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| import os | |
| import cv2 | |
| import numpy as np | |
| from PIL import Image, ImageDraw, ImageFont | |
| from ultralytics import YOLO | |
| import sqlite3 | |
| from io import BytesIO | |
| from scipy.stats import norm | |
| # Load YOLO models | |
| try: | |
| yolo_model_cataract = YOLO('best-cataract-seg.pt') | |
| yolo_model_object_detection = YOLO('best-cataract-od.pt') | |
| print("YOLO models loaded successfully.") | |
| except Exception as e: | |
| print(f"Error loading YOLO models: {e}") | |
| def calculate_ratios(red_values, green_values, blue_values, total_pixels): | |
| if total_pixels == 0: | |
| return 0, 0, 0 | |
| red_ratio = np.sum(red_values) / total_pixels | |
| green_ratio = np.sum(green_values) / total_pixels | |
| blue_ratio = np.sum(blue_values) / total_pixels | |
| total_ratio = red_ratio + green_ratio + blue_ratio | |
| if total_ratio > 0: | |
| red_quantity = (red_ratio / total_ratio) * 255 | |
| green_quantity = (green_ratio / total_ratio) * 255 | |
| blue_quantity = (blue_ratio / total_ratio) * 255 | |
| else: | |
| red_quantity, green_quantity, blue_quantity = 0, 0, 0 | |
| return red_quantity, green_quantity, blue_quantity | |
| def cataract_staging(red_quantity, green_quantity, blue_quantity): | |
| # Assuming you have already defined your mean and std for each class and each RGB channel | |
| # Example mean and std based on earlier discussion | |
| mean_mature_red = 73.37 | |
| std_mature_red = (90.12 - 41.49) / 4 | |
| mean_mature_green = 89.48 | |
| std_mature_green = (97.67 - 83.39) / 4 | |
| mean_mature_blue = 92.15 | |
| std_mature_blue = (117.82 - 75.37) / 4 | |
| mean_normal_red = 67.84 | |
| std_normal_red = (107.02 - 56.19) / 4 | |
| mean_normal_green = 84.85 | |
| std_normal_green = (89.89 - 80.74) / 4 | |
| mean_normal_blue = 102.31 | |
| std_normal_blue = (111.34 - 65.58) / 4 | |
| mean_immature_red = 68.83 | |
| std_immature_red = (85.95 - 41.49) / 4 | |
| mean_immature_green = 89.43 | |
| std_immature_green = (97.67 - 83.39) / 4 | |
| mean_immature_blue = 96.74 | |
| std_immature_blue = (117.82 - 78.41) / 4 | |
| # Calculate likelihoods for each class | |
| likelihood_mature = ( | |
| norm.pdf(red_quantity, mean_mature_red, std_mature_red) * | |
| norm.pdf(green_quantity, mean_mature_green, std_mature_green) * | |
| norm.pdf(blue_quantity, mean_mature_blue, std_mature_blue) | |
| ) | |
| likelihood_normal = ( | |
| norm.pdf(red_quantity, mean_normal_red, std_normal_red) * | |
| norm.pdf(green_quantity, mean_normal_green, std_normal_green) * | |
| norm.pdf(blue_quantity, mean_normal_blue, std_normal_blue) | |
| ) | |
| likelihood_immature = ( | |
| norm.pdf(red_quantity, mean_immature_red, std_immature_red) * | |
| norm.pdf(green_quantity, mean_immature_green, std_immature_green) * | |
| norm.pdf(blue_quantity, mean_immature_blue, std_immature_blue) | |
| ) | |
| # Define prior probabilities (assuming equal prior for simplicity) | |
| prior_mature = 1/3 | |
| prior_normal = 1/3 | |
| prior_immature = 1/3 | |
| # Apply Bayes' theorem to compute posterior probabilities | |
| posterior_mature = likelihood_mature * prior_mature | |
| posterior_normal = likelihood_normal * prior_normal | |
| posterior_immature = likelihood_immature * prior_immature | |
| # Determine the stage based on maximum posterior probability | |
| stages = { | |
| posterior_mature: "Mature", | |
| posterior_normal: "Normal", | |
| posterior_immature: "Immature" | |
| } | |
| max_posterior = max(posterior_mature, posterior_normal, posterior_immature) | |
| stage = stages[max_posterior] | |
| return stage | |
| def add_watermark(image): | |
| try: | |
| logo = Image.open('image-logo.png').convert("RGBA") | |
| image = image.convert("RGBA") | |
| # Resize logo | |
