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])
self.assertTrue(np.allclose(imgs[j], test_img_roi), msg="In imgs[{}]".format(j))
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, coords = 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])
self.assertTrue(np.allclose(imgs[j], test_img_roi), msg="In imgs[{}]".format(j))
def test_init(seed=16):
np.random.seed(seed)
# Read data from files
# Read learning set
image_data = np.genfromtxt(ANNOTATION_LEARNING_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']))
train_set_without_negatives = files
# Read test set
image_data = np.genfromtxt(ANNOTATION_TEST_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']))
test_set_without_negatives = files
return {
'train_set': train_set_without_negatives,
'test_set': test_set_without_negatives
}
def test_init(seed=16):
np.random.seed(seed)
# Read data from files
# Read learning set
image_data = np.genfromtxt(ANNOTATION_LEARNING_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']))
train_set_without_negatives = files
# Read test set
image_data = np.genfromtxt(ANNOTATION_TEST_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']))
test_set_without_negatives = files
return {'train_set': train_set_without_negatives, 'test_set': test_set_without_negatives}
