Question about boxes in evaluation

[VanillaChelle]

Hi, thanks for your work.

I have a question about the boxes mapping in keras_code/B_Online_Aggregation_Eval.py.

In line 251, we get the groundtruth box with x, y, dx, dy:
x, y, dx, dy = f_list[1], f_list[2], f_list[3], f_list[4]
and make them in multiples of 16 in line 254-255:
f_x, f_y, f_dx, f_dy = int((x - (x % 16)) / 16), int((y - (y % 16)) / 16), \ int((dx - (dx % 16)) / 16), int((dy - (dy % 16)) / 16)
The query image is processed with resizing to either 1024*720 or 720*1024 in line 276:
img_p = preprocess_images(img, size[0], size[1], mean_value)
The processed image is then fed into the network to get features in line 278-279:
features, cams, roi = extract_feat_cam_fast(model, get_output, conv_layer_features, 1, img_p, num_cams, class_list[0, 0:num_cams])
However, after that we get the descriptors by slicing features with f_x, f_y, f_dx, f_dy in line 281-282:
d_wp = weighted_cam_pooling(features[:, :, f_y:f_dy, f_x:f_dx], cams[:, :, f_y:f_dy, f_x:f_dx])

I'm a little confused since we get the features by feeding the resized images, while the box indices for slicing features are not resized accordingly. I'll be really grateful for your reply :)