[FFmpeg-devel] [PATCH V7 4/6] lavu: add side data AV_FRAME_DATA_BOUNDING_BOXES

[Lynne]

Apr 9, 2021, 16:35 by bygrandao at gmail.com:

> Em sex., 9 de abr. de 2021 Ã s 01:13, Guo, Yejun <yejun.guo at intel.com> escreveu:
>
>>
>>
>>
>> > -----Original Message-----
>> > From: ffmpeg-devel <ffmpeg-devel-bounces at ffmpeg.org> On Behalf Of Lynne
>> > Sent: 2021年4月9日 0:57
>> > To: FFmpeg development discussions and patches <ffmpeg-devel at ffmpeg.org>
>> > Subject: Re: [FFmpeg-devel] [PATCH V7 4/6] lavu: add side data
>> > AV_FRAME_DATA_BOUNDING_BOXES
>> >
>>
>> First of all, thanks for the quick replies, I see, all the discussions/comments are to
>> make this patch better, thank you.
>>
>> > >> >
>> > >> >> >> > +
>> > >> >> >> > +typedef struct AVBoundingBoxHeader {
>> > >> >> >> > +    /**
>> > >> >> >> > +     * Information about how the bounding box is generated.
>> > >> >> >> > +     * for example, the DNN model name.
>> > >> >> >> > +     */
>> > >> >> >> > +    char source[128];
>> > >> >> >> > +
>> > >> >> >> > +    /**
>> > >> >> >> > +     * The size of frame when it is detected.
>> > >> >> >> > +     */
>> > >> >> >> > +    int frame_width;
>> > >> >> >> > +    int frame_height;
>> > >> >> >> >
>> > >> >> >>
>> > >> >> >> Why? This side data is attached to AVFrames only, where we
>> > >> >> >> already have width and height.
>> > >> >> >>
>> > >> >> >
>> > >> >> > The detection result will be used by other filters, for example,
>> > >> >> > dnn_classify (see https://github.com/guoyejun/ffmpeg/tree/classify).
>> > >> >> >
>> > >> >> > The filter dnn_detect detects all the objects (cat, dog, person ...) in a
>> > >> >> > frame, while dnn_classify classifies one detected object (for example,
>> > >> person)
>> > >> >> > for its attribute (for example, emotion, etc.)
>> > >> >> >
>> > >> >> > The filter dnn_classify have to check if the frame size is changed after
>> > >> >> > it is detected, to handle the below filter chain:
>> > >> >> > dnn_detect -> scale -> dnn_classify
>> > >> >> >
>> > >> >>
>> > >> >> This doesn't look good. Why is dnn_classify needing to know
>> > >> >> the original frame size at all?
>> > >> >>
>> > >> >
>> > >> > For example, the original size of the frame is 100*100, and dnn_detect
>> > >> > detects a face at place (10, 10) -> (30, 40), such data will be saved in
>> > >> > AVBoundingBox.top/left/right/bottom.
>> > >> >
>> > >> > Then, the frame is scaled into 50*50.
>> > >> >
>> > >> > Then, dnn_classify is used to analyze the emotion of the face, it needs to
>> > >> > know the frame size (100*100) when it is detected, otherwise, it does not
>> > >> > work with just (10,10), (30,40) and 50*50.
>> > >> >
>> > >>
>> > >> Why can't the scale filter also rescale this side data as well?
>> > >>
>> > >
>> > > I'm afraid that we could not make sure all such filters (including filters in the
>> > > future) to do the rescale. And in the previous discussion, I got to know that
>> > > 'many other existing side-data types are invalidated by scaling'.
>> > >
>> > > So, we need frame_width and frame_height here.
>> > >
>> >
>> > No, you don't. You just need to make sure filters which change resolution
>> > or do cropping also change the side data parameters.
>> > It's called maintainership. As-is, this won't even work with cropping,
>> > only with basic aspect ratio preserving scaling.
>> > For the lack of a better term, this is a hack.
>>
>> As discussed in previous email, for the frame size change case, dnn_classify
>> (and other filters which use the detection result, for example drawbox) can
>> just output a warning message to tell user what happens, and don't do the
>> classification, otherwise, it will give a wrong/weird result which makes the
>> user confused.
>>
>> >
>> > I would accept just specifying that if the frame dimensions are
>> > altered in any way, the side-data is no longer valid and it's up
>> > to users to figure that out by out of bound coordinates.
>> > This is what we currently do with video_enc_params.
>>
>> frame_width/frame_height is not perfect (for the cases such as: scale down
>> + crop + scale up to the same size), but it provides more info than the checking
>> of 'out of bound coordinates'. There are many other possible issues when the
>> coordinates are within the frame.
>>
>> If we think we'd better not let user get more info from the warning message,
>> I'm ok to remove them.
>>
>> I'll remove them if there's another comment supporting the removal, and
>> there's no objection.
>>
>> >
>> >
>> > >> >> >> > diff --git a/libavutil/frame.h b/libavutil/frame.h
>> > >> >> >> > index a5ed91b20a..41e22de02a 100644
>> > >> >> >> > --- a/libavutil/frame.h
>> > >> >> >> > +++ b/libavutil/frame.h
>> > >> >> >> > @@ -198,6 +198,13 @@ enum AVFrameSideDataType {
>> > >> >> >> >  * Must be present for every frame which should have film grain
>> > >> applied.
>> > >> >> >> >  */
>> > >> >> >> >  AV_FRAME_DATA_FILM_GRAIN_PARAMS,
>> > >> >> >> > +
>> > >> >> >> > +    /**
>> > >> >> >> > +     * Bounding boxes for object detection and classification, the
