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| # import basic libraries | |
| import numpy as np | |
| import streamlit as st | |
| import cv2 | |
| from deepface import DeepFace as dfc | |
| from PIL import Image | |
| import os | |
| st.set_page_config(page_title='Face-detection-analysis', page_icon=None, layout='centered', initial_sidebar_state='auto') | |
| # function to load image | |
| try: | |
| face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') | |
| except Exception: | |
| st.write("Error loading cascade classifiers") | |
| def face_detect(img): | |
| img = np.array(img.convert("RGB")) | |
| face = face_cascade.detectMultiScale(image=img) | |
| # draw rectangle around face | |
| for (x, y, w, h) in face: | |
| cv2.rectangle(img=img, pt1=(x, y), pt2=(x + w, y + h), color=(255, 0, 0), thickness=2) | |
| roi = img[y:y + h, x:x + w] | |
| return img, face | |
| # analyze image | |
| def analyze_image(img): | |
| prediction = dfc.analyze(img_path=img) | |
| return prediction | |
| #function for webcam | |
| def detect_web(image): | |
| faces = face_cascade.detectMultiScale( | |
| image=image, scaleFactor=1.3, minNeighbors=5) | |
| for (x, y, w, h) in faces: | |
| cv2.rectangle(img=image, pt1=(x, y), pt2=( | |
| x + w, y + h), color=(255, 0, 0), thickness=2) | |
| return image, faces | |
| def main(): | |
| # Face Analysis Application # | |
| st.markdown("<h1 style='text-align: center;'>Face Detection and Analysis </h1>", unsafe_allow_html=True) | |
| activiteis = ["Home", "Analyze Face", "About"] | |
| choice = st.sidebar.selectbox("Select Activity", activiteis) | |
| st.sidebar.markdown( | |
| """ Developed by [Vivek] (https://github.com/7Vivek)""") | |
| st.sidebar.markdown( | |
| """ Checkout complete project [here] (https://github.com/7Vivek/Face-detection-analysis)""") | |
| # C0C0C0 | |
| if choice == "Home": | |
| html_temp_home1 = """<div style="background-color:#1E2839;padding:10px"> | |
| <h4 style="color:white;text-align:center;"> | |
| Face detection and Face feature analysis application using OpenCV, DeepFace and Streamlit.</h4> | |
| </div> | |
| </br>""" | |
| st.image('https://cdn.dribbble.com/users/1373613/screenshots/5510801/media/b82469d51c432c2ff65c0158334cfabf.gif',use_column_width=True) | |
| st.markdown(html_temp_home1, unsafe_allow_html=True) | |
| st.write(""" | |
| Application Functionalities. | |
| 1. Face feature analysis such as emotion, gender and age.""") | |
| elif choice == "Analyze Face": | |
| st.subheader("Analyze facial features such as emotion, age and gender.") | |
| image_file = st.file_uploader("Upload image you want to analyze", type=['jpg', 'png', 'jpeg']) | |
| if image_file is not None: | |
| #read image using PIL | |
| image_loaded = Image.open(image_file) | |
| #detect faces in image | |
| result_img, result_face = face_detect(image_loaded) | |
| st.image(result_img, use_column_width=True) | |
| st.success("found {} face\n".format(len(result_face))) | |
| if st.button("Analyze image"): | |
| # convert image to array | |
| new_image = np.array(image_loaded.convert('RGB')) | |
| img = cv2.cvtColor(new_image, 1) | |
| gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) | |
| #analyze features of face | |
| result = analyze_image(img) | |
| # st.write(result) | |
| st.write("Analysis summary") | |
| st.write("Face emotion is ", result["dominant_emotion"], "in image.") | |
| st.write("Gender recognized as", result["gender"], "in image.") | |
| st.write("Age is", result["age"], "years.") | |
| else: | |
| pass | |
| #st.write("Click on Analyze image ") | |
| elif choice == "About": | |
| st.subheader("About this app") | |
| html_temp_about1= """<div style="background-color:#283347;padding:10px"> | |
| <h4 style="color:white;text-align:center;"> | |
| Face detection and Face feature analysis application using OpenCV, DeepFace and Streamlit.</h4> | |
| </div> | |
| </br>""" | |
| st.markdown(html_temp_about1, unsafe_allow_html=True) | |
| html_temp4 = """ | |
| <div style="background-color:#434E61;padding:10px"> | |
| <h4 style="color:white;text-align:center;">This Application is developed by Vivek Limbad using Streamlit Framework, Opencv and DeepFace library for demonstration purpose. If you have any suggestion or want to comment just write a mail at [email protected]. </h4> | |
| <h4 style="color:white;text-align:center;">Thanks for Visiting </h4> | |
| </div> | |
| <br></br> | |
| <br></br>""" | |
| st.markdown(html_temp4, unsafe_allow_html=True) | |
| else: | |
| pass | |
| if __name__ == '__main__': | |
| main() | |