def test_prepare(self):

dataset_path = "c:/_Hive/_diploma/LISA Traffic Sign Dataset/signDatabasePublicFramesOnly/vid0/frameAnnotations-vid_cmp2.avi_annotations/"

annotation_path = dataset_path + 'frameAnnotations.csv'

image_data = np.genfromtxt(annotation_path, delimiter=';', names=True, dtype=None)

files = dict()

for image in image_data:

filepath = image['Filename']

if filepath not in files:

img = Image(filepath)

img.add_sign(label=image['Annotation_tag'],

coordinates=(image['Upper_left_corner_X'], image['Upper_left_corner_Y'],

image['Lower_right_corner_X'], image['Lower_right_corner_Y']))

files[filepath] = img

else:

files[filepath].add_sign(label=image['Annotation_tag'],

coordinates=(image['Upper_left_corner_X'], image['Upper_left_corner_Y'],

image['Lower_right_corner_X'], image['Lower_right_corner_Y']))

images = np.array(list(files.keys()))

images.sort()

lbl = np.array([files.get(key).get_coordinates() for key in images])

print(images[0].decode('utf8'))

imgs, lbls = prepare_images.prepare(dataset_path + images[0].decode('utf8'), lbl[0])

test_img = cv2.imread(dataset_path + images[0].decode('utf8'), cv2.IMREAD_UNCHANGED)

# noinspection PyAugmentAssignment

test_img = test_img / 255

for j in range(lbls.shape[0]):

# Rectangle = namedtuple('Rectangle', ['xmin', 'ymin', 'xmax', 'ymax'])

(x1, y1, x2, y2) = (coords[j].xmin, coords[j].ymin, coords[j].xmax, coords[j].ymax)

test_img_roi = np.array(

[test_img[y1:y2, x1:x2, 0], test_img[y1:y2, x1:x2, 1], test_img[y1:y2, x1:x2, 2]])

# if j > 325:

# prepare_images.show_roi([imgs[j], test_img_roi])