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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 "libavutil/file_open.h"
#include "libavutil/mem.h"
#include "libavutil/opt.h"
#include "filters.h"
#include "dnn_filter_common.h"
#include "internal.h"
#include "video.h"
#include "libavutil/time.h"
#include "libavutil/avstring.h"
#include "libavutil/detection_bbox.h"
#include "libavutil/fifo.h"
typedef enum {
DDMT_SSD,
DDMT_YOLOV1V2,
DDMT_YOLOV3,
DDMT_YOLOV4
} DNNDetectionModelType;
typedef struct DnnDetectContext {
const AVClass *class;
DnnContext dnnctx;
float confidence;
char *labels_filename;
char **labels;
int label_count;
DNNDetectionModelType model_type;
int cell_w;
int cell_h;
int nb_classes;
AVFifo *bboxes_fifo;
int scale_width;
int scale_height;
char *anchors_str;
float *anchors;
int nb_anchor;
} 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 = DNN_OV }, INT_MIN, INT_MAX, FLAGS, .unit = "backend" },
#if (CONFIG_LIBTENSORFLOW == 1)
{ "tensorflow", "tensorflow backend flag", 0, AV_OPT_TYPE_CONST, { .i64 = DNN_TF }, 0, 0, FLAGS, .unit = "backend" },
#endif
#if (CONFIG_LIBOPENVINO == 1)
{ "openvino", "openvino backend flag", 0, AV_OPT_TYPE_CONST, { .i64 = DNN_OV }, 0, 0, FLAGS, .unit = "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 },
{ "model_type", "DNN detection model type", OFFSET2(model_type), AV_OPT_TYPE_INT, { .i64 = DDMT_SSD }, INT_MIN, INT_MAX, FLAGS, .unit = "model_type" },
{ "ssd", "output shape [1, 1, N, 7]", 0, AV_OPT_TYPE_CONST, { .i64 = DDMT_SSD }, 0, 0, FLAGS, .unit = "model_type" },
{ "yolo", "output shape [1, N*Cx*Cy*DetectionBox]", 0, AV_OPT_TYPE_CONST, { .i64 = DDMT_YOLOV1V2 }, 0, 0, FLAGS, .unit = "model_type" },
{ "yolov3", "outputs shape [1, N*D, Cx, Cy]", 0, AV_OPT_TYPE_CONST, { .i64 = DDMT_YOLOV3 }, 0, 0, FLAGS, .unit = "model_type" },
{ "yolov4", "outputs shape [1, N*D, Cx, Cy]", 0, AV_OPT_TYPE_CONST, { .i64 = DDMT_YOLOV4 }, 0, 0, FLAGS, .unit = "model_type" },
{ "cell_w", "cell width", OFFSET2(cell_w), AV_OPT_TYPE_INT, { .i64 = 0 }, 0, INTMAX_MAX, FLAGS },
{ "cell_h", "cell height", OFFSET2(cell_h), AV_OPT_TYPE_INT, { .i64 = 0 }, 0, INTMAX_MAX, FLAGS },
{ "nb_classes", "The number of class", OFFSET2(nb_classes), AV_OPT_TYPE_INT, { .i64 = 0 }, 0, INTMAX_MAX, FLAGS },
{ "anchors", "anchors, splited by '&'", OFFSET2(anchors_str), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS },
{ NULL }
};
AVFILTER_DEFINE_CLASS(dnn_detect);
static inline float sigmoid(float x) {
return 1.f / (1.f + exp(-x));
}
static inline float linear(float x) {
return x;
}
static int dnn_detect_get_label_id(int nb_classes, int cell_size, float *label_data)
{
float max_prob = 0;
int label_id = 0;
for (int i = 0; i < nb_classes; i++) {
if (label_data[i * cell_size] > max_prob) {
max_prob = label_data[i * cell_size];
label_id = i;
}
}
return label_id;
}
static int dnn_detect_parse_anchors(char *anchors_str, float **anchors)
{
char *saveptr = NULL, *token;
float *anchors_buf;
int nb_anchor = 0, i = 0;
while(anchors_str[i] != '\0') {
if(anchors_str[i] == '&')
nb_anchor++;
i++;
}
nb_anchor++;
anchors_buf = av_mallocz(nb_anchor * sizeof(**anchors));
if (!anchors_buf) {
return 0;
}
for (int i = 0; i < nb_anchor; i++) {
token = av_strtok(anchors_str, "&", &saveptr);
if (!token) {
av_freep(&anchors_buf);
return 0;
}
anchors_buf[i] = strtof(token, NULL);
anchors_str = NULL;
}
*anchors = anchors_buf;
return nb_anchor;
}
/* Calculate Intersection Over Union */
static float dnn_detect_IOU(AVDetectionBBox *bbox1, AVDetectionBBox *bbox2)
{
float overlapping_width = FFMIN(bbox1->x + bbox1->w, bbox2->x + bbox2->w) - FFMAX(bbox1->x, bbox2->x);
float overlapping_height = FFMIN(bbox1->y + bbox1->h, bbox2->y + bbox2->h) - FFMAX(bbox1->y, bbox2->y);
