stage2
parent
848295ae5f
commit
af1000ca39
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@ -1,2 +1,4 @@
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lib
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lib64
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uploads
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students_data.pkl
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123
app.py
123
app.py
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@ -1,77 +1,86 @@
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from flask import Flask, request, jsonify, render_template, send_from_directory, make_response
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from flask_cors import CORS
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import face_recognition
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from flask import Flask, request, jsonify
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import os
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import numpy as np
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import face_recognition
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import pickle
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from werkzeug.utils import secure_filename
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from time import time
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app = Flask(__name__)
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CORS(app)
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#pip install Flask flask-cors face_recognition
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app.config['UPLOAD_FOLDER'] = 'uploads/'
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app.config['STUDENT_DATA_FILE'] = 'students_data.pkl'
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# Directory to save student images and encodings
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STUDENT_IMAGES_DIR = 'student_images'
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STUDENT_ENCODINGS_FILE = 'student_encodings.pkl'
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if not os.path.exists(app.config['UPLOAD_FOLDER']):
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os.makedirs(app.config['UPLOAD_FOLDER'])
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# Ensure the directory exists
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os.makedirs(STUDENT_IMAGES_DIR, exist_ok=True)
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# Load existing encodings if available
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if os.path.exists(STUDENT_ENCODINGS_FILE):
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with open(STUDENT_ENCODINGS_FILE, 'rb') as f:
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student_encodings = pickle.load(f)
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# Load or initialize student data
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if os.path.exists(app.config['STUDENT_DATA_FILE']):
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with open(app.config['STUDENT_DATA_FILE'], 'rb') as f:
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students_db = pickle.load(f)
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else:
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student_encodings = {}
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students_db = {}
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@app.route('/upload', methods=['POST'])
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def upload_images():
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student_id = request.form.get('student_id')
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if 'images' not in request.files or not student_id:
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return jsonify({"error": "No images or student ID provided"}), 400
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def save_student_data():
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with open(app.config['STUDENT_DATA_FILE'], 'wb') as f:
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pickle.dump(students_db, f)
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images = request.files.getlist('images')
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encodings = []
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@app.route('/upl', methods=['POST'])
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def upload_image():
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if 'face' not in request.files or 'student_id' not in request.form:
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return jsonify({'error': 'Image or student_id not provided'}), 400
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for image in images:
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image_path = os.path.join(STUDENT_IMAGES_DIR, f"{student_id}_{image.filename}")
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image.save(image_path)
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image = request.files['face']
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student_id = request.form['student_id']
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timestamp = int(time())
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filename = secure_filename(f"{timestamp}.jpg")
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student_folder = os.path.join(app.config['UPLOAD_FOLDER'], student_id)
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if not os.path.exists(student_folder):
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os.makedirs(student_folder)
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filepath = os.path.join(student_folder, filename)
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image.save(filepath)
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# Load the image and get the face encodings
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img = face_recognition.load_image_file(image_path)
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img_encodings = face_recognition.face_encodings(img)
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# Load the image file into a numpy array
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image_array = face_recognition.load_image_file(filepath)
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# Get the face encoding for the image
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face_encodings = face_recognition.face_encodings(image_array)
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if img_encodings:
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encodings.append(img_encodings[0])
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if encodings:
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student_encodings[student_id] = np.mean(encodings, axis=0)
