| Message ID | 1577435660-11904-1-git-send-email-yejun.guo@intel.com |
|---|---|
| State | New |
| Headers | show |
> -----Original Message----- > From: Guo, Yejun > Sent: Friday, December 27, 2019 4:34 PM > To: ffmpeg-devel@ffmpeg.org > Cc: Guo, Yejun <yejun.guo@intel.com> > Subject: [PATCH 2/2] vf_dnn_processing: add support for more formats gray8 > and grayf32 this patch set asks for review, thanks. btw, I'll add the fate test after this patch set is reviewed.
Em sex., 27 de dez. de 2019 às 05:42, Guo, Yejun <yejun.guo@intel.com> escreveu: > > The following is a python script to halve the value of the gray > image. It demos how to setup and execute dnn model with python+tensorflow. > It also generates .pb file which will be used by ffmpeg. > > import tensorflow as tf > import numpy as np > from skimage import color > from skimage import io > in_img = io.imread('input.jpg') > in_img = color.rgb2gray(in_img) > io.imsave('ori_gray.jpg', np.squeeze(in_img)) > in_data = np.expand_dims(in_img, axis=0) > in_data = np.expand_dims(in_data, axis=3) > filter_data = np.array([0.5]).reshape(1,1,1,1).astype(np.float32) > filter = tf.Variable(filter_data) > x = tf.placeholder(tf.float32, shape=[1, None, None, 1], name='dnn_in') > y = tf.nn.conv2d(x, filter, strides=[1, 1, 1, 1], padding='VALID', name='dnn_out') > sess=tf.Session() > sess.run(tf.global_variables_initializer()) > graph_def = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, ['dnn_out']) > tf.train.write_graph(graph_def, '.', 'halve_gray_float.pb', as_text=False) > print("halve_gray_float.pb generated, please use \ > path_to_ffmpeg/tools/python/convert.py to generate halve_gray_float.model\n") > output = sess.run(y, feed_dict={x: in_data}) > output = output * 255.0 > output = output.astype(np.uint8) > io.imsave("out.jpg", np.squeeze(output)) > > To do the same thing with ffmpeg: > - generate halve_gray_float.pb with the above script > - generate halve_gray_float.model with tools/python/convert.py > - try with following commands > ./ffmpeg -i input.jpg -vf format=grayf32,dnn_processing=model=halve_gray_float.model:input=dnn_in:output=dnn_out:dnn_backend=native out.native.png > ./ffmpeg -i input.jpg -vf format=grayf32,dnn_processing=model=halve_gray_float.pb:input=dnn_in:output=dnn_out:dnn_backend=tensorflow out.tf.png > > Signed-off-by: Guo, Yejun <yejun.guo@intel.com> > --- > doc/filters.texi | 6 ++ > libavfilter/vf_dnn_processing.c | 168 ++++++++++++++++++++++++++++++---------- > 2 files changed, 132 insertions(+), 42 deletions(-) > > diff --git a/doc/filters.texi b/doc/filters.texi > index f467378..57a129d 100644 > --- a/doc/filters.texi > +++ b/doc/filters.texi > @@ -9075,6 +9075,12 @@ Halve the red channle of the frame with format rgb24: > ffmpeg -i input.jpg -vf format=rgb24,dnn_processing=model=halve_first_channel.model:input=dnn_in:output=dnn_out:dnn_backend=native out.native.png > @end example > > +@item > +Halve the pixel value of the frame with format gray32f: > +@example > +ffmpeg -i input.jpg -vf format=grayf32,dnn_processing=model=halve_gray_float.model:input=dnn_in:output=dnn_out:dnn_backend=native -y out.native.png > +@end example > + > @end itemize > > @section drawbox > diff --git a/libavfilter/vf_dnn_processing.c b/libavfilter/vf_dnn_processing.c > index 4a6b900..13273f2 100644 > --- a/libavfilter/vf_dnn_processing.c > +++ b/libavfilter/vf_dnn_processing.c > @@ -104,12 +104,20 @@ static int query_formats(AVFilterContext *context) > { > 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_NONE > }; > AVFilterFormats *fmts_list = ff_make_format_list(pix_fmts); > return ff_set_common_formats(context, fmts_list); > } > > +#define LOG_FORMAT_CHANNEL_MISMATCH() \ > + av_log(ctx, AV_LOG_ERROR, \ > + "the frame's format %s does not match " \ > + "the model input channel %d\n", \ > + av_get_pix_fmt_name(fmt), \ > + model_input->channels); > + > static int check_modelinput_inlink(const DNNData *model_input, const AVFilterLink *inlink) > { > AVFilterContext *ctx = inlink->dst; > @@ -131,17 +139,34 @@ static int check_modelinput_inlink(const DNNData *model_input, const AVFilterLin > case AV_PIX_FMT_RGB24: > case AV_PIX_FMT_BGR24: > if (model_input->channels != 3) { > - av_log(ctx, AV_LOG_ERROR, "the frame's input format %s does not match " > - "the model input channels %d\n", > - av_get_pix_fmt_name(fmt), > - model_input->channels); > + LOG_FORMAT_CHANNEL_MISMATCH(); > return AVERROR(EIO); > } > if (model_input->dt != DNN_FLOAT && model_input->dt != DNN_UINT8) { > av_log(ctx, AV_LOG_ERROR, "only support dnn models with input data type as float32 and uint8.