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>

configure

@@ -3555,6 +3555,7 @@ 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"

doc/filters.texi

@@ -10127,6 +10127,46 @@ 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
 

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

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;

libavfilter/vf_dnn_detect.c

@@ -0,0 +1,421 @@
+/*
+ * 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 "libavutil/avstring.h"
+#include "libavutil/detection_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);
+
+static int dnn_detect_post_proc(AVFrame *frame, DNNData *output, uint32_t nb, 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_bboxes = 0;
+    AVFrameSideData *sd;
+    AVDetectionBBox *bbox;
+    AVDetectionBBoxHeader *header;
+
+    sd = av_frame_get_side_data(frame, AV_FRAME_DATA_DETECTION_BBOXES);
+    if (sd) {
+        av_log(filter_ctx, AV_LOG_ERROR, "already have bounding boxes in side data.\n");
+        return -1;
+    }
+
+    for (int i = 0; i < proposal_count; ++i) {
+        float conf = detections[i * detect_size + 2];
+        if (conf < 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) {
+        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];
+
+        bbox = av_get_detection_bbox(header, i);
+
+        if (conf < 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 * 10000), 10000);
+        bbox->classify_count = 0;
+
+        if (ctx->labels && label_id < ctx->label_count) {
+            av_strlcpy(bbox->detect_label, ctx->labels[label_id], sizeof(bbox->detect_label));
+        } else {
+            snprintf(bbox->detect_label, sizeof(bbox->detect_label), "%d", label_id);
+        }
+
+        nb_bboxes--;
+        if (nb_bboxes == 0) {
+            break;
+        }
+    }
+
+    return 0;
+}
+
+static void free_detect_labels(DnnDetectContext *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_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 >= 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_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_detect_post_proc(&ctx->dnnctx, 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, in);
+    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, in) != 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,
+};