from ResUnet import *
model = ResUnet(input_size=(512, 512, 3),
start_neurons=16,
keep_prob=1,
block_size=1)
weight = "Model/Luna/ResUnet.h5"
if os.path.isfile(weight): model.load_weights(weight)
model_checkpoint = ModelCheckpoint(weight,
monitor='val_acc',
verbose=1,
save_best_only=True)
y_pred = model.predict(x_test)
y_pred_threshold = []
i = 0
for y in y_pred:
_, temp = cv2.threshold(y, 0.5, 1, cv2.THRESH_BINARY)
y_pred_threshold.append(temp)
y = y * 255
cv2.imwrite('./Luna/test/result/%d.png' % i, y)
i += 1
y_test = list(np.ravel(y_test))
y_pred_threshold = list(np.ravel(y_pred_threshold))
tn, fp, fn, tp = confusion_matrix(y_test, y_pred_threshold).ravel()
print('Accuracy:', accuracy_score(y_test, y_pred_threshold))
import os
import cv2
import numpy as np
from sklearn.metrics import recall_score, roc_auc_score, accuracy_score, confusion_matrix
from keras.callbacks import ModelCheckpoint
from util import *
import scipy.misc as mc
import math
data_location = ''
testing_images_loc = data_location + 'Drive/test/images/'
testing_label_loc = data_location + 'Drive/test/label/'
test_files = os.listdir(testing_images_loc)
test_data = []
test_label = []
desired_size = 592
for i in test_files:
im = mc.imread(testing_images_loc + i)
label = mc.imread(testing_label_loc + i.split('_')[0] + '_manual1.png')
old_size = im.shape[:2] # old_size is in (height, width) format
delta_w = desired_size - old_size[1]
delta_h = desired_size - old_size[0]
top, bottom = delta_h // 2, delta_h - (delta_h // 2)
left, right = delta_w // 2, delta_w - (delta_w // 2)
color = [0, 0, 0]
color2 = [0]
new_im = cv2.copyMakeBorder(im,
top,
bottom,
left,
right,