def get_densenet():
model = DenseNet(nb_dense_block=2, classes=1)
model.summary()
# compile model
print('compile model...')
optimizer = optimizers.Adam()
model.compile(loss='mean_squared_error', optimizer=optimizer)
return model
if K.image_dim_ordering() == 'th':
# Transpose image dimensions (Theano uses the channels as the 1st dimension)
im = im.transpose((2,0,1))
# Use pre-trained weights for Theano backend
weights_path = 'imagenet_models/densenet121_weights_th.h5'
else:
# Use pre-trained weights for Tensorflow backend
weights_path = 'imagenet_models/densenet121_weights_tf.h5'
# Insert a new dimension for the batch_size
im = np.expand_dims(im, axis=0)
# Test pretrained model
model = DenseNet(reduction=0.5, classes=1000, weights_path=weights_path)
sgd = SGD(lr=1e-2, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])
out = model.predict(im)
warnings.filterwarnings('ignore', '.*do not.*',)
# Load ImageNet classes file
classes = []
with open('resources/classes.txt', 'r') as list_:
for line in list_:
classes.append(line.rstrip('\n'))
print('Prediction: '+str(classes[np.argmax(out)]))
def main():
# create directory to save the preprocessed images
if not os.path.exists('preprocessed_images'):
os.mkdir('preprocessed_images')
print("Reading Arguments: ")
args = get_arguments()
try:
images = open(args.img_list).readlines()
except Exception as e:
print("Unable to read the image list")
print(e)
exit()
print("Processing {} images.".format(len(images)))
header = ['image_name']
header.extend(list(range(1,13)))
rows = []
# Initialize the pretrained model
print("Intializing the pretrained model")
model = DenseNet(reduction=0.5, classes=cfg.num_classes, weights_path=cfg.weights_path)
sgd = SGD(lr=1e-2, decay=1e-4, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])
print("Model loaded sucessfully!")
for img_name in images:
img_name = img_name.strip()
image_row = dict()
image_row['image_name'] = img_name.split('/')[-1]
image = cv2.imread(img_name)
(h, w) = image.shape[:2]
center = (w/2, h/2)
M = cv2.getRotationMatrix2D(center, 180, 1.0)
image = cv2.warpAffine(image, M, (w, h))
im_name = osp.join('preprocessed_images', img_name.split('/')[-1])
cv2.imwrite(im_name, image)
if args.grid_type == 12:
gridw, gridh = cfg.GRIDW, cfg.GRIDH
else:
raise ValueError(INVALID_GRID_TYPE)
# Pre-process the image.
crop_dims = args.crop_dims
if type(crop_dims) == str:
crop_dims = eval(crop_dims)
rgb = RGBPreprocess(crop_dims)
data = rgb.process_img(cv2.cvtColor(image, cv2.COLOR_BGR2RGB), image_row['image_name'], args.debug, gridh, gridw)
# run for each grid location.
for i, im in enumerate(data):
# preprocess the image
im = Image.fromarray(im)
im = im.resize((224, 224), Image.LANCZOS)
im = np.asarray(im, np.float64)
im = np.expand_dims(im, axis=0)
im = mean_subtraction(im)
# Run prediction
out = model.predict(im)
predict = np.argmax(out)
# write prediction to the row dictionary.
image_row[i+1] = cfg.class_names[predict]
rows.append(image_row)
# write to csv
with open(args.output_file, 'w') as csvf:
writer = csv.DictWriter(csvf, fieldnames=header)
writer.writeheader()
for row in rows:
writer.writerow(row)
weight_path = "{}_weights.best.hdf5".format('model')
sgd = SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)
earlystopping = EarlyStopping(monitor='val_loss',
min_delta=0,
patience=16,
verbose=0,
mode='auto')
checkpoint = ModelCheckpoint(weight_path,
monitor='val_loss',
verbose=1,
save_best_only=True,
mode='min',
save_weights_only=True)
tensorboard = TensorBoard(log_dir="./logs/{}".format(time()))
model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])
hist = model.fit(x_train,
y_train,
batch_size=128,
epochs=50,
callbacks=[earlystopping, checkpoint, tensorboard],
validation_split=0.3)
test_loss, test_acc = model.evaluate(x_test, y_test,
batch_size=128) # original basize 32
# print(hist.history)
# Plot training & validation accuracy values
plt.plot(hist.history['acc'])
plt.plot(hist.history['val_acc'])