| basewidth = 100 | |
| wpercent = (basewidth / float(logo.size[0])) | |
| hsize = int((float(wpercent) * logo.size[1])) | |
| logo = logo.resize((basewidth, hsize), Image.LANCZOS) | |
| # Position logo | |
| position = (image.width - logo.width - 10, image.height - logo.height - 10) | |
| # Composite image | |
| transparent = Image.new('RGBA', (image.width, image.height), (0, 0, 0, 0)) | |
| transparent.paste(image, (0, 0)) | |
| transparent.paste(logo, position, mask=logo) | |
| return transparent.convert("RGB") | |
| except Exception as e: | |
| print(f"Error adding watermark: {e}") | |
| return image | |
| def predict_and_visualize(image): | |
| try: | |
| pil_image = Image.fromarray(image.astype('uint8'), 'RGB') | |
| orig_size = pil_image.size | |
| results = yolo_model_cataract(pil_image) | |
| raw_response = str(results) | |
| masked_image = np.array(pil_image) | |
| mask_image = np.zeros_like(masked_image) | |
| red_quantity, green_quantity, blue_quantity = 0, 0, 0 | |
| total_pixels = 0 | |
| if len(results) > 0: | |
| result = results[0] | |
| if hasattr(result, 'masks') and result.masks is not None and len(result.masks) > 0: | |
| mask = np.array(result.masks.data.cpu().squeeze().numpy()) | |
| mask_resized = np.array(Image.fromarray(mask).resize(orig_size, Image.NEAREST)) | |
| red_mask = np.zeros_like(masked_image) | |
| red_mask[mask_resized > 0.5] = [255, 0, 0] | |
| alpha = 0.5 | |
| blended_image = cv2.addWeighted(masked_image, 1 - alpha, red_mask, alpha, 0) | |
| pupil_pixels = np.array(pil_image)[mask_resized > 0.5] | |
| total_pixels = pupil_pixels.shape[0] | |
| red_values = pupil_pixels[:, 0] | |
| green_values = pupil_pixels[:, 1] | |
| blue_values = pupil_pixels[:, 2] | |
| red_quantity, green_quantity, blue_quantity = calculate_ratios(red_values, green_values, blue_values, total_pixels) | |
| stage = cataract_staging(red_quantity, green_quantity, blue_quantity) | |
| # Add text to the blended image | |
| combined_pil_image = Image.fromarray(blended_image) | |
| draw = ImageDraw.Draw(combined_pil_image) | |
| # Load a larger font (adjust the size as needed) | |
| font_size = 48 # Example font size | |
| try: | |
| font = ImageFont.truetype("font.ttf", size=font_size) | |
| except IOError: | |
| font = ImageFont.load_default() | |
| print("Error: cannot open resource, using default font.") | |
| text = f"Red quantity: {red_quantity:.2f}\nGreen quantity: {green_quantity:.2f}\nBlue quantity: {blue_quantity:.2f}\nStage: {stage}" | |
| # Calculate text bounding box | |
| text_bbox = draw.textbbox((0, 0), text, font=font) | |
| text_width, text_height = text_bbox[2] - text_bbox[0], text_bbox[3] - text_bbox[1] | |
| text_x = 20 | |
| text_y = 40 | |
| padding = 10 | |
| # Draw a filled rectangle for the background | |
| draw.rectangle( | |
| [text_x - padding, text_y - padding, text_x + text_width + padding, text_y + text_height + padding], | |
| fill="black" | |
| ) | |
| # Draw text on top of the rectangle | |
| draw.text((text_x, text_y), text, fill=(255, 255, 255, 255), font=font) | |
| # Add watermark to the image | |
| combined_pil_image_with_watermark = add_watermark(combined_pil_image) | |
| return np.array(combined_pil_image_with_watermark), red_quantity, green_quantity, blue_quantity, raw_response, stage | |
| return image, 0, 0, 0, "No mask detected.", "Unknown" | |
| except Exception as e: | |
| print("Error:", e) | |
| return np.zeros_like(image), 0, 0, 0, str(e), "Error" | |
| def check_duplicate_entry(conn, red_quantity, green_quantity, blue_quantity, stage): | |
| cursor = conn.cursor() | |
| query = '''SELECT COUNT(*) FROM cataract_results WHERE red_quantity=? AND green_quantity=? AND blue_quantity=? AND stage=?''' | |
| cursor.execute(query, (red_quantity, green_quantity, blue_quantity, stage)) | |
