mmdet

import os import torch from mmdet.apis import DetInferencer import mmcv # For image loading if not using DetInferencer's internal loading # Ensure an image file exists for the demo # For simplicity, create a dummy image or download one img_path = 'demo_image.jpg' if not os.path.exists(img_path): # Using a common utility to create a simple dummy image from PIL import Image img = Image.new('RGB', (640, 480), color = 'red') img.save(img_path) print(f"Created dummy image: {img_path}") # Initialize the DetInferencer with a pre-trained model. # The model weights will be automatically downloaded. # Using device='cpu' for broader compatibility. # rtmdet_tiny is a lightweight model for quick demo. inferencer = DetInferencer(model='rtmdet_tiny_8xb32-300e_coco', device='cpu') # Perform inference on the image # The result contains detections, bounding boxes, labels, and scores. result = inferencer(img_path, show=False) # show=False to prevent immediate display # Print detected objects (example for the first image if batched) if isinstance(result['predictions'], list) and len(result['predictions']) > 0: first_image_predictions = result['predictions'][0] print(f"Detected objects in {img_path}:") for box, label, score in zip(first_image_predictions['bboxes'], first_image_predictions['labels'], first_image_predictions['scores']): print(f" Label: {label}, Score: {score:.2f}, BBox: {list(map(int, box))}") # To save the visualization, specify an output directory output_dir = 'mmdet_output' os.makedirs(output_dir, exist_ok=True) inferencer(img_path, out_dir=output_dir) print(f"Inference results saved to {output_dir}") # Clean up the dummy image and output directory (optional) os.remove(img_path) # For a full cleanup, you might also remove output_dir recursively # import shutil; shutil.rmtree(output_dir)