Delete main.py

main.py

@@ -1,119 +0,0 @@
-import face_recognition
-import numpy as np
-import pickle
-from mtcnn import MTCNN
-from PIL import Image
-import cv2
-import faiss
-import imgaug.augmenters as iaa
-import os
-import gradio as gr
-
-def detect_and_align_face(image_path):
-    detector = MTCNN()
-    image = cv2.imread(image_path)
-    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    detections = detector.detect_faces(image_rgb)
-    
-    if len(detections) == 0:
-        raise ValueError("No face detected in the image.")
-    
-    detection = detections[0]
-    x, y, width, height = detection['box']
-    keypoints = detection['keypoints']
-    face = image_rgb[y:y+height, x:x+width]
-    
-    left_eye = keypoints['left_eye']
-    right_eye = keypoints['right_eye']
-    delta_x = right_eye[0] - left_eye[0]
-    delta_y = right_eye[1] - left_eye[1]
-    angle = np.arctan2(delta_y, delta_x) * (180.0 / np.pi)
-    
-    center = ((x + x + width) // 2, (y + y + height) // 2)
-    rot_matrix = cv2.getRotationMatrix2D(center, angle, scale=1.0)
-    aligned_image = cv2.warpAffine(image_rgb, rot_matrix, (image_rgb.shape[1], image_rgb.shape[0]))
-    aligned_face = aligned_image[y:y+height, x:x+width]
-    
-    return Image.fromarray(aligned_face)
-
-def load_encodings(file_path):
-    with open(file_path, "rb") as file:
-        data = pickle.load(file)
-    return np.array(data["encodings"]), data["labels"]
-
-def save_encodings(encodings, labels, file_path):
-    data = {"encodings": encodings, "labels": labels}
-    with open(file_path, "wb") as file:
-        pickle.dump(data, file)
-
-def create_faiss_index(known_encodings):
-    dimension = known_encodings.shape[1]
-    index = faiss.IndexFlatL2(dimension)
-    index.add(known_encodings)
-    return index
-
-def encode_face(image):
-    img_array = np.array(image)
-    encodings = face_recognition.face_encodings(img_array)
-    return encodings[0] if encodings else None
-
-def augment_image(image, num_augmented=5):
-    image = np.array(image)
-    aug = iaa.Sequential([
-        iaa.Fliplr(0.5),  # horizontal flips
-        iaa.Affine(rotate=(-25, 25)),  # rotation
-        iaa.AdditiveGaussianNoise(scale=(0, 0.05*255)),  # noise
-        iaa.Multiply((0.8, 1.2)),  # brightness
-        iaa.GaussianBlur(sigma=(0.0, 1.0))  # blur
-    ])
-    augmented_images = [Image.fromarray(aug(image=image)) for _ in range(num_augmented)]
-    return augmented_images
-
-def update_dataset_with_verified_image(image, encodings_file, label, num_augmented=5):
-    known_encodings, known_labels = load_encodings(encodings_file)
-    augmented_images = augment_image(image, num_augmented=num_augmented)
-    images_to_encode = [image] + augmented_images
-    for img in images_to_encode:
-        img_array = np.array(img)
-        encoding = face_recognition.face_encodings(img_array)[0]
-        known_encodings = np.append(known_encodings, [encoding], axis=0)
-        known_labels.append(label)
-    save_encodings(known_encodings, known_labels, encodings_file)
-
-def verify_face_with_faiss(image, encodings_file, similarity_threshold=70, num_augmented=5):
-    aligned_face = image.convert("RGB")
-    target_encoding = face_recognition.face_encodings(np.array(aligned_face))[0].reshape(1, -1)
-    
-    known_encodings, known_labels = load_encodings(encodings_file)
-    known_encodings = np.array(known_encodings)
-    
-    index = create_faiss_index(known_encodings)
-    
-    distances, indices = index.search(target_encoding, 1)
-    
-    best_match_index = indices[0][0]
-    best_similarity_percentage = (1 - distances[0][0]) * 100
-    
-    is_match = best_similarity_percentage >= similarity_threshold
-    
-    if is_match:
-        matched_label = known_labels[best_match_index]
-        update_dataset_with_verified_image(image, encodings_file, matched_label, num_augmented=num_augmented)
-        return True, f"Match found: {matched_label}, Similarity: {best_similarity_percentage:.2f}%"
-    else:
-        return False, "No match found."
-
-# Define the Gradio interface
-def gradio_interface(image, similarity_threshold=70):
-    encodings_file = "face_encoding.pkl"
-    result, message = verify_face_with_faiss(image, encodings_file, similarity_threshold=similarity_threshold)
-    return message
-
-# Launch the Gradio interface
-iface = gr.Interface(fn=gradio_interface, 
-                     inputs=[gr.Image(type="pil"), gr.Slider(0, 100, value=70, label="Similarity Threshold")], 
-                     outputs="text", 
-                     title="Face Recognition with MTCNN and FAISS",
-                     description="Upload an image to see if it matches any face in the database.")
-
-iface.launch()