mirror of https://git.ffmpeg.org/ffmpeg.git
861 lines
29 KiB
C
861 lines
29 KiB
C
/*
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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 "libavutil/file_open.h"
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#include "libavutil/opt.h"
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#include "filters.h"
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#include "dnn_filter_common.h"
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#include "internal.h"
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#include "video.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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#include "libavutil/fifo.h"
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typedef enum {
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DDMT_SSD,
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DDMT_YOLOV1V2,
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DDMT_YOLOV3,
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DDMT_YOLOV4
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} DNNDetectionModelType;
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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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DNNDetectionModelType model_type;
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int cell_w;
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int cell_h;
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int nb_classes;
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AVFifo *bboxes_fifo;
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int scale_width;
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int scale_height;
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char *anchors_str;
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float *anchors;
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int nb_anchor;
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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 = DNN_OV }, INT_MIN, INT_MAX, FLAGS, "backend" },
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#if (CONFIG_LIBTENSORFLOW == 1)
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{ "tensorflow", "tensorflow backend flag", 0, AV_OPT_TYPE_CONST, { .i64 = DNN_TF }, 0, 0, FLAGS, "backend" },
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#endif
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#if (CONFIG_LIBOPENVINO == 1)
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{ "openvino", "openvino backend flag", 0, AV_OPT_TYPE_CONST, { .i64 = DNN_OV }, 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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{ "model_type", "DNN detection model type", OFFSET2(model_type), AV_OPT_TYPE_INT, { .i64 = DDMT_SSD }, INT_MIN, INT_MAX, FLAGS, "model_type" },
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{ "ssd", "output shape [1, 1, N, 7]", 0, AV_OPT_TYPE_CONST, { .i64 = DDMT_SSD }, 0, 0, FLAGS, "model_type" },
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{ "yolo", "output shape [1, N*Cx*Cy*DetectionBox]", 0, AV_OPT_TYPE_CONST, { .i64 = DDMT_YOLOV1V2 }, 0, 0, FLAGS, "model_type" },
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{ "yolov3", "outputs shape [1, N*D, Cx, Cy]", 0, AV_OPT_TYPE_CONST, { .i64 = DDMT_YOLOV3 }, 0, 0, FLAGS, "model_type" },
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{ "yolov4", "outputs shape [1, N*D, Cx, Cy]", 0, AV_OPT_TYPE_CONST, { .i64 = DDMT_YOLOV4 }, 0, 0, FLAGS, "model_type" },
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{ "cell_w", "cell width", OFFSET2(cell_w), AV_OPT_TYPE_INT, { .i64 = 0 }, 0, INTMAX_MAX, FLAGS },
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{ "cell_h", "cell height", OFFSET2(cell_h), AV_OPT_TYPE_INT, { .i64 = 0 }, 0, INTMAX_MAX, FLAGS },
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{ "nb_classes", "The number of class", OFFSET2(nb_classes), AV_OPT_TYPE_INT, { .i64 = 0 }, 0, INTMAX_MAX, FLAGS },
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{ "anchors", "anchors, splited by '&'", OFFSET2(anchors_str), 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 inline float sigmoid(float x) {
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return 1.f / (1.f + exp(-x));
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}
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static inline float linear(float x) {
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return x;
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}
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static int dnn_detect_get_label_id(int nb_classes, int cell_size, float *label_data)
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{
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float max_prob = 0;
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int label_id = 0;
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for (int i = 0; i < nb_classes; i++) {
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if (label_data[i * cell_size] > max_prob) {
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max_prob = label_data[i * cell_size];
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label_id = i;
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}
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}
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return label_id;
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}
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static int dnn_detect_parse_anchors(char *anchors_str, float **anchors)
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{
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char *saveptr = NULL, *token;
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float *anchors_buf;
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int nb_anchor = 0, i = 0;
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while(anchors_str[i] != '\0') {
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if(anchors_str[i] == '&')
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nb_anchor++;
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i++;
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}
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nb_anchor++;
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anchors_buf = av_mallocz(nb_anchor * sizeof(**anchors));
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if (!anchors_buf) {
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return 0;
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}
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for (int i = 0; i < nb_anchor; i++) {
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token = av_strtok(anchors_str, "&", &saveptr);
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if (!token) {
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av_freep(&anchors_buf);
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return 0;
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}
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anchors_buf[i] = strtof(token, NULL);
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anchors_str = NULL;
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}
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*anchors = anchors_buf;
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return nb_anchor;
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}
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/* Calculate Intersection Over Union */
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static float dnn_detect_IOU(AVDetectionBBox *bbox1, AVDetectionBBox *bbox2)
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{
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float overlapping_width = FFMIN(bbox1->x + bbox1->w, bbox2->x + bbox2->w) - FFMAX(bbox1->x, bbox2->x);
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float overlapping_height = FFMIN(bbox1->y + bbox1->h, bbox2->y + bbox2->h) - FFMAX(bbox1->y, bbox2->y);
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float intersection_area =
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(overlapping_width < 0 || overlapping_height < 0) ? 0 : overlapping_height * overlapping_width;
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float union_area = bbox1->w * bbox1->h + bbox2->w * bbox2->h - intersection_area;
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return intersection_area / union_area;
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}
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static int dnn_detect_parse_yolo_output(AVFrame *frame, DNNData *output, int output_index,
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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 detection_boxes, box_size;
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int cell_w = 0, cell_h = 0, scale_w = 0, scale_h = 0;
