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| import streamlit as st | |
| from PIL import Image | |
| import cv2 | |
| import numpy as np | |
| import cvlib as cv | |
| from cvlib.object_detection import draw_bbox | |
| # Streamlit UI | |
| st.title("Object Detection with YOLO") | |
| uploaded_image = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"]) | |
| if uploaded_image is not None: | |
| image = Image.open(uploaded_image) | |
| st.image(image, caption="Uploaded Image", use_column_width=True) | |
| if st.button("Detect Objects"): | |
| st.write("Detecting objects...") | |
| # Convert the uploaded image to a NumPy array | |
| image_np = np.array(image) | |
| # Use YOLOv3 for object detection | |
| bbox, label, conf = cv.detect_common_objects(image_np) | |
| # Draw bounding boxes on the image | |
| output_image = draw_bbox(image_np, bbox, label, conf) | |
| # Convert the NumPy array back to an image | |
| output_image = Image.fromarray(output_image) | |
| # Display the image with bounding boxes | |
| st.image(output_image, caption="Objects Detected", use_column_width=True) | |
| st.text("Upload an image and click the 'Detect Objects' button to see object detection results using YOLO.") | |