| try: |
| import detectron2 |
| except: |
| import os |
| os.system('pip install git+https://github.com/facebookresearch/detectron2.git') |
|
|
| from inference import * |
| import gradio as gr |
| import glob |
|
|
| def gradio_app(image_path): |
| """Helper function to run inference on provided image""" |
|
|
| predictions, out_pil = run_inference(image_path) |
|
|
| return out_pil |
|
|
|
|
| |
| |
| |
|
|
| title = "MBARI Monterey Bay Benthic Supercategory" |
| description = "Gradio demo for MBARI Monterey Bay Benthic Supercategory: This " \ |
| "is a RetinaNet model fine-tuned from the Detectron2 object " \ |
| "detection platform's ResNet backbone to identify 20 benthic " \ |
| "supercategories drawn from MBARI's remotely operated vehicle " \ |
| "image data collected in Monterey Bay off the coast of Central " \ |
| "California. The data is drawn from FathomNet and consists of " \ |
| "32779 images that contain a total of 80683 localizations. The " \ |
| "model was trained on an 85/15 train/validation split at the " \ |
| "image level. DOI: 10.5281/zenodo.5571043. " |
|
|
| examples = glob.glob("images/*.png") |
|
|
| gr.Interface(gradio_app, |
| inputs=[gr.inputs.Image(type="filepath")], |
| outputs=gr.outputs.Image(type="pil"), |
| enable_queue=True, |
| title=title, |
| description=description, |
| examples=examples).launch() |
|
|
