Face_Detection.py

import streamlit as st
import cv2
from cv2 import *
from PIL import Image, ImageEnhance
import numpy as np
import os


@st.cache
def load_img(img):
    im = Image.open(img)
    return im


face_cascade = cv2.CascadeClassifier(
    'haarcascades/haarcascade_frontalface_alt.xml')
eyes_cascade = cv2.CascadeClassifier(
    'haarcascades/haarcascade_eye.xml')
smile_cascade = cv2.CascadeClassifier(
    'haarcascades/haarcascade_smile.xml')


def detect_faces(our_image):
    new_img = np.array(our_image.convert('RGB'))
    img = cv2.cvtColor(new_img, 1)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # Detect Face
    faces = face_cascade.detectMultiScale(gray, 1.1, 4)
    # Draw Rectangle
    for(x, y, w, h) in faces:
        cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
    return img, faces


def detect_eyes(our_image):
    new_img = np.array(our_image.convert('RGB'))
    img = cv2.cvtColor(new_img, 1)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # Detect Eyes
    eyes = eyes_cascade.detectMultiScale(gray, 1.3, 5)
    # Draw Rectangle
    for(ex, ey, ew, eh) in eyes:
        cv2.rectangle(img, (ex, ey), (ex+ew, ey+eh), (0, 255, 0), 2)
    return img


def detect_smiles(our_image):
    new_img = np.array(our_image.convert('RGB'))
    img = cv2.cvtColor(new_img, 1)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # r_gray = gray[y:y+h, x:x+w]
    # Detect Smile
    smiles = smile_cascade.detectMultiScale(gray, 1.1, 4)
    # Draw Rectangle
    for(x, y, w, h) in smiles:
        cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 5)
    return img


def cartonize_image(our_image):
    new_img = np.array(our_image.convert('RGB'))
    img = cv2.cvtColor(new_img, 1)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # Edges
    gray = cv2.medianBlur(gray, 5)
    edges = cv2.adaptiveThreshold(
        gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 9, 2)
    # Color
    color = cv2.bilateralFilter(img, 9, 300, 300)
    # Cartoon
    cartoon = cv2.bitwise_and(color, color, mask=edges)

    return cartoon


def cannize_image(our_image):
    new_img = np.array(our_image.convert('RGB'))
    img = cv2.cvtColor(new_img, 1)
    img = cv2.GaussianBlur(img, (11, 11), 0)
    canny = cv2.Canny(img, 100, 150)
    return canny


def main():
    st.title("Face Detection App")
    st.text("Built with Streamlit and Open CV")

    activities = ["Detection", "About"]
    choice = st.sidebar.selectbox("Select Activity", activities)

    if choice == 'Detection':
        st.subheader("Face Detection")

        image_file = st.file_uploader(
            "Upload Image", type=['jpg', 'png', 'jpeg'])

        if image_file is not None:
            our_image = Image.open(image_file)
            st.text("Original Image")
            st.image(our_image, width=500)

        enhance_type = st.sidebar.radio(
            "Enhance Type", ["Original", "Gray-Scale", "Contrast", "Brightness", "Blurring"])
        if enhance_type == "Gray-Scale":
            new_img = np.array(our_image.convert('RGB'))
            img = cv2.cvtColor(new_img, 1)
            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
            st.image(gray, width=500)
        if enhance_type == "Contrast":
            c_rate = st.sidebar.slider("Contrast", 0.5, 3.5)
            enhancer = ImageEnhance.Contrast(our_image)
            img_output = enhancer.enhance(c_rate)
            st.image(img_output, width=500)
        if enhance_type == "Brightness":
            c_rate = st.sidebar.slider("Brightness", 0.5, 3.5)
            enhancer = ImageEnhance.Brightness(our_image)
            img_output = enhancer.enhance(c_rate)
            st.image(img_output, width=500)
        if enhance_type == "Blurring":
            new_img = np.array(our_image.convert('RGB'))
            blur_rate = st.sidebar.slider("Blur", 0.5, 3.5)
            img = cv2.cvtColor(new_img, 1)
            blur_img = cv2.GaussianBlur(img, (11, 11), blur_rate)
            st.image(blur_img, width=500)

        # Face Detection
        task = ["Faces", "Smiles", "Eyes", "Cannize", "Cartonize"]
        feature_choice = st.sidebar.selectbox("Find Features", task)
        if st.button("Process"):

            if feature_choice == 'Faces':
                result_img, result_faces = detect_faces(our_image)
                st.image(result_img, width=500)
                st.success("Found {} faces".format(len(result_faces)))

            elif feature_choice == 'Eyes':
                result_img = detect_eyes(our_image)
                st.image(result_img, width=500)

            elif feature_choice == 'Smiles':
                result_img = detect_smiles(our_image)
                st.image(result_img, width=500)

            elif feature_choice == 'Cannize':
                result_img = cannize_image(our_image)
                st.image(result_img, width=500)

            elif feature_choice == 'Cartonize':
                result_img = cartonize_image(our_image)
                st.image(result_img, width=500)

    elif choice == 'About':
        st.subheader("About")
        st.text("A Face Detection Application built with Streamlit and Open CV in Python. \nIt provides features of \n- Editing Your Image\n- Face Detection\n- Smile Detection\n- Eyes Detection\n- Cartoonizing your image")
        st.subheader("\n\n\nBuilt by Mihir Pesswani")


if __name__ == "__main__":
    main()