Comments (3)
Hi,
I use https://github.com/D-X-Y/SAN/blob/master/cache_data/generate_300W.py#L38 to generate the bounding box on 300W. Specifically, two kinds of the bounding box are used. One is "OD", it uses the official bounding box provided by the 300W website (https://ibug.doc.ic.ac.uk/media/uploads/competitions/bounding_boxes.zip). The other is "GT", use the tight bounding box around the facial landmarks.
I'm not surprised that using other bounding boxes would decrease the performance. Because our framework is not designed for the robustness w.r.t. different detectors. For our SAN, the detector used for training should be the same as the detector used for test.
from landmark-detection.
Hi @D-X-Y πππ:
I am new to this topic, maybe some questions sound silly, thanks for your patience and guidance πππππ.
I have seen your answer in #issue 14. How did you get the bounding box in link? Is this the βGTβ, tight bounding box? It looks like the predefined bounding box imported from mat data.
Now I need to execute your code on my own image (single), how should I locate the tight bounding box, can you share the instruction link if exists? or can you tell me how did you do on the bounding box of ../cache_data/cache/test_1.png, that looks pretty good.
Best,
XG
from landmark-detection.
The facial bounding box format followed by 300W dataset is ( top-left x, top-left y, bottom right x, bottom right y). The other formats like MTCNN follows (top-left x, top-left y, width, height). I took a out of dataset image and used MTCNN to detect bounding box and changed the format and I am getting facial landmarks ish accurate.
from landmark-detection.
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from landmark-detection.