float intersection_area =
(overlapping_width < 0 || overlapping_height < 0) ? 0 : overlapping_height * overlapping_width;
float union_area = bbox1->w * bbox1->h + bbox2->w * bbox2->h - intersection_area;
return intersection_area / union_area;
}
static int dnn_detect_parse_yolo_output(AVFrame *frame, DNNData *output, int output_index,
AVFilterContext *filter_ctx)
{
DnnDetectContext *ctx = filter_ctx->priv;
float conf_threshold = ctx->confidence;
int detection_boxes, box_size;
int cell_w = 0, cell_h = 0, scale_w = 0, scale_h = 0;
int nb_classes = ctx->nb_classes;
float *output_data = output[output_index].data;
float *anchors = ctx->anchors;
AVDetectionBBox *bbox;
float (*post_process_raw_data)(float x) = linear;
int is_NHWC = 0;
if (ctx->model_type == DDMT_YOLOV1V2) {
cell_w = ctx->cell_w;
cell_h = ctx->cell_h;
scale_w = cell_w;
scale_h = cell_h;
} else {
if (output[output_index].dims[2] != output[output_index].dims[3] &&
output[output_index].dims[2] == output[output_index].dims[1]) {
is_NHWC = 1;
cell_w = output[output_index].dims[2];
cell_h = output[output_index].dims[1];
} else {
cell_w = output[output_index].dims[3];
cell_h = output[output_index].dims[2];
}
scale_w = ctx->scale_width;
scale_h = ctx->scale_height;
}
box_size = nb_classes + 5;
switch (ctx->model_type) {
case DDMT_YOLOV1V2:
case DDMT_YOLOV3:
post_process_raw_data = linear;
break;
case DDMT_YOLOV4:
post_process_raw_data = sigmoid;
break;
}
if (!cell_h || !cell_w) {
av_log(filter_ctx, AV_LOG_ERROR, "cell_w and cell_h are detected\n");
return AVERROR(EINVAL);
}
if (!nb_classes) {
av_log(filter_ctx, AV_LOG_ERROR, "nb_classes is not set\n");
return AVERROR(EINVAL);
}
if (!anchors) {
av_log(filter_ctx, AV_LOG_ERROR, "anchors is not set\n");
return AVERROR(EINVAL);
}
if (output[output_index].dims[1] * output[output_index].dims[2] *
output[output_index].dims[3] % (box_size * cell_w * cell_h)) {
av_log(filter_ctx, AV_LOG_ERROR, "wrong cell_w, cell_h or nb_classes\n");
return AVERROR(EINVAL);
}
detection_boxes = output[output_index].dims[1] *
output[output_index].dims[2] *
output[output_index].dims[3] / box_size / cell_w / cell_h;
anchors = anchors + (detection_boxes * output_index * 2);
/**
* find all candidate bbox
* yolo output can be reshaped to [B, N*D, Cx, Cy]
* Detection box 'D' has format [`x`, `y`, `h`, `w`, `box_score`, `class_no_1`, ...,]
**/
for (int box_id = 0; box_id < detection_boxes; box_id++) {
for (int cx = 0; cx < cell_w; cx++)
for (int cy = 0; cy < cell_h; cy++) {
float x, y, w, h, conf;
float *detection_boxes_data;
int label_id;
if (is_NHWC) {
detection_boxes_data = output_data +
((cy * cell_w + cx) * detection_boxes + box_id) * box_size;
conf = post_process_raw_data(detection_boxes_data[4]);
} else {
detection_boxes_data = output_data + box_id * box_size * cell_w * cell_h;
conf = post_process_raw_data(
detection_boxes_data[cy * cell_w + cx + 4 * cell_w * cell_h]);
}
if (is_NHWC) {
x = post_process_raw_data(detection_boxes_data[0]);
y = post_process_raw_data(detection_boxes_data[1]);
w = detection_boxes_data[2];
h = detection_boxes_data[3];
label_id = dnn_detect_get_label_id(ctx->nb_classes, 1, detection_boxes_data + 5);
conf = conf * post_process_raw_data(detection_boxes_data[label_id + 5]);
} else {
x = post_process_raw_data(detection_boxes_data[cy * cell_w + cx]);
y = post_process_raw_data(detection_boxes_data[cy * cell_w + cx + cell_w * cell_h]);
w = detection_boxes_data[cy * cell_w + cx + 2 * cell_w * cell_h];
h = detection_boxes_data[cy * cell_w + cx + 3 * cell_w * cell_h];
label_id = dnn_detect_get_label_id(ctx->nb_classes, cell_w * cell_h,
detection_boxes_data + cy * cell_w + cx + 5 * cell_w * cell_h);
