def test_get_next_image(self):
img = imread(self.test_path_image, IMREAD_COLOR)
img = cvtColor(img, COLOR_BGR2RGB)
img_resized = resize(img, (224, 224))
img_final = img_resized / 255.0
img_actual = modelUtils.get_image_at_index([self.test_path_image], 0)
assert_allclose(img_actual, img_final)
def test_create_training_data(self):
expected_x_value = modelUtils.get_image_at_index(
[self.test_path_image], 0)
expected_y_value = 0
makeDataset.create_training_data(1, 'x_data_test', 'y_data_test')
actual_x_value = np.load('x_data_test.npy')[0] / 255.0
actual_y_value = np.load('y_data_test.npy')[0]
assert_allclose(expected_x_value, actual_x_value)
self.assertEqual(expected_y_value, actual_y_value)
def test_generator(self):
expected = ([modelUtils.get_image_at_index([self.test_path_image],
0)], 0)
for batch in modelUtils.generator(array([self.test_path_image]),
array([self.test_path_descriptions]),
1):
actual_image, actual_label = batch
expected_image, expected_label = expected
self.assertEqual(len(actual_image), len(expected_image))
self.assertEqual(actual_label, expected_label)
break
else:
thisplot[0].set_color('red')
num_rows = 5
num_cols = 3
num_images = num_rows * num_cols
test_images = []
test_labels = []
for i in range(num_images):
index = int(random.choice(len(X_file_names), 1))
file_name = os.path.join(DATADIR, X_file_names[index])
print(file_name)
image = get_image_at_index(np.array([file_name]), 0)
test_images.append(image)
test_labels.append(get_label(os.path.join(DATADESC, y_file_names[index])))
model = load_model('resmodel')
predictions = model.predict(np.array(test_images))
plt.figure(figsize=(2 * 2 * num_cols, 2 * num_rows))
for i in range(num_images):
plt.subplot(num_rows, 2 * num_cols, 2 * i + 1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(num_rows, 2 * num_cols, 2 * i + 2)
plot_value_array(i, predictions, test_labels)
plt.show()
X_file_names = np.array(os.listdir(DATADIR))
y_file_names = np.array(os.listdir(DATADESC))
num_rows = 5
num_cols = 3
num_images = num_rows * num_cols
images = []
images_aug = []
for i in range(num_images):
index = int(random.choice(len(X_file_names), 1))
#test_images.append(cv2.resize(cv2.imread(os.path.join(DATADIR,X_file_names[index])),(224,224)))
file_name = os.path.join(DATADIR, X_file_names[index])
print(file_name)
image = modelUtils.get_image_at_index(np.array([file_name]), 0)
image_aug = modelUtils.get_image_at_index_with_transform(
np.array([file_name]), 0)
images.append(image)
images_aug.append(image_aug)
plt.figure(figsize=(2 * 2 * num_cols, 2 * num_rows))
for i in range(num_images):
plt.subplot(num_rows, 2 * num_cols, 2 * i + 1)
plt.imshow(images[i])
plt.xticks([])
plt.yticks([])
plt.subplot(num_rows, 2 * num_cols, 2 * i + 2)
plt.imshow(images_aug[i])
plt.xticks([])
plt.yticks([])