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import cv2 as cv
from mtcnn.mtcnn import MTCNN
from keras_facenet import FaceNet
import numpy as np
import pickle

detector = MTCNN()
embedder = FaceNet()

model_path = 'svm_model_160x160.pkl'
with open(model_path, 'rb') as f:
    model = pickle.load(f)

with open('label_encoder.pkl', 'rb') as f:
    encoder = pickle.load(f)

def get_embedding(face_img):
    face_img = face_img.astype('float32')
    face_img = np.expand_dims(face_img, axis=0)
    yhat = embedder.embeddings(face_img)
    return yhat[0]

image_path = 'TestFolder/Test/dd07a081161bb345ea0a14.jpg'
frame = cv.imread(image_path)
frame = cv.resize(frame, (1280, 600))
rgb_frame = cv.cvtColor(frame, cv.COLOR_BGR2RGB)

results = detector.detect_faces(rgb_frame)

for result in results:
    x, y, width, height = result['box']
    x, y, width, height = abs(x), abs(y), abs(width), abs(height)

    face_img = frame[y:y + height, x:x + width]
    face_img = cv.resize(face_img, (160, 160))

    test_im = get_embedding(face_img)
    test_im = [test_im]
    ypreds = model.predict(test_im)
    proba = model.predict_proba(test_im)
    label = encoder.inverse_transform(ypreds)[0]

    cv.rectangle(frame, (x, y), (x + width, y + height), (0, 255, 0), 2)
    prob = proba[0][ypreds[0]]
    if prob < 0.6:
        label_text = "KhongTheNhanDang"
    else:
        label_text = f"{label} ({prob * 100:.2f}%)"
    cv.putText(frame, label_text, (x, y - 10), cv.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)

cv.imshow('Image', frame)
cv.waitKey(0)
cv.destroyAllWindows()