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int nb_classes = ctx->nb_classes;
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float *output_data = output[output_index].data;
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float *anchors = ctx->anchors;
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AVDetectionBBox *bbox;
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float (*post_process_raw_data)(float x) = linear;
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int is_NHWC = 0;
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if (ctx->model_type == DDMT_YOLOV1V2) {
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cell_w = ctx->cell_w;
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cell_h = ctx->cell_h;
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scale_w = cell_w;
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scale_h = cell_h;
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} else {
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if (output[output_index].dims[2] != output[output_index].dims[3] &&
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output[output_index].dims[2] == output[output_index].dims[1]) {
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is_NHWC = 1;
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cell_w = output[output_index].dims[2];
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cell_h = output[output_index].dims[1];
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} else {
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cell_w = output[output_index].dims[3];
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cell_h = output[output_index].dims[2];
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}
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scale_w = ctx->scale_width;
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scale_h = ctx->scale_height;
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}
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box_size = nb_classes + 5;
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switch (ctx->model_type) {
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case DDMT_YOLOV1V2:
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case DDMT_YOLOV3:
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post_process_raw_data = linear;
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break;
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case DDMT_YOLOV4:
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post_process_raw_data = sigmoid;
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break;
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}
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if (!cell_h || !cell_w) {
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av_log(filter_ctx, AV_LOG_ERROR, "cell_w and cell_h are detected\n");
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return AVERROR(EINVAL);
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}
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if (!nb_classes) {
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av_log(filter_ctx, AV_LOG_ERROR, "nb_classes is not set\n");
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return AVERROR(EINVAL);
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}
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if (!anchors) {
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av_log(filter_ctx, AV_LOG_ERROR, "anchors is not set\n");
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return AVERROR(EINVAL);
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}
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if (output[output_index].dims[1] * output[output_index].dims[2] *
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output[output_index].dims[3] % (box_size * cell_w * cell_h)) {
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av_log(filter_ctx, AV_LOG_ERROR, "wrong cell_w, cell_h or nb_classes\n");
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return AVERROR(EINVAL);
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}
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detection_boxes = output[output_index].dims[1] *
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output[output_index].dims[2] *
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output[output_index].dims[3] / box_size / cell_w / cell_h;
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anchors = anchors + (detection_boxes * output_index * 2);
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/**
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* find all candidate bbox
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* yolo output can be reshaped to [B, N*D, Cx, Cy]
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* Detection box 'D' has format [`x`, `y`, `h`, `w`, `box_score`, `class_no_1`, ...,]
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**/
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for (int box_id = 0; box_id < detection_boxes; box_id++) {
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for (int cx = 0; cx < cell_w; cx++)
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for (int cy = 0; cy < cell_h; cy++) {
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float x, y, w, h, conf;
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float *detection_boxes_data;
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int label_id;
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if (is_NHWC) {
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detection_boxes_data = output_data +
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((cy * cell_w + cx) * detection_boxes + box_id) * box_size;
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conf = post_process_raw_data(detection_boxes_data[4]);
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} else {
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detection_boxes_data = output_data + box_id * box_size * cell_w * cell_h;
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conf = post_process_raw_data(
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detection_boxes_data[cy * cell_w + cx + 4 * cell_w * cell_h]);
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}
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if (is_NHWC) {
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x = post_process_raw_data(detection_boxes_data[0]);
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y = post_process_raw_data(detection_boxes_data[1]);
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w = detection_boxes_data[2];
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h = detection_boxes_data[3];
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label_id = dnn_detect_get_label_id(ctx->nb_classes, 1, detection_boxes_data + 5);
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conf = conf * post_process_raw_data(detection_boxes_data[label_id + 5]);
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} else {
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x = post_process_raw_data(detection_boxes_data[cy * cell_w + cx]);
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y = post_process_raw_data(detection_boxes_data[cy * cell_w + cx + cell_w * cell_h]);
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w = detection_boxes_data[cy * cell_w + cx + 2 * cell_w * cell_h];
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h = detection_boxes_data[cy * cell_w + cx + 3 * cell_w * cell_h];
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label_id = dnn_detect_get_label_id(ctx->nb_classes, cell_w * cell_h,
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detection_boxes_data + cy * cell_w + cx + 5 * cell_w * cell_h);
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conf = conf * post_process_raw_data(
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detection_boxes_data[cy * cell_w + cx + (label_id + 5) * cell_w * cell_h]);
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}
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if (conf < conf_threshold) {
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continue;
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}
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bbox = av_mallocz(sizeof(*bbox));
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if (!bbox)
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return AVERROR(ENOMEM);
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bbox->w = exp(w) * anchors[box_id * 2] * frame->width / scale_w;
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bbox->h = exp(h) * anchors[box_id * 2 + 1] * frame->height / scale_h;
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bbox->x = (cx + x) / cell_w * frame->width - bbox->w / 2;
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bbox->y = (cy + y) / cell_h * frame->height - bbox->h / 2;
