lavfi: add filter dnn_detect for object detection
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,showinfo -f null - We'll see the detect result as below: [Parsed_showinfo_1 @ 0x560c21ecbe40] side data - detection bounding boxes: [Parsed_showinfo_1 @ 0x560c21ecbe40] source: face-detection-adas-0001.xml [Parsed_showinfo_1 @ 0x560c21ecbe40] index: 0, region: (1005, 813) -> (1086, 905), label: face, confidence: 10000/10000. [Parsed_showinfo_1 @ 0x560c21ecbe40] 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.guo@intel.com>
This commit is contained in:
Guo, Yejun
parent
e942b4bbaa
commit
aa9ffdaa1e
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@ -3555,6 +3555,7 @@ derain_filter_select="dnn"
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deshake_filter_select="pixelutils"
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deshake_opencl_filter_deps="opencl"
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dilation_opencl_filter_deps="opencl"
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dnn_detect_filter_select="dnn"
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dnn_processing_filter_select="dnn"
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drawtext_filter_deps="libfreetype"
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drawtext_filter_suggest="libfontconfig libfribidi"
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@ -10127,6 +10127,46 @@ ffmpeg -i INPUT -f lavfi -i nullsrc=hd720,geq='r=128+80*(sin(sqrt((X-W/2)*(X-W/2
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@end example
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@end itemize
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@section dnn_detect
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Do object detection with deep neural networks.
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The filter accepts the following options:
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@table @option
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@item dnn_backend
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Specify which DNN backend to use for model loading and execution. This option accepts
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only openvino now, tensorflow backends will be added.
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@item model
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Set path to model file specifying network architecture and its parameters.
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Note that different backends use different file formats.
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@item input
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Set the input name of the dnn network.
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@item output
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Set the output name of the dnn network.
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@item confidence
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Set the confidence threshold (default: 0.5).
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@item labels
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Set path to label file specifying the mapping between label id and name.
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Each label name is written in one line, tailing spaces and empty lines are skipped.
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The first line is the name of label id 0 (usually it is 'background'),
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and the second line is the name of label id 1, etc.
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The label id is considered as name if the label file is not provided.
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@item backend_configs
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Set the configs to be passed into backend
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@item async
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use DNN async execution if set (default: set),
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roll back to sync execution if the backend does not support async.
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@end table
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@anchor{dnn_processing}
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@section dnn_processing
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@ -245,6 +245,7 @@ OBJS-$(CONFIG_DILATION_FILTER) += vf_neighbor.o
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OBJS-$(CONFIG_DILATION_OPENCL_FILTER) += vf_neighbor_opencl.o opencl.o \
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opencl/neighbor.o
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OBJS-$(CONFIG_DISPLACE_FILTER) += vf_displace.o framesync.o
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OBJS-$(CONFIG_DNN_DETECT_FILTER) += vf_dnn_detect.o
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OBJS-$(CONFIG_DNN_PROCESSING_FILTER) += vf_dnn_processing.o
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OBJS-$(CONFIG_DOUBLEWEAVE_FILTER) += vf_weave.o
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OBJS-$(CONFIG_DRAWBOX_FILTER) += vf_drawbox.o
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@ -230,6 +230,7 @@ extern AVFilter ff_vf_detelecine;
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extern AVFilter ff_vf_dilation;
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extern AVFilter ff_vf_dilation_opencl;
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extern AVFilter ff_vf_displace;
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extern AVFilter ff_vf_dnn_detect;
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extern AVFilter ff_vf_dnn_processing;
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extern AVFilter ff_vf_doubleweave;
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extern AVFilter ff_vf_drawbox;
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@ -0,0 +1,421 @@
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/*
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* This file is part of FFmpeg.
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*
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* FFmpeg is free software; you can redistribute it and/or
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* modify it under the terms of the GNU Lesser General Public
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* License as published by the Free Software Foundation; either
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* version 2.1 of the License, or (at your option) any later version.
