Guo, Yejun: > Below are the example steps to do object detection: > > 1. download and install l_openvino_toolkit_p_2021.1.110.tgz from > https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit/download.html > or, we can get source code (tag 2021.1), build and install. > 2. export LD_LIBRARY_PATH with openvino settings, for example: > .../deployment_tools/inference_engine/lib/intel64/:.../deployment_tools/inference_engine/external/tbb/lib/ > 3. rebuild ffmpeg from source code with configure option: > --enable-libopenvino > --extra-cflags='-I.../deployment_tools/inference_engine/include/' > --extra-ldflags='-L.../deployment_tools/inference_engine/lib/intel64' > 4. download model files and test image > wget > https://github.com/guoyejun/ffmpeg_dnn/raw/main/models/openvino/2021.1/face-detection-adas-0001.bin > wget > https://github.com/guoyejun/ffmpeg_dnn/raw/main/models/openvino/2021.1/face-detection-adas-0001.xml > wget > https://github.com/guoyejun/ffmpeg_dnn/raw/main/models/openvino/2021.1/face-detection-adas-0001.label > wget https://github.com/guoyejun/ffmpeg_dnn/raw/main/images/cici.jpg > 5. run ffmpeg with: > ./ffmpeg -i cici.jpg -vf > dnn_detect=dnn_backend=openvino:model=face-detection-adas-0001.xml:input=data:output=detection_out:confidence=0.6:labels=face-detection-adas-0001.label > -f null - > > We'll see the detect result as below: > [Parsed_dnn_detect_0 @ 0x56226644e540] frame->private_ref (bounding boxes): > [Parsed_dnn_detect_0 @ 0x56226644e540] source: face-detection-adas-0001.xml > [Parsed_dnn_detect_0 @ 0x56226644e540] index: 0, region: (1005, 813) -> > (1086, 905), label: face, confidence: 10000/10000. > [Parsed_dnn_detect_0 @ 0x56226644e540] index: 1, region: (888, 839) -> (967, > 926), label: face, confidence: 6917/10000. > > There are two faces detected with confidence 100% and 69.17%. > > Signed-off-by: Guo, Yejun <yejun....@intel.com> > --- > configure | 1 + > doc/filters.texi | 40 +++ > libavfilter/Makefile | 1 + > libavfilter/allfilters.c | 1 + > libavfilter/dnn/dnn_backend_openvino.c | 12 + > libavfilter/dnn_filter_common.c | 7 + > libavfilter/dnn_filter_common.h | 1 + > libavfilter/dnn_interface.h | 6 +- > libavfilter/vf_dnn_detect.c | 462 +++++++++++++++++++++++++ > 9 files changed, 529 insertions(+), 2 deletions(-) > create mode 100644 libavfilter/vf_dnn_detect.c > > diff --git a/configure b/configure > index 336301cb40..cdac292c2f 100755 > --- a/configure > +++ b/configure > @@ -3549,6 +3549,7 @@ derain_filter_select="dnn" > deshake_filter_select="pixelutils" > deshake_opencl_filter_deps="opencl" > dilation_opencl_filter_deps="opencl" > +dnn_detect_filter_select="dnn" > dnn_processing_filter_select="dnn" > drawtext_filter_deps="libfreetype" > drawtext_filter_suggest="libfontconfig libfribidi" > diff --git a/doc/filters.texi b/doc/filters.texi > index 426cb158da..55368c6f1b 100644 > --- a/doc/filters.texi > +++ b/doc/filters.texi > @@ -10133,6 +10133,46 @@ ffmpeg -i INPUT -f lavfi -i > nullsrc=hd720,geq='r=128+80*(sin(sqrt((X-W/2)*(X-W/2 > @end example > @end itemize > > +@section dnn_detect > + > +Do object detection with deep neural networks. > + > +The filter accepts the following options: > + > +@table @option > +@item dnn_backend > +Specify which DNN backend to use for model loading and execution. This > option accepts > +only openvino now, tensorflow backends will be added. > + > +@item