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# Save the encodings to file
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with open(STUDENT_ENCODINGS_FILE, 'wb') as f:
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pickle.dump(student_encodings, f)
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return jsonify({"message": "Images uploaded and processed successfully"}), 200
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if len(face_encodings) > 0:
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# Assuming the first face found in the image is the correct one
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students_db[student_id] = face_encodings[0]
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save_student_data()
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return jsonify({'message': 'Image uploaded and student registered successfully', 'student_id': student_id}), 200
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else:
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return jsonify({"error": "No faces found in the uploaded images"}), 400
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return jsonify({'error': 'No faces found in the image'}), 400
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@app.route('/recognize', methods=['POST'])
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def recognize():
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if 'image' not in request.files:
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return jsonify({"error": "No image provided"}), 400
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@app.route('/rec', methods=['POST'])
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def recognize_image():
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if 'face' not in request.files:
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return jsonify({'error': 'Image not provided'}), 400
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image = request.files['image']
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img = face_recognition.load_image_file(image)
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img_encodings = face_recognition.face_encodings(img)
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image = request.files['face']
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# Load the uploaded image file into a numpy array
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unknown_image = face_recognition.load_image_file(image)
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# Get the face encodings for the uploaded image
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unknown_encodings = face_recognition.face_encodings(unknown_image)
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if img_encodings:
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img_encoding = img_encodings[0]
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distances = {student_id: np.linalg.norm(encoding - img_encoding) for student_id, encoding in student_encodings.items()}
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student_id = min(distances, key=distances.get)
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if len(unknown_encodings) > 0:
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unknown_encoding = unknown_encodings[0]
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best_match_id = None
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best_match_score = None
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return jsonify({"student_id": student_id}), 200
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else:
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return jsonify({"error": "No face found in the uploaded image"}), 400
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for student_id, known_encoding in students_db.items():
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# Calculate the distance between the known encoding and the uploaded image encoding
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distance = face_recognition.face_distance([known_encoding], unknown_encoding)[0]
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# Convert distance to accuracy score (the closer the distance, the higher the accuracy)
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accuracy_score = (1 - distance) * 100
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if best_match_score is None or accuracy_score > best_match_score:
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best_match_id = student_id
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best_match_score = accuracy_score
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if best_match_id:
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return jsonify({'student_id': best_match_id, 'accuracy_score': best_match_score}), 200
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return jsonify({'error': 'Student not recognized'}), 404
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if __name__ == '__main__':
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app.run(debug=True, host="0.0.0.0", port=5005)
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80
app2.py
80
app2.py
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from flask import Flask, request, jsonify, render_template, send_from_directory, make_response
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from flask_restful import Api, Resource
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import os
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import face_recognition
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from werkzeug.utils import secure_filename
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from PIL import Image
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import numpy as np
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app = Flask(__name__)
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api = Api(app)
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UPLOAD_FOLDER = 'upload'
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if not os.path.exists(UPLOAD_FOLDER):
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os.makedirs(UPLOAD_FOLDER)
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app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
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# Helper function to save image
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def save_image(image, roll_number):
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roll_number_folder = os.path.join(app.config['UPLOAD_FOLDER'], roll_number)
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if not os.path.exists(roll_number_folder):
