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import sys, os |
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import gradio as gr |
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import plotly.express as px |
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import numpy as np |
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import random |
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from ultralytics import YOLO |
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from src.sahi_onnx import * |
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from sahi.predict import get_sliced_prediction |
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from sahi.utils.cv import visualize_object_predictions |
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import PIL |
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model_base = 'onnx_models' |
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def inference( |
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im:gr.inputs.Image=None, |
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model_path:gr.inputs.Dropdown='YOLOv8n', |
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conf_thr:gr.inputs.Slider=0.25 |
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): |
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model = Yolov8onnxDetectionModel(model_path=f'{model_base}/{model_path}/best.onnx', |
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config_path=f'{model_base}/{model_path}/args.yaml'' |
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device='cpu', |
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confidence_threshold=conf_thr, |
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image_size=640) |
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res = get_sliced_prediction(im, model, slice_width=320, |
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slice_height=320, overlap_height_ratio=0.2, |
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overlap_width_ratio=0.2, verbose=0) |
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img = PIL.Image.open(im) |
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visual_result = visualize_object_predictions(image=np.array(img), |
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object_prediction_list=res.object_prediction_list, |
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text_size=0.3, |
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rect_th=1) |
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fig = px.imshow(visual_result['image']) |
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fig.update_layout(showlegend=False, hovermode=False) |
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fig.update_xaxes(visible=False) |
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fig.update_yaxes(visible=False) |
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return fig |
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inputs = [ |
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gr.Image(type='filepath', label='Input'), |
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gr.components.Dropdown([ |
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'YOLOv8n', |
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'YOLOv8s', |
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'YOLOv8m', |
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'YOLOv8l', |
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'YOLOv8x' |
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], |
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value='YOLOv8n', label='Model'), |
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gr.components.Slider(minimum=0.0, maximum=1.0, value=0.25, step=0.05, label='Confidence Threshold'), |
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] |
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outputs = [ |
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gr.Plot(label='Predictions') |
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] |
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example_images = [[f'examples/{f}'] for f in os.listdir('examples')] |
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gr.Interface( |
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fn=inference, |
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inputs=inputs, |
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outputs=outputs, |
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allow_flagging='never', |
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examples=example_images, |
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cache_examples=False, |
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examples_per_page=10, |
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title='Marine vessel detection from Sentinel 2 images', |
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description="""Detecting marine vessels from Sentinel 2 imagery. |
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Each example image covers 1500x1500 pixels.""" |
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).launch() |
