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ffmpeg/libavfilter/vf_dnn_detect.c

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/*
* 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_LIBTENSORFLOW == 1)
{ "tensorflow", "tensorflow backend flag", 0, AV_OPT_TYPE_CONST, { .i64 = 1 }, 0, 0, FLAGS, "backend" },
#endif
#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_ov(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_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 int dnn_detect_post_proc_tf(AVFrame *frame, DNNData *output, AVFilterContext *filter_ctx)
{
DnnDetectContext *ctx = filter_ctx->priv;
int proposal_count;
float conf_threshold = ctx->confidence;
float *conf, *position, *label_id, x0, y0, x1, y1;
int nb_bboxes = 0;
AVFrameSideData *sd;
AVDetectionBBox *bbox;
AVDetectionBBoxHeader *header;
proposal_count = *(float *)(output[0].data);
conf = output[1].data;
position = output[3].data;
label_id = output[2].data;
sd = av_frame_get_side_data(frame, AV_FRAME_DATA_DETECTION_BBOXES);
if (sd) {
av_log(filter_ctx, AV_LOG_ERROR, "already have dnn bounding boxes in side data.\n");
return -1;
}
for (int i = 0; i < proposal_count; ++i) {
if (conf[i] < 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) {
y0 = position[i * 4];
x0 = position[i * 4 + 1];
y1 = position[i * 4 + 2];
x1 = position[i * 4 + 3];
bbox = av_get_detection_bbox(header, i);
if (conf[i] < 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[i] * 10000), 10000);
bbox->classify_count = 0;
if (ctx->labels && label_id[i] < ctx->label_count) {
av_strlcpy(bbox->detect_label, ctx->labels[(int)label_id[i]], sizeof(bbox->detect_label));
} else {
snprintf(bbox->detect_label, sizeof(bbox->detect_label), "%d", (int)label_id[i]);
}
nb_bboxes--;
if (nb_bboxes == 0) {
break;
}
}
return 0;
}
static int dnn_detect_post_proc(AVFrame *frame, DNNData *output, uint32_t nb, AVFilterContext *filter_ctx)
{
DnnDetectContext *ctx = filter_ctx->priv;
DnnContext *dnn_ctx = &ctx->dnnctx;
switch (dnn_ctx->backend_type) {
case DNN_OV:
return dnn_detect_post_proc_ov(frame, output, filter_ctx);
case DNN_TF:
return dnn_detect_post_proc_tf(frame, output, filter_ctx);
default:
avpriv_report_missing_feature(filter_ctx, "Current dnn backend does not support detect filter\n");
return AVERROR(EINVAL);
}
}
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 = avpriv_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 int check_output_nb(DnnDetectContext *ctx, DNNBackendType backend_type, int output_nb)
{
switch(backend_type) {
case DNN_TF:
if (output_nb != 4) {
av_log(ctx, AV_LOG_ERROR, "Only support tensorflow detect model with 4 outputs, \
but get %d instead\n", output_nb);
return AVERROR(EINVAL);
}
return 0;
case DNN_OV:
if (output_nb != 1) {
av_log(ctx, AV_LOG_ERROR, "Dnn detect filter with openvino backend needs 1 output only, \
but get %d instead\n", output_nb);
return AVERROR(EINVAL);
}
return 0;
default:
avpriv_report_missing_feature(ctx, "Dnn detect filter does not support current backend\n");
return AVERROR(EINVAL);
}
return 0;
}
static av_cold int dnn_detect_init(AVFilterContext *context)
{
DnnDetectContext *ctx = context->priv;
DnnContext *dnn_ctx = &ctx->dnnctx;
int ret;
ret = ff_dnn_init(&ctx->dnnctx, DFT_ANALYTICS_DETECT, context);
if (ret < 0)
return ret;
ret = check_output_nb(ctx, dnn_ctx->backend_type, dnn_ctx->nb_outputs);
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 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
};
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 != 0) {
return -1;
}
do {
AVFrame *in_frame = NULL;
AVFrame *out_frame = NULL;
async_state = ff_dnn_get_result(&ctx->dnnctx, &in_frame, &out_frame);
if (async_state == DAST_SUCCESS) {
ret = ff_filter_frame(outlink, in_frame);
if (ret < 0)
return ret;
if (out_pts)
*out_pts = in_frame->pts + pts;
}
av_usleep(5000);
} while (async_state >= DAST_NOT_READY);
return 0;
}
static int dnn_detect_activate(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(&ctx->dnnctx, in, NULL) != 0) {
return AVERROR(EIO);
}
}
} while (ret > 0);
// drain all processed frames
do {
AVFrame *in_frame = NULL;
AVFrame *out_frame = NULL;
async_state = ff_dnn_get_result(&ctx->dnnctx, &in_frame, &out_frame);
if (async_state == DAST_SUCCESS) {
ret = ff_filter_frame(outlink, in_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 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,
},
};
static const AVFilterPad dnn_detect_outputs[] = {
{
.name = "default",
.type = AVMEDIA_TYPE_VIDEO,
},
};
const 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,
FILTER_INPUTS(dnn_detect_inputs),
FILTER_OUTPUTS(dnn_detect_outputs),
FILTER_PIXFMTS_ARRAY(pix_fmts),
.priv_class = &dnn_detect_class,
.activate = dnn_detect_activate,
};
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