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os.makedirs(roll_number_folder)
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image_count = len(os.listdir(roll_number_folder)) + 1
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image_path = os.path.join(roll_number_folder, f"{image_count}.jpg")
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image.save(image_path)
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return image_path
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# Helper function to load known faces
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def load_known_faces():
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known_faces = []
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known_roll_numbers = []
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for roll_number in os.listdir(app.config['UPLOAD_FOLDER']):
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roll_number_folder = os.path.join(app.config['UPLOAD_FOLDER'], roll_number)
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for filename in os.listdir(roll_number_folder):
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image_path = os.path.join(roll_number_folder, filename)
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image = face_recognition.load_image_file(image_path)
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encodings = face_recognition.face_encodings(image)
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if encodings:
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known_faces.append(encodings[0])
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known_roll_numbers.append(roll_number)
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return known_faces, known_roll_numbers
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class UploadImage(Resource):
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def post(self):
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roll_number = request.form['roll_number']
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if 'image' not in request.files:
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return jsonify({"error": "No image provided"}), 400
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image = request.files['image']
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filename = secure_filename(image.filename)
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image = Image.open(image)
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image_path = save_image(image, roll_number)
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return jsonify({"message": f"Image saved as {image_path}"}), 200
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class RecognizeStudent(Resource):
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def post(self):
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if 'image' not in request.files:
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return jsonify({"error": "No image provided"}), 400
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image = request.files['image']
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filename = secure_filename(image.filename)
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image = face_recognition.load_image_file(image)
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unknown_encodings = face_recognition.face_encodings(image)
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if not unknown_encodings:
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return jsonify({"error": "No faces found in the image"}), 400
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unknown_encoding = unknown_encodings[0]
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known_faces, known_roll_numbers = load_known_faces()
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results = face_recognition.compare_faces(known_faces, unknown_encoding)
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if True in results:
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matched_index = results.index(True)
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roll_number = known_roll_numbers[matched_index]
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return jsonify({"roll_number": roll_number}), 200
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else:
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return jsonify({"error": "No matching student found"}), 404
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api.add_resource(UploadImage, '/upload')
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api.add_resource(RecognizeStudent, '/recognize')
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if __name__ == '__main__':
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app.run(debug=True, host="0.0.0.0", port=5005)
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84
app3.py
84
app3.py
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@ -1,84 +0,0 @@
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from flask import Flask, request, jsonify, render_template, send_from_directory, make_response
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from flask_restful import Api, Resource
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import os
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import face_recognition
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from werkzeug.utils import secure_filename
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from PIL import Image
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import numpy as np
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app = Flask(__name__)
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api = Api(app)
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UPLOAD_FOLDER = 'upload'
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if not os.path.exists(UPLOAD_FOLDER):
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os.makedirs(UPLOAD_FOLDER)
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app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
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# Helper function to save image
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def save_image(image, roll_number):
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roll_number_folder = os.path.join(app.config['UPLOAD_FOLDER'], roll_number)
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if not os.path.exists(roll_number_folder):
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os.makedirs(roll_number_folder)
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image_count = len(os.listdir(roll_number_folder)) + 1
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image_path = os.path.join(roll_number_folder, f"{image_count}.jpg")
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image.save(image_path)
