input.data = oprd->data;
input.dt = oprd->data_type;
if (do_ioproc) {
- if (native_model->model->pre_proc != NULL) {
- native_model->model->pre_proc(in_frame, &input, native_model->model->filter_ctx);
+ if (native_model->model->frame_pre_proc != NULL) {
+ native_model->model->frame_pre_proc(in_frame, &input, native_model->model->filter_ctx);
} else {
ff_proc_from_frame_to_dnn(in_frame, &input, native_model->model->func_type, ctx);
}
output.dt = oprd->data_type;
if (do_ioproc) {
- if (native_model->model->post_proc != NULL) {
- native_model->model->post_proc(out_frame, &output, native_model->model->filter_ctx);
+ if (native_model->model->frame_post_proc != NULL) {
+ native_model->model->frame_post_proc(out_frame, &output, native_model->model->filter_ctx);
} else {
ff_proc_from_dnn_to_frame(out_frame, &output, ctx);
}
for (int i = 0; i < request->task_count; ++i) {
task = request->tasks[i];
if (task->do_ioproc) {
- if (ov_model->model->pre_proc != NULL) {
- ov_model->model->pre_proc(task->in_frame, &input, ov_model->model->filter_ctx);
+ if (ov_model->model->frame_pre_proc != NULL) {
+ ov_model->model->frame_pre_proc(task->in_frame, &input, ov_model->model->filter_ctx);
} else {
ff_proc_from_frame_to_dnn(task->in_frame, &input, ov_model->model->func_type, ctx);
}
for (int i = 0; i < request->task_count; ++i) {
task = request->tasks[i];
if (task->do_ioproc) {
- if (task->ov_model->model->post_proc != NULL) {
- task->ov_model->model->post_proc(task->out_frame, &output, task->ov_model->model->filter_ctx);
+ if (task->ov_model->model->frame_post_proc != NULL) {
+ task->ov_model->model->frame_post_proc(task->out_frame, &output, task->ov_model->model->filter_ctx);
} else {
ff_proc_from_dnn_to_frame(task->out_frame, &output, ctx);
}
input.data = (float *)TF_TensorData(input_tensor);
if (do_ioproc) {
- if (tf_model->model->pre_proc != NULL) {
- tf_model->model->pre_proc(in_frame, &input, tf_model->model->filter_ctx);
+ if (tf_model->model->frame_pre_proc != NULL) {
+ tf_model->model->frame_pre_proc(in_frame, &input, tf_model->model->filter_ctx);
} else {
ff_proc_from_frame_to_dnn(in_frame, &input, tf_model->model->func_type, ctx);
}
output.dt = TF_TensorType(output_tensors[i]);
if (do_ioproc) {
- if (tf_model->model->post_proc != NULL) {
- tf_model->model->post_proc(out_frame, &output, tf_model->model->filter_ctx);
+ if (tf_model->model->frame_post_proc != NULL) {
+ tf_model->model->frame_post_proc(out_frame, &output, tf_model->model->filter_ctx);
} else {
ff_proc_from_dnn_to_frame(out_frame, &output, ctx);
}
return 0;
}
+int ff_dnn_set_frame_proc(DnnContext *ctx, FramePrePostProc pre_proc, FramePrePostProc post_proc)
+{
+ ctx->model->frame_pre_proc = pre_proc;
+ ctx->model->frame_post_proc = post_proc;
+ return 0;
+}
+
DNNReturnType ff_dnn_get_input(DnnContext *ctx, DNNData *input)
{
return ctx->model->get_input(ctx->model->model, input, ctx->model_inputname);
int ff_dnn_init(DnnContext *ctx, DNNFunctionType func_type, AVFilterContext *filter_ctx);
+int ff_dnn_set_frame_proc(DnnContext *ctx, FramePrePostProc pre_proc, FramePrePostProc post_proc);
DNNReturnType ff_dnn_get_input(DnnContext *ctx, DNNData *input);
DNNReturnType ff_dnn_get_output(DnnContext *ctx, int input_width, int input_height, int *output_width, int *output_height);
DNNReturnType ff_dnn_execute_model(DnnContext *ctx, AVFrame *in_frame, AVFrame *out_frame);
DNNColorOrder order;
} DNNData;
+typedef int (*FramePrePostProc)(AVFrame *frame, DNNData *model, AVFilterContext *filter_ctx);
+
typedef struct DNNModel{
// Stores model that can be different for different backends.
void *model;
const char *output_name, int *output_width, int *output_height);
// set the pre process to transfer data from AVFrame to DNNData
// the default implementation within DNN is used if it is not provided by the filter
- int (*pre_proc)(AVFrame *frame_in, DNNData *model_input, AVFilterContext *filter_ctx);
+ FramePrePostProc frame_pre_proc;
// set the post process to transfer data from DNNData to AVFrame
// the default implementation within DNN is used if it is not provided by the filter
- int (*post_proc)(AVFrame *frame_out, DNNData *model_output, AVFilterContext *filter_ctx);
+ FramePrePostProc frame_post_proc;
} DNNModel;
// Stores pointers to functions for loading, executing, freeing DNN models for one of the backends.