model > +Set path to model file specifying network architecture and its parameters. > +Note that different backends use different file formats. > + > +@item input > +Set the input name of the dnn network. > + > +@item output > +Set the output name of the dnn network. > + > +@item confidence > +Set the confidence threshold (default: 0.5). > + > +@item labels > +Set path to label file specifying the mapping between label id and name. > +Each label name is written in one line, tailing spaces and empty lines are > skipped. > +The first line is the name of label id 0 (usually it is 'background'), > +and the second line is the name of label id 1, etc. > +The label id is considered as name if the label file is not provided. > + > +@item backend_configs > +Set the configs to be passed into backend > + > +@item async > +use DNN async execution if set (default: set), > +roll back to sync execution if the backend does not support async. > + > +@end table > + > @anchor{dnn_processing} > @section dnn_processing > > diff --git a/libavfilter/Makefile b/libavfilter/Makefile > index 359ea7f903..b14c0ecdb9 100644 > --- a/libavfilter/Makefile > +++ b/libavfilter/Makefile > @@ -245,6 +245,7 @@ OBJS-$(CONFIG_DILATION_FILTER) += > vf_neighbor.o > OBJS-$(CONFIG_DILATION_OPENCL_FILTER) += vf_neighbor_opencl.o > opencl.o \ > opencl/neighbor.o > OBJS-$(CONFIG_DISPLACE_FILTER) += vf_displace.o framesync.o > +OBJS-$(CONFIG_DNN_DETECT_FILTER) += vf_dnn_detect.o > OBJS-$(CONFIG_DNN_PROCESSING_FILTER) += vf_dnn_processing.o > OBJS-$(CONFIG_DOUBLEWEAVE_FILTER) += vf_weave.o > OBJS-$(CONFIG_DRAWBOX_FILTER) += vf_drawbox.o > diff --git a/libavfilter/allfilters.c b/libavfilter/allfilters.c > index 452c030706..85419ec3f1 100644 > --- a/libavfilter/allfilters.c > +++ b/libavfilter/allfilters.c > @@ -230,6 +230,7 @@ extern AVFilter ff_vf_detelecine; > extern AVFilter ff_vf_dilation; > extern AVFilter ff_vf_dilation_opencl; > extern AVFilter ff_vf_displace; > +extern AVFilter ff_vf_dnn_detect; > extern AVFilter ff_vf_dnn_processing; > extern AVFilter ff_vf_doubleweave; > extern AVFilter ff_vf_drawbox; > diff --git a/libavfilter/dnn/dnn_backend_openvino.c > b/libavfilter/dnn/dnn_backend_openvino.c > index 5be053b7f8..928d84b744 100644 > --- a/libavfilter/dnn/dnn_backend_openvino.c > +++ b/libavfilter/dnn/dnn_backend_openvino.c > @@ -621,6 +621,12 @@ DNNReturnType ff_dnn_execute_model_ov(const DNNModel > *model, const char *input_n > return DNN_ERROR; > } > > + if (model->func_type != DFT_PROCESS_FRAME) { > + if (!out_frame) { > + out_frame = in_frame; > + } > + } > + > if (nb_output != 1) { > // currently, the filter does not need multiple outputs, > // so we just pending the support until we really need it. > @@ -674,6 +680,12 @@ DNNReturnType ff_dnn_execute_model_async_ov(const > DNNModel *model, const char *i > return DNN_ERROR; > } > > + if (model->func_type != DFT_PROCESS_FRAME) { > + if (!out_frame) { > + out_frame = in_frame; > + } > + } > + > task = av_malloc(sizeof(*task)); > if (!task) { > av_log(ctx, AV_LOG_ERROR, "unable to alloc memory for task item.