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return image_path
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# Helper function to load known faces
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def load_known_faces():
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known_faces = []
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known_roll_numbers = []
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for roll_number in os.listdir(app.config['UPLOAD_FOLDER']):
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roll_number_folder = os.path.join(app.config['UPLOAD_FOLDER'], roll_number)
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for filename in os.listdir(roll_number_folder):
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image_path = os.path.join(roll_number_folder, filename)
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image = face_recognition.load_image_file(image_path)
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encodings = face_recognition.face_encodings(image)
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if encodings:
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known_faces.append(encodings[0])
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known_roll_numbers.append(roll_number)
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return known_faces, known_roll_numbers
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@app.route('/')
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def index():
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return render_template('./upload.html')
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class UploadImage(Resource):
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def post(self):
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roll_number = request.form['roll_number']
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if 'image' not in request.files:
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return jsonify({"error": "No image provided"}), 400
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image = request.files['image']
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filename = secure_filename(image.filename)
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image = Image.open(image)
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image_path = save_image(image, roll_number)
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return jsonify({"message": f"Image saved as {image_path}"}), 200
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class RecognizeStudent(Resource):
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def post(self):
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if 'image' not in request.files:
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return jsonify({"error": "No image provided"}), 400
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image = request.files['image']
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filename = secure_filename(image.filename)
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image = face_recognition.load_image_file(image)
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unknown_encodings = face_recognition.face_encodings(image)
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if not unknown_encodings:
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return jsonify({"error": "No faces found in the image"}), 400
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unknown_encoding = unknown_encodings[0]
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known_faces, known_roll_numbers = load_known_faces()
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results = face_recognition.compare_faces(known_faces, unknown_encoding)
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if True in results:
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matched_index = results.index(True)
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roll_number = known_roll_numbers[matched_index]
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return jsonify({"roll_number": roll_number}), 200
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else:
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return jsonify({"error": "No matching student found"}), 404
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api.add_resource(UploadImage, '/upload')
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api.add_resource(RecognizeStudent, '/recognize')
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if __name__ == '__main__':
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app.run(debug=True, host="0.0.0.0", port=5005)
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93
app4.py
93
app4.py
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import os
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from flask import Flask, request, jsonify
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import face_recognition
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import pickle
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from PIL import Image
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import numpy as np
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app = Flask(__name__)
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UPLOAD_FOLDER = 'student_roll/uploads/'
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FACE_DATA_FOLDER = 'student_roll/face_data/'
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os.makedirs(UPLOAD_FOLDER, exist_ok=True)
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os.makedirs(FACE_DATA_FOLDER, exist_ok=True)
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def convert_image_to_8bit_rgb(image_path):
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with Image.open(image_path) as img:
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if img.mode != 'RGB':
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img = img.convert('RGB')
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img = img.convert('RGB') # Ensure conversion to 8-bit per channel
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img_array = np.array(img)
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return img_array
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@app.route('/upload', methods=['POST'])
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def upload_image():
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if 'image' not in request.files:
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return jsonify({"error": "No image part in the request"}), 400
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file = request.files['image']
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if file.filename == '':
