app.py

from turn import get_ice_servers
import cv2
import numpy as np
import time
import math
import streamlit as st
import av

from tensorflow.keras.models import load_model
from scipy.signal import convolve2d
from skimage import color
from skimage import io
from sklearn.metrics import accuracy_score

# VECTORIZATION the u factor
import matplotlib.pyplot as plt
import os
from PIL import Image
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv1D, MaxPooling1D, Flatten, Dropout
from tensorflow.keras.optimizers import Adam
from streamlit_webrtc import webrtc_streamer
import dlib

num_bins = 256

# Load the model
model = load_model('best_model_HQ_enhanced_v12.h5')
# model2 = load_model('best_model_HQ_v9.h5')
# model3 = load_model('best_model_HQ_enhanced_v12.h5')
def u_sliding_factor(image_channel, P):
    result = np.zeros(image_channel.shape, np.float32)

    # Define the sliding window size
    window_size = (3, 3)

    # Create the convolution kernel
    kernel = np.ones(window_size, np.float32)
    kernel[1, 1] = 0
    kernel = kernel / (2 * P)
    kernal2 = np.zeros(window_size, np.float32)
    kernal2[1, 1] = 1
    kernal2 = kernal2 / 2

    # Perform the convolution using scipy's convolve2d
    convolution_matrix = cv2.filter2D(image_channel, -1, kernel) + cv2.filter2D(image_channel, -1, kernal2)
    result = convolution_matrix[1:-1, 1:-1]

    return result.astype(np.float32)

def C_list_calculate(P):
    C = []
    for count in range(1, 9):
        c_value = ((P - count) * (count - 1)) / math.floor(((P - 1) / 2)**2)
        C.append(c_value)
    return C

def ED_LBP_Sliding_Matrix(I, P):
    # Define the amount of padding
    padding_amount = 1

    # Pad the array with zeros
    I = np.pad(I, pad_width=padding_amount, mode='constant')
    K = (2**P) - 1
    C_list = C_list_calculate(8)
    u_fac_matrix = u_sliding_factor(I.astype(np.float32), P)
    slid_factor = np.zeros((u_fac_matrix.shape), np.float32)
    m, n = u_fac_matrix.shape
    ED_LBP = np.zeros(u_fac_matrix.shape, np.float32)
    ED_LBP_matrix = np.zeros((u_fac_matrix.shape), np.float32)
    K_matrix = np.ones(u_fac_matrix.shape).astype(np.float32) * K
    offsets = [(0, 1), (0, 2), (1, 2), (2, 2), (2, 1), (2, 0), (1, 0), (0, 0)]
    count = 1

    for offset in offsets:
        row_offset, col_offset = offset
        sliding_matrix = I[row_offset:row_offset + m, col_offset:col_offset + n].astype(np.float32) - u_fac_matrix.astype(np.float32)
        slid_factor = np.maximum(sliding_matrix, 0).astype(np.float32)
        k_norm = K_matrix.astype(np.float32) - u_fac_matrix.astype(np.float32)
        k_norm_nonzero = np.where(k_norm == 0, 1e-10, k_norm)
        A_factor = np.where(k_norm != 0, slid_factor / k_norm_nonzero, 0)
        ED_LBP_matrix = (A_factor.astype(np.float32) * C_list[count - 1]) + np.ones(A_factor.shape).astype(np.float32)
        ED_LBP = ED_LBP + np.where(sliding_matrix >= 0, 2**((count - 1) * ED_LBP_matrix.astype(np.float32)), 0)
        count = count + 1

    ED_LBP = np.where(ED_LBP > 255, 255, np.round(ED_LBP))

    return ED_LBP.astype(int)

def compute_histogram(image, num_bins):
    hist = cv2.calcHist([image], [0], None, [num_bins], [0, num_bins])
    hist = hist / hist.sum()  # Normalize the histogram
    return hist

def spatial_pyramid(image, num_bins):
    ED_LBP_image = np.zeros((image.shape), np.int16)
    num_channels = image.shape[2]
    histograms = []

    for channel in range(num_channels):
        ED_LBP_image[:, :, channel] = ED_LBP_Sliding_Matrix(image[:, :, channel].astype(np.int16), 8)

        # Level 0: Compute histogram for the entire channel
        H1_channel = compute_histogram(ED_LBP_image[:, :, channel].astype(np.uint8), num_bins).ravel()

        # Level 2: Compute histograms for 4x4 grids
        grid_size = 4
        H2_channel = np.empty((grid_size, grid_size, num_bins))
        grid_height, grid_width = ED_LBP_image[:, :, channel].shape[0] // grid_size, ED_LBP_image[:, :, channel].shape[1] // grid_size
        for m in range(grid_size):
            for n in range(grid_size):
                grid_image = ED_LBP_image[m * grid_height: (m + 1) * grid_height,
                                n * grid_width: (n + 1) * grid_width, channel]
                H2_channel[m, n] = compute_histogram(grid_image.astype(np.uint8), num_bins).ravel()

        H2_channel = H2_channel.reshape(-1)

        # Concatenate histograms from level 0 and level 2
        Hs_channel = np.concatenate((H1_channel, H2_channel))
        histograms.append(Hs_channel)

    # Concatenate histograms from all channels
    feature_vector = np.concatenate(histograms)
    return feature_vector


# Add custom CSS styles
st.markdown(
    """
    <style>
    .st-title {
        font-size: 24px;  /* Larger font for the title */
        text-align: center;
    }
    .st-text {
        font-size: 16px;  /* Smaller font for the text */
        text-align: center;
        margin: 10px 0;
    }
    .st-button {
        font-size: 20px;
    }
    .centered {
        display: flex;
        justify-content: center;
        align-items: center;
        flex-direction: column;
    }
    </style>
    """,
    unsafe_allow_html=True
)

