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| import streamlit as st | |
| import cv2 | |
| from ultralytics import YOLO | |
| from PIL import Image | |
| import numpy as np | |
| # Load the YOLOv8 model | |
| model = YOLO('yolov8n.pt') | |
| # Streamlit app | |
| def main(): | |
| st.title("Object Detection - General Use") | |
| st.write("This is a general use object detection space using YOLOv8. For more complex projects, video, or real-time object detection, can be implemented.") | |
| st.header("Upload an Image") | |
| # Upload image | |
| uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"]) | |
| if uploaded_file is not None: | |
| # Read the uploaded image | |
| image = Image.open(uploaded_file) | |
| image_array = np.array(image) | |
| # Perform object detection | |
| results = model.predict(image_array) | |
| # Display the detected objects | |
| for result in results: | |
| boxes = result.boxes | |
| for box in boxes: | |
| x1, y1, x2, y2 = box.xyxy[0] | |
| x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) | |
| cv2.rectangle(image_array, (x1, y1), (x2, y2), (0, 255, 0), 2) | |
| conf = box.conf[0] | |
| cls = int(box.cls[0]) | |
| label = f"{model.names[cls]} ({conf:.2f})" | |
| cv2.putText(image_array, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2) | |
| # Convert the image array back to PIL Image | |
| image = Image.fromarray(image_array) | |
| # Display the image with detected objects | |
| st.image(image, caption='Detected Objects') | |
| if not uploaded_file: | |
| # Display example image | |
| st.header("Example Result") | |
| example_image = Image.open("example.jpeg") | |
| st.image(example_image, caption='Cars are picked out in green and the person is distinguished from the motorcycle', use_column_width=True) | |
| if __name__ == "__main__": | |
| main() |