| import glob | |
| import gradio as gr | |
| from inference import * | |
| from PIL import Image | |
| def gradio_app(image_path): | |
| """A function that send the file to the inference pipeline, and filters | |
| some predictions before outputting to gradio interface.""" | |
| predictions = run_inference(image_path) | |
| out_img = Image.fromarray(predictions.render()[0]) | |
| return out_img | |
| title = "MBARI Monterey Bay Benthic" | |
| description = "Gradio demo for MBARI Monterey Bay Benthic: This model was " \ | |
| "trained on 691 classes using 33,667 localized images from " \ | |
| "MBARI’s Video Annotation and Reference System (VARS). Note: " \ | |
| "only a subset of the VARS database is uploaded to FathomNet " \ | |
| "because of institutional concept embargos. For training, " \ | |
| "images were split 80/20 train/test. Classes were selected " \ | |
| "because they are commonly observed concepts (primarily " \ | |
| "benthic organisms, along with equipment and marine litter or " \ | |
| "trash) within the Monterey Bay and Submarine Canyon system " \ | |
| "from 500 to 4000 m deep. Many of these organisms will be seen " \ | |
| "throughout the entire NE Pacific within the continental " \ | |
| "slope, shelf, and abyssal regions. We used the PyTorch " \ | |
| "framework and the yolov5 ‘YOLOv5x’ pretrained checkpoint to " \ | |
| "train for 28 epochs with a batch size of 18 and image size of " \ | |
| "640 pixels. DOI: 10.5281/zenodo.5539915 " | |
| 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() |