# Define the app title
st.markdown("<h1 class='st-title'>نظام كشف التزييف</h1>", unsafe_allow_html=True)
st.markdown("<p class='st-text'>قم بقراءة شروط الاستخدام في الاسفل قبل استخدام النظام</p>", unsafe_allow_html=True)


picture = st.camera_input("Take a picture")


if picture:
    bytes_data = picture.getvalue()
    frm = cv2.imdecode(np.frombuffer(bytes_data, np.uint8), cv2.IMREAD_COLOR)
    img_h, img_w, img_c = frm.shape
    frm = cv2.flip(frm,1)
    features_list=[]
    features_list2=[]
    # Check if there are face landmarks detected
    gray = cv2.cvtColor(frm, cv2.COLOR_BGR2GRAY)
    average_brightness = cv2.mean(gray)[0]
    # st.text(str(average_brightness))
    
    if average_brightness < 100:
        st.text("إضاءة غير جيدة")
        st.text("انتقل الى مكان جيد الإضاءة")
    else:
    
        # Initialize the face detector
        faceDetector = dlib.get_frontal_face_detector()
        
        # Assuming 'frm' is your input image
        faces = faceDetector(frm, 0)
        
        # Check if there are any faces detected
        if len(faces) > 0:
            largest_face = None
            max_area = 0
        
            # Find the largest face
            for face in faces:
                w = face.right() - face.left()
                h = face.bottom() - face.top()
                area = w * h
                if area > max_area:
                    max_area = area
                    largest_face = face
        
                        # If a largest face is detected
            if largest_face is not None:
                # Calculate the expanded dimensions
                expansion_factor = 1.5
                x = largest_face.left()
                y = largest_face.top()
                w = largest_face.right() - x
                h = largest_face.bottom() - y
        
                expanded_x = max(0, int(x - w * (expansion_factor - 1) / 2))
                expanded_y = max(0, int(y - h * (expansion_factor - 1) / 2))
                expanded_w = min(frm.shape[1], int(w * expansion_factor))
                expanded_h = min(frm.shape[0], int(h * expansion_factor))
        
                # Adjust the width and height to avoid going beyond image boundaries
                expanded_w = min(expanded_w, frm.shape[1] - expanded_x)
                expanded_h = min(expanded_h, frm.shape[0] - expanded_y)
        
                # Extract the expanded face area
                # face_img = frm[expanded_y:expanded_y + expanded_h, expanded_x:expanded_x + expanded_w]
                face_img = frm[y:y + h, x:x + w]

                # Now, 'face_img' contains the largest face. You can process it further as needed.
                if np.array(face_img).size != 0 :
                    target_size = (512, 512)
                    resized_face = cv2.resize(face_img, target_size)                
                    # Perform spatial pyramid feature extraction
                    rgb_features = spatial_pyramid(cv2.cvtColor(resized_face, cv2.COLOR_BGR2RGB), num_bins)
                    hsv_features = spatial_pyramid(cv2.cvtColor(resized_face, cv2.COLOR_BGR2HSV), num_bins)
                    ycbcr_features = spatial_pyramid(cv2.cvtColor(resized_face, cv2.COLOR_BGR2YCrCb), num_bins)
            
            
                    if rgb_features.size > 0 and hsv_features.size > 0 and ycbcr_features.size > 0:
                        combined_features = np.concatenate((rgb_features, hsv_features, ycbcr_features))
                        features_list.append(combined_features)
                    if len(features_list) > 0:
                        X_array = np.array(features_list)
                        # print(X_array.shape)
                        X_test_array_reshaped = np.expand_dims(X_array, axis=-1)
                        # prediction = model3.predict(X_test_array_reshaped)
                        # prediction2 = model2.predict(X_test_array_reshaped)
                        prediction3 = model.predict(X_test_array_reshaped)
                        if prediction3 >=0.5:
                            st.text("Real")
                            st.text(str(prediction3[0]))
                        else:
                            st.text("Fack")
                            st.text(str(prediction3[0]))
                        # if prediction >= 0.85 and prediction2 >= 0.0000000000001:
                        #     st.text("صورة حقيقية")
                        #     # st.text(str(prediction[0]))
                        #     # st.text(str(prediction2[0]))
                        # else:
                        #     st.text("صورة مزيفة")
                        #     st.text(str(prediction[0]))
                        #     # st.text(str(prediction2[0]))
            else:
                st.text("لا يوجد وجه")
        else:
            st.text("لا يوجد وجه")
    # Provide guidance for users
    st.markdown("<p class='st-text'>في حالة حصلت على نتيجة غير حقيقية احرص على تحقيق الشروط في الاسفل</p>", unsafe_allow_html=True)
                
    st.image(picture)
# Define the app title
st.markdown("<h1 class='st-title'>شروط الاستخدام</h1>", unsafe_allow_html=True)

# Add informative text with centered alignment
st.markdown("<p class='st-text'>يجب توفر إضاءة جيدة</p>", unsafe_allow_html=True)
st.markdown("<p class='st-text'>يجب استخدام كاميرا هاتف بدقة جيدة</p>", unsafe_allow_html=True)
st.markdown("<p class='st-text'>يجب ان يكون الوجه مقابلا للشاشة بشكل مستقيم</p>", unsafe_allow_html=True)
st.markdown("<p class='st-text'>التركيز على مكان الكاميرا عند الالتقاط</p>", unsafe_allow_html=True)
st.markdown("<p class='st-text'>الحرص على ان لا يكون خلفك خلفية تعكس الضوء مثل الزجاج</p>", unsafe_allow_html=True)
st.markdown("<p class='st-text'>يفضل ان يكون خلفك خلفية صلدة مثل الجدار</p>", unsafe_allow_html=True)