\n"); > diff --git a/libavfilter/dnn_filter_common.c b/libavfilter/dnn_filter_common.c > index 413adba406..92b696e710 100644 > --- a/libavfilter/dnn_filter_common.c > +++ b/libavfilter/dnn_filter_common.c > @@ -64,6 +64,13 @@ int ff_dnn_init(DnnContext *ctx, DNNFunctionType > func_type, AVFilterContext *fil > return 0; > } > > +int ff_dnn_set_proc(DnnContext *ctx, PRE_POST_PROC pre_proc, PRE_POST_PROC > post_proc) > +{ > + ctx->model->pre_proc = pre_proc; > + ctx->model->post_proc = post_proc; > + return 0; > +} > + > DNNReturnType ff_dnn_get_input(DnnContext *ctx, DNNData *input) > { > return ctx->model->get_input(ctx->model->model, input, > ctx->model_inputname); > diff --git a/libavfilter/dnn_filter_common.h b/libavfilter/dnn_filter_common.h > index 79c4d3efe3..0e88b88bdd 100644 > --- a/libavfilter/dnn_filter_common.h > +++ b/libavfilter/dnn_filter_common.h > @@ -48,6 +48,7 @@ typedef struct DnnContext { > > > int ff_dnn_init(DnnContext *ctx, DNNFunctionType func_type, AVFilterContext > *filter_ctx); > +int ff_dnn_set_proc(DnnContext *ctx, PRE_POST_PROC pre_proc, PRE_POST_PROC > post_proc); > DNNReturnType ff_dnn_get_input(DnnContext *ctx, DNNData *input); > DNNReturnType ff_dnn_get_output(DnnContext *ctx, int input_width, int > input_height, int *output_width, int *output_height); > DNNReturnType ff_dnn_execute_model(DnnContext *ctx, AVFrame *in_frame, > AVFrame *out_frame); > diff --git a/libavfilter/dnn_interface.h b/libavfilter/dnn_interface.h > index d3a0c58a61..90a08129f4 100644 > --- a/libavfilter/dnn_interface.h > +++ b/libavfilter/dnn_interface.h > @@ -63,6 +63,8 @@ typedef struct DNNData{ > DNNColorOrder order; > } DNNData; > > +typedef int (*PRE_POST_PROC)(AVFrame *frame, DNNData *model, AVFilterContext > *filter_ctx);
Why uppercase? It is a typedef, not a macro. > + > typedef struct DNNModel{ > // Stores model that can be different for different backends. > void *model; > @@ -80,10 +82,10 @@ typedef struct DNNModel{ > const char *output_name, int *output_width, > int *output_height); > // set the pre process to transfer data from AVFrame to DNNData > // the default implementation within DNN is used if it is not provided > by the filter > - int (*pre_proc)(AVFrame *frame_in, DNNData *model_input, AVFilterContext > *filter_ctx); > + PRE_POST_PROC pre_proc; > // set the post process to transfer data from DNNData to AVFrame > // the default implementation within DNN is used if it is not provided > by the filter > - int (*post_proc)(AVFrame *frame_out, DNNData *model_output, > AVFilterContext *filter_ctx); > + PRE_POST_PROC post_proc; Spurious change. > } DNNModel; > > // Stores pointers to functions for loading, executing, freeing DNN models > for one of the backends. > diff --git a/libavfilter/vf_dnn_detect.c b/libavfilter/vf_dnn_detect.c > new file mode 100644 > index 0000000000..13b82579a9 > --- /dev/null > +++ b/libavfilter/vf_dnn_detect.c > @@ -0,0 +1,462 @@ > +/* > + * Copyright (c) 2021 > + * > + * This file is part of FFmpeg. > + * > + * FFmpeg is free software; you can redistribute it and/or > + * modify it under the terms of the GNU Lesser General Public > + * License as published by the Free Software Foundation; either > + * version 2.1 of the License, or (at your option) any later version. > + * > + * FFmpeg is distributed in the hope that it will be useful, > + * but WITHOUT ANY WARRANTY; without even the implied warranty of > + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU > + * Lesser General Public License for more details. > + * > + * You should have received a copy of the GNU Lesser General Public > + * License along with FFmpeg; if not, write to the Free Software > + * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 > USA > + */ > + > +/** > + * @file > + * implementing an object detecting filter using deep learning networks. > + */ > + > +#include "libavformat/avio.h" > +#include "libavutil/opt.h" > +#include "libavutil/pixdesc.h" > +#include "libavutil/avassert.h" > +#include "libavutil/imgutils.h" > +#include "filters.h" > +#include "dnn_filter_common.h" > +#include "formats.h" > +#include "internal.h" > +#include "libavutil/time.h" > +#include "bbox.h" > + > +typedef struct DnnDetectContext { > + const AVClass *class; > + DnnContext dnnctx; > + float confidence; > + char *labels_filename; > + char **labels; > + int label_count; > +} DnnDetectContext; > + > +#define OFFSET(x) offsetof(DnnDetectContext, dnnctx.x) > +#define OFFSET2(x) offsetof(DnnDetectContext, x) > +#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_LIBOPENVINO == 1) > + { "openvino", "openvino backend flag", 0, > AV_OPT_TYPE_CONST, { .i64 = 2 }, 0, 0, FLAGS, "backend" }, > +#endif > + DNN_COMMON_OPTIONS > + { "confidence", "threshold of confidence", OFFSET2(confidence), > AV_OPT_TYPE_FLOAT, { .dbl = 0.5 }, 0, 1, FLAGS}, > + { "labels", "path to labels file", OFFSET2(labels_filename), > AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS }, > + { NULL } > +}; > + > +AVFILTER_DEFINE_CLASS(dnn_detect); > + > +// remove this function once we update vf_drawbox/text > +// to visualize the bbox > +static void dump_boundingbox(AVFilterContext *ctx, AVFrame *frame) > +{ > + int nb_bbox = 0; > + BoundingBox *bbox; > + BoundingBoxHeader *header; > + > + if (!frame->private_ref) > + return; > + > + header = (BoundingBoxHeader *)frame->private_ref->data; > + bbox = (BoundingBox *)(header + 1); > + > + if (!header->bbox_size || (frame->private_ref->size - sizeof(*header)) % > header->bbox_size != 0) { > + av_log(ctx, AV_LOG_ERROR, "invalid size of bounding box\n"); > + return; > + } > + > + nb_bbox = (frame->private_ref->size - sizeof(*header)) / > header->bbox_size; > + av_log(ctx, AV_LOG_INFO, "frame->private_ref (bounding boxes):\n"); > + av_log(ctx, AV_LOG_INFO, "source: %s\n", header->source); > + for (int i = 0; i < nb_bbox; i++) { > + av_log(ctx, AV_LOG_INFO, "index: %d, region: (%d, %d) -> (%d, %d), > label: %s, confidence: %d/%d.\n", > + i, bbox->left, bbox->top, bbox->right, > bbox->bottom, > + bbox->detect_label, > bbox->detect_confidence.num, bbox->detect_confidence.den); > + if (bbox->classify_count > 0) { > + for (int j = 0; j < bbox->classify_count; j++) { > + av_log(ctx, AV_LOG_INFO, "\t\tclassify: label: %s, > confidence: %d/%d.\n", > + bbox->classify_labels[j], > bbox->classify_confidences[j].num, bbox->classify_confidences[j].den); > + } > + } > + bbox++; > + } > +} > + > +static int dnn_detect_post_proc(AVFrame *frame, DNNData *output, > AVFilterContext *filter_ctx) > +{ > + DnnDetectContext *ctx = filter_ctx->priv; > + float conf_threshold = ctx->confidence; > + int proposal_count = output->height; > + int detect_size = output->width; > + float *detections = output->data; > + int nb_bbox = 0; > + BoundingBox *bbox; > + BoundingBoxHeader *header; > + > + for (int i = 0; i < proposal_count; ++i) { > + float conf = detections[i * detect_size + 2]; > + if (conf < conf_threshold) { > + continue; > + } > + nb_bbox++; > + } > + > + if (nb_bbox == 0) { > + av_log(filter_ctx, AV_LOG_VERBOSE, "nothing detected in this > frame.