keras-mac.py

keras-mac.py

import time
import numpy as np
import cv2
from PIL import Image
import keras
from keras import backend as K
from keras.layers.core import Dense
from keras.optimizers import Adam
from keras.metrics import categorical_crossentropy
from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing import image
from keras.models import Model
from keras.applications import imagenet_utils
from sklearn.metrics import confusion_matrix
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten, Input

NUM_CLASSES = 2
LEARNING_RATE = 0.0001
EPOCHS = 20
DENSE_UNITS = 100

TRAINING_DATA = []  # Example array to be trained
TRAINING_LABELS = []  # Label array


# Load mobilenet model
def loadModel():
    mobilenet = keras.applications.mobilenet.MobileNet()
    flatten = Flatten(input_shape=(7, 7, 1024))(mobilenet.get_layer('conv_pw_13_relu').output)
    fc1 = Dense(DENSE_UNITS, activation='relu')(flatten)
    fc2 = Dense(NUM_CLASSES)(flatten)
    output = Activation('softmax')(fc2)
    model = Model(mobilenet.input, output)
    # make all layers untrainable by freezing weights (except for last two layers)
    for l, layer in enumerate(model.layers[:-3]):
        layer.trainable = False
    # ensure the last layer is trainable/not frozen
    for l, layer in enumerate(model.layers[-3:]):
        layer.trainable = True
    return model


# function prepares an image to keras
def prepare_frame(frame):
    img = Image.fromarray(frame, 'RGB')
    img = img.resize((224, 224))
    img_array = np.array(img)
    img_array_extended = np.expand_dims(img_array, axis=0).astype('float32')
    processed = keras.applications.mobilenet.preprocess_input(img_array_extended)
    return processed


def addExample(example, label):  # add examples to training data set
    encoded_y = keras.utils.np_utils.to_categorical(label, num_classes=NUM_CLASSES)  # make one-hot
    TRAINING_LABELS.append(encoded_y)
    TRAINING_DATA.append(example[0])
    print('add example for label %d' % label)


# load the model
model = loadModel()
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

import numpy as np
import cv2

cap = cv2.VideoCapture(0)
cap.set(3, 640)  # set video widht
cap.set(4, 480)  # set video height

while (True):
    # Capture frame-by-frame
    ret, frame = cap.read()
    # Our operations on the frame come here
    # gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Display the resulting frame
    cv2.imshow('frame', frame)
    key = cv2.waitKey(1)

    # cv2.putText(frame, "id={}, count={}".format("0", "0"), (30, 30), cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 255), 2)
    cv2.putText(frame, "face count={}".format(0), (100, 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 2)
    if key == ord('1'):  # add example in class 0
        addExample(prepare_frame(frame), 0)

    if key == ord('2'):  # add example in class 1
        addExample(prepare_frame(frame), 1)

    if key == ord('t'):  # train
        model.fit(np.array(TRAINING_DATA), np.array(TRAINING_LABELS), epochs=EPOCHS, batch_size=8)

    if key == ord('p'):  # predict
        processed_image = prepare_frame(frame)  # prepare frame
        prediction = model.predict(processed_image)
        result = np.argmax(prediction)  # imagenet_utils.decode_predictions(prediction)
        print('predict %d' % result)

    if key == ord('q'):  # quit
        break

# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()