83 lines
2.9 KiB
Python
83 lines
2.9 KiB
Python
import numpy as np
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import cv2
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from skimage.feature import local_binary_pattern
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import joblib
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# Load the trained model from file
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knn_model = joblib.load('facemodel.joblib')
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def extract_lbp_features(image, radius=1, n_points=8, method='uniform'):
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lbp = local_binary_pattern(image, n_points, radius, method)
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(hist, _) = np.histogram(lbp.ravel(), bins=np.arange(0, n_points + 3), range=(0, n_points + 2))
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# Normalize the histogram
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hist = hist.astype("float")
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hist /= (hist.sum() + 1e-7)
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return hist
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# Fungsi untuk deteksi wajah dan cropping
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def detect_and_crop_faces(image_path):
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# Baca gambar
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original_image = cv2.imread(image_path)
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gray_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
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# Peningkatan kontras
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gray_image = cv2.equalizeHist(gray_image)
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# Muat pre-trained Haar Cascade untuk deteksi wajah
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face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
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# Deteksi wajah pada gambar dengan parameter yang disesuaikan
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faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.4, minNeighbors=6, minSize=(50, 50))
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# Cek jumlah wajah yang terdeteksi
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print(f'{image_path}: {len(faces)} face(s) detected')
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# Loop melalui setiap wajah yang terdeteksi, potong, dan tampilkan
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for i, (x, y, w, h) in enumerate(faces, 1):
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# Pemotongan (Cropping) wajah
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cropped_face = original_image[y:y + h, x:x + w]
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# # Tampilkan gambar wajah yang terpotong
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# cv2.imshow(f'Cropped Face {i}', cropped_face)
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#
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# # Tunggu 1 detik sebelum menampilkan gambar berikutnya
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# cv2.waitKey(1000)
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return faces
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# Path ke gambar wajah uji
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test_image_path = 'images/testface/Alia Bhatt/Alia Bhatt_77.jpg'
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# Deteksi wajah dan cropping pada gambar uji
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test_faces = detect_and_crop_faces(test_image_path)
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# Loop melalui setiap wajah yang terdeteksi di gambar uji
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for i, (x, y, w, h) in enumerate(test_faces, 1):
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# Pemotongan (Cropping) wajah
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cropped_test_face = cv2.imread(test_image_path)[y:y + h, x:x + w]
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# Resize gambar menjadi 100x100 piksel
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resized_test_face = cv2.resize(cropped_test_face, (100, 100))
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# Konversi gambar resized ke grayscale
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gray_resized_test_face = cv2.cvtColor(resized_test_face, cv2.COLOR_BGR2GRAY)
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# Normalisasi intensitas
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normalized_test_face = cv2.normalize(gray_resized_test_face, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX)
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# Ekstraksi fitur LBP untuk gambar uji
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test_lbp_features = extract_lbp_features(normalized_test_face, radius=1, n_points=8, method='uniform')
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# Tampilkan nilai ekstraksi LBP ke konsol
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print(f'LBP Features for face: {test_lbp_features}')
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# Prediksi kelas menggunakan model K-NN
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predicted_class = knn_model.predict([test_lbp_features])[0]
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# Tampilkan hasil prediksi
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print(f'Predicted class for face {i}: {predicted_class}')
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# Tunggu sampai tombol ditekan dan tutup jendela
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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