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| import streamlit as st | |
| from PIL import Image | |
| from prediction import prediction | |
| from prediction import confidence | |
| from prediction import iou_thresold | |
| from prediction import Display_Confidence | |
| from prediction import Display_Class | |
| import streamlit as st | |
| import time | |
| import os | |
| # Global variables | |
| uploaded_file = None | |
| path_to_image = None | |
| def make_prediction(): | |
| global confidence | |
| global path_to_image | |
| global uploaded_file | |
| global iou_thresold | |
| if uploaded_file is not None: | |
| with st.spinner(f"Detecting heads in the image. Please wait..."): | |
| annotatedImage = prediction(path_to_image, confidence, | |
| disp_Class=Display_Class, disp_Confidence=Display_Confidence) | |
| st.image(annotatedImage, caption=f'Model Prediction') | |
| def upload_file(): | |
| global path_to_image | |
| global uploaded_file | |
| global confidence | |
| uploaded_file = st.file_uploader("Upload an image",type=['jpg','png','jpeg']) | |
| if uploaded_file is not None: | |
| path_to_image = "image/"+uploaded_file.name | |
| image = Image.open(uploaded_file) | |
| # Save image to the directory 'image' if it doesn't exist | |
| if not os.path.exists(path_to_image): | |
| image.save(path_to_image) | |
| make_prediction() | |
| def side_bar(): | |
| global confidence | |
| global uploaded_file | |
| global iou_thresold | |
| global Display_Confidence | |
| global Display_Class | |
| with st.sidebar: | |
| st.subheader("Modify parameters") | |
| confidence = st.slider('Confidence %', 0, 100, 80) | |
| iou_thresold = st.slider('IOU Threshold %', 0, 100, 30) | |
| # Checkboxes to display class and confidence for each detection | |
| Display_Class = st.checkbox('Display Class', value=True) | |
| Display_Confidence = st.checkbox('Display Confidence', value=True) | |
| url = "https://github.com/AbelKidane-abita/Reports" | |
| # st.write("check out this [link](%s)" % url) | |
| st.markdown("[GitHub](%s)" % url) | |
| def main_func(): | |
| st.title('YoloV8 Head Detector Model') #display title | |
| st.text('This is a YoloV8 object detection model that detects human heads.') #display description | |
| side_bar() #display side bar | |
| upload_file() #display the button to upload the file from file explorer | |
| if __name__=='__main__': | |
| main_func() |