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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)
if encoding:
known_encodings = np.append(known_encodings, [encoding[0]], 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_encodings = face_recognition.face_encodings(np.array(aligned_face))
if not target_encodings:
return False, "No face detected in the provided image. Please try with another image."
target_encoding = target_encodings[0].reshape(1, -1)
known_encodings, known_labels = load_encodings(encodings_file)
if len(known_encodings) == 0:
return False, "No known faces in the database. Please add some faces first."
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."
# Gradio Interface Function
def gradio_interface(image, similarity_threshold=70):
if image is None:
return "Error: No image provided. Please upload an image or take one using the webcam."
encodings_file = "./face_encoding.pkl"
try:
result, message = verify_face_with_faiss(image, encodings_file, similarity_threshold=similarity_threshold)
return message
except Exception as e:
return f"An error occurred: {str(e)}"
# Gradio Interface Setup
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(share=True)
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