\n"); > + return 0; > + } > + > + if (frame->private_ref) { > + av_log(filter_ctx, AV_LOG_ERROR, "frame->private_ref is already > occupied\n"); > + return -1; > + } > + > + frame->private_ref = av_buffer_alloc(sizeof(*header) + sizeof(*bbox) * > nb_bbox); > + if (!frame->private_ref) { > + av_log(filter_ctx, AV_LOG_ERROR, "failed to allocate buffer for %d > bboxes\n", nb_bbox); > + return AVERROR(ENOMEM);; Double ; > + } > + > + header = (BoundingBoxHeader *)frame->private_ref->data; > + strncpy(header->source, ctx->dnnctx.model_filename, > sizeof(header->source)); > + header->source[sizeof(header->source) - 1] = '\0'; > + header->bbox_size = sizeof(*bbox); > + > + bbox = (BoundingBox *)(header + 1); This does not guarantee proper alignment. You could use a flexible array member for that. > + for (int i = 0; i < proposal_count; ++i) { > + int av_unused image_id = (int)detections[i * detect_size + 0]; > + int label_id = (int)detections[i * detect_size + 1]; > + float conf = detections[i * detect_size + 2]; > + float x0 = detections[i * detect_size + 3]; > + float y0 = detections[i * detect_size + 4]; > + float x1 = detections[i * detect_size + 5]; > + float y1 = detections[i * detect_size + 6]; > + > + if (conf < conf_threshold) { > + continue; > + } > + > + bbox->left = (int)(x0 * frame->width); > + bbox->right = (int)(x1 * frame->width); > + bbox->top = (int)(y0 * frame->height); > + bbox->bottom = (int)(y1 * frame->height); > + > + bbox->detect_confidence = av_make_q((int)(conf * 10000), 10000); > + bbox->classify_count = 0; > + > + if (ctx->labels && label_id < ctx->label_count) { > + strcpy(bbox->detect_label, ctx->labels[label_id]); > + } else { > + snprintf(bbox->detect_label, BBOX_LABEL_NAME_MAX_LENGTH, "%d", > label_id); > + } > + > + nb_bbox--; > + if (nb_bbox == 0) { > + break; > + } > + bbox++; > + } > + > + dump_boundingbox(filter_ctx, frame); > + > + return 0; > +} > + > +static void free_detect_labels(DnnDetectContext *ctx) > +{ > + for (int i = 0; i < ctx->label_count; i++) { > + av_freep(&ctx->labels[i]); > + } > + av_freep(&ctx->labels); > +} > + > +static int read_detect_label_file(AVFilterContext *context) > +{ > + int line_len; > + FILE *file; > + DnnDetectContext *ctx = context->priv; > + > + file = av_fopen_utf8(ctx->labels_filename, "r"); > + if (!file){ > + av_log(context, AV_LOG_ERROR, "failed to open file %s\n", > ctx->labels_filename); > + return AVERROR(EINVAL); > + } > + > + while (!feof(file)) { > + char *label; > + char buf[256]; > + if (!fgets(buf, 256, file)) { > + break; > + } > + > + line_len = strlen(buf); > + while (line_len) { > + int i = line_len - 1; > + if (buf[i] == '\n' || buf[i] == '\r' || buf[i] == ' ') { > + buf[i] = '\0'; > + line_len--; > + } else { > + break; > + } > + } > + > + if (line_len == 0) // empty line > + continue; > + > + if (line_len > BBOX_LABEL_NAME_MAX_LENGTH) { > + av_log(context, AV_LOG_ERROR, "label %s too long\n", buf); > + fclose(file); > + free_detect_labels(ctx); > + return AVERROR(EINVAL); > + } > + > + label = av_strdup(buf); > + if (!label) { > + av_log(context, AV_LOG_ERROR, "failed to allocate memory for > label %s\n", buf); > + fclose(file); > + free_detect_labels(ctx); > + return AVERROR(ENOMEM); > + } > + > + if (av_dynarray_add_nofree(&ctx->labels, &ctx->label_count, label) < > 0) { > + av_log(context, AV_LOG_ERROR, "failed to do