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return jsonify({"error": "No selected file"}), 400
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student_roll = request.form.get('student_roll')
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if not student_roll:
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return jsonify({"error": "Student roll number is required"}), 400
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file_path = os.path.join(UPLOAD_FOLDER, f"{student_roll}.jpg")
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file.save(file_path)
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try:
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# Convert image to 8-bit RGB if necessary
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image = convert_image_to_8bit_rgb(file_path)
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face_encodings = face_recognition.face_encodings(image)
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if not face_encodings:
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return jsonify({"error": "No face found in the image"}), 400
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face_data_path = os.path.join(FACE_DATA_FOLDER, f"{student_roll}.pkl")
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with open(face_data_path, 'wb') as f:
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pickle.dump(face_encodings[0], f)
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return jsonify({"message": "Image and face data uploaded successfully"}), 200
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except Exception as e:
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return jsonify({"error": str(e)}), 500
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@app.route('/recognize_student', methods=['POST'])
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def recognize_student():
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if 'image' not in request.files:
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return jsonify({"error": "No image part in the request"}), 400
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file = request.files['image']
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if file.filename == '':
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return jsonify({"error": "No selected file"}), 400
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file_path = os.path.join(UPLOAD_FOLDER, "temp.jpg")
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file.save(file_path)
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try:
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# Convert image to 8-bit RGB if necessary
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image = convert_image_to_8bit_rgb(file_path)
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face_encodings = face_recognition.face_encodings(image)
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if not face_encodings:
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return jsonify({"error": "No face found in the image"}), 400
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uploaded_face_encoding = face_encodings[0]
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# Load all stored face data
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for face_data_file in os.listdir(FACE_DATA_FOLDER):
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with open(os.path.join(FACE_DATA_FOLDER, face_data_file), 'rb') as f:
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known_face_encoding = pickle.load(f)
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matches = face_recognition.compare_faces([known_face_encoding], uploaded_face_encoding)
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if matches[0]:
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student_roll = os.path.splitext(face_data_file)[0]
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return jsonify({"student_roll": student_roll}), 200
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return jsonify({"error": "No matching student found"}), 404
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except Exception as e:
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return jsonify({"error": str(e)}), 500
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if __name__ == '__main__':
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app.run(debug=True, host="0.0.0.0", port=5005)
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81
app5.py
81
app5.py
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from flask import Flask, request, jsonify
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import os
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import face_recognition
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import pickle
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from werkzeug.utils import secure_filename
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app = Flask(__name__)
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app.config['UPLOAD_FOLDER'] = 'uploads/'
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app.config['STUDENT_DATA_FILE'] = 'students_data.pkl'
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if not os.path.exists(app.config['UPLOAD_FOLDER']):
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os.makedirs(app.config['UPLOAD_FOLDER'])
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# Load or initialize student data
|
||||
if os.path.exists(app.config['STUDENT_DATA_FILE']):
|
||||
with open(app.config['STUDENT_DATA_FILE'], 'rb') as f:
|
||||
students_db = pickle.load(f)
|
||||
else:
|
||||
students_db = {}
|
||||
|
||||
def save_student_data():
|
||||
with open(app.config['STUDENT_DATA_FILE'], 'wb') as f:
|
||||
pickle.dump(students_db, f)
|
||||
|
||||
@app.route('/upload', methods=['POST'])
|
||||
def upload_image():
|
||||
if 'image' not in request.files or 'student_id' not in request.form:
|
||||
return jsonify({'error': 'Image or student_id not provided'}), 400
|
||||
|
||||
image = request.files['image']
|
||||
student_id = request.form['student_id']
|
||||
filename = secure_filename(f"{student_id}_{image.filename}")
|
||||
filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
|
||||
image.save(filepath)
|
||||
|
||||
# Load the image file into a numpy array
|
||||
image = face_recognition.load_image_file(filepath)
|
||||
# Get the face encoding for the image
|
||||
face_encodings = face_recognition.face_encodings(image)
|
||||
|
||||
if len(face_encodings) > 0:
|
||||
# Assuming the first face found in the image is the correct one
|
||||
students_db[student_id] = face_encodings[0]
|
||||
save_student_data()
|
||||
return jsonify({'message': 'Image uploaded and student registered successfully', 'student_id': student_id}), 200
|
||||
else:
|
||||
