mediapipe

import mediapipe as mp from mediapipe.tasks import python from mediapipe.tasks.python import vision import numpy as np import os # Placeholder for a real model file. Download a .task file (e.g., efficientdet_lite0.tflite) # from MediaPipe's model zoo (https://developers.google.com/mediapipe/solutions/object_detector) # or use your own. For local testing, ensure the file exists. # Example: model_path = '~/mediapipe_models/efficientdet_lite0.tflite' model_path = os.environ.get('MEDIAPIPE_MODEL_PATH', 'object_detector.tflite') # Replace with actual model path or env var try: # Create a BaseOptions object with the model asset path. # For GPU acceleration on supported platforms, add delegate=python.BaseOptions.Delegate.GPU base_options = python.BaseOptions(model_asset_path=model_path) # Create an ObjectDetectorOptions object. options = vision.ObjectDetectorOptions(base_options=base_options, score_threshold=0.25, max_results=5) # Create an ObjectDetector. detector = vision.ObjectDetector.create_from_options(options) # Create a dummy image (e.g., a blank white image) for demonstration. # In a real application, you'd load an image from a file or camera. dummy_image_np = np.zeros((224, 224, 3), dtype=np.uint8) + 255 # White 224x224 image mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=dummy_image_np) # Perform object detection on the image. detection_result = detector.detect(mp_image) # Print the detection results. print("Detection results:") if detection_result.detections: for detection in detection_result.detections: for category in detection.categories: print(f" Category: {category.category_name}, Score: {category.score:.2f}") bbox = detection.bounding_box print(f" Bounding Box: (x:{bbox.origin_x}, y:{bbox.origin_y}, w:{bbox.width}, h:{bbox.height})") else: print(" No objects detected.") except FileNotFoundError: print(f"Error: Model file not found at '{model_path}'. Please ensure the model exists or update MEDIAPIPE_MODEL_PATH environment variable.") except Exception as e: print(f"An error occurred: {e}")