Yuvnish017

data = np.load('dataset.npz', allow_pickle=True)
X = data['arr_0']
Y = data['arr_1']

X = list(X)
Y = list(Y)

for i in range(len(X)):
    img = X[i]
    img = cv2.resize(img, (32, 32))

Yuvnish017

aug = ImageDataGenerator(rotation_range=20, 
                         zoom_range=0.2, 
                         horizontal_flip=True)
hist = model.fit(aug.flow(X_train, Y_train, batch_size=128), 
                 batch_size=128, 
                 epochs=200, 
                 validation_data=(X_test, Y_test))

Yuvnish017

def full_face_detection_pipeline(input_image_path):
    face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
    eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
    detector = dlib.get_frontal_face_detector()
    predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
    fa = FaceAligner(predictor, desiredFaceWidth=256)
    test_image = cv2.imread(input_image_path)
    test_image = imutils.resize(test_image, width=800)
    test_image_gray = cv2.cvtColor(test_image, cv2.COLOR_BGR2GRAY)
    rects = detector(test_image_gray, 2)

Yuvnish017

figure = plt.figure(figsize=(5, 5))
predicted_image = cv2.imread(full_face_detection_pipeline('sleepy-driver.jpg'))
predicted_image = cv2.cvtColor(predicted_image, cv2.COLOR_BGR2RGB)
plt.imshow(predicted_image)
plt.axis('off')
plt.show()

Yuvnish017

train_data_generator = ImageDataGenerator(rotation_range=360, 
                                    width_shift_range=0.0, 
                                    height_shift_range=0.0, 
#                                     brightness_range=[0.5, 1.5],
                                    horizontal_flip=True, 
                                    vertical_flip=True)

x = list(x_train)
y = list(y_train)

Yuvnish017

def parkinson_disease_detection_model(input_shape=(128, 128, 1)):
    regularizer = tf.keras.regularizers.l2(0.001)
    model = Sequential()
    model.add(Input(shape=input_shape))
    model.add(Conv2D(128, (5, 5), padding='same', strides=(1, 1), name='conv1', activation='relu', 
                     kernel_initializer='glorot_uniform', kernel_regularizer=regularizer))
    model.add(MaxPool2D((9, 9), strides=(3, 3)))

    model.add(Conv2D(64, (5, 5), padding='same', strides=(1, 1), name='conv2', activation='relu', 
                     kernel_initializer='glorot_uniform', kernel_regularizer=regularizer))

Yuvnish017

hist = model.fit(x_train, y_train, batch_size=128, epochs=70, validation_data=(x_test, y_test))

Yuvnish017

labels = ['Healthy', 'Parkinson']
image_healthy = cv2.imread('Parkinson_disease_detection/test_image_healthy.png')
image_parkinson = cv2.imread('Parkinson_disease_detection/test_image_parkinson.png')

image_healthy = cv2.resize(image_healthy, (128, 128))
image_healthy = cv2.cvtColor(image_healthy, cv2.COLOR_BGR2GRAY)
image_healthy = np.array(image_healthy)
image_healthy = np.expand_dims(image_healthy, axis=0)
image_healthy = np.expand_dims(image_healthy, axis=-1)

Yuvnish017

figure = plt.figure(figsize=(2, 2))
img_healthy = np.squeeze(image_healthy, axis=0)
plt.imshow(img_healthy)
plt.axis('off')
plt.title(f'Prediction by the model: {labels[np.argmax(ypred_healthy[0], axis=0)]}')
plt.show()

Yuvnish017

figure = plt.figure(figsize=(2, 2))
img_parkinson = np.squeeze(image_parkinson, axis=0)
plt.imshow(img_parkinson)
plt.axis('off')
plt.title(f'Prediction by the model: {labels[np.argmax(ypred_parkinson[0], axis=0)]}')
plt.show()