add training_model.py

.gitignore

@@ -1,3 +1,4 @@
 .idea/
 *.pyc
+/dlib/
 /venvFR/

training_model.py

@@ -0,0 +1,61 @@
+import os
+import pickle
+import sys
+import face_recognition
+
+
+def train_model_by_img(name):
+
+    if not os.path.exists("dataset"):
+        print("[ERROR] there is no directory 'dataset'")
+        sys.exit()
+
+    known_encodings = []
+    images = os.listdir("dataset")
+
+    # print(images)
+
+    for(i, image) in enumerate(images):
+        print(f"[+] processing img {i + 1}/{len(images)}")
+        # print(image)
+
+        face_img = face_recognition.load_image_file(f"dataset/{image}")
+        face_enc = face_recognition.face_encodings(face_img)[0]
+
+        # print(face_enc)
+
+        if len(known_encodings) == 0:
+            known_encodings.append(face_enc)
+        else:
+            for item in range(0, len(known_encodings)):
+                result = face_recognition.compare_faces([face_enc], known_encodings[item])
+                # print(result)
+
+                if result[0]:
+                    known_encodings.append(face_enc)
+                    # print("Same person!")
+                    break
+                else:
+                    # print("Another person!")
+                    break
+
+    # print(known_encodings)
+    # print(f"Length {len(known_encodings)}")
+
+    data = {
+        "name": name,
+        "encodings": known_encodings
+    }
+
+    with open(f"{name}_encodings.pickle", "wb") as file:
+        file.write(pickle.dumps(data))
+
+    return f"[INFO] File {name}_encodings.pickle successfully created"
+
+
+def main():
+    print(train_model_by_img("person_name"))
+
+
+if __name__ == '__main__':
+    main()