File size: 2,361 Bytes
2e01643 cb55219 e3951d6 cb55219 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | import torch, subprocess, sys
print("CUDA available:", torch.cuda.is_available())
try:
import detectron2
except:
print("Installing detectron2...")
subprocess.check_call([
sys.executable, "-m", "pip", "install",
"git+https://github.com/facebookresearch/detectron2.git"
])
import detectron2
print("Detectron2 ready")
import gradio as gr
import cv2
import numpy as np
from detectron2.config import get_cfg
from detectron2.engine import DefaultPredictor
from detectron2.utils.visualizer import Visualizer, ColorMode
from detectron2.data import MetadataCatalog
# Load model
cfg = get_cfg()
cfg.merge_from_file("config.yaml")
cfg.MODEL.WEIGHTS = "model_final.pth"
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
cfg.MODEL.DEVICE = "cpu" # HF Spaces free tier is CPU
MetadataCatalog.get("__unused").set(thing_classes=["ship"])
predictor = DefaultPredictor(cfg)
def detect_ships(image, confidence_threshold):
"""Run ship detection on uploaded image."""
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = confidence_threshold
# Convert PIL → BGR numpy
img_bgr = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
outputs = predictor(img_bgr)
instances = outputs["instances"].to("cpu")
# Filter by threshold
keep = instances.scores >= confidence_threshold
instances = instances[keep]
metadata = MetadataCatalog.get("__unused")
v = Visualizer(img_bgr[:, :, ::-1], metadata=metadata,
scale=1.0, instance_mode=ColorMode.IMAGE)
out = v.draw_instance_predictions(instances)
result_img = out.get_image()
num_ships = len(instances)
scores = instances.scores.tolist()
info = f"Detected {num_ships} ship(s)\n"
if scores:
info += "Confidence scores: " + ", ".join([f"{s:.2f}" for s in scores])
return result_img, info
demo = gr.Interface(
fn=detect_ships,
inputs=[
gr.Image(type="pil", label="Upload SAR Image"),
gr.Slider(0.1, 0.9, value=0.5, step=0.05, label="Confidence Threshold")
],
outputs=[
gr.Image(type="numpy", label="Detection Result"),
gr.Textbox(label="Detection Info")
],
title="🚢 HRSID Ship Detection",
description="Upload a SAR image to detect ships using Faster R-CNN trained on HRSID dataset.",
examples=[]
)
if __name__ == "__main__":
demo.launch()
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