OpenCV Face Recognition model using DeepFace

FaceRecognition.py

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+from deepface import DeepFace
+import cv2
+import time
+import streamlit as st
+from PIL import Image
+
+windowsHolder = st.empty()
+value = st.empty()
+
+def main():
+    cap = cv2.VideoCapture(0)
+    while True:
+        time.sleep(0.5)
+        _, img = cap.read()
+
+        if img is None:
+            break
+
+        #cv2.imshow("Window", img)
+        windowsHolder.image(img, channels="BGR")
+        extract_faces(img)
+        if cv2.waitKey(1) & 0xFF == ord("q"):
+            break
+    cap.release()
+    cv2.destroyAllWindows()
+
+    return
+
+
+def extract_faces(raw_img):
+    test = DeepFace.extract_faces(raw_img, detector_backend="mtcnn", align=True)
+
+    if test:
+        faces = []
+
+        for face_obj in test:
+            facial_area = face_obj["facial_area"]
+            faces.append(
+                (
+                    facial_area["x"],
+                    facial_area["y"],
+                    facial_area["w"],
+                    facial_area["h"],
+                )
+            )
+
+        detected_faces = []
+        for x, y, w, h in faces:
+            detected_face = raw_img[int(y) : int(y + h), int(x) : int(x + w)]
+            detected_faces.append(detected_face)
+            recognition(detected_face)
+            print("Face detected")
+            time.sleep(0.6)
+            return
+
+
+def recognition(img):
+    print("Starting recognition")
+    dfs = DeepFace.find(
+        img_path=img,
+        db_path="/Users/futuregadgetlab/Desktop/DB",
+        detector_backend="mtcnn",
+        model_name="VGG-Face",
+        distance_metric="euclidean_l2",
+        enforce_detection=False,
+    )
+
+    found_non_empty_df = False
+
+    for df in dfs:
+        if len(df) != 0:
+            for _, row in df.iterrows():
+                if row["VGG-Face_euclidean_l2"] < 0.6:
+                    print("Matched!")
+                    with value.container():
+                        st.write("Matched! Welcome to expo 2023")
+                    st.balloons()
+                    found_non_empty_df = True
+                    break
+
+            if found_non_empty_df:
+                break  # Exit the outer loop if a match is found in any DataFrame
+
+    if not found_non_empty_df:
+        print("SIke")
+        with value.container():
+            st.warning("You don't exist, my friend!")
+    time.sleep(1)
+
+
+if __name__ == "__main__":
+    main()