av_dynarray_add\n"); > + fclose(file); > + free_detect_labels(ctx); 1. You are leaking label here. 2. You are repeating yourself with the cleanup code. 3. When you return an error in a filter's init function, the filter's uninit function is called automatically. In this case this means that free_detect_labels is called twice which is not only wasteful, but harmful: You are freeing ctx->labels (and all labels contained in it) in the first run, but you are not resetting the number of labels. If ctx->label_count is > 0, there will be a segfault when free_detect_labels is called the second time. 4. Return the error code. (5. I consider your use of av_log for every error to be excessive.) > + return AVERROR(ENOMEM); > + } > + } > + > + fclose(file); > + return 0; > +} > + > +static av_cold int dnn_detect_init(AVFilterContext *context) > +{ > + DnnDetectContext *ctx = context->priv; > + int ret = ff_dnn_init(&ctx->dnnctx, DFT_ANALYTICS_DETECT, context); > + if (ret < 0) > + return ret; > + ff_dnn_set_proc(&ctx->dnnctx, NULL, dnn_detect_post_proc); > + > + if (ctx->labels_filename) { > + return read_detect_label_file(context); > + } > + return 0; > +} > + > +static int dnn_detect_query_formats(AVFilterContext *context) > +{ > + static const enum AVPixelFormat pix_fmts[] = { > + AV_PIX_FMT_RGB24, AV_PIX_FMT_BGR24, > + AV_PIX_FMT_GRAY8, AV_PIX_FMT_GRAYF32, > + AV_PIX_FMT_YUV420P, AV_PIX_FMT_YUV422P, > + AV_PIX_FMT_YUV444P, AV_PIX_FMT_YUV410P, AV_PIX_FMT_YUV411P, > + AV_PIX_FMT_NV12, > + AV_PIX_FMT_NONE > + }; > + AVFilterFormats *fmts_list = ff_make_format_list(pix_fmts); > + return ff_set_common_formats(context, fmts_list); > +} > + > +static int dnn_detect_filter_frame(AVFilterLink *inlink, AVFrame *in) > +{ > + AVFilterContext *context = inlink->dst; > + AVFilterLink *outlink = context->outputs[0]; > + DnnDetectContext *ctx = context->priv; > + DNNReturnType dnn_result; > + > + dnn_result = ff_dnn_execute_model(&ctx->dnnctx, in, NULL); > + if (dnn_result != DNN_SUCCESS){ > + av_log(ctx, AV_LOG_ERROR, "failed to execute model\n"); > + av_frame_free(&in); > + return AVERROR(EIO); > + } > + > + return ff_filter_frame(outlink, in); > +} > + > +static int dnn_detect_activate_sync(AVFilterContext *filter_ctx) > +{ > + AVFilterLink *inlink = filter_ctx->inputs[0]; > + AVFilterLink *outlink = filter_ctx->outputs[0]; > + AVFrame *in = NULL; > + int64_t pts; > + int ret, status; > + int got_frame = 0; > + > + FF_FILTER_FORWARD_STATUS_BACK(outlink, inlink); > + > + do { > + // drain all input frames > + ret = ff_inlink_consume_frame(inlink, &in); > + if (ret < 0) > + return ret; > + if (ret > 0) { > + ret = dnn_detect_filter_frame(inlink, in); > + if (ret < 0) > + return ret; > + got_frame = 1; > + } > + } while (ret > 0); > + > + // if frame got, schedule to next filter > + if (got_frame) > + return 0; > + > + if (ff_inlink_acknowledge_status(inlink, &status, &pts)) { > + if (status == AVERROR_EOF) { > + ff_outlink_set_status(outlink, status, pts); > + return ret; > + } > + } > + > + FF_FILTER_FORWARD_WANTED(outlink, inlink); > + > + return FFERROR_NOT_READY; > +} > + > +static int dnn_detect_flush_frame(AVFilterLink *outlink, int64_t pts, > int64_t *out_pts) > +{ > + DnnDetectContext *ctx = outlink->src->priv; > + int ret; > + DNNAsyncStatusType async_state; > + > + ret = ff_dnn_flush(&ctx->dnnctx); > + if (ret != DNN_SUCCESS) { > + return -1; > + } > + > + do { > + AVFrame *in_frame = NULL; > + AVFrame *out_frame = NULL; > + async_state = ff_dnn_get_async_result(&ctx->dnnctx, &in_frame, > &out_frame); > + if (out_frame) { > + av_assert0(in_frame == out_frame); > + ret = ff_filter_frame(outlink, out_frame); > + if (ret < 0) > + return ret; > + if (out_pts) > + *out_pts = out_frame->pts + pts; > + } > + av_usleep(5000); > + } while (async_state >= DAST_NOT_READY); > + > + return 0; > +} > + > +static int dnn_detect_activate_async(AVFilterContext *filter_ctx) > +{ > + AVFilterLink *inlink = filter_ctx->inputs[0]; > + AVFilterLink *outlink = filter_ctx->outputs[0]; > + DnnDetectContext *ctx = filter_ctx->priv; > + AVFrame *in = NULL; > + int64_t pts; > + int ret, status; > + int got_frame = 0; > + int async_state; > + > + FF_FILTER_FORWARD_STATUS_BACK(outlink, inlink); > + > + do { > + // drain all input frames > + ret = ff_inlink_consume_frame(inlink, &in); > + if (ret < 0) > + return ret; > + if (ret > 0) { > + if (ff_dnn_execute_model_async(&ctx->dnnctx, in, NULL) != > DNN_SUCCESS) { > + return AVERROR(EIO); > + } > + } > + } while (ret > 0); > + > + // drain all processed frames > + do { > + AVFrame *in_frame = NULL; > + AVFrame *out_frame = NULL; > + async_state = ff_dnn_get_async_result(&ctx->dnnctx, &in_frame, > &out_frame); > + if (out_frame) { > + av_assert0(in_frame == out_frame); > + ret = ff_filter_frame(outlink, out_frame); > + if (ret < 0) > + return ret; > + got_frame = 1; > + } > + } while (async_state == DAST_SUCCESS); > + > + // if frame got, schedule to next filter > + if (got_frame) > + return 0; > + > + if (ff_inlink_acknowledge_status(inlink, &status, &pts)) { > + if (status == AVERROR_EOF) { > + int64_t out_pts = pts; > + ret = dnn_detect_flush_frame(outlink, pts, &out_pts); > + ff_outlink_set_status(outlink, status, out_pts); > + return ret; > + } > + } > + > + FF_FILTER_FORWARD_WANTED(outlink, inlink); > + > + return 0; > +} > + > +static int dnn_detect_activate(AVFilterContext *filter_ctx) > +{ > + DnnDetectContext *ctx = filter_ctx->priv; > + > + if (ctx->dnnctx.async) > + return dnn_detect_activate_async(filter_ctx); > + else > + return dnn_detect_activate_sync(filter_ctx); > +} > + > +static av_cold void dnn_detect_uninit(AVFilterContext *context) > +{ > + DnnDetectContext *ctx = context->priv; > + ff_dnn_uninit(&ctx->dnnctx); > + free_detect_labels(ctx); > +} > + > +static const AVFilterPad dnn_detect_inputs[] = { > + { > + .name = "default", > + .type = AVMEDIA_TYPE_VIDEO, > + }, > + { NULL } > +}; > + > +static const AVFilterPad dnn_detect_outputs[] = { > + { > + .name = "default", > + .type = AVMEDIA_TYPE_VIDEO, > + }, > + { NULL } > +}; > + > +AVFilter ff_vf_dnn_detect = { > + .name = "dnn_detect", > + .description = NULL_IF_CONFIG_SMALL("Apply DNN detect filter to the > input."), > + .priv_size = sizeof(DnnDetectContext), > + .init = dnn_detect_init, > + .uninit = dnn_detect_uninit, > + .query_formats = dnn_detect_query_formats, > + .inputs = dnn_detect_inputs, > + .outputs = dnn_detect_outputs, > + .priv_class = &dnn_detect_class, > + .activate = dnn_detect_activate, > +}; > _______________________________________________ ffmpeg-devel mailing list ffmpeg-devel@ffmpeg.org https://ffmpeg.org/mailman/listinfo/ffmpeg-devel To unsubscribe, visit link above, or email ffmpeg-devel-requ...@ffmpeg.org with subject "unsubscribe".