conf = conf * post_process_raw_data(
detection_boxes_data[cy * cell_w + cx + (label_id + 5) * cell_w * cell_h]);
}
if (conf < conf_threshold) {
continue;
}
bbox = av_mallocz(sizeof(*bbox));
if (!bbox)
return AVERROR(ENOMEM);
bbox->w = exp(w) * anchors[box_id * 2] * frame->width / scale_w;
bbox->h = exp(h) * anchors[box_id * 2 + 1] * frame->height / scale_h;
bbox->x = (cx + x) / cell_w * frame->width - bbox->w / 2;
bbox->y = (cy + y) / cell_h * frame->height - bbox->h / 2;
bbox->detect_confidence = av_make_q((int)(conf * 10000), 10000);
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);
}
if (av_fifo_write(ctx->bboxes_fifo, &bbox, 1) < 0) {
av_freep(&bbox);
return AVERROR(ENOMEM);
}
bbox = NULL;
}
}
return 0;
}
static int dnn_detect_fill_side_data(AVFrame *frame, AVFilterContext *filter_ctx)
{
DnnDetectContext *ctx = filter_ctx->priv;
float conf_threshold = ctx->confidence;
AVDetectionBBox *bbox;
int nb_bboxes = 0;
AVDetectionBBoxHeader *header;
if (av_fifo_can_read(ctx->bboxes_fifo) == 0) {
av_log(filter_ctx, AV_LOG_VERBOSE, "nothing detected in this frame.\n");
return 0;
}
/* remove overlap bboxes */
for (int i = 0; i < av_fifo_can_read(ctx->bboxes_fifo); i++){
av_fifo_peek(ctx->bboxes_fifo, &bbox, 1, i);
for (int j = 0; j < av_fifo_can_read(ctx->bboxes_fifo); j++) {
AVDetectionBBox *overlap_bbox;
av_fifo_peek(ctx->bboxes_fifo, &overlap_bbox, 1, j);
if (!strcmp(bbox->detect_label, overlap_bbox->detect_label) &&
av_cmp_q(bbox->detect_confidence, overlap_bbox->detect_confidence) < 0 &&
dnn_detect_IOU(bbox, overlap_bbox) >= conf_threshold) {
bbox->classify_count = -1; // bad result
nb_bboxes++;
break;
}
}
}
nb_bboxes = av_fifo_can_read(ctx->bboxes_fifo) - nb_bboxes;
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));
while(av_fifo_can_read(ctx->bboxes_fifo)) {
AVDetectionBBox *candidate_bbox;
av_fifo_read(ctx->bboxes_fifo, &candidate_bbox, 1);
if (nb_bboxes > 0 && candidate_bbox->classify_count != -1) {
bbox = av_get_detection_bbox(header, header->nb_bboxes - nb_bboxes);
memcpy(bbox, candidate_bbox, sizeof(*bbox));
nb_bboxes--;
}
av_freep(&candidate_bbox);
}
return 0;
}
static int dnn_detect_post_proc_yolo(AVFrame *frame, DNNData *output, AVFilterContext *filter_ctx)
{
int ret = 0;
ret = dnn_detect_parse_yolo_output(frame, output, 0, filter_ctx);
if (ret < 0)
return ret;
ret = dnn_detect_fill_side_data(frame, filter_ctx);
if (ret < 0)
return ret;
return 0;
}
static int dnn_detect_post_proc_yolov3(AVFrame *frame, DNNData *output,
AVFilterContext *filter_ctx, int nb_outputs)
{
int ret = 0;
for (int i = 0; i < nb_outputs; i++) {
ret = dnn_detect_parse_yolo_output(frame, output, i, filter_ctx);
if (ret < 0)
return ret;
}
ret = dnn_detect_fill_side_data(frame, filter_ctx);
if (ret < 0)
return ret;
return 0;
}
static int dnn_detect_post_proc_ssd(AVFrame *frame, DNNData *output, int nb_outputs,
AVFilterContext *filter_ctx)
{
DnnDetectContext *ctx = filter_ctx->priv;
float conf_threshold = ctx->confidence;
int proposal_count = 0;
int detect_size = 0;
float *detections = NULL, *labels = NULL;
int nb_bboxes = 0;
AVDetectionBBoxHeader *header;
AVDetectionBBox *bbox;
int scale_w = ctx->scale_width;
int scale_h = ctx->scale_height;
if (nb_outputs == 1 && output->dims[3] == 7) {
proposal_count = output->dims[2];
detect_size = output->dims[3];
detections = output->data;
} else if (nb_outputs == 2 && output[0].dims[3] == 5) {
proposal_count = output[0].dims[2];
detect_size = output[0].dims[3];
detections = output[0].data;
labels = output[1].data;
} else if (nb_outputs == 2 && output[1].dims[3] == 5) {
proposal_count = output[1].dims[2];
detect_size = output[1].dims[3];
detections = output[1].data;
labels = output[0].data;
} else {
