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| import streamlit as st | |
| import cv2 | |
| import numpy as np | |
| import PIL | |
| from pathlib import Path | |
| # Local Modules | |
| import settings | |
| import helper | |
| from helper import _display_detected_frames # Ensure this import | |
| # Setting page layout | |
| st.set_page_config( | |
| page_title="Object Detection using YOLO", | |
| page_icon="🤖", | |
| layout="wide", | |
| initial_sidebar_state="expanded" | |
| ) | |
| # Main page heading | |
| st.title("Object Detection using YOLOv10 - CPU Only") | |
| # Sidebar | |
| st.sidebar.header("ML Model Config") | |
| # Model Options | |
| confidence = float(st.sidebar.slider("Select Model Confidence", 25, 100, 40)) / 100 | |
| # Load Pre-trained ML Model | |
| model_path = Path(settings.DETECTION_MODEL) | |
| try: | |
| model = helper.load_model(model_path) | |
| except Exception as ex: | |
| st.error(f"Unable to load model. Check the specified path: {model_path}") | |
| st.error(ex) | |
| st.sidebar.header("Image/Video Config") | |
| source_radio = st.sidebar.radio("Select Source", settings.SOURCES_LIST) | |
| source_img = None | |
| # If image is selected | |
| if source_radio == settings.IMAGE: | |
| source_img = st.sidebar.file_uploader("Choose an image...", type=("jpg", "jpeg", "png", 'bmp', 'webp')) | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| try: | |
| if source_img is None: | |
| default_image_path = str(settings.DEFAULT_IMAGE) | |
| default_image = PIL.Image.open(default_image_path) | |
| st.image(default_image_path, caption="Default Image", use_column_width=True) | |
| else: | |
| uploaded_image = PIL.Image.open(source_img) | |
| st.image(source_img, caption="Uploaded Image", use_column_width=True) | |
| except Exception as ex: | |
| st.error("Error occurred while opening the image.") | |
| st.error(ex) | |
| with col2: | |
| if source_img is None: | |
| default_detected_image_path = str(settings.DEFAULT_DETECT_IMAGE) | |
| default_detected_image = PIL.Image.open(default_detected_image_path) | |
| st.image(default_detected_image_path, caption='Detected Image', use_column_width=True) | |
| else: | |
| if st.sidebar.button('Detect Objects'): | |
| try: | |
| # Convert PIL image to OpenCV format | |
| image_np = np.array(uploaded_image) | |
| _display_detected_frames(confidence, model, st, image_np) | |
| except Exception as ex: | |
| st.error("Error occurred while detecting objects.") | |
| st.error(ex) | |
| # If video is selected | |
| elif source_radio == settings.VIDEO: | |
| helper.play_stored_video(confidence, model) | |