示例#1
0
 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))
示例#3
0
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
    }
示例#4
0
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}