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*
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* FFmpeg is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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* Lesser General Public License for more details.
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*
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* You should have received a copy of the GNU Lesser General Public
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* License along with FFmpeg; if not, write to the Free Software
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* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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*/
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/**
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* @file
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* implementing an object detecting filter using deep learning networks.
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*/
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#include "libavformat/avio.h"
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#include "libavutil/opt.h"
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#include "libavutil/pixdesc.h"
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#include "libavutil/avassert.h"
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#include "libavutil/imgutils.h"
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#include "filters.h"
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#include "dnn_filter_common.h"
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#include "formats.h"
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#include "internal.h"
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#include "libavutil/time.h"
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#include "libavutil/avstring.h"
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#include "libavutil/detection_bbox.h"
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typedef struct DnnDetectContext {
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const AVClass *class;
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DnnContext dnnctx;
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float confidence;
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char *labels_filename;
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char **labels;
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int label_count;
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} DnnDetectContext;
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#define OFFSET(x) offsetof(DnnDetectContext, dnnctx.x)
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#define OFFSET2(x) offsetof(DnnDetectContext, x)
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#define FLAGS AV_OPT_FLAG_FILTERING_PARAM | AV_OPT_FLAG_VIDEO_PARAM
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static const AVOption dnn_detect_options[] = {
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{ "dnn_backend", "DNN backend", OFFSET(backend_type), AV_OPT_TYPE_INT, { .i64 = 2 }, INT_MIN, INT_MAX, FLAGS, "backend" },
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#if (CONFIG_LIBOPENVINO == 1)
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{ "openvino", "openvino backend flag", 0, AV_OPT_TYPE_CONST, { .i64 = 2 }, 0, 0, FLAGS, "backend" },
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#endif
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DNN_COMMON_OPTIONS
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{ "confidence", "threshold of confidence", OFFSET2(confidence), AV_OPT_TYPE_FLOAT, { .dbl = 0.5 }, 0, 1, FLAGS},
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{ "labels", "path to labels file", OFFSET2(labels_filename), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS },
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{ NULL }
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};
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AVFILTER_DEFINE_CLASS(dnn_detect);
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static int dnn_detect_post_proc(AVFrame *frame, DNNData *output, uint32_t nb, AVFilterContext *filter_ctx)
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{
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DnnDetectContext *ctx = filter_ctx->priv;
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float conf_threshold = ctx->confidence;
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int proposal_count = output->height;
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int detect_size = output->width;
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float *detections = output->data;
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int nb_bboxes = 0;
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AVFrameSideData *sd;
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AVDetectionBBox *bbox;
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AVDetectionBBoxHeader *header;
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sd = av_frame_get_side_data(frame, AV_FRAME_DATA_DETECTION_BBOXES);
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if (sd) {
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av_log(filter_ctx, AV_LOG_ERROR, "already have bounding boxes in side data.\n");
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return -1;
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}
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for (int i = 0; i < proposal_count; ++i) {
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float conf = detections[i * detect_size + 2];
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if (conf < conf_threshold) {
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continue;
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}
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nb_bboxes++;
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}
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if (nb_bboxes == 0) {
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av_log(filter_ctx, AV_LOG_VERBOSE, "nothing detected in this frame.\n");
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return 0;
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}
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header = av_detection_bbox_create_side_data(frame, nb_bboxes);
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if (!header) {
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av_log(filter_ctx, AV_LOG_ERROR, "failed to create side data with %d bounding boxes\n", nb_bboxes);
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return -1;
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}
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av_strlcpy(header->source, ctx->dnnctx.model_filename, sizeof(header->source));
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for (int i = 0; i < proposal_count; ++i) {
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int av_unused image_id = (int)detections[i * detect_size + 0];
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int label_id = (int)detections[i * detect_size + 1];
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float conf = detections[i * detect_size + 2];
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float x0 = detections[i * detect_size + 3];
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float y0 = detections[i * detect_size + 4];
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float x1 = detections[i * detect_size + 5];
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float y1 = detections[i * detect_size + 6];
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bbox = av_get_detection_bbox(header, i);
