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| import numpy as np | |
| import pandas as pd | |
| from ultralytics import YOLO | |
| import streamlit as st | |
| import cv2 | |
| import base64 | |
| import time | |
| import shutil | |
| import os | |
| from PIL import Image | |
| import base64 | |
| import random | |
| st.set_page_config(layout="wide",initial_sidebar_state="expanded", | |
| page_icon='🔎',page_title='Poth-Hole Detector') | |
| image_directory = "val" # Assuming "val" is the directory name | |
| # Get a list of image filenames in the directory | |
| image_filenames = [filename for filename in os.listdir(image_directory) if filename.endswith(".jpg")] | |
| # Function to generate a random image from the list of filenames | |
| def get_random_image(): | |
| if not image_filenames: | |
| return None | |
| random_image_filename = random.choice(image_filenames) | |
| random_image_path = os.path.join(image_directory, random_image_filename) | |
| return random_image_path | |
| def get_video_base64(video_path): | |
| with open(video_path, "rb") as file: | |
| video_bytes = file.read() | |
| base64_encoded = base64.b64encode(video_bytes).decode("utf-8") | |
| return base64_encoded | |
| video_path = "deep1.mp4" | |
| video_base64 = get_video_base64(video_path) | |
| video_html = f""" | |
| <style> | |
| #myVideo {{ | |
| position: fixed; | |
| right: 0; | |
| bottom: 0; | |
| min-width: 100%; | |
| min-height: 100%; | |
| }} | |
| .content {{ | |
| position: fixed; | |
| bottom: 0; | |
| background: rgba(0, 0, 0, 0.5); | |
| color: #f1f1f1; | |
| width: 100%; | |
| padding: 20px; | |
| }} | |
| </style> | |
| <video autoplay loop muted id="myVideo"> | |
| <source type="video/mp4" src="data:video/mp4;base64,{video_base64}"> | |
| </video> | |
| """ | |
| st.markdown(video_html, unsafe_allow_html=True) | |
| # Define custom style for the glowing text | |
| glowing_text_style = ''' | |
| <style> | |
| .glowing-text { | |
| font-family: 'Arial Black', sans-serif; | |
| font-size: 48px; | |
| text-align: center; | |
| animation: glowing 2s infinite; | |
| } | |
| @keyframes glowing { | |
| 0% { color: #FF9933; } /* Saffron color */ | |
| 25% { color: #FFFFFF; } /* White color */ | |
| 50% { color: #128807; } /* Green color */ | |
| 75% { color: #0000FF; } /* Blue color */ | |
| 100% { color: #FF9933; } /* Saffron color */ | |
| } | |
| </style> | |
| ''' | |
| # Display the glowing text using st.markdown | |
| st.markdown(glowing_text_style, unsafe_allow_html=True) | |
| st.markdown(f'<p class="glowing-text">🕳️ PothHole Detector 🕳️</p>', unsafe_allow_html=True) | |
| def upload(): | |
| image=None | |
| image_filename=None | |
| initial_image = st.camera_input('Take a picture') | |
| original_image = initial_image | |
| temp_path = None | |
| if initial_image is not None: | |
| image_filename = f"{int(time.time())}.jpg" | |
| bytes_data = initial_image.getvalue() | |
| image = cv2.imdecode(np.frombuffer(bytes_data, np.uint8), cv2.IMREAD_COLOR) | |
| return image, original_image,image_filename | |
| def process_line(line, image_np,counter): | |
| # Process a single line from the labels.txt file | |
| bresults = line.split() | |
| if len(bresults) >=5: | |
| names={0:'POTH_HOLE'} | |
| xc, yc, nw, nh = map(float, bresults[1:5]) | |
| h, w = image_np.shape[0], image_np.shape[1] | |
| xc *= w | |
| yc *= h | |
| nw *= w | |
| nh *= h | |
| top_left = (int(xc - nw / 2), int(yc - nh / 2)) | |
| bottom_right = (int(xc + nw / 2), int(yc + nh / 2)) | |
| # Draw bounding box | |
| cv2.rectangle(image_np, top_left, bottom_right, (4, 29, 255), 3, cv2.LINE_4) | |
| # Draw label text | |
