DataGenerator - Bounding box scaling Issue

[rdbch]

Hi,

As trying to train my model I have found the following issue in the DataGenerator.py file.

In read_image_and_gt(), the original width and height are stored after the image was resized thus resulting in the fact that the scaling coefficients for X and Y will always be 1. If you were to train on images with different sizes this could be a major problem, because the bounding boxes won't be scaled accordingly.

def read_image_and_gt(img_files, gt_files, config):
.............
    for img_name, gt_name in zip(img_files, gt_files):

        #open img
        img = cv2.imread(img_name).astype(np.float32, copy=False)

        # scale image
        img = cv2.resize( img, (config.IMAGE_WIDTH, config.IMAGE_HEIGHT))

        #subtract means
        img = (img - np.mean(img))/ np.std(img)

        #store original height and width?
        orig_h, orig_w, _ = [float(v) for v in img.shape]

    .............................................................
       # scale annotation
        x_scale = config.IMAGE_WIDTH / orig_w
        y_scale = config.IMAGE_HEIGHT / orig_h

        # scale boxes
        bboxes_per_file[:, 0::2] = bboxes_per_file[:, 0::2] * x_scale
        bboxes_per_file[:, 1::2] = bboxes_per_file[:, 1::2] * y_scale