| import sys, os | |
| import gradio as gr | |
| import plotly.express as px | |
| import numpy as np | |
| import random | |
| from ultralytics import YOLO | |
| from sahi.models.yolov8 import * | |
| from sahi.predict import get_sliced_prediction | |
| from sahi.utils.cv import visualize_object_predictions | |
| import PIL | |
| model_base = "https://huggingface.co/mayrajeo/marine-vessel-detection/resolve/main/" | |
| def inference( | |
| im:gr.inputs.Image=None, | |
| model_path:gr.inputs.Dropdown=None, | |
| conf_thr:gr.inputs.Slider=0.25 | |
| ): | |
| model = Yolov8DetectionModel(model_path=f'{model_base}/{model_path}/best.pt', | |
| device='cpu', | |
| confidence_threshold=conf_thr, | |
| image_size=640) | |
| res = get_sliced_prediction(im, model, slice_width=320, | |
| slice_height=320, overlap_height_ratio=0.2, | |
| overlap_width_ratio=0.2, verbose=0) | |
| img = PIL.Image.open(im) | |
| visual_result = visualize_object_predictions(image=np.array(img), | |
| object_prediction_list=res.object_prediction_list, | |
| text_size=0.3, | |
| rect_th=1) | |
| fig = px.imshow(visual_result['image']) | |
| fig.update_layout(showlegend=False, hovermode=False) | |
| fig.update_xaxes(visible=False) | |
| fig.update_yaxes(visible=False) | |
| return fig | |
| inputs = [ | |
| gr.Image(type='filepath', label='Input'), | |
| gr.components.Dropdown([ | |
| 'YOLOv8n', | |
| 'YOLOv8s', | |
| 'YOLOv8m', | |
| 'YOLOv8l', | |
| 'YOLOv8x' | |
| ], | |
| value='YOLOv8n', label='Model'), | |
| gr.components.Slider(minimum=0.0, maximum=1.0, value=0.25, step=0.05, label='Confidence Threshold'), | |
| ] | |
| outputs = [ | |
| gr.Plot(label='Predictions') | |
| ] | |
| example_images = [[f'examples/{f}'] for f in os.listdir('examples')] | |
| gr.Interface( | |
| fn=inference, | |
| inputs=inputs, | |
| outputs=outputs, | |
| allow_flagging='never', | |
| examples=example_images, | |
| examples_per_page=32, | |
| title='Boat detection from Sentinel 2 images', | |
| description="""Detecting marine vessels from Sentinel 2 imagery. | |
| Each example image covers 1500x1500 pixels.""" | |
| ).launch(share=True, server_name='0.0.0.0') |