[FFmpeg-devel,6/6] lavfi/dnn_classify: add filter dnn_classify for classification based on detection bounding boxes

Message ID	20210418100802.19017-6-yejun.guo@intel.com
State	Accepted
Headers	show Delivered-To: andriy.gelman@gmail.com Received-SPF: pass (google.com: domain of ffmpeg-devel-bounces@ffmpeg.org designates 79.124.17.100 as permitted sender) client-ip=79.124.17.100; IronPort-SDR: 8l7vHjzNlWB514JXTBrpXvLvr9k2HoxwHAw+37Auc6HzM4MIiKwBO8G/oMRSTcjX65mhBWBVll qj28Elx+UwJw== IronPort-SDR: OqxfeA8KCT/2opOy28KUTXtvWt0Jw1J+Gs4lvXnIwZBi8d3x3uzih8pRmrw+gkg9pa4zldJwDU DFVBdKbWDrNg== From: "Guo, Yejun" <yejun.guo@intel.com> To: ffmpeg-devel@ffmpeg.org Date: Sun, 18 Apr 2021 18:08:02 +0800 Message-Id: <20210418100802.19017-6-yejun.guo@intel.com> In-Reply-To: <20210418100802.19017-1-yejun.guo@intel.com> References: <20210418100802.19017-1-yejun.guo@intel.com> Subject: [FFmpeg-devel] [PATCH 6/6] lavfi/dnn_classify: add filter dnn_classify for classification based on detection bounding boxes Precedence: list Reply-To: FFmpeg development discussions and patches <ffmpeg-devel@ffmpeg.org> Cc: yejun.guo@intel.com MIME-Version: 1.0 Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: 7bit Errors-To: ffmpeg-devel-bounces@ffmpeg.org Sender: "ffmpeg-devel" <ffmpeg-devel-bounces@ffmpeg.org> Content-Length: 16023
Series	[FFmpeg-devel,1/6] lavfi/dnn_backend_openvino.c: unify code for infer request for sync/async \| expand [FFmpeg-devel,1/6] lavfi/dnn_backend_openvino.c: unify code for infer request for sync/async [FFmpeg-devel,2/6] lavfi/dnn_backend_openvino.c: add InferenceItem between TaskItem and RequestItem [FFmpeg-devel,3/6] lavfi/dnn_backend_openvino.c: move the logic for batch mode earlier [FFmpeg-devel,4/6] lavfi/dnn: refine dnn interface to add DNNExecBaseParams [FFmpeg-devel,5/6] lavfi/dnn: add classify support with openvino backend [FFmpeg-devel,6/6] lavfi/dnn_classify: add filter dnn_classify for classification based on detectio…

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andriy/PPC64_make_fate	success	Make fate finished