| #label = names[int(bresults[0])] | |
| label = f'{names[int(bresults[0])]}-{counter}' | |
| text_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 2, 3)[0] | |
| text_width, text_height = text_size | |
| text_x = (top_left[0] + bottom_right[0] - text_width) // 2 + 100 | |
| text_y = top_left[1] - 10 | |
| cv2.putText(image_np, label, (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2) | |
| sidebar_option = st.sidebar.radio("Select an option", ("Take picture for prediction", "Upload file")) | |
| def main(): | |
| if sidebar_option == "Take picture for prediction": | |
| if st.checkbox('Take a picture for prediction'): | |
| image, original_image,image_filename= upload() | |
| if original_image is not None and original_image is not None and len(image_filename)!=0 and st.button('Prediction'): # Check if original_image is not None | |
| st.info('Wait for the results...!') | |
| #image1=cv2.imread(image) | |
| counter=1 | |
| names={0:'POTH_HOLE' | |
| } | |
| model=YOLO('best.pt') | |
| result = model.predict(image,save=True,save_txt=True) | |
| txt_files_exist = any(filename.endswith('.txt') for filename in os.listdir('runs/detect/predict/labels')) | |
| if txt_files_exist: | |
| lis=open('runs/detect/predict/labels/image0.txt','r').readlines() | |
| for line in lis: | |
| process_line(line, image,counter) | |
| counter+=1 | |
| with st.spinner('Wait for the results...!'): | |
| time.sleep(5) | |
| st.image(image,use_column_width=True) | |
| st.balloons() | |
| try: | |
| if os.path.exists('runs'): | |
| shutil.rmtree('runs') | |
| st.session_state.original_image = None # Clear the original_image variable | |
| except Exception as e: | |
| st.error(f"An error occurred: {e}") | |
| else: | |
| st.warning('⚠️Please check your image') | |
| st.info("📷✨ **Encountering the 'Please check your image' error?**") | |
| st.write( | |
| """ | |
| Our algorithm may not have been able to predict the content of your image. To improve results, consider the following: | |
| 👉 **Verify image quality and resolution.** | |
| 👉 **Ensure the image is clear and well-lit.** | |
| 👉 **Check if the image meets our specified format requirements.** | |
| 👉 **Consider alternative images for better results.** | |
| Our aim is to provide accurate predictions, and addressing these aspects can make a significant difference. If the issue persists, please reach out to our support team. We're here to help! 🤝🔧 | |
| """ | |
| ) | |
| try: | |
| if os.path.exists('runs'): | |
| shutil.rmtree('runs') | |
| st.session_state.original_image = None # Clear the original_image variable | |
| except Exception as e: | |
| st.error(f"An error occurred: {e}") | |
| elif sidebar_option == "Upload file": | |
| fileimage=st.file_uploader('Upload the file for detection 📁',type=['jpg','jpeg','png']) | |
| st.info("If you haven't filed, our system will employ a default image for prediction 📁. Simply press the 'Predict' button and directly upload your file for analysis 🧐.") | |
| if st.button('Predict'): | |
| if True: | |
| if fileimage is None: | |
| default_image=get_random_image() | |
| st.warning('⚠️ We are using random image from our backend!.') | |
| st.info('Wait for the results...!') | |
| counter=1 | |
| pic=Image.open(default_image) | |
| image_np = np.array(pic) | |
| names={0:'POTH_HOLE' | |
| } | |
| mod1=YOLO('best.pt') | |
| mod1.predict(image_np,save=True,save_txt=True) | |
| txt_files_exist = any(filename.endswith('.txt') for filename in os.listdir('runs/detect/predict/labels')) | |
| if txt_files_exist: | |
| lis=open('runs/detect/predict/labels/image0.txt','r').readlines() | |