| count = cursor.fetchone()[0] | |
| return count > 0 | |
| def save_cataract_prediction_to_db(image, red_quantity, green_quantity, blue_quantity, stage): | |
| database = "cataract_results.db" | |
| conn = create_connection(database) | |
| if conn: | |
| create_cataract_table(conn) | |
| # Check for duplicate entries | |
| if check_duplicate_entry(conn, red_quantity, green_quantity, blue_quantity, stage): | |
| conn.close() | |
| return "Duplicate entry found, not saving.", "Duplicate entry detected." | |
| sql = '''INSERT INTO cataract_results(image, red_quantity, green_quantity, blue_quantity, stage) VALUES(?,?,?,?,?)''' | |
| cur = conn.cursor() | |
| # Convert the image to bytes | |
| buffered = BytesIO() | |
| image.save(buffered, format="PNG") | |
| img_bytes = buffered.getvalue() | |
| cur.execute(sql, (img_bytes, red_quantity, green_quantity, blue_quantity, stage)) | |
| conn.commit() | |
| conn.close() | |
| return "Data saved successfully", f"Red: {red_quantity}, Green: {green_quantity}, Blue: {blue_quantity}, Stage: {stage}" | |
| return "Failed to save data", "No connection to the database." | |
| def combined_prediction(image): | |
| blended_image, red_quantity, green_quantity, blue_quantity, raw_response, stage = predict_and_visualize(image) | |
| save_message, debug_info = save_cataract_prediction_to_db(Image.fromarray(blended_image), red_quantity, green_quantity, blue_quantity, stage) | |
| return blended_image, red_quantity, green_quantity, blue_quantity, raw_response, stage, save_message, debug_info | |
| def create_connection(db_file): | |
| """ Create a database connection to the SQLite database """ | |
| conn = None | |
| try: | |
| conn = sqlite3.connect(db_file) | |
| return conn | |
| except sqlite3.Error as e: | |
| print(e) | |
| return conn | |
| def create_cataract_table(conn): | |
| """ Create the cataract results table if it does not exist """ | |
| create_table_sql = """ CREATE TABLE IF NOT EXISTS cataract_results ( | |
| id integer PRIMARY KEY, | |
| image blob, | |
| red_quantity real, | |
| green_quantity real, | |
| blue_quantity real, | |
| stage text | |
| ); """ | |
| try: | |
| cursor = conn.cursor() | |
| cursor.execute(create_table_sql) | |
| except sqlite3.Error as e: | |
| print(e) | |
| def predict_object_detection(image): | |
| try: | |
| image_np = np.array(image) | |
| results = yolo_model_object_detection(image_np) | |
| image_with_boxes = image_np.copy() | |
| raw_predictions = [] | |
| for result in results[0].boxes: | |
| label = "Normal" if result.cls.item() == 1 else "Cataract" | |
| confidence = result.conf.item() | |
| xmin, ymin, xmax, ymax = map(int, result.xyxy[0]) | |
| cv2.rectangle(image_with_boxes, (xmin, ymin), (xmax, ymax), (255, 0, 0), 2) | |
| font_scale = 1.0 | |
| thickness = 2 | |
| text = f'{label} {confidence:.2f}' | |
| (text_width, text_height), baseline = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, font_scale, thickness) | |
| cv2.rectangle(image_with_boxes, (xmin, ymin - text_height - baseline), (xmin + text_width, ymin), (0, 0, 0), cv2.FILLED) | |
| cv2.putText(image_with_boxes, text, (xmin, ymin - baseline), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 255, 255), thickness) | |
| raw_predictions.append(f"Label: {label}, Confidence: {confidence:.2f}, Box: [{xmin}, {ymin}, {xmax}, {ymax}]") | |
| raw_predictions_str = "\n".join(raw_predictions) | |
| # Convert image_with_boxes to PIL image and add watermark | |
| image_with_boxes_pil = Image.fromarray(image_with_boxes) | |
| image_with_boxes_pil_with_watermark = add_watermark(image_with_boxes_pil) | |
| return np.array(image_with_boxes_pil_with_watermark), raw_predictions_str | |
| except Exception as e: | |
| print("Error in object detection:", e) | |
| return np.zeros_like(image), str(e) |