[FFmpeg-devel] [PATCH V2 4/4] dnn/vf_dnn_detect: add tensorflow output parse support
Guo, Yejun
yejun.guo at intel.com
Tue May 11 05:55:39 EEST 2021
> -----Original Message-----
> From: ffmpeg-devel <ffmpeg-devel-bounces at ffmpeg.org> On Behalf Of Guo,
> Yejun
> Sent: 2021年5月10日 14:14
> To: FFmpeg development discussions and patches
> <ffmpeg-devel at ffmpeg.org>
> Subject: Re: [FFmpeg-devel] [PATCH V2 4/4] dnn/vf_dnn_detect: add
> tensorflow output parse support
>
>
>
> > -----Original Message-----
> > From: ffmpeg-devel <ffmpeg-devel-bounces at ffmpeg.org> On Behalf Of Ting
> > Fu
> > Sent: 2021年5月6日 16:46
> > To: ffmpeg-devel at ffmpeg.org
> > Subject: [FFmpeg-devel] [PATCH V2 4/4] dnn/vf_dnn_detect: add
> tensorflow
> > output parse support
> >
> > Testing model is tensorflow offical model in github repo, please refer
> >
> https://github.com/tensorflow/models/blob/master/research/object_detecti
> > on/g3doc/tf1_detection_zoo.md
> > to download the detect model as you need.
> > For example, local testing was carried on with
> > 'ssd_mobilenet_v2_coco_2018_03_29.tar.gz', and
> > used one image of dog in
> >
> https://github.com/tensorflow/models/blob/master/research/object_detecti
> > on/test_images/image1.jpg
> >
> > Testing command is:
> > ./ffmpeg -i image1.jpg -vf
> > dnn_detect=dnn_backend=tensorflow:input=image_tensor:output=\
> >
> "num_detections&detection_scores&detection_classes&detection_boxes":m
> > odel=ssd_mobilenet_v2_coco.pb,\
> > showinfo -f null -
> >
> > We will see the result similar as below:
> > [Parsed_showinfo_1 @ 0x33e65f0] side data - detection bounding boxes:
> > [Parsed_showinfo_1 @ 0x33e65f0] source: ssd_mobilenet_v2_coco.pb
> > [Parsed_showinfo_1 @ 0x33e65f0] index: 0, region: (382, 60) ->
> > (1005, 593), label: 18, confidence: 9834/10000.
> > [Parsed_showinfo_1 @ 0x33e65f0] index: 1, region: (12, 8) -> (328,
> > 549), label: 18, confidence: 8555/10000.
> > [Parsed_showinfo_1 @ 0x33e65f0] index: 2, region: (293, 7) -> (682,
> > 458), label: 1, confidence: 8033/10000.
> > [Parsed_showinfo_1 @ 0x33e65f0] index: 3, region: (342, 0) -> (690,
> > 325), label: 1, confidence: 5878/10000.
> >
> > There are two boxes of dog with cores 94.05% & 93.45% and two boxes of
> > person with scores 80.33% & 58.78%.
> >
> > Signed-off-by: Ting Fu <ting.fu at intel.com>
> > ---
> > libavfilter/vf_dnn_detect.c | 95
> > ++++++++++++++++++++++++++++++++++++-
> > 1 file changed, 94 insertions(+), 1 deletion(-)
> >
> > diff --git a/libavfilter/vf_dnn_detect.c b/libavfilter/vf_dnn_detect.c
> > index 7d39acb653..818b53a052 100644
> > --- a/libavfilter/vf_dnn_detect.c
> > +++ b/libavfilter/vf_dnn_detect.c
> > @@ -48,6 +48,9 @@ typedef struct DnnDetectContext {
> > #define FLAGS AV_OPT_FLAG_FILTERING_PARAM |
> > AV_OPT_FLAG_VIDEO_PARAM
> > static const AVOption dnn_detect_options[] = {
> > { "dnn_backend", "DNN backend",
> > OFFSET(backend_type), AV_OPT_TYPE_INT, { .i64 = 2 },
> > INT_MIN, INT_MAX, FLAGS, "backend" },
> > +#if (CONFIG_LIBTENSORFLOW == 1)
> > + { "tensorflow", "tensorflow backend flag", 0,
> > AV_OPT_TYPE_CONST, { .i64 = 1 }, 0, 0, FLAGS, "backend" },
> > +#endif
> > #if (CONFIG_LIBOPENVINO == 1)
> > { "openvino", "openvino backend flag", 0,
> > AV_OPT_TYPE_CONST, { .i64 = 2 }, 0, 0, FLAGS, "backend" },
> > #endif
> > @@ -59,7 +62,7 @@ static const AVOption dnn_detect_options[] = {
> >
> > AVFILTER_DEFINE_CLASS(dnn_detect);
> >
> > -static int dnn_detect_post_proc(AVFrame *frame, DNNData *output,