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if (conf < conf_threshold) {
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continue;
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}
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bbox->x = (int)(x0 * frame->width);
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bbox->w = (int)(x1 * frame->width) - bbox->x;
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bbox->y = (int)(y0 * frame->height);
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bbox->h = (int)(y1 * frame->height) - bbox->y;
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bbox->detect_confidence = av_make_q((int)(conf * 10000), 10000);
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bbox->classify_count = 0;
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if (ctx->labels && label_id < ctx->label_count) {
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av_strlcpy(bbox->detect_label, ctx->labels[label_id], sizeof(bbox->detect_label));
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} else {
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snprintf(bbox->detect_label, sizeof(bbox->detect_label), "%d", label_id);
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}
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nb_bboxes--;
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if (nb_bboxes == 0) {
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break;
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}
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}
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return 0;
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}
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static void free_detect_labels(DnnDetectContext *ctx)
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{
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for (int i = 0; i < ctx->label_count; i++) {
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av_freep(&ctx->labels[i]);
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}
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ctx->label_count = 0;
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av_freep(&ctx->labels);
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}
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static int read_detect_label_file(AVFilterContext *context)
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{
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int line_len;
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FILE *file;
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DnnDetectContext *ctx = context->priv;
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file = av_fopen_utf8(ctx->labels_filename, "r");
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if (!file){
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av_log(context, AV_LOG_ERROR, "failed to open file %s\n", ctx->labels_filename);
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return AVERROR(EINVAL);
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}
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while (!feof(file)) {
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char *label;
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char buf[256];
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if (!fgets(buf, 256, file)) {
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break;
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}
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line_len = strlen(buf);
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while (line_len) {
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int i = line_len - 1;
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if (buf[i] == '\n' || buf[i] == '\r' || buf[i] == ' ') {
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buf[i] = '\0';
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line_len--;
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} else {
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break;
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}
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}
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if (line_len == 0) // empty line
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continue;
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if (line_len >= AV_DETECTION_BBOX_LABEL_NAME_MAX_SIZE) {
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av_log(context, AV_LOG_ERROR, "label %s too long\n", buf);
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fclose(file);
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return AVERROR(EINVAL);
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}
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label = av_strdup(buf);
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if (!label) {
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av_log(context, AV_LOG_ERROR, "failed to allocate memory for label %s\n", buf);
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fclose(file);
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return AVERROR(ENOMEM);
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}
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if (av_dynarray_add_nofree(&ctx->labels, &ctx->label_count, label) < 0) {
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av_log(context, AV_LOG_ERROR, "failed to do av_dynarray_add\n");
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fclose(file);
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av_freep(&label);
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return AVERROR(ENOMEM);
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}
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}
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fclose(file);
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return 0;
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}
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static av_cold int dnn_detect_init(AVFilterContext *context)
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{
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DnnDetectContext *ctx = context->priv;
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int ret = ff_dnn_init(&ctx->dnnctx, DFT_ANALYTICS_DETECT, context);
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if (ret < 0)
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return ret;
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ff_dnn_set_detect_post_proc(&ctx->dnnctx, dnn_detect_post_proc);
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if (ctx->labels_filename) {
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return read_detect_label_file(context);
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}
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return 0;
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}
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static int dnn_detect_query_formats(AVFilterContext *context)
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{
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static const enum AVPixelFormat pix_fmts[] = {
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AV_PIX_FMT_RGB24, AV_PIX_FMT_BGR24,
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AV_PIX_FMT_GRAY8, AV_PIX_FMT_GRAYF32,
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AV_PIX_FMT_YUV420P, AV_PIX_FMT_YUV422P,
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AV_PIX_FMT_YUV444P, AV_PIX_FMT_YUV410P, AV_PIX_FMT_YUV411P,
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AV_PIX_FMT_NV12,
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AV_PIX_FMT_NONE
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};
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AVFilterFormats *fmts_list = ff_make_format_list(pix_fmts);
