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import cv2
import matplotlib.pyplot as plt
import streamlit as st
from deepface import DeepFace
import mediapipe
import os
import tempfile
backends = [
'opencv',
'ssd',
'dlib',
'mtcnn',
'fastmtcnn',
'retinaface',
'mediapipe',
'yolov8',
'yunet',
'centerface',
]
metrics = ["cosine", "euclidean", "euclidean_l2"]
models = [
"VGG-Face",
"Facenet",
"Facenet512",
"OpenFace",
"DeepFace",
"DeepID",
"ArcFace",
"Dlib",
"SFace",
"GhostFaceNet",
]
def verify(img1, img2, model_name, backend, metric):
# Save the uploaded images to temporary files
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)
img1p = cv2.cvtColor(img1p, cv2.COLOR_BGR2RGB)
img2p = cv2.cvtColor(img2p, cv2.COLOR_BGR2RGB)
face_detect = mediapipe.solutions.face_detection
face_detector = face_detect.FaceDetection(min_detection_confidence=0.6)
width1, height1 = img1p.shape[1], img1p.shape[0]
width2, height2 = img2p.shape[1], img2p.shape[0]
result1 = face_detector.process(img1p)
result2 = face_detector.process(img2p)
if result1.detections is not None:
for face in result1.detections:
if face.score[0] > 0.80:
bounding_box = face.location_data.relative_bounding_box
x = int(bounding_box.xmin * width1)
w = int(bounding_box.width * width1)
y = int(bounding_box.ymin * height1)
h = int(bounding_box.height * height1)
cv2.rectangle(img1p, (x, y), (x+w, y+h), color=(126, 133, 128), thickness=10)
if result2.detections is not None:
for face in result2.detections:
if face.score[0] > 0.80:
bounding_box = face.location_data.relative_bounding_box
x = int(bounding_box.xmin * width2)
w = int(bounding_box.width * width2)
y = int(bounding_box.ymin * height2)
h = int(bounding_box.height * height2)
cv2.rectangle(img2p, (x, y), (x+w, y+h), color=(126, 133, 128), thickness=10)
st.image([img1p, img2p], caption=["Image 1", "Image 2"], width=200)
face = DeepFace.verify(img1p, img2p, model_name=model_name, detector_backend=backend, distance_metric=metric)
verification = face["verified"]
if verification:
st.write("Matched")
else:
st.write("Not Matched")
# Streamlit app
def main():
st.title("Face Verification App")
tab_selection = st.sidebar.selectbox("Select Functionality", ["Face Verification", "Face Recognition", "Celebrity Lookalike", "Age and Emotions Detection"])
if tab_selection == "Face Verification":
st.header("Face Verification")
model_name = st.selectbox("Select Model", models)
backend = st.selectbox("Select Backend", backends)
metric = st.selectbox("Select Metric", metrics)
uploaded_img1 = st.file_uploader("Upload Image 1", type=["jpg", "png"])
uploaded_img2 = st.file_uploader("Upload Image 2", type=["jpg", "png"])
if uploaded_img1 and uploaded_img2:
if st.button("Verify Faces"):
verify(uploaded_img1, uploaded_img2, model_name, backend, metric)
# Run the app
if __name__ == "__main__":
main()
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