> > uint32_t nb, AVFilterContext *filter_ctx)
> > +static int dnn_detect_post_proc_ov(AVFrame *frame, DNNData *output,
> > AVFilterContext *filter_ctx)
> > {
> > DnnDetectContext *ctx = filter_ctx->priv;
> > float conf_threshold = ctx->confidence;
> > @@ -136,6 +139,96 @@ static int dnn_detect_post_proc(AVFrame *frame,
> > DNNData *output, uint32_t nb, AV
> > return 0;
> > }
> >
> > +static int dnn_detect_post_proc_tf(AVFrame *frame, DNNData *output,
> > AVFilterContext *filter_ctx)
> > +{
> > + DnnDetectContext *ctx = filter_ctx->priv;
> > + int proposal_count;
> > + float conf_threshold = ctx->confidence;
> > + float *conf, *position, *label_id, x0, y0, x1, y1;
> > + int nb_bboxes = 0;
> > + AVFrameSideData *sd;
> > + AVDetectionBBox *bbox;
> > + AVDetectionBBoxHeader *header;
> > +
> > + proposal_count = *(float *)(output[0].data);
> > + conf = output[1].data;
> > + position = output[3].data;
> > + label_id = output[2].data;
> > +
> > + sd = av_frame_get_side_data(frame,
> > AV_FRAME_DATA_DETECTION_BBOXES);
> > + if (sd) {
> > + av_log(filter_ctx, AV_LOG_ERROR, "already have dnn bounding
> > boxes in side data.\n");
> > + return -1;
> > + }
> > +
> > + for (int i = 0; i < proposal_count; ++i) {
> > + if (conf[i] < conf_threshold)
> > + continue;
> > + nb_bboxes++;
> > + }
> > +
> > + if (nb_bboxes == 0) {
> > + av_log(filter_ctx, AV_LOG_VERBOSE, "nothing detected in this
> > frame.\n");
> > + return 0;
> > + }
> > +
> > + header = av_detection_bbox_create_side_data(frame, nb_bboxes);
> > + if (!header) {
> > + av_log(filter_ctx, AV_LOG_ERROR, "failed to create side data
> > with %d bounding boxes\n", nb_bboxes);
> > + return -1;
> > + }
> > +
> > + av_strlcpy(header->source, ctx->dnnctx.model_filename,
> > sizeof(header->source));
> > +
> > + for (int i = 0; i < proposal_count; ++i) {
> > + y0 = position[i * 4];
> > + x0 = position[i * 4 + 1];
> > + y1 = position[i * 4 + 2];
> > + x1 = position[i * 4 + 3];
> > +
> > + bbox = av_get_detection_bbox(header, i);
> > +
> > + if (conf[i] < conf_threshold) {
> > + continue;
> > + }
> > +
> > + bbox->x = (int)(x0 * frame->width);
> > + bbox->w = (int)(x1 * frame->width) - bbox->x;
> > + bbox->y = (int)(y0 * frame->height);
> > + bbox->h = (int)(y1 * frame->height) - bbox->y;
> > +
> > + bbox->detect_confidence = av_make_q((int)(conf[i] * 10000),
> > 10000);
> > + bbox->classify_count = 0;
> > +
> > + if (ctx->labels && label_id[i] < ctx->label_count) {
> > + av_strlcpy(bbox->detect_label, ctx->labels[(int)label_id[i]],
> > sizeof(bbox->detect_label));
> > + } else {
> > + snprintf(bbox->detect_label, sizeof(bbox->detect_label),
> "%d",
> > (int)label_id[i]);
> > + }
> > +
> > + nb_bboxes--;
> > + if (nb_bboxes == 0) {
> > + break;
> > + }
> > + }
> > + return 0;
> > +}
> > +
> > +static int dnn_detect_post_proc(AVFrame *frame, DNNData *output,
> > uint32_t nb, AVFilterContext *filter_ctx)
> > +{
> > + DnnDetectContext *ctx = filter_ctx->priv;
> > + DnnContext *dnn_ctx = &ctx->dnnctx;
> > + switch (dnn_ctx->backend_type) {
> > + case DNN_OV:
> > + return dnn_detect_post_proc_ov(frame, output, filter_ctx);
> > + case DNN_TF:
> > + return dnn_detect_post_proc_tf(frame, output, filter_ctx);
> > + default:
> > + avpriv_report_missing_feature(filter_ctx, "Current dnn backend
> do
> > not support detect filter\n");
>
> do -> does, changed locally, will push tomorrow if there's no objection,
> thanks.
>
pushed
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