[FFmpeg-cvslog] dnn/vf_dnn_detect.c: add tensorflow output parse support
Ting Fu
git at videolan.org
Tue May 11 05:55:17 EEST 2021
ffmpeg | branch: master | Ting Fu <ting.fu at intel.com> | Thu May 6 16:46:10 2021 +0800| [c38bc5634d8eb4540cb8dfa616eafcc0b3c85e59] | committer: Guo, Yejun
dnn/vf_dnn_detect.c: 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_detection/g3doc/tf2_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_detection/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":model=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>
Signed-off-by: Guo, Yejun <yejun.guo at intel.com>
> http://git.videolan.org/gitweb.cgi/ffmpeg.git/?a=commit;h=c38bc5634d8eb4540cb8dfa616eafcc0b3c85e59
---
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..d23e30aedd 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 does not support detect filter\n");
+ return AVERROR(EINVAL);
+ }
+}
+
static void free_detect_labels(DnnDetectContext *ctx)
{
for (int i = 0; i < ctx->label_count; i++) {
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