import os import csv from crater_cnn import Network as CNN from crater_nn import Network as NN import Param import argparse # This script will go through all image tiles and detects crater area using sliding window method. # Then, write results as a csv file to the results folder. The results of this script is the input to the remove_duplicates.py script. # you need to provide the tile image name as argument after --tileimg command. For instance tile1_24 param = Param.Param() cwd = os.getcwd() # setup CNN cnn = CNN(img_shape=(50, 50, 1)) cnn.add_convolutional_layer(5, 16) cnn.add_convolutional_layer(5, 36) cnn.add_flat_layer() cnn.add_fc_layer(size=64, use_relu=True) cnn.add_fc_layer(size=16, use_relu=True) cnn.add_fc_layer(size=2, use_relu=False) cnn.finish_setup() # model.set_data(data) # restore previously trained CNN model cnn_model_path = os.path.join(cwd, 'models/cnn/crater_model_cnn.ckpt') cnn.restore(cnn_model_path) # go through all the tile folders gt_list = ["1_24", "1_25", "2_24", "2_25", "3_24", "3_25"]
#preprocess('tile3_24' , img_dimensions=(50, 50))
#preprocess('tile3_25' , img_dimensions=(50, 50))
from crater_loader import load_crater_data
from crater_data import Data
# Load dataxcv
images, labels, hot_one = load_crater_data()
data = Data(images, hot_one, random_state=42)
print("Size of:")
print("- Training-set:\t\t{}".format(len(data.train.labels)))
print("- Test-set:\t\t{}".format(len(data.test.labels)))
print("- Validation-set:\t{}".format(len(data.validation.labels)))
model = Network(img_shape=(50, 50, 1))
model.add_flat_layer()
model.add_fc_layer(size=50 * 50, use_relu=True)
model.add_fc_layer(size=16, use_relu=True)
model.add_fc_layer(size=2, use_relu=False)
model.finish_setup()
#model.set_data(data)
cnn = Network(img_shape=(50, 50, 1))
cnn.add_convolutional_layer(5, 16)
cnn.add_convolutional_layer(5, 36)
cnn.add_flat_layer()
cnn.add_fc_layer(size=64, use_relu=True)
cnn.add_fc_layer(size=16, use_relu=True)
cnn.add_fc_layer(size=2, use_relu=False)
cnn.finish_setup()
# preprocess the west region images (tile1_24, tile1_25)
preprocess(img_dimensions=(50, 50), 'tile1_24')
preprocess(img_dimensions=(50, 50), 'tile1_25')
from crater_loader import load_crater_data
from crater_data import Data
# Load data
images, labels, hot_one = load_crater_data()
data = Data(images, hot_one, random_state=42)
print("Size of:")
print("- Training-set:\t\t{}".format(len(data.train.labels)))
print("- Test-set:\t\t{}".format(len(data.test.labels)))
model = Network(img_shape=(50, 50, 1))
model.add_flat_layer()
model.add_fc_layer(size=50 * 50, use_relu=True)
model.add_fc_layer(size=16, use_relu=True)
model.add_fc_layer(size=2, use_relu=False)
model.finish_setup()
model.set_data(data)
model_path = os.path.join(cwd, 'results', 'models', 'crater_model_nn.ckpt')
#model.restore(model_path)
model.print_test_accuracy()
model.optimize(epochs=20)
model.save(model_path)
from lime import lime_image
from skimage.segmentation import mark_boundaries
import matplotlib.pyplot as plt
from crater_cnn import Network
from crater_plots import plot_image, plot_conv_weights, plot_conv_layer
from keras.applications import imagenet_utils
from keras.preprocessing import image
from skimage.color import gray2rgb, rgb2gray
cwd = os.getcwd()
input_shape = (50, 50)
preprocess = imagenet_utils.preprocess_input
# setup NN
nn = Network(img_shape=(50, 50, 1))
nn.add_flat_layer()
nn.add_fc_layer(size=50 * 50, use_relu=True)
nn.add_fc_layer(size=16, use_relu=True)
nn.add_fc_layer(size=2, use_relu=False)
nn.finish_setup()
# model.set_data(data)
# restore previously trained CNN model
print("loading the pre-trained NN model")
nn_model_path = os.path.join(cwd, 'results', 'nn_models',
'crater_east_model_nn.ckpt')
nn.restore(nn_model_path)
def transform_img_fn(path_list):
# preprocess the west region images (tile1_24, tile1_25)
preprocess('tile1_24', img_dimensions=(50, 50))
preprocess('tile1_25', img_dimensions=(50, 50))
preprocess('tile2_24', img_dimensions=(50, 50))
preprocess('tile2_25', img_dimensions=(50, 50))
preprocess('tile3_24', img_dimensions=(50, 50))
preprocess('tile3_25', img_dimensions=(50, 50))
from crater_loader import load_crater_data
from crater_data import KCV_Data
# Load data
images, labels, hot_one = load_crater_data()
# define model
model = Network(img_shape=(50, 50, 1))
model.add_flat_layer()
model.add_fc_layer(size=50 * 50, use_relu=True)
model.add_fc_layer(size=16, use_relu=True)
model.add_fc_layer(size=2, use_relu=False)
model.finish_setup()
# perform k fold cross validation
kf = KFold(n_splits=5)
i = 1
f1_avg = 0.0
acc_avg = 0.0
for train_index, test_index in kf.split(images):
X_train, X_test = images[train_index], images[test_index]
Y_train, Y_test = hot_one[train_index], hot_one[test_index]
from skimage.transform import pyramid_gaussian import cv2 as cv from helper import sliding_window import time import os import csv from crater_cnn import Network from crater_plots import plot_image, plot_conv_weights, plot_conv_layer cwd = os.getcwd() model = Network(img_shape=(30, 30, 1)) model.add_convolutional_layer(5, 16) model.add_convolutional_layer(5, 36) model.add_flat_layer() model.add_fc_layer(size=128, use_relu=True) model.add_fc_layer(size=2, use_relu=False) model.finish_setup() # model.set_data(data) model_path = os.path.join(cwd, 'model.ckpt') model.restore(model_path) path = './images/tiles' img = cv.imread(path + '/tile3_24.pgm', 0) img = cv.normalize(img, img, 0, 255, cv.NORM_MINMAX) / 255.0 crater_list = [] win_sizes = range(20, 30, 2) # loop over the image pyramid
import os from crater_cnn import Network from crater_plots import plot_image, plot_conv_weights, plot_conv_layer cwd = os.getcwd() #preprocess(img_dimensions=(30, 30)) from crater_loader import load_crater_data from crater_data import Data # Load data images, labels, hot_one = load_crater_data() data = Data(images, hot_one, random_state=42) model = Network(img_shape=(30, 30, 1)) model.add_convolutional_layer(5, 16) model.add_convolutional_layer(5, 36) model.add_flat_layer() model.add_fc_layer(size=128, use_relu=True) model.add_fc_layer(size=2, use_relu=False) model.finish_setup() model.set_data(data) model_path = os.path.join(cwd, 'model.ckpt') model.restore(model_path) image1 = data.test.images[7] image2 = data.test.images[14] print(model.predict([image1])) print(model.predict([image1, image2]))