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bbox->detect_confidence = av_make_q((int)(conf * 10000), 10000);
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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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if (av_fifo_write(ctx->bboxes_fifo, &bbox, 1) < 0) {
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av_freep(&bbox);
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return AVERROR(ENOMEM);
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}
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bbox = NULL;
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}
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}
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return 0;
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}
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static int dnn_detect_fill_side_data(AVFrame *frame, 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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AVDetectionBBox *bbox;
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int nb_bboxes = 0;
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AVDetectionBBoxHeader *header;
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if (av_fifo_can_read(ctx->bboxes_fifo) == 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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/* remove overlap bboxes */
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for (int i = 0; i < av_fifo_can_read(ctx->bboxes_fifo); i++){
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av_fifo_peek(ctx->bboxes_fifo, &bbox, 1, i);
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for (int j = 0; j < av_fifo_can_read(ctx->bboxes_fifo); j++) {
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AVDetectionBBox *overlap_bbox;
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av_fifo_peek(ctx->bboxes_fifo, &overlap_bbox, 1, j);
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if (!strcmp(bbox->detect_label, overlap_bbox->detect_label) &&
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av_cmp_q(bbox->detect_confidence, overlap_bbox->detect_confidence) < 0 &&
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dnn_detect_IOU(bbox, overlap_bbox) >= conf_threshold) {
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bbox->classify_count = -1; // bad result
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nb_bboxes++;
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break;
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}
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}
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}
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nb_bboxes = av_fifo_can_read(ctx->bboxes_fifo) - nb_bboxes;
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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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while(av_fifo_can_read(ctx->bboxes_fifo)) {
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AVDetectionBBox *candidate_bbox;
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av_fifo_read(ctx->bboxes_fifo, &candidate_bbox, 1);
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if (nb_bboxes > 0 && candidate_bbox->classify_count != -1) {
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bbox = av_get_detection_bbox(header, header->nb_bboxes - nb_bboxes);
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memcpy(bbox, candidate_bbox, sizeof(*bbox));
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nb_bboxes--;
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}
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av_freep(&candidate_bbox);
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}
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return 0;
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}
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static int dnn_detect_post_proc_yolo(AVFrame *frame, DNNData *output, AVFilterContext *filter_ctx)
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{
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int ret = 0;
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ret = dnn_detect_parse_yolo_output(frame, output, 0, filter_ctx);
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if (ret < 0)
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return ret;
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ret = dnn_detect_fill_side_data(frame, filter_ctx);
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if (ret < 0)
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return ret;
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return 0;
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}
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static int dnn_detect_post_proc_yolov3(AVFrame *frame, DNNData *output,
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AVFilterContext *filter_ctx, int nb_outputs)
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{
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int ret = 0;
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for (int i = 0; i < nb_outputs; i++) {
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ret = dnn_detect_parse_yolo_output(frame, output, i, filter_ctx);
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if (ret < 0)
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return ret;
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}
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ret = dnn_detect_fill_side_data(frame, filter_ctx);
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if (ret < 0)
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return ret;
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return 0;
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}
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static int dnn_detect_post_proc_ssd(AVFrame *frame, DNNData *output, int nb_outputs,
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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 = 0;
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int detect_size = 0;
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float *detections = NULL, *labels = NULL;
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int nb_bboxes = 0;
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AVDetectionBBoxHeader *header;
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AVDetectionBBox *bbox;
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int scale_w = ctx->scale_width;
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int scale_h = ctx->scale_height;
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if (nb_outputs == 1 && output->dims[3] == 7) {
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proposal_count = output->dims[2];
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detect_size = output->dims[3];
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detections = output->data;
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} else if (nb_outputs == 2 && output[0].dims[3] == 5) {
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proposal_count = output[0].dims[2];
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detect_size = output[0].dims[3];
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detections = output[0].data;
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labels = output[1].data;
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} else if (nb_outputs == 2 && output[1].dims[3] == 5) {
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proposal_count = output[1].dims[2];
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detect_size = output[1].dims[3];
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detections = output[1].data;
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labels = output[0].data;
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} else {
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av_log(filter_ctx, AV_LOG_ERROR, "Model output shape doesn't match ssd requirement.\n");
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return AVERROR(EINVAL);
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}
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if (proposal_count == 0)
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return 0;
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for (int i = 0; i < proposal_count; ++i) {
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float conf;
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if (nb_outputs == 1)
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conf = detections[i * detect_size + 2];
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else
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conf = detections[i * detect_size + 4];
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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) {
|
|
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,
|
|
};
|