av_log(filter_ctx, AV_LOG_ERROR, "Model output shape doesn't match ssd requirement.\n");
return AVERROR(EINVAL);
}
if (proposal_count == 0)
return 0;
for (int i = 0; i < proposal_count; ++i) {
float conf;
if (nb_outputs == 1)
conf = detections[i * detect_size + 2];
else
conf = detections[i * detect_size + 4];
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;
float conf, x0, y0, x1, y1;
if (nb_outputs == 1) {
label_id = (int)detections[i * detect_size + 1];
conf = detections[i * detect_size + 2];
x0 = detections[i * detect_size + 3];
y0 = detections[i * detect_size + 4];
x1 = detections[i * detect_size + 5];
y1 = detections[i * detect_size + 6];
} else {
label_id = (int)labels[i];
x0 = detections[i * detect_size] / scale_w;
y0 = detections[i * detect_size + 1] / scale_h;
x1 = detections[i * detect_size + 2] / scale_w;
y1 = detections[i * detect_size + 3] / scale_h;
conf = detections[i * detect_size + 4];
}
if (conf < conf_threshold) {
continue;
}
bbox = av_get_detection_bbox(header, header->nb_bboxes - nb_bboxes);
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_ov(AVFrame *frame, DNNData *output, int nb_outputs,
AVFilterContext *filter_ctx)
{
AVFrameSideData *sd;
DnnDetectContext *ctx = filter_ctx->priv;
int ret = 0;
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;
}
switch (ctx->model_type) {
case DDMT_SSD:
ret = dnn_detect_post_proc_ssd(frame, output, nb_outputs, filter_ctx);
if (ret < 0)
return ret;
break;
case DDMT_YOLOV1V2:
ret = dnn_detect_post_proc_yolo(frame, output, filter_ctx);
if (ret < 0)
return ret;
break;
case DDMT_YOLOV3:
case DDMT_YOLOV4:
ret = dnn_detect_post_proc_yolov3(frame, output, filter_ctx, nb_outputs);
if (ret < 0)
return ret;
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, nb, 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:
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;
ctx->bboxes_fifo = av_fifo_alloc2(1, sizeof(AVDetectionBBox *), AV_FIFO_FLAG_AUTO_GROW);
if (!ctx->bboxes_fifo)
return AVERROR(ENOMEM);
ff_dnn_set_detect_post_proc(&ctx->dnnctx, dnn_detect_post_proc);
if (ctx->labels_filename) {
return read_detect_label_file(context);
}
if (ctx->anchors_str) {
ret = dnn_detect_parse_anchors(ctx->anchors_str, &ctx->anchors);
if (!ctx->anchors) {
av_log(context, AV_LOG_ERROR, "failed to parse anchors_str\n");
return AVERROR(EINVAL);
}
ctx->nb_anchor = ret;
}
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;
AVDetectionBBox *bbox;
ff_dnn_uninit(&ctx->dnnctx);
while(av_fifo_can_read(ctx->bboxes_fifo)) {
av_fifo_read(ctx->bboxes_fifo, &bbox, 1);
av_freep(&bbox);
}
av_fifo_freep2(&ctx->bboxes_fifo);
av_freep(&ctx->anchors);
free_detect_labels(ctx);
}
static int config_input(AVFilterLink *inlink)
{
AVFilterContext *context = inlink->dst;
DnnDetectContext *ctx = context->priv;
DNNData model_input;
int ret, width_idx, height_idx;
ret = ff_dnn_get_input(&ctx->dnnctx, &model_input);
if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "could not get input from the model\n");
return ret;
}
width_idx = dnn_get_width_idx_by_layout(model_input.layout);
height_idx = dnn_get_height_idx_by_layout(model_input.layout);
ctx->scale_width = model_input.dims[width_idx] == -1 ? inlink->w :
model_input.dims[width_idx];
ctx->scale_height = model_input.dims[height_idx] == -1 ? inlink->h :
model_input.dims[height_idx];
return 0;
}
static const AVFilterPad dnn_detect_inputs[] = {
{
.name = "default",
.type = AVMEDIA_TYPE_VIDEO,
.config_props = config_input,
},
};
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(ff_video_default_filterpad),
FILTER_PIXFMTS_ARRAY(pix_fmts),
.priv_class = &dnn_detect_class,
.activate = dnn_detect_activate,
};
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