| /* |
| * 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 classification 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" |
| |
| typedef struct DnnClassifyContext { |
| const AVClass *class; |
| DnnContext dnnctx; |
| float confidence; |
| char *labels_filename; |
| char *target; |
| char **labels; |
| int label_count; |
| } DnnClassifyContext; |
| |
| #define OFFSET(x) offsetof(DnnClassifyContext, dnnctx.x) |
| #define OFFSET2(x) offsetof(DnnClassifyContext, x) |
| #define FLAGS AV_OPT_FLAG_FILTERING_PARAM | AV_OPT_FLAG_VIDEO_PARAM |
| static const AVOption dnn_classify_options[] = { |
| { "dnn_backend", "DNN backend", OFFSET(backend_type), AV_OPT_TYPE_INT, { .i64 = DNN_OV }, INT_MIN, INT_MAX, FLAGS, .unit = "backend" }, |
| #if (CONFIG_LIBOPENVINO == 1) |
| { "openvino", "openvino backend flag", 0, AV_OPT_TYPE_CONST, { .i64 = DNN_OV }, 0, 0, FLAGS, .unit = "backend" }, |
| #endif |
| { "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 }, |
| { "target", "which one to be classified", OFFSET2(target), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS }, |
| { NULL } |
| }; |
| |
| AVFILTER_DNN_DEFINE_CLASS(dnn_classify); |
| |
| static int dnn_classify_post_proc(AVFrame *frame, DNNData *output, uint32_t bbox_index, AVFilterContext *filter_ctx) |
| { |
| DnnClassifyContext *ctx = filter_ctx->priv; |
| float conf_threshold = ctx->confidence; |
| AVDetectionBBoxHeader *header; |
| AVDetectionBBox *bbox; |
| float *classifications; |
| uint32_t label_id; |
| float confidence; |
| AVFrameSideData *sd; |
| int output_size = output->dims[3] * output->dims[2] * output->dims[1]; |
| if (output_size <= 0) { |
| return -1; |
| } |
| |
| sd = av_frame_get_side_data(frame, AV_FRAME_DATA_DETECTION_BBOXES); |
| if (!sd) { |
| av_log(filter_ctx, AV_LOG_ERROR, "Cannot get side data in dnn_classify_post_proc\n"); |
| return -1; |
| } |
| header = (AVDetectionBBoxHeader *)sd->data; |
| |
| if (bbox_index == 0) { |
| av_strlcat(header->source, ", ", sizeof(header->source)); |
| av_strlcat(header->source, ctx->dnnctx.model_filename, sizeof(header->source)); |
| } |
| |
| classifications = output->data; |
| label_id = 0; |
| confidence= classifications[0]; |
| for (int i = 1; i < output_size; i++) { |
| if (classifications[i] > confidence) { |
| label_id = i; |
| confidence= classifications[i]; |
| } |
| } |
| |
| if (confidence < conf_threshold) { |
| return 0; |
| } |
| |
| bbox = av_get_detection_bbox(header, bbox_index); |
| bbox->classify_confidences[bbox->classify_count] = av_make_q((int)(confidence * 10000), 10000); |
| |
| if (ctx->labels && label_id < ctx->label_count) { |
| av_strlcpy(bbox->classify_labels[bbox->classify_count], ctx->labels[label_id], sizeof(bbox->classify_labels[bbox->classify_count])); |
| } else { |
| snprintf(bbox->classify_labels[bbox->classify_count], sizeof(bbox->classify_labels[bbox->classify_count]), "%d", label_id); |
| } |
| |
| bbox->classify_count++; |
| |
| return 0; |
| } |
| |
| static void free_classify_labels(DnnClassifyContext *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_classify_label_file(AVFilterContext *context) |
| { |
| int line_len; |
| FILE *file; |
| DnnClassifyContext *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 av_cold int dnn_classify_init(AVFilterContext *context) |
| { |
| DnnClassifyContext *ctx = context->priv; |
| int ret = ff_dnn_init(&ctx->dnnctx, DFT_ANALYTICS_CLASSIFY, context); |
| if (ret < 0) |
| return ret; |
| ff_dnn_set_classify_post_proc(&ctx->dnnctx, dnn_classify_post_proc); |
| |
| if (ctx->labels_filename) { |
| return read_classify_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_classify_flush_frame(AVFilterLink *outlink, int64_t pts, int64_t *out_pts) |
| { |
| DnnClassifyContext *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_classify_activate(AVFilterContext *filter_ctx) |
| { |
| AVFilterLink *inlink = filter_ctx->inputs[0]; |
| AVFilterLink *outlink = filter_ctx->outputs[0]; |
| DnnClassifyContext *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_classification(&ctx->dnnctx, in, NULL, ctx->target) != 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_classify_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_classify_uninit(AVFilterContext *context) |
| { |
| DnnClassifyContext *ctx = context->priv; |
| ff_dnn_uninit(&ctx->dnnctx); |
| free_classify_labels(ctx); |
| } |
| |
| const AVFilter ff_vf_dnn_classify = { |
| .name = "dnn_classify", |
| .description = NULL_IF_CONFIG_SMALL("Apply DNN classify filter to the input."), |
| .priv_size = sizeof(DnnClassifyContext), |
| .preinit = ff_dnn_filter_init_child_class, |
| .init = dnn_classify_init, |
| .uninit = dnn_classify_uninit, |
| FILTER_INPUTS(ff_video_default_filterpad), |
| FILTER_OUTPUTS(ff_video_default_filterpad), |
| FILTER_PIXFMTS_ARRAY(pix_fmts), |
| .priv_class = &dnn_classify_class, |
| .activate = dnn_classify_activate, |
| }; |