>> > >> data is
>> > >> >> a
>> > >> >> >> AVBoundingBoxHeader
>> > >> >> >> > +     * followed with an array of AVBoudingBox, and
>> > >> >> >> AVBoundingBoxHeader.nb_bboxes is the number
>> > >> >> >> > +     * of array element.
>> > >> >> >> > +     */
>> > >> >> >> > +    AV_FRAME_DATA_BOUNDING_BOXES,
>> > >> >> >> >  };
>> > >> >> >> >
>> > >> >> >>
>> > >> >> >> Finally, why call it a Bounding Box? It's not descriptive at all.
>> > >> >> >> How about "Object Classification"? It makes much more sense, it's
>> > >> >> >> exactly what this is. So AVObjectClassification, AVObjectClassification,
>> > >> >> >> AV_FRAME_DATA_OBJECT_CLASSIFICATION and so on.
>> > >> >> >>
>> > >> >> >
>> > >> >> > In object detection papers, bounding box is usually used.
>> > >> >> > We'd better use the same term, imho, thanks.
>> > >> >> >
>> > >> >>
>> > >> >> Not in this case, API users won't have any idea what this is or what
>> > >> >> it's for. This is user-facing code after all.
>> > >> >> Papers in fields can get away with overloading language, but we're
>> > >> >> trying to make a concise API. Object classification makes sense
>> > >> >> because this is exactly what this is.
>> > >> >>
>> > >> >
>> > >> > The term bounding box is dominating the domain, for example, even
>> > >> > HEVC spec uses this term, see page 317 of
>> > >> >
>> > >>
>> > https://www.itu.int/rec/dologin_pub.asp?lang=e&id=T-REC-H.265-201911-I!!P
>> > >> DF-E&type=items
>> > >> >
>> > >> > also copy some here for your convenient.
>> > >> > ar_bounding_box_top[ ar_object_idx[ i ] ] u(16)
>> > >> > ar_bounding_box_left[ ar_object_idx[ i ] ] u(16)
>> > >> > ar_bounding_box_width[ ar_object_idx[ i ] ] u(16)
>> > >> > ar_bounding_box_height[ ar_object_idx[ i ] ] u(16)
>> > >> >
>> > >> > I would prefer to use bounding box.
>> > >> >
>> > >>
>> > >> It's for an entirely different thing, and like I said, it's just an overloaded
>> > >> language because they can get away. We're trying to be generic.
>> > >> This side data is for detecting _and_ classifying objects. In fact, most of
>> > >> the structure is dedicated towards classifying. If you'd like users to actually
>> > >> use this, give it a good name and don't leave them guessing what this
>> > >> structure is by throwing vague jargon some other paper or spec has
>> > >> because it's close enough.
>> > >>
>> > >
>> > > all the people in the domain accepts bounding box, they can understand this
>> > > struct name easily and clearly, they might be bothered if we use another
>> > name.
>> > >
>> > > btw, AVObjectClassification confuses people who just want object detection.
>> > >
>> >
>> > As I said, the name "bounding box" makes no sense once it gets overloaded
>> > with object classification.
>>
>> dnn_detect creates an array of 'bounding box' for all detected objects, and
>> dnn_classify assigns attributes for a set of bounding boxes (with same object
>> id). 'bounding box' serves both detection and classification properly.
>>
>>
>> > Object classification is still the main use of the filters,
>> > because the original proposal was to have all of this info be ffmpeg-private,
>> > which would forbid simple object detection.
>>
>> The original proposal is to add it as side data which is ffmpeg-public, and then,
>> we spent much time discussing/trying with ffmpeg-private as an temporary
>> method, and since it is not good to be temporary, we now switch back to
>> ffmpeg-public.
>>
>> During the whole period, we don't have any intention to
>> 'forbid simple object detection', not quite understand your point here.
>>
>>
>> > So I still maintain this should be called "Object classification". I'd accept
>> > "Object detection" as well, but definitely not "bounding box".
>>
>> imho, ' Object detection' and ' Object classification' are worse, they just
>> describe one aspect of the struct. The users might just use filter dnn_detect,
>> they might use filters dnn_detect + dnn_classify.
>>
>>
>> >
>> > Since the decision was made to make the side data public, we have to make
>> > very sure it contains no hacks or is impossible to extend, since we don't want
>> > to have an
>> > "AV_SIDE_DATA_OBJECT_CLASSIFICATION_VERSION_2_SORRY_WE_SCREWED_
>> > UP"
>> > faster than you can say "Recursive cascade correlation artificial neural
>> > networks".
>>
>> sorry, not quite understand your point here.
>>
>> 'bounding box' is designed for general purpose to contain the info for
>> detection/classification. It doesn't matter which DNN model is used, it doesn't
>> matter if a traditional algorithm (non-dnn) is used.
>>
>> I'm open to use a better name. And bounding box is the best one for me till now.
>> Everyone in the domain knows the exact meaning of bounding box without
>> extra explanation. This word has been extended/evolved with such meaning in
>> this domain.
>>
> +1
>
> I think it is wise to use the name which is widely used in the field.
>

Way to go, ignoring half the exchange to voice an opinion after we
came to an agreement.