.style.display = 'inline-block'; + cancelButton.style.display = 'none'; + + // Show a popup or notification for training completion + alert(data.message || "Training completed successfully!"); + } else if (data.status === 'failed') { + // Update UI: change "Cancel Training" to "Retrain Model" + trainingButton.style.display = 'inline-block'; + cancelButton.style.display = 'none'; + + // Show a popup or notification for training failure + alert(data.message || "Training failed. Please try again."); + } +}); + +socket.on('button_update', (data) => { + const retrainButton = document.getElementById('retrainModelButton'); + const cancelButton = document.getElementById('cancelTrainingButton'); + + if (data.action === 'retrain') { + // Update to "Retrain Model" button + retrainButton.style.display = 'inline-block'; + retrainButton.textContent = 'Retrain Model'; + retrainButton.classList.replace('btn-danger', 'btn-primary'); + cancelButton.style.display = 'none'; + } +}); + +function updateButtonToRetrainModel() { + const button = document.getElementById('retrainModelButton'); + button.innerText = "Retrain Model"; + button.classList.replace("btn-danger", "btn-primary"); + button.disabled = false; +} + + +socket.on('training_status', (data) => { + const retrainButton = document.getElementById('retrainModelButton'); + const cancelButton = document.getElementById('cancelTrainingButton'); + + if (data.status === 'completed') { + retrainButton.style.display = 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    Segmentation Tools

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    Interactive Segmentation Tool

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    Input Image
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    Segmented Image
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    Automatic Segmentation Tool

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    Input Image
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    Processed Image
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    Segmentation Results

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    Image NameChip #Chip AreaVoid %Max Void %
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    + + + + + + + + + + diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/utils/__pycache__/__init__.cpython-311.pyc b/utils/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7471c80806948ab99a064501a26cfe7dc8ccd3ae Binary files /dev/null and b/utils/__pycache__/__init__.cpython-311.pyc differ diff --git a/utils/__pycache__/helpers.cpython-311.pyc b/utils/__pycache__/helpers.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..184f8d72303fed8f7c7f52427fe8ea12b9b20f24 Binary files /dev/null and b/utils/__pycache__/helpers.cpython-311.pyc differ diff --git a/utils/__pycache__/predictor.cpython-311.pyc b/utils/__pycache__/predictor.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e9b4bcb5833e2f0a6243900d0e56562f56922a16 Binary files /dev/null and b/utils/__pycache__/predictor.cpython-311.pyc differ diff --git a/utils/helpers.py b/utils/helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..129147f109dc13541220e126be1ab7e9b43f8f7d --- /dev/null +++ b/utils/helpers.py @@ -0,0 +1,74 @@ +import numpy as np +from PIL import Image +import cv2 +import os +import shutil + +def blend_mask_with_image(image, mask, color): + """Blend the mask with the original image using a transparent color overlay.""" + mask_rgb = np.stack([mask * color[i] for i in range(3)], axis=-1) + blended = (0.7 * image + 0.3 * mask_rgb).astype(np.uint8) + return blended + +def save_mask_as_png(mask, path): + """Save the binary mask as a PNG.""" + mask_image = Image.fromarray((mask * 255).astype(np.uint8)) + mask_image.save(path) + +def convert_mask_to_yolo(mask_path, image_path, class_id, output_path, append=False): + """ + Convert a binary mask to YOLO-compatible segmentation labels. + + Args: + mask_path (str): Path to the binary mask image. + image_path (str): Path to the corresponding image. + class_id (int): Class ID (e.g., 0 for void, 1 for chip). + output_path (str): Path to save the YOLO label (.txt) file. + append (bool): Whether to append labels to the file. + + Returns: + None + """ + try: + # Load the binary mask + mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE) + if mask is None: + raise ValueError(f"Mask not found or invalid: {mask_path}") + + # Load the corresponding image to get dimensions + image = cv2.imread(image_path) + if image is None: + raise ValueError(f"Image not found or invalid: {image_path}") + + h, w = image.shape[:2] # Image height and width + + # Find contours in the mask + contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + # Determine file mode: "w" for overwrite or "a" for append + file_mode = "a" if append else "w" + + # Open the output .txt file + with open(output_path, file_mode) as label_file: + for contour in contours: + # Simplify the contour points to reduce the number of vertices + epsilon = 0.01 * cv2.arcLength(contour, True) # Tolerance for approximation + contour = cv2.approxPolyDP(contour, epsilon, True) + + # Normalize contour points (polygon vertices) + normalized_vertices = [] + for point in contour: + x, y = point[0] # Extract x, y from the point + x_normalized = x / w + y_normalized = y / h + normalized_vertices.extend([x_normalized, y_normalized]) + + # Write the polygon annotation to the label file + if len(normalized_vertices) >= 6: # At least 3 points required for a polygon + label_file.write(f"{class_id} " + " ".join(f"{v:.6f}" for v in normalized_vertices) + "\n") + + print(f"YOLO segmentation label saved: {output_path}") + + except Exception as e: + print(f"Error converting mask to YOLO format: {e}") + raise RuntimeError(f"Failed to convert {mask_path} for class {class_id}: {e}") diff --git a/utils/predictor.py b/utils/predictor.py new file mode 100644 index 0000000000000000000000000000000000000000..0e0e358117a71ea711b1d221034341a23599f0f0 --- /dev/null +++ b/utils/predictor.py @@ -0,0 +1,26 @@ +import numpy as np +from sam2.build_sam import build_sam2 +from sam2.sam2_image_predictor import SAM2ImagePredictor + +class Predictor: + def __init__(self, model_cfg, checkpoint, device): + self.device = device + self.model = build_sam2(model_cfg, checkpoint, device=device) + self.predictor = SAM2ImagePredictor(self.model) + self.image_set = False + + def set_image(self, image): + """Set the image for SAM prediction.""" + self.image = image + self.predictor.set_image(image) + self.image_set = True + + def predict(self, point_coords, point_labels, multimask_output=False): + """Run SAM prediction.""" + if not self.image_set: + raise RuntimeError("An image must be set with .set_image(...) before mask prediction.") + return self.predictor.predict( + point_coords=point_coords, + point_labels=point_labels, + multimask_output=multimask_output + ) diff --git a/yolo11n.pt b/yolo11n.pt new file mode 100644 index 0000000000000000000000000000000000000000..c7723db027d009343e9682f261370833fd6f0d84 --- /dev/null +++ b/yolo11n.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0ebbc80d4a7680d14987a577cd21342b65ecfd94632bd9a8da63ae6417644ee1 +size 5613764