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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")
@st.cache
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()
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