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| from ultralytics import YOLO | |
| import cv2 | |
| import os | |
| from PIL import Image | |
| import numpy as np | |
| import glob | |
| import sys | |
| import argparse | |
| sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) | |
| from utils import download_yolo_face_detection | |
| def initialize_yolo_model(yolo_model_path): | |
| """Initialize YOLO model.""" | |
| if not os.path.exists(yolo_model_path): | |
| download_yolo_face_detection.download_yolo_face_detection_model() | |
| return YOLO(yolo_model_path) | |
| def process_image_results(image, image_rgb, boxes): | |
| """Process bounding boxes and crop faces for a single image.""" | |
| bounding_boxes, cropped_faces = [], [] | |
| for box in boxes: | |
| x1, y1, x2, y2 = map(int, box) | |
| if x2 > x1 and y2 > y1 and x1 >= 0 and y1 >= 0 and x2 <= image.shape[1] and y2 <= image.shape[0]: | |
| bounding_boxes.append([x1, y1, x2, y2]) | |
| cropped_face = image_rgb[y1:y2, x1:x2] | |
| if cropped_face.size > 0: | |
| pil_image = Image.fromarray(cropped_face).resize((112, 112), Image.Resampling.BILINEAR) | |
| cropped_faces.append(pil_image) | |
| return np.array(bounding_boxes, dtype=np.int32) if bounding_boxes else np.empty((0, 4), dtype=np.int32), cropped_faces | |
| def process_batch(model, image_paths, all_bounding_boxes, all_cropped_faces): | |
| """Process images in batch mode using list comprehensions for efficiency.""" | |
| # Validate and load images, filter out invalid ones | |
| valid_data = [(cv2.imread(path), path) for path in image_paths if os.path.exists(path)] | |
| valid_images, valid_image_paths = zip(*[(img, path) for img, path in valid_data if img is not None]) if valid_data else ([], []) | |
| # Append empty results for invalid images | |
| for path in image_paths: | |
| if not os.path.exists(path) or cv2.imread(path) is None: | |
| all_bounding_boxes.append(np.empty((0, 4), dtype=np.int32)) | |
| all_cropped_faces.append([]) | |
| print(f"Warning: {'not found' if not os.path.exists(path) else 'failed to load'} {path}. Skipping.") | |
| # Process valid images | |
| if valid_images: | |
| images_rgb = [cv2.cvtColor(img, cv2.COLOR_BGR2RGB) for img in valid_images] | |
| results = model.predict(source=valid_image_paths, conf=0.25, iou=0.45, verbose=False) | |
| # Process results with comprehension | |
| [all_bounding_boxes.append(bboxes) and all_cropped_faces.append(faces) | |
| for bboxes, faces in [process_image_results(img, rgb, result.boxes.xyxy.cpu().numpy()) | |
| for img, rgb, result in zip(valid_images, images_rgb, results)]] | |
| def process_individual(model, image_paths, all_bounding_boxes, all_cropped_faces): | |
| """Process images individually.""" | |
| for image_path in image_paths: | |
| if not os.path.exists(image_path): | |
| print(f"Warning: {image_path} not found. Skipping.") | |
| all_bounding_boxes.append(np.empty((0, 4), dtype=np.int32)) | |
| all_cropped_faces.append([]) | |
| continue | |
| image = cv2.imread(image_path) | |
| if image is None: | |
| print(f"Warning: Failed to load {image_path}. Skipping.") | |
| all_bounding_boxes.append(np.empty((0, 4), dtype=np.int32)) | |
| all_cropped_faces.append([]) | |
| continue | |
| image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| results = model(image_path, conf=0.25, iou=0.45, verbose=False) | |
| for result in results: | |
| boxes = result.boxes.xyxy.cpu().numpy() | |
| bboxes, faces = process_image_results(image, image_rgb, boxes) | |
| all_bounding_boxes.append(bboxes) | |
| all_cropped_faces.append(faces) | |
| def face_yolo_detection(yolo_model_path, image_paths, use_batch=True): | |
| """Perform face detection using YOLOv11 with batch or individual processing.""" | |
| model = initialize_yolo_model(yolo_model_path) | |
| all_bounding_boxes, all_cropped_faces = [], [] | |
| if use_batch: | |
| process_batch(model, image_paths, all_bounding_boxes, all_cropped_faces) | |
| else: | |
| process_individual(model, image_paths, all_bounding_boxes, all_cropped_faces) | |
| # for i, (bboxes, faces) in enumerate(zip(all_bounding_boxes, all_cropped_faces)): | |
| # print(f"\nImage {i+1} ({image_paths[i]}):") | |
| # print("Bounding Boxes:", bboxes) | |
| # print("Cropped Faces:", [f"Face {j+1}: {face}" for j, face in enumerate(faces)]) | |
| return all_bounding_boxes, all_cropped_faces | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser(description="YOLOv11 face detection") | |
| parser.add_argument("--use-batch", action="store_true", default=True, help="Use batch processing (default: True)") | |
| parser.add_argument("--image-dir", type=str, default="test/test_images", help="Input image directory") | |
| parser.add_argument("--yolo-model-path", type=str, default="checkpoints/yolo11_face_detection/model.pt", help="YOLO model path") | |
| args = parser.parse_args() | |
| image_paths = (glob.glob(os.path.join(args.image_dir, "*.[jJ][pP][gG]")) + | |
| glob.glob(os.path.join(args.image_dir, "*.[pP][nN][gG]")) | |
| ) | |
| if args.yolo_model_path: | |
| yolo_model_path = args.yolo_model_path | |
| else: | |
| yolo_model_path = os.path.join("checkpoints", "yolo11_face_detection", "model.pt") | |
| # import time | |
| # t1 = time.time() | |
| # all_bounding_boxes, all_cropped_faces = face_yolo_detection(yolo_model_path, image_paths, args.use_batch) | |
| # print("Time taken:", time.time() - t1) | |
| import time | |
| num_runs = 50 | |
| batch_times, individual_times = [], [] | |
| # Benchmark batch processing | |
| for _ in range(num_runs): | |
| t1 = time.time() | |
| face_yolo_detection(yolo_model_path, image_paths, use_batch=True) | |
| batch_times.append(time.time() - t1) | |
| # Benchmark individual processing | |
| for _ in range(num_runs): | |
| t1 = time.time() | |
| face_yolo_detection(yolo_model_path, image_paths, use_batch=False) | |
| individual_times.append(time.time() - t1) | |
| # Calculate and print average times | |
| avg_batch_time = sum(batch_times) / num_runs | |
| avg_individual_time = sum(individual_times) / num_runs | |
| print(f"\nBenchmark Results (over {num_runs} runs):") | |
| print(f"Average Batch Processing Time: {avg_batch_time:.4f} seconds") | |
| print(f"Average Individual Processing Time: {avg_individual_time:.4f} seconds") |