return jsonify({'error': 'No faces found in the image'}), 400
|
||||
|
||||
@app.route('/recognize_image', methods=['POST'])
|
||||
def recognize_image():
|
||||
if 'image' not in request.files:
|
||||
return jsonify({'error': 'Image not provided'}), 400
|
||||
|
||||
image = request.files['image']
|
||||
# Load the uploaded image file into a numpy array
|
||||
unknown_image = face_recognition.load_image_file(image)
|
||||
# Get the face encodings for the uploaded image
|
||||
unknown_encodings = face_recognition.face_encodings(unknown_image)
|
||||
|
||||
if len(unknown_encodings) > 0:
|
||||
unknown_encoding = unknown_encodings[0]
|
||||
best_match_id = None
|
||||
best_match_score = None
|
||||
|
||||
for student_id, known_encoding in students_db.items():
|
||||
# Calculate the distance between the known encoding and the uploaded image encoding
|
||||
distance = face_recognition.face_distance([known_encoding], unknown_encoding)[0]
|
||||
# Convert distance to accuracy score (the closer the distance, the higher the accuracy)
|
||||
accuracy_score = (1 - distance) * 100
|
||||
|
||||
if best_match_score is None or accuracy_score > best_match_score:
|
||||
best_match_id = student_id
|
||||
best_match_score = accuracy_score
|
||||
|
||||
if best_match_id:
|
||||
return jsonify({'student_id': best_match_id, 'accuracy_score': best_match_score}), 200
|
||||
|
||||
return jsonify({'error': 'Student not recognized'}), 404
|
||||
|
||||
if __name__ == '__main__':
|
||||
app.run(debug=True, host="0.0.0.0", port=5005)
|
||||
86
app6.py
86
app6.py
|
|
@ -1,86 +0,0 @@
|
|||
from flask import Flask, request, jsonify
|
||||
import os
|
||||
import face_recognition
|
||||
import pickle
|
||||
from werkzeug.utils import secure_filename
|
||||
from time import time
|
||||
|
||||
app = Flask(__name__)
|
||||
app.config['UPLOAD_FOLDER'] = 'uploads/'
|
||||
app.config['STUDENT_DATA_FILE'] = 'students_data.pkl'
|
||||
|
||||
if not os.path.exists(app.config['UPLOAD_FOLDER']):
|
||||
os.makedirs(app.config['UPLOAD_FOLDER'])
|
||||
|
||||
# Load or initialize student data
|
||||
if os.path.exists(app.config['STUDENT_DATA_FILE']):
|
||||
with open(app.config['STUDENT_DATA_FILE'], 'rb') as f:
|
||||
students_db = pickle.load(f)
|
||||
else:
|
||||
students_db = {}
|
||||
|
||||
def save_student_data():
|
||||
with open(app.config['STUDENT_DATA_FILE'], 'wb') as f:
|
||||
pickle.dump(students_db, f)
|
||||
|
||||
@app.route('/upl', methods=['POST'])
|
||||
def upload_image():
|
||||
if 'face' not in request.files or 'student_id' not in request.form:
|
||||
return jsonify({'error': 'Image or student_id not provided'}), 400
|
||||
|
||||
image = request.files['face']
|
||||
student_id = request.form['student_id']
|
||||
timestamp = int(time())
|
||||
filename = secure_filename(f"{timestamp}.jpg")
|
||||
student_folder = os.path.join(app.config['UPLOAD_FOLDER'], student_id)
|
||||
if not os.path.exists(student_folder):
|
||||
os.makedirs(student_folder)
|
||||
filepath = os.path.join(student_folder, filename)
|
||||
image.save(filepath)
|
||||
|
||||
# Load the image file into a numpy array
|
||||
image_array = face_recognition.load_image_file(filepath)
|
||||
# Get the face encoding for the image
|
||||
face_encodings = face_recognition.face_encodings(image_array)
|
||||
|
||||
if len(face_encodings) > 0:
|
||||
# Assuming the first face found in the image is the correct one
|
||||
students_db[student_id] = face_encodings[0]
|
||||
save_student_data()
|
||||
return jsonify({'message': 'Image uploaded and student registered successfully', 'student_id': student_id}), 200
|
||||
else:
|
||||
return jsonify({'error': 'No faces found in the image'}), 400
|
||||
|
||||
@app.route('/rec', methods=['POST'])
|
||||
def recognize_image():
|
||||
if 'image' not in request.files:
|
||||
return jsonify({'error': 'Image not provided'}), 400
|
||||
|
||||
image = request.files['image']
|
||||
# Load the uploaded image file into a numpy array
|
||||
unknown_image = face_recognition.load_image_file(image)
|
||||
# Get the face encodings for the uploaded image
|
||||
unknown_encodings = face_recognition.face_encodings(unknown_image)
|
||||
|
||||
if len(unknown_encodings) > 0:
|
||||
unknown_encoding = unknown_encodings[0]
|
||||
best_match_id = None
|
||||
best_match_score = None
|
||||
|
||||
for student_id, known_encoding in students_db.items():
|
||||
# Calculate the distance between the known encoding and the uploaded image encoding
|
||||
distance = face_recognition.face_distance([known_encoding], unknown_encoding)[0]
|
||||
# Convert distance to accuracy score (the closer the distance, the higher the accuracy)
|
||||
accuracy_score = (1 - distance) * 100
|
||||
|
||||
if best_match_score is None or accuracy_score > best_match_score:
|
||||
best_match_id = student_id
|
||||
best_match_score = accuracy_score
|
||||
|
||||
if best_match_id:
|
||||
return jsonify({'student_id': best_match_id, 'accuracy_score': best_match_score}), 200
|
||||
|
||||
return jsonify({'error': 'Student not recognized'}), 404
|
||||
|
||||
if __name__ == '__main__':
|
||||
app.run(debug=True, host="0.0.0.0", port=5005)
|
||||
Binary file not shown.
|
|
@ -1,18 +0,0 @@
|
|||
<!DOCTYPE html>
|
||||
<html>
|
||||
<head>
|
||||
<title>Upload Student Image</title>
|
||||
</head>
|
||||
<body>
|
||||
<h1>Upload Student Image</h1>
|
||||
<form action="/upload" method="post" enctype="multipart/form-data">
|
||||
<label for="roll_number">Roll Number:</label>
|
||||
<input type="text" id="roll_number" name="roll_number" required>
|
||||
<br><br>
|
||||
<label for="image">Select image:</label>
|
||||
<input type="file" id="image" name="image" accept="image/*" required>
|
||||
<br><br>
|
||||
<input type="submit" value="Upload">
|
||||
</form>
|
||||
</body>
|
||||
</html>
|
||||
29
upload.py
29
upload.py
|
|
@ -1,29 +0,0 @@
|
|||
from flask import Flask, render_template, request, jsonify, make_response
|
||||
import os
|
||||
|
||||
app = Flask(__name__)
|
||||
UPLOAD_FOLDER = 'uploads'
|
||||
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
|
||||
|
||||
@app.route('/')
|
||||
def upload_form():
|
||||
return render_template('upload.html')
|
||||
|
||||
@app.route('/upload', methods=['POST'])
|
||||
def upload_file():
|
||||
folder_number = request.form['roll_number']
|
||||
images = request.files.getlist('images[]')
|
||||
|
||||
folder_path = os.path.join(app.config['UPLOAD_FOLDER'], folder_number)
|
||||
os.makedirs(folder_path, exist_ok=True)
|
||||
|
||||
for image in images:
|
||||
if image.filename == '':
|
||||
continue
|
||||
filename = image.filename
|
||||
image.save(os.path.join(folder_path, filename))
|
||||
|
||||
return make_response(jsonify('success'), 200)
|
||||
|
||||
if __name__ == '__main__':
|
||||
app.run(debug=True, host="0.0.0.0", port=5005)
|
||||
Loading…
Reference in New Issue