| import streamlit as st |
| from ultralytics import YOLO |
| from PIL import Image |
| import numpy as np |
| import cv2 |
|
|
| st.set_page_config(page_title="OceanCV Object Detection", page_icon="🦑") |
|
|
| st.title("OceanCV FirstPass Detector") |
| st.write("Upload marine imagery to detect underwater objects.") |
|
|
| @st.cache_resource |
| def load_model(): |
| return YOLO("OceanCV_FirstPass.pt") |
|
|
| model = load_model() |
|
|
| uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"]) |
|
|
| conf = st.slider("Confidence Threshold", 0.1, 1.0, 0.5) |
| iou = st.slider("IOU Threshold", 0.1, 1.0, 0.45) |
|
|
| if uploaded_file is not None: |
| image = Image.open(uploaded_file) |
| st.image(image, caption="Uploaded Image", use_column_width=True) |
| |
| if st.button("Detect Objects"): |
| with st.spinner("Analyzing..."): |
| results = model.predict(image, conf=conf, iou=iou, imgsz=1024) |
| |
| res_plotted = results[0].plot() |
| res_rgb = cv2.cvtColor(res_plotted, cv2.COLOR_BGR2RGB) |
| |
| st.image(res_rgb, caption="Processed Image", use_column_width=True) |
| |
| st.write(f"Detected {len(results[0].boxes)} objects.") |
|
|
