Add app.py

app.py

@@ -0,0 +1,76 @@
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+import face_recognition
+import os
+import numpy as np
+import pickle
+
+app = Flask(__name__)
+CORS(app)
+#pip install Flask flask-cors face_recognition
+
+# Directory to save student images and encodings
+STUDENT_IMAGES_DIR = 'student_images'
+STUDENT_ENCODINGS_FILE = 'student_encodings.pkl'
+
+# Ensure the directory exists
+os.makedirs(STUDENT_IMAGES_DIR, exist_ok=True)
+
+# Load existing encodings if available
+if os.path.exists(STUDENT_ENCODINGS_FILE):
+    with open(STUDENT_ENCODINGS_FILE, 'rb') as f:
+        student_encodings = pickle.load(f)
+else:
+    student_encodings = {}
+
+@app.route('/upload_images', methods=['POST'])
+def upload_images():
+    student_id = request.form.get('student_id')
+    if 'images' not in request.files or not student_id:
+        return jsonify({"error": "No images or student ID provided"}), 400
+
+    images = request.files.getlist('images')
+    encodings = []
+
+    for image in images:
+        image_path = os.path.join(STUDENT_IMAGES_DIR, f"{student_id}_{image.filename}")
+        image.save(image_path)
+
+        # Load the image and get the face encodings
+        img = face_recognition.load_image_file(image_path)
+        img_encodings = face_recognition.face_encodings(img)
+
+        if img_encodings:
+            encodings.append(img_encodings[0])
+
+    if encodings:
+        student_encodings[student_id] = np.mean(encodings, axis=0)
+
+        # Save the encodings to file
+        with open(STUDENT_ENCODINGS_FILE, 'wb') as f:
+            pickle.dump(student_encodings, f)
+
+        return jsonify({"message": "Images uploaded and processed successfully"}), 200
+    else:
+        return jsonify({"error": "No faces found in the uploaded images"}), 400
+
+@app.route('/recognize', methods=['POST'])
+def recognize():
+    if 'image' not in request.files:
+        return jsonify({"error": "No image provided"}), 400
+
+    image = request.files['image']
+    img = face_recognition.load_image_file(image)
+    img_encodings = face_recognition.face_encodings(img)
+
+    if img_encodings:
+        img_encoding = img_encodings[0]
+        distances = {student_id: np.linalg.norm(encoding - img_encoding) for student_id, encoding in student_encodings.items()}
+        student_id = min(distances, key=distances.get)
+
+        return jsonify({"student_id": student_id}), 200
+    else:
+        return jsonify({"error": "No face found in the uploaded image"}), 400
+
+if __name__ == '__main__':
+    app.run(debug=True)