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return ff_set_common_formats(context, fmts_list);
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}
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static int dnn_detect_filter_frame(AVFilterLink *inlink, AVFrame *in)
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{
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AVFilterContext *context = inlink->dst;
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AVFilterLink *outlink = context->outputs[0];
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DnnDetectContext *ctx = context->priv;
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DNNReturnType dnn_result;
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dnn_result = ff_dnn_execute_model(&ctx->dnnctx, in, in);
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if (dnn_result != DNN_SUCCESS){
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av_log(ctx, AV_LOG_ERROR, "failed to execute model\n");
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av_frame_free(&in);
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return AVERROR(EIO);
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}
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return ff_filter_frame(outlink, in);
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}
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static int dnn_detect_activate_sync(AVFilterContext *filter_ctx)
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{
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AVFilterLink *inlink = filter_ctx->inputs[0];
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AVFilterLink *outlink = filter_ctx->outputs[0];
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AVFrame *in = NULL;
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int64_t pts;
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int ret, status;
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int got_frame = 0;
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FF_FILTER_FORWARD_STATUS_BACK(outlink, inlink);
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do {
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// drain all input frames
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ret = ff_inlink_consume_frame(inlink, &in);
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if (ret < 0)
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return ret;
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if (ret > 0) {
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ret = dnn_detect_filter_frame(inlink, in);
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if (ret < 0)
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return ret;
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got_frame = 1;
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}
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} while (ret > 0);
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// if frame got, schedule to next filter
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if (got_frame)
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return 0;
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if (ff_inlink_acknowledge_status(inlink, &status, &pts)) {
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if (status == AVERROR_EOF) {
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ff_outlink_set_status(outlink, status, pts);
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return ret;
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}
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}
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FF_FILTER_FORWARD_WANTED(outlink, inlink);
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return FFERROR_NOT_READY;
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}
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static int dnn_detect_flush_frame(AVFilterLink *outlink, int64_t pts, int64_t *out_pts)
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{
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DnnDetectContext *ctx = outlink->src->priv;
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int ret;
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DNNAsyncStatusType async_state;
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ret = ff_dnn_flush(&ctx->dnnctx);
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if (ret != DNN_SUCCESS) {
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return -1;
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}
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do {
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AVFrame *in_frame = NULL;
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AVFrame *out_frame = NULL;
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async_state = ff_dnn_get_async_result(&ctx->dnnctx, &in_frame, &out_frame);
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if (out_frame) {
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av_assert0(in_frame == out_frame);
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ret = ff_filter_frame(outlink, out_frame);
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if (ret < 0)
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return ret;
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if (out_pts)
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*out_pts = out_frame->pts + pts;
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}
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av_usleep(5000);
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} while (async_state >= DAST_NOT_READY);
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return 0;
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}
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static int dnn_detect_activate_async(AVFilterContext *filter_ctx)
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{
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AVFilterLink *inlink = filter_ctx->inputs[0];
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AVFilterLink *outlink = filter_ctx->outputs[0];
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DnnDetectContext *ctx = filter_ctx->priv;
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AVFrame *in = NULL;
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int64_t pts;
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int ret, status;
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int got_frame = 0;
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int async_state;
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FF_FILTER_FORWARD_STATUS_BACK(outlink, inlink);
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do {
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// drain all input frames
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ret = ff_inlink_consume_frame(inlink, &in);
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if (ret < 0)
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return ret;
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if (ret > 0) {
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if (ff_dnn_execute_model_async(&ctx->dnnctx, in, in) != DNN_SUCCESS) {
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return AVERROR(EIO);
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}
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}
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} while (ret > 0);
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// drain all processed frames
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do {
|
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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,
|
||||
};
|
||||
Loading…
Reference in New Issue