diff --git a/configure b/configure index cc1013fb1d..d1fc0d05a7 100755 --- a/configure +++ b/configure @@ -3555,6 +3555,7 @@ derain_filter_select="dnn" deshake_filter_select="pixelutils" deshake_opencl_filter_deps="opencl" dilation_opencl_filter_deps="opencl" +dnn_classify_filter_select="dnn" dnn_detect_filter_select="dnn" dnn_processing_filter_select="dnn" drawtext_filter_deps="libfreetype" diff --git a/doc/filters.texi b/doc/filters.texi index 68f17dd563..9975db7326 100644 --- a/doc/filters.texi +++ b/doc/filters.texi @@ -10127,6 +10127,42 @@ 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_classify + +Do classification with deep neural networks based on bounding boxes. + +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, +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 + +@end table + @section dnn_detect Do object detection with deep neural networks. diff --git a/libavfilter/Makefile b/libavfilter/Makefile index b77f2276a4..dd4decdd71 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_CLASSIFY_FILTER) += vf_dnn_classify.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 diff --git a/libavfilter/allfilters.c b/libavfilter/allfilters.c index 0d2bf7bbee..9b24a2da29 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_classify; extern AVFilter ff_vf_dnn_detect; extern AVFilter ff_vf_dnn_processing; extern AVFilter ff_vf_doubleweave; diff --git a/libavfilter/vf_dnn_classify.c b/libavfilter/vf_dnn_classify.c new file mode 100644 index 0000000000..dd61f743d6 --- /dev/null +++ b/libavfilter/vf_dnn_classify.c @@ -0,0 +1,330 @@ +/* + * 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 classification 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 "libavutil/avstring.h" +#include "libavutil/detection_bbox.h" + +typedef struct DnnClassifyContext { + const AVClass *class; + DnnContext dnnctx; + float confidence; + char *labels_filename; + char *target; + char **labels; + int label_count; +} DnnClassifyContext; + +#define OFFSET(x) offsetof(DnnClassifyContext, dnnctx.x) +#define OFFSET2(x) offsetof(DnnClassifyContext, x) +#define FLAGS AV_OPT_FLAG_FILTERING_PARAM | AV_OPT_FLAG_VIDEO_PARAM +static const AVOption dnn_classify_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 }, + { "target", "which one to be classified", OFFSET2(target), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS }, + { NULL } +}; + +AVFILTER_DEFINE_CLASS(dnn_classify); + +static int dnn_classify_post_proc(AVFrame *frame, DNNData *output, uint32_t bbox_index, AVFilterContext *filter_ctx) +{ + DnnClassifyContext *ctx = filter_ctx->priv; + float conf_threshold = ctx->confidence; + AVDetectionBBoxHeader *header; + AVDetectionBBox *bbox; + float *classifications; + uint32_t label_id; + float confidence; + AVFrameSideData *sd; + + if (output->channels <= 0) { + return -1; + } + + sd = av_frame_get_side_data(frame, AV_FRAME_DATA_DETECTION_BBOXES); + header = (AVDetectionBBoxHeader *)sd->data; + + if (bbox_index == 0) { + av_strlcat(header->source, ", ", sizeof(header->source)); + av_strlcat(header->source, ctx->dnnctx.model_filename, sizeof(header->source)); + } + + classifications = output->data; + label_id = 0; + confidence= classifications[0]; + for (int i = 1; i < output->channels; i++) { + if (classifications[i] > confidence) { + label_id = i; + confidence= classifications[i]; + } + } + + if (confidence < conf_threshold) { + return 0; + } + + bbox = av_get_detection_bbox(header, bbox_index); + bbox->classify_confidences[bbox->classify_count] = av_make_q((int)(confidence * 10000), 10000); + + if (ctx->labels && label_id < ctx->label_count) { + av_strlcpy(bbox->classify_labels[bbox->classify_count], ctx->labels[label_id], sizeof(bbox->classify_labels[bbox->classify_count])); + } else { + snprintf(bbox->classify_labels[bbox->classify_count], sizeof(bbox->classify_labels[bbox->classify_count]), "%d", label_id); + } + + bbox->classify_count++; + + return 0; +} + +static void free_classify_labels(DnnClassifyContext *ctx) +{ + for (int i = 0; i < ctx->label_count; i++) { + av_freep(&ctx->labels[i]); + } + ctx->label_count = 0; + av_freep(&ctx->labels); +} + +static int read_classify_label_file(AVFilterContext *context) +{ + int line_len; + FILE *file; + DnnClassifyContext *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 >= AV_DETECTION_BBOX_LABEL_NAME_MAX_SIZE) { + av_log(context, AV_LOG_ERROR, "label %s too long\n", buf); + fclose(file); + 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); + 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); + av_freep(&label); + return AVERROR(ENOMEM); + } + } + + fclose(file); + return 0; +} + +static av_cold int dnn_classify_init(AVFilterContext *context) +{ + DnnClassifyContext *ctx = context->priv; + int ret = ff_dnn_init(&ctx->dnnctx, DFT_ANALYTICS_CLASSIFY, context); + if (ret < 0) + return ret; + ff_dnn_set_classify_post_proc(&ctx->dnnctx, dnn_classify_post_proc); + + if (ctx->labels_filename) { + return read_classify_label_file(context); + } + return 0; +} + +static int dnn_classify_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_classify_flush_frame(AVFilterLink *outlink, int64_t pts, int64_t *out_pts) +{ + DnnClassifyContext *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_classify_activate(AVFilterContext *filter_ctx) +{ + AVFilterLink *inlink = filter_ctx->inputs[0]; + AVFilterLink *outlink = filter_ctx->outputs[0]; + DnnClassifyContext *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_classification(&ctx->dnnctx, in, in, ctx->target) != 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_classify_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 av_cold void dnn_classify_uninit(AVFilterContext *context) +{ + DnnClassifyContext *ctx = context->priv; + ff_dnn_uninit(&ctx->dnnctx); + free_classify_labels(ctx); +} + +static const AVFilterPad dnn_classify_inputs[] = { + { + .name = "default", + .type = AVMEDIA_TYPE_VIDEO, + }, + { NULL } +}; + +static const AVFilterPad dnn_classify_outputs[] = { + { + .name = "default", + .type = AVMEDIA_TYPE_VIDEO, + }, + { NULL } +}; + +AVFilter ff_vf_dnn_classify = { + .name = "dnn_classify", + .description = NULL_IF_CONFIG_SMALL("Apply DNN classify filter to the input."), + .priv_size = sizeof(DnnClassifyContext), + .init = dnn_classify_init, + .uninit = dnn_classify_uninit, + .query_formats = dnn_classify_query_formats, + .inputs = dnn_classify_inputs, + .outputs = dnn_classify_outputs, + .priv_class = &dnn_classify_class, + .activate = dnn_classify_activate, +};

[FFmpeg-devel,6/6] lavfi/dnn_classify: add filter dnn_classify for classification based on detection bounding boxes

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