Upload app.py

app.py

@@ -0,0 +1,105 @@
+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)
+
+    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 result2.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()