| with st.spinner('Wait for the results...!'): | |
| time.sleep(5) | |
| for line in lis: | |
| process_line(line, image_np,counter) | |
| counter+=1 | |
| col1,col2=st.columns(2) | |
| with col1: | |
| st.info('Original Image!') | |
| st.image(default_image,use_column_width=True) | |
| with col2: | |
| st.info('Detected Image!') | |
| st.image(image_np,use_column_width=True) | |
| st.balloons() | |
| try: | |
| if os.path.exists('runs'): | |
| shutil.rmtree('runs') | |
| st.session_state.original_image = None # Clear the original_image variable | |
| except Exception as e: | |
| st.error(f"An error occurred: {e}") | |
| else: | |
| st.warning('⚠️Please check your image') | |
| st.info("📷✨ **Encountering the 'Please check your image' error?**") | |
| st.write( | |
| """ | |
| Our algorithm may not have been able to predict the content of your image. To improve results, consider the following: | |
| 👉 **Verify image quality and resolution.** | |
| 👉 **Ensure the image is clear and well-lit.** | |
| 👉 **Check if the image meets our specified format requirements.** | |
| 👉 **Consider alternative images for better results.** | |
| Our aim is to provide accurate predictions, and addressing these aspects can make a significant difference. If the issue persists, please reach out to our support team. We're here to help! 🤝🔧 | |
| """ | |
| ) | |
| try: | |
| if os.path.exists('runs'): | |
| shutil.rmtree('runs') | |
| st.session_state.original_image = None # Clear the original_image variable | |
| except Exception as e: | |
| st.error(f"An error occurred: {e}") | |
| else: | |
| st.info('Wait for the results...!') | |
| counter=1 | |
| pic=Image.open(fileimage) | |
| image_np = np.array(pic) | |
| names={0:'POTH_HOLE' | |
| } | |
| mod1=YOLO('best.pt') | |
| mod1.predict(image_np,save=True,save_txt=True) | |
| txt_files_exist = any(filename.endswith('.txt') for filename in os.listdir('runs/detect/predict/labels')) | |
| if txt_files_exist: | |
| lis=open('runs/detect/predict/labels/image0.txt','r').readlines() | |
| with st.spinner('Wait for the results...!'): | |
| time.sleep(5) | |
| for line in lis: | |
| process_line(line, image_np,counter) | |
| counter+=1 | |
| col1,col2=st.columns(2) | |
| with col1: | |
| st.info('Original Image!') | |
| st.image(fileimage,use_column_width=True) | |
| with col2: | |
| st.info('Detected Image!') | |
| st.image(image_np,use_column_width=True) | |
| st.balloons() | |
| try: | |
| if os.path.exists('runs'): | |
| shutil.rmtree('runs') | |
| st.session_state.original_image = None # Clear the original_image variable | |
| except Exception as e: | |
| st.error(f"An error occurred: {e}") | |
| else: | |
| st.warning('⚠️Please check your image') | |
| st.info("📷✨ **Encountering the 'Please check your image' error?**") | |
| st.write( | |
| """ | |
| Our algorithm may not have been able to predict the content of your image. To improve results, consider the following: | |
| 👉 **Verify image quality and resolution.** | |
| 👉 **Ensure the image is clear and well-lit.** | |
| 👉 **Check if the image meets our specified format requirements.** | |
| 👉 **Consider alternative images for better results.** | |
| Our aim is to provide accurate predictions, and addressing these aspects can make a significant difference. If the issue persists, please reach out to our support team. We're here to help! 🤝🔧 | |
| """ | |
| ) | |
| try: | |
| if os.path.exists('runs'): | |
| shutil.rmtree('runs') | |
| st.session_state.original_image = None # Clear the original_image variable | |
| except Exception as e: | |
| st.error(f"An error occurred: {e}") | |
| if __name__ == '__main__': | |
| main() | |