updated embedding function

API/route.py

@@ -63,6 +63,80 @@ def load_and_preprocess_image(img_path, target_size=(160, 160)):
     img_array /= 255.0
     return img_array
 
+def calculate_embeddings(image_filename):
+
+    """
+    Calculate embeddings for the provided image.
+    
+    Args:
+        image_filename (str): The path to the image file.
+        
+    Returns:
+        list: A list of embeddings for the image.
+    """
+
+    face_image_data = DeepFace.extract_faces(
+        image_filename, detector_backend='mtcnn', enforce_detection=False,
+    )
+    new_image_path = f'Images/Faces/tmp.jpg'
+
+    if face_image_data[0]['face'] is not None:
+        plt.imsave(new_image_path, face_image_data[0]['face'])
+
+        img_array = load_and_preprocess_image(new_image_path)
+        model=load_model('Model/embedding_trial3.h5')
+        embedding = model.predict(img_array)[0]
+        embedding_list = embedding.tolist()
+        logging.info(f'Embedding created')
+
+        return embedding_list
+
+@router.post('/recalculate_embeddings')
+async def recalculate_embeddings():
+    """
+    Recalculate embeddings for all the images in the database.
+
+    Returns:
+        dict: A dictionary with a success message.
+
+    Raises:
+        None
+    """
+    logging.info('Recalculating embeddings')
+    employees_mongo = client2.find(collection2)
+    for employee in employees_mongo:
+        print(employee, type(employee))
+        embeddings = []
+
+        # In the initial version, the images were stored in the 'Image' field
+        if 'Images' in employee:
+            images = employee['Images']
+        else:
+            images = employee['Image']
+
+        for encoded_image in images:
+            encoded_image = encoded_image.replace('data:image/png;base64,', '')
+            encoded_image = encoded_image.strip()
+            encoded_image += '=' * (-len(encoded_image) % 4)
+            img_recovered = base64.b64decode(encoded_image)
+            pil_image = Image.open(BytesIO(img_recovered))
+            image_filename = f'{employee["Name"]}.png'
+            pil_image.save(image_filename)
+            logging.debug(f'Image saved {employee["Name"]}')
+            embeddings.append(calculate_embeddings(image_filename))
+            # os.remove(image_filename)
+
+        logging.debug(f'About to update Embeddings: {embeddings}')
+        # Store the data in the database
+        client2.update_one(
+            collection2,
+            {'EmployeeCode': employee['EmployeeCode']},
+            {'$set': {'embeddings': embeddings, 'Images': images}},
+        )
+
+    return {'message': 'Embeddings Recalculated successfully'}
+
+
 # To create new entries of employee
 @router.post('/create_new_faceEntry')
 async def create_new_faceEntry(Employee: Employee):
@@ -102,19 +176,8 @@ async def create_new_faceEntry(Employee: Employee):
         #     image_filename, model_name='Facenet512', detector_backend='mtcnn',
         # )
 
-        face_image_data = DeepFace.extract_faces(
-            image_filename, detector_backend='mtcnn', enforce_detection=False,
-        )
-        if face_image_data[0]['face'] is not None:
-            plt.imsave(f'Images/Faces/{Name}.jpg', face_image_data[0]['face'])
-
-            img_array = load_and_preprocess_image(f'Images/Faces/{Name}.jpg')
-            model=load_model('Model/embedding_trial3.h5')
-            embedding = model.predict(img_array)[0]
-            embedding_list = embedding.tolist()
-            embeddings.append(embedding_list)
-            logging.info(f'Embedding created Embeddings for {Name}')
-        os.remove(image_filename)
+        embeddings.append(calculate_embeddings(image_filename))
+        # os.remove(image_filename)
 
     logging.debug(f'About to insert Embeddings: {embeddings}')
     # Store the data in the database
@@ -250,19 +313,9 @@ async def update_employees(EmployeeCode: int, Employee: UpdateEmployee):
             #     image_filename, model_name='Facenet', detector_backend='mtcnn',
             # )
 
-            face_image_data = DeepFace.extract_faces(
-                image_filename, detector_backend='mtcnn', enforce_detection=False,
-            )
-            if face_image_data[0]['face'] is not None:
-                plt.imsave(f'Images/Faces/{Employee.Name}.jpg', face_image_data[0]['face'])
-
-                img_array = load_and_preprocess_image(f'Images/Faces/{Employee.Name}.jpg')
-                model=load_model('Model/embedding_trial3.h5')
-                embedding = model.predict(img_array)[0]
-                embedding_list = embedding.tolist()
-                embeddings.append(embedding_list)
-                logging.info(f'Embedding created Embeddings for {Employee.Name}')
-            os.remove(image_filename)
+            embeddings.append(calculate_embeddings(image_filename))
+            # os.remove(image_filename)
+
         Employee_data['embeddings'] = embeddings
 
         try: