FinalLast

FastAPI.py

@@ -23,11 +23,16 @@ config = {
 firebase = pyrebase.initialize_app(config)
 storage = firebase.storage()
 
-
+# Define the folder containing face images in the Firebase Storage bucket
+storage_folder = "Faces/"
 
 
 app = FastAPI()
 
+class ImgSave(BaseModel):
+    image_url: HttpUrl
+    user_name: str
+
 class ImgInput(BaseModel):
     image_url: HttpUrl
 
@@ -69,7 +74,48 @@ def recognize_face(image_url: HttpUrl) -> ImgOutput:
 
 
 
+def add_face(image_url: HttpUrl,user_name : str) -> ImgOutput:
+    # Downloading image
+    response = requests.get(image_url)
+    with open("examp.jpg", 'wb') as file:
+        file.write(response.content)
+
+    # Load the stored face encodings and labels from the pickle file
+    with open("face_encodings.pkl", "rb") as file:
+        data = pickle.load(file)
+        face_encodings = data["encodings"]
+        labels = data["labels"]
+
+    # Load a new image you want to recognize
+    new_image = cv2.imread("examp.jpg")
+
+    # Convert the BGR image to RGB
+    rgb_img = cv2.cvtColor(new_image, cv2.COLOR_BGR2RGB)
+
+    encode = face_recognition.face_encodings(new_image)[0]
+    face_encodings.append(encode)
+    labels.append(user_name)
+
+    # Delete the temporary downloaded image
+    os.remove("examp.jpg")
+
+    # Save the encodings and labels to a pickle file
+    data = {"encodings": face_encodings, "labels": labels}
+    with open("face_encodings.pkl", "wb") as file:
+        pickle.dump(data, file)
+
+    # Upload the pickle file to Firebase Storage
+    pkl_blob = storage.child(f"{storage_folder}pkl/face_encodings.pkl")
+    pkl_blob.put("face_encodings.pkl")
+
+
 @app.post('/')
 async def scoring_endpoint(item:ImgInput):
     result = recognize_face(item.image_url)
-    return result
+    return result
+
+
+@app.post('/user/')
+async def scoring_endpoint(item:ImgSave):
+    add_face(item.image_url, item.user_name)
+    return ImgOutput(label="User Saved")

encode_pkl.py

@@ -1,65 +0,0 @@
-import os
-import cv2
-import face_recognition
-import pickle
-import pyrebase
-
-# Initialize Firebase using Pyrebase
-config = {
-    "apiKey": "AIzaSyClnRJAnrJgAgkYjuYnlvu-CJ6Cxyklebo",
-    "databaseURL": "https://console.firebase.google.com/project/socioverse-2025/database/socioverse-2025-default-rtdb/data/~2F",
-    "authDomain": "socioverse-2025.firebaseapp.com",
-    "projectId": "socioverse-2025",
-    "storageBucket": "socioverse-2025.appspot.com",
-    "messagingSenderId": "689574504641",
-    "appId": "1:689574504641:web:a22f6a2fa343e4221acc40",
-    "serviceAccount":"socioverse-2025-firebase-adminsdk-gcc6m-6bfb53e6d9.json"
-}
-
-firebase = pyrebase.initialize_app(config)
-storage = firebase.storage()
-
-# Define the folder containing face images in the Firebase Storage bucket
-storage_folder = "Faces/"
-
-# Create an array to store encodings and corresponding labels
-face_encodings = []
-labels = []
-
-# List all files in the Firebase Storage folder
-blobs = storage.child(storage_folder).list_files()
-
-
-for blob in blobs:
-    if blob.name.startswith(storage_folder) and (blob.name.endswith(".jpeg") or blob.name.endswith(".jpg") or blob.name.endswith(".png")):
-        # Download the image to a local file
-        url = storage.child(f"{blob.name}").get_url(None)
-
-        storage.child(url).download(f"{blob.name}","temp.jpeg")
-        # Load the image using OpenCV
-        img = cv2.imread("temp.jpeg")
-
-        # Convert the BGR image to RGB
-        rgb_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-
-        encode = face_recognition.face_encodings(img)[0]
-        face_encodings.append(encode)
-        name_parts = blob.name.split('/')
-        name = name_parts[-1]
-        name_parts = name.split('$')
-        name = name_parts[0]
-        labels.append(name)
-
-        # Delete the temporary downloaded image
-        os.remove("temp.jpeg")
-
-# Save the encodings and labels to a pickle file
-data = {"encodings": face_encodings, "labels": labels}
-with open("face_encodings.pkl", "wb") as file:
-    pickle.dump(data, file)
-
-# Upload the pickle file to Firebase Storage
-pkl_blob = storage.child(f"{storage_folder}pkl/face_encodings.pkl")
-pkl_blob.put("face_encodings.pkl")
-
-print("Face encodings and labels saved to Firebase Storage in 'faces/pkl' folder as 'face_encodings.pkl'.")

predict.py

@@ -1,45 +0,0 @@
-import face_recognition
-import pickle
-import cv2
-import pyrebase
-
-
-# Initialize Firebase using Pyrebase
-config = {
-    "apiKey": "AIzaSyClnRJAnrJgAgkYjuYnlvu-CJ6Cxyklebo",
-    "databaseURL": "https://console.firebase.google.com/project/socioverse-2025/database/socioverse-2025-default-rtdb/data/~2F",
-    "authDomain": "socioverse-2025.firebaseapp.com",
-    "projectId": "socioverse-2025",
-    "storageBucket": "socioverse-2025.appspot.com",
-    "messagingSenderId": "689574504641",
-    "appId": "1:689574504641:web:a22f6a2fa343e4221acc40",
-    "serviceAccount":"socioverse-2025-firebase-adminsdk-gcc6m-6bfb53e6d9.json"
-}
-
-firebase = pyrebase.initialize_app(config)
-storage = firebase.storage()
-
-
-storage.child().download("Faces/pkl/face_encodings.pkl","face_encodings.pkl")
-
-# Load the stored face encodings and labels from the pickle file
-with open("face_encodings.pkl", "rb") as file:
-    data = pickle.load(file)
-    face_encodings = data["encodings"]
-    labels = data["labels"]
-
-# Load a new image you want to recognize
-new_image = cv2.imread("download.jpg")
-
-
-new_face_encoding = face_recognition.face_encodings(new_image)
-
-if len(new_face_encoding) == 0:
-    print("No faces found in the new image.")
-else:
-    # Compare the new face encoding to the stored encodings
-    results = face_recognition.compare_faces(face_encodings, new_face_encoding[0])
-
-    for i, result in enumerate(results):
-        if result:
-            print(f"Recognised : {labels[i]}")