add app

README.md

@@ -1,6 +1,6 @@
 ---
 title: Marine Vessel Detection
-emoji: ⚡
+emoji: 🛥️
 colorFrom: green
 colorTo: indigo
 sdk: gradio

app.py

@@ -0,0 +1,70 @@
+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')

requirements.txt

@@ -0,0 +1,4 @@
+torch
+ultralytics
+plotly
+sahi