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import cv2
import streamlit as st
import tempfile
import torch
from torchvision import transforms
from mtcnn import MTCNN
from skimage.feature import hog
import joblib
import numpy as np
# Preprocessing for Siamese Model
def preprocess_image_siamese(img):
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor()
])
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
return transform(img)
# Preprocessing for SVM model (converting to grayscale)
def preprocess_image_svm(img):
img = cv2.resize(img, (224, 224))
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
return img
# Extract HOG Features
def extract_hog_features(img):
hog_features = hog(img, orientations=9, pixels_per_cell=(16, 16), cells_per_block=(4, 4))
return hog_features
# Detect faces using MTCNN
def get_face(img):
detector = MTCNN()
faces = detector.detect_faces(img)
if faces:
x1, y1, w, h = faces[0]['box']
x1, y1 = abs(x1), abs(y1)
x2, y2 = x1 + w, y1 + h
return img[y1:y2, x1:x2]
return None
# Function to verify face (either HOG-SVM or Siamese model)
def verify(img1, img2, model_type, anchor_img):
with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_img1:
temp_img1.write(img1.read())
temp_img1_path = temp_img1.name
with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_img2:
temp_img2.write(img2.read())
temp_img2_path = temp_img2.name
img1p = cv2.imread(temp_img1_path)
img2p = cv2.imread(temp_img2_path)
face1 = get_face(img1p)
face2 = get_face(img2p)
if face1 is not None and face2 is not None:
st.image([face1, face2], caption=["Image 1", "Image 2"], width=200)
if model_type == "HOG-SVM":
with open('./svm.pkl', 'rb') as f:
svm = joblib.load(f)
with open('./pca.pkl', 'rb') as f:
pca = joblib.load(f)
face1 = preprocess_image_svm(face1)
face2 = preprocess_image_svm(face2)
hog1 = extract_hog_features(face1)
hog2 = extract_hog_features(face2)
hog1_pca = pca.transform([hog1])
hog2_pca = pca.transform([hog2])
pred1 = svm.predict(hog1_pca)
pred2 = svm.predict(hog2_pca)
if pred1 == 1 and pred2 == 1:
st.write("Matched")
else:
st.write("Not Matched")
else:
st.write("Face not detected in one or both images")
# Main function to handle Streamlit interaction
def main():
st.title("Real-time Face Verification App")
model_type = st.selectbox("Select Model", ["Siamese", "HOG-SVM"])
anchor_img = st.file_uploader("Select Anchor Image", type=["jpg", "png"])
if model_type == "Siamese":
# Implement Siamese model choice logic here if needed
st.write("Using Siamese Network")
elif model_type == "HOG-SVM":
# Implement HOG-SVM logic here
st.write("Using HOG-SVM")
# Camera Input for Face Detection
run_detection = st.checkbox("Start Camera")
if run_detection:
cap = cv2.VideoCapture(0) # Start camera
while True:
ret, frame = cap.read()
if not ret:
st.write("Failed to grab frame.")
break
# Detect face in the current frame
face = get_face(frame)
if face is not None:
# Draw bounding box around detected face
x1, y1, x2, y2 = face[0], face[1], face[2], face[3] # Update face coordinates
cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
# Show bounding box
st.image(frame, channels="BGR", use_column_width=True)
# Stop camera when ESC is pressed
key = cv2.waitKey(1) & 0xFF
if key == 27: # ESC key
break
cap.release()
cv2.destroyAllWindows()
if __name__ == "__main__":
main()
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