Create face_recog.py

face_recog.py

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+from flask import Flask, request, jsonify
+import face_recognition
+import cv2
+import numpy as np
+import os
+from pathlib import Path
+import base64
+
+# Initialize Flask app
+app = Flask(__name__)
+
+# Set the maximum file upload size (50MB)
+app.config['MAX_CONTENT_LENGTH'] = 50 * 1024 * 1024  # 50MB
+
+# Define the path to dataset and test image
+dataset_folder_path = '/content/sample_data/dataset'
+
+# Load and encode faces from the dataset folder
+def load_face_encodings(dataset_folder_path):
+    known_face_encodings = []
+    known_face_names = []
+
+    for img_path in Path(dataset_folder_path).glob("*.jpg"):
+        image = face_recognition.load_image_file(img_path)
+        encodings = face_recognition.face_encodings(image)
+
+        if encodings:
+            known_face_encodings.append(encodings[0])
+            known_face_names.append(img_path.stem)
+
+    return known_face_encodings, known_face_names
+
+# Load and process the uploaded test image
+def load_test_image(test_image):
+    test_image_rgb = cv2.imdecode(np.fromstring(test_image.read(), np.uint8), cv2.IMREAD_COLOR)
+    test_image_rgb = cv2.cvtColor(test_image_rgb, cv2.COLOR_BGR2RGB)
+    face_locations = face_recognition.face_locations(test_image_rgb)
+    face_encodings = face_recognition.face_encodings(test_image_rgb, face_locations)
+    return test_image_rgb, face_locations, face_encodings
+
+# Compare faces from the test image with the dataset faces
+def compare_faces(known_face_encodings, known_face_names, test_image_encodings):
+    matches = []
+    for test_encoding in test_image_encodings:
+        results = face_recognition.compare_faces(known_face_encodings, test_encoding)
+        if True in results:
+            first_match_index = results.index(True)
+            matches.append(known_face_names[first_match_index])
+        else:
+            matches.append("Unknown")
+    return matches
+
+# Convert image to base64
+def convert_image_to_base64(image):
+    _, buffer = cv2.imencode('.jpg', image)
+    img_bytes = buffer.tobytes()
+    img_base64 = base64.b64encode(img_bytes).decode('utf-8')
+    return img_base64
+
+# Flask route for face recognition
+@app.route('/recognize_faces', methods=['POST'])
+def recognize_faces():
+    if 'image' not in request.files:
+        return jsonify({"error": "No image file provided!"}), 400
+
+    # Load known face encodings
+    known_face_encodings, known_face_names = load_face_encodings(dataset_folder_path)
+    
+    image_file = request.files['image']
+    test_image_rgb, face_locations, test_image_encodings = load_test_image(image_file)
+    
+    matches = compare_faces(known_face_encodings, known_face_names, test_image_encodings)
+
+    for (top, right, bottom, left), name in zip(face_locations, matches):
+        cv2.rectangle(test_image_rgb, (left, top), (right, bottom), (0, 0, 255), 2)
+        font = cv2.FONT_HERSHEY_DUPLEX
+        cv2.putText(test_image_rgb, name, (left + 6, bottom - 6), font, 0.5, (255, 255, 255), 1)
+
+    result_image_base64 = convert_image_to_base64(test_image_rgb)
+    return jsonify({'recognized_faces': matches, 'image': result_image_base64})
+
+@app.route('/')
+def hello_world():
+    return 'Welcome to the Face Recognition API! Upload an image for recognition.'
+
+# Run Flask app
+if __name__ == '__main__':
+    app.run()