import os
import numpy as np
from keras.preprocessing.image import img_to_array, load_img
from data_utils import prepare_input_image_batch, postprocess_output, resize
from model_utils import generate_mobilenet_model
IMAGE_FOLDER_PATH = r"D:\Yue\Documents\Datasets\MSCOCO\val\valset\\"
batch_size = 10
image_size = 256
model = generate_mobilenet_model(img_size=image_size)
model.load_weights('weights/model.h5')
X = []
files = os.listdir(IMAGE_FOLDER_PATH)
files = files[:100]
for i, filename in enumerate(files):
img = img_to_array(load_img(os.path.join(IMAGE_FOLDER_PATH, filename))) / 255.
img = resize(img, (image_size, image_size, 3)) * 255. # resize needs floats to be in 0-1 range, preprocess needs in 0-255 range
X.append(img)
if i % (len(files) // 20) == 0:
print("Loaded %0.2f percentage of images from directory" % (i / float(len(files)) * 100))
X = np.array(X, dtype='float32')
print("Images loaded. Shape = ", X.shape)
X_lab, X_features = prepare_input_image_batch(X, batchsize=batch_size)
sepia_filter = np.array([[.393, .769, .189], [.349, .686, .168], [.272, .534, .131]])
scale = 0.01
sepia_img = img.dot(sepia_filter.T * scale)
sepia_img /= sepia_img.max()
sepia_img = skimage.img_as_ubyte(sepia_img)
return sepia_img
#variance_generator = lambda i,j,h: 0.25*(i+j)/1022. + 0.001
#variances = np.fromfunction(variance_generator,(224,224,3))
#sepia_img = skimage.util.random_noise(sepia_img, mode='gaussian', seed=1)
#sepia_img = skimage.util.random_noise(sepia_img, mode='localvar', local_vars=variances)
#return skimage.img_as_ubyte(skimage.color.rgb2gray(sepia_img))
return sepia_img
model = generate_mobilenet_model()
model.load_weights(_MODEL_WEIGHTS_NAME)
def get_images_names(path):
images = os.listdir(path)
final = []
for image in images:
final.append(image.rstrip())
return images
images_names = get_images_names(_IMAGE_PATH)
for img in images_names:
x_b = img_to_array(load_img(_IMAGE_PATH+img, target_size= (224,224)))
x_b = np.expand_dims(x_b, axis=0)
x_b = x_b/255 #conversion to a range of -1 to 1. Explanation saved.
import os
from data_utils import train_generator, val_batch_generator
from model_utils import generate_mobilenet_model
from train_utils import TensorBoardBatch
from keras.callbacks import ModelCheckpoint
nb_train_images = 60000 # there are 82783 images in MS-COCO, set this to how many samples you want to train on.
batch_size = 125
model = generate_mobilenet_model(lr=1e-3)
model.summary()
# continue training if weights are available
#if os.path.exists('weights/mobilenet_model.h5'):
# model.load_weights('weights/mobilenet_model.h5')
# use Batchwise TensorBoard callback
tensorboard = TensorBoardBatch(batch_size=batch_size)
checkpoint = ModelCheckpoint('weights/mobilenet_model_v2.h5',
monitor='loss',
verbose=1,
save_best_only=True,
save_weights_only=True)
callbacks = [checkpoint, tensorboard]
model.fit_generator(generator=train_generator(batch_size),
steps_per_epoch=nb_train_images // batch_size,
epochs=100,
verbose=1,