ffmpeg/libavfilter/vf_dnn_detect.c
Guo, Yejun aa9ffdaa1e 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>
2021-04-17 17:27:02 +08:00

422 lines
12 KiB
C

/*
* 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,
};