ffmpeg | branch: master | Wenbin Chen <wenbin.c...@intel.com> | Tue Dec 12 10:33:33 2023 +0800| [a882fc029493ef2691360dcba360b0e998c628b4] | committer: Guo Yejun
libavfilter/vf_dnn_detect: Add yolov3 support Add yolov3 support. The difference of yolov3 is that it has multiple outputs in different scale to perform better on both large and small object. The model detail refer to: https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolo-v3-tf Signed-off-by: Wenbin Chen <wenbin.c...@intel.com> Reviewed-by: Guo Yejun <yejun....@intel.com> > http://git.videolan.org/gitweb.cgi/ffmpeg.git/?a=commit;h=a882fc029493ef2691360dcba360b0e998c628b4 --- libavfilter/vf_dnn_detect.c | 28 +++++++++++++++++++++++++++- 1 file changed, 27 insertions(+), 1 deletion(-) diff --git a/libavfilter/vf_dnn_detect.c b/libavfilter/vf_dnn_detect.c index 35c0508c50..4c537cf255 100644 --- a/libavfilter/vf_dnn_detect.c +++ b/libavfilter/vf_dnn_detect.c @@ -35,6 +35,7 @@ typedef enum { DDMT_SSD, DDMT_YOLOV1V2, + DDMT_YOLOV3 } DNNDetectionModelType; typedef struct DnnDetectContext { @@ -73,6 +74,7 @@ static const AVOption dnn_detect_options[] = { { "model_type", "DNN detection model type", OFFSET2(model_type), AV_OPT_TYPE_INT, { .i64 = DDMT_SSD }, INT_MIN, INT_MAX, FLAGS, "model_type" }, { "ssd", "output shape [1, 1, N, 7]", 0, AV_OPT_TYPE_CONST, { .i64 = DDMT_SSD }, 0, 0, FLAGS, "model_type" }, { "yolo", "output shape [1, N*Cx*Cy*DetectionBox]", 0, AV_OPT_TYPE_CONST, { .i64 = DDMT_YOLOV1V2 }, 0, 0, FLAGS, "model_type" }, + { "yolov3", "outputs shape [1, N*D, Cx, Cy]", 0, AV_OPT_TYPE_CONST, { .i64 = DDMT_YOLOV3 }, 0, 0, FLAGS, "model_type" }, { "cell_w", "cell width", OFFSET2(cell_w), AV_OPT_TYPE_INT, { .i64 = 0 }, 0, INTMAX_MAX, FLAGS }, { "cell_h", "cell height", OFFSET2(cell_h), AV_OPT_TYPE_INT, { .i64 = 0 }, 0, INTMAX_MAX, FLAGS }, { "nb_classes", "The number of class", OFFSET2(nb_classes), AV_OPT_TYPE_INT, { .i64 = 0 }, 0, INTMAX_MAX, FLAGS }, @@ -151,6 +153,11 @@ static int dnn_detect_parse_yolo_output(AVFrame *frame, DNNData *output, int out cell_h = ctx->cell_h; scale_w = cell_w; scale_h = cell_h; + } else { + cell_w = output[output_index].width; + cell_h = output[output_index].height; + scale_w = ctx->scale_width; + scale_h = ctx->scale_height; } box_size = nb_classes + 5; @@ -178,6 +185,7 @@ static int dnn_detect_parse_yolo_output(AVFrame *frame, DNNData *output, int out output[output_index].height * output[output_index].width / box_size / cell_w / cell_h; + anchors = anchors + (detection_boxes * output_index * 2); /** * find all candidate bbox * yolo output can be reshaped to [B, N*D, Cx, Cy] @@ -290,6 +298,21 @@ static int dnn_detect_post_proc_yolo(AVFrame *frame, DNNData *output, AVFilterCo return 0; } +static int dnn_detect_post_proc_yolov3(AVFrame *frame, DNNData *output, + AVFilterContext *filter_ctx, int nb_outputs) +{ + int ret = 0; + for (int i = 0; i < nb_outputs; i++) { + ret = dnn_detect_parse_yolo_output(frame, output, i, filter_ctx); + if (ret < 0) + return ret; + } + ret = dnn_detect_fill_side_data(frame, filter_ctx); + if (ret < 0) + return ret; + return 0; +} + static int dnn_detect_post_proc_ssd(AVFrame *frame, DNNData *output, AVFilterContext *filter_ctx) { DnnDetectContext *ctx = filter_ctx->priv; @@ -386,8 +409,11 @@ static int dnn_detect_post_proc_ov(AVFrame *frame, DNNData *output, int nb_outpu ret = dnn_detect_post_proc_yolo(frame, output, filter_ctx); if (ret < 0) return ret; + case DDMT_YOLOV3: + ret = dnn_detect_post_proc_yolov3(frame, output, filter_ctx, nb_outputs); + if (ret < 0) + return ret; } - return 0; } _______________________________________________ ffmpeg-cvslog mailing list ffmpeg-cvslog@ffmpeg.org https://ffmpeg.org/mailman/listinfo/ffmpeg-cvslog To unsubscribe, visit link above, or email ffmpeg-cvslog-requ...@ffmpeg.org with subject "unsubscribe".
