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Yuvnish017 / defining_model.py
Created July 10, 2021 06:38
Parkinson_disease_detection
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
Yuvnish017 / data_augmentaion.py
Created July 10, 2021 06:23
Parkinson_disease_detection
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
Yuvnish017 / test1.py
Last active June 29, 2021 06:59
Driver Drowsiness Detection
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
Yuvnish017 / test_pipeline.py
Created June 29, 2021 06:51
Driver Drowsiness Detection
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
Yuvnish017 / training_model.py
Created June 29, 2021 06:27
Driver Drowsiness Detection
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
Yuvnish017 / load_dataset.py
Created June 29, 2021 06:22
Driver Drowsiness Detection
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
Yuvnish017 / CNN_model.py
Created June 29, 2021 06:02
Driver Drowsiness Detection
def driver_drowsiness_detection_model(input_shape=(32, 32, 3)):
model = Sequential()
model.add(Input(shape=input_shape))
model.add(Conv2D(32, (3, 3), padding='same', strides=(1, 1), name='conv1', activation='relu',
kernel_initializer=glorot_uniform(seed=0)))
model.add(BatchNormalization())
model.add(Conv2D(32, (3, 3), padding='same', strides=(1, 1), name='conv2', activation='relu',
kernel_initializer=glorot_uniform(seed=0)))
model.add(BatchNormalization())
model.add(Dropout(0.2))
model = cv2.dnn.readNetFromTorch('udnie.t7')
image = cv2.imread('yuvnish malhotra photo.jpg')
(h, w) = image.shape[:2]
image = cv2.resize(image, (600, h))
(h, w, c) = image.shape
print(h, w, c)
blob = cv2.dnn.blobFromImage(image, 1.0, (w, h), (103.939, 116.779, 123.680), swapRB=False, crop=False)
optimizer = keras.optimizers.SGD(
keras.optimizers.schedules.ExponentialDecay(
initial_learning_rate=100.0, decay_steps=500, decay_rate=0.96
)
)
base_image = preprocess_image('yuvnish malhotra photo.jpg')
style_reference_image = preprocess_image('style_image3.jpg')
combination_image = tf.Variable(preprocess_image('yuvnish malhotra photo.jpg'))
print(base_image.shape)
def compute_loss(combination_image, base_image, style_reference_image):
input_tensor = tf.concat(
[base_image, style_reference_image, combination_image], axis=0
)
features = feature_extractor(input_tensor)
# Initialize the loss
loss = tf.zeros(shape=())
# Add content loss