\n"); > return AVERROR(EIO); > } > - break; > + return 0; > + case AV_PIX_FMT_GRAY8: > + if (model_input->channels != 1) { > + LOG_FORMAT_CHANNEL_MISMATCH(); > + return AVERROR(EIO); > + } > + if (model_input->dt != DNN_UINT8) { > + av_log(ctx, AV_LOG_ERROR, "only support dnn models with input data type uint8.\n"); > + return AVERROR(EIO); > + } > + return 0; > + case AV_PIX_FMT_GRAYF32: > + if (model_input->channels != 1) { > + LOG_FORMAT_CHANNEL_MISMATCH(); > + return AVERROR(EIO); > + } > + if (model_input->dt != DNN_FLOAT) { > + av_log(ctx, AV_LOG_ERROR, "only support dnn models with input data type float32.\n"); > + return AVERROR(EIO); > + } > + return 0; > default: > av_log(ctx, AV_LOG_ERROR, "%s not supported.\n", av_get_pix_fmt_name(fmt)); > return AVERROR(EIO); > @@ -206,28 +231,58 @@ static int config_output(AVFilterLink *outlink) > > static int copy_from_frame_to_dnn(DNNData *dnn_input, const AVFrame *frame) > { > - // extend this function to support more formats > - av_assert0(frame->format == AV_PIX_FMT_RGB24 || frame->format == AV_PIX_FMT_BGR24); > - > - if (dnn_input->dt == DNN_FLOAT) { > - float *dnn_input_data = dnn_input->data; > - for (int i = 0; i < frame->height; i++) { > - for(int j = 0; j < frame->width * 3; j++) { > - int k = i * frame->linesize[0] + j; > - int t = i * frame->width * 3 + j; > - dnn_input_data[t] = frame->data[0][k] / 255.0f; > + switch (frame->format) { > + case AV_PIX_FMT_RGB24: > + case AV_PIX_FMT_BGR24: > + if (dnn_input->dt == DNN_FLOAT) { > + float *dnn_input_data = dnn_input->data; > + for (int i = 0; i < frame->height; i++) { > + for(int j = 0; j < frame->width * 3; j++) { > + int k = i * frame->linesize[0] + j; > + int t = i * frame->width * 3 + j; > + dnn_input_data[t] = frame->data[0][k] / 255.0f; > + } > + } > + } else { > + uint8_t *dnn_input_data = dnn_input->data; > + av_assert0(dnn_input->dt == DNN_UINT8); > + for (int i = 0; i < frame->height; i++) { > + for(int j = 0; j < frame->width * 3; j++) { > + int k = i * frame->linesize[0] + j; > + int t = i * frame->width * 3 + j; > + dnn_input_data[t] = frame->data[0][k]; > + } > } > } > - } else { > - uint8_t *dnn_input_data = dnn_input->data; > - av_assert0(dnn_input->dt == DNN_UINT8); > - for (int i = 0; i < frame->height; i++) { > - for(int j = 0; j < frame->width * 3; j++) { > - int k = i * frame->linesize[0] + j; > - int t = i * frame->width * 3 + j; > - dnn_input_data[t] = frame->data[0][k]; > + return 0; > + case AV_PIX_FMT_GRAY8: > + { > + uint8_t *dnn_input_data = dnn_input->data; > + av_assert0(dnn_input->dt == DNN_UINT8); > + for (int i = 0; i < frame->height; i++) { > + for(int j = 0; j < frame->width; j++) { > + int k = i * frame->linesize[0] + j; > + int t = i * frame->width + j; > + dnn_input_data[t] = frame->data[0][k]; > + } > } > } > + return 0; > + case AV_PIX_FMT_GRAYF32: > + { > + float *dnn_input_data = dnn_input->data; > + av_assert0(dnn_input->dt == DNN_FLOAT); > + for (int i = 0; i < frame->height; i++) { > + for(int j = 0; j < frame->width; j++) { > + int k = i * frame->linesize[0] + j * sizeof(float); > + int t = i * frame->width + j; > + dnn_input_data[t] = *(float*)(frame->data[0] + k); > + } > + } > + } > + return 0; > + default: > + return AVERROR(EIO); > } > > return 0; > @@ -235,28 +290,58 @@ static int copy_from_frame_to_dnn(DNNData *dnn_input, const AVFrame *frame) > > static int copy_from_dnn_to_frame(AVFrame *frame, const DNNData *dnn_output) > { > - // extend this function to support more formats > - av_assert0(frame->format == AV_PIX_FMT_RGB24 || frame->format == AV_PIX_FMT_BGR24); > - > - if (dnn_output->dt == DNN_FLOAT) { > - float *dnn_output_data = dnn_output->data; > - for (int i = 0; i < frame->height; i++) { > - for(int j = 0; j < frame->width * 3; j++) { > - int k = i * frame->linesize[0] + j; > - int t = i * frame->width * 3 + j; > - frame->data[0][k] = av_clip_uintp2((int)(dnn_output_data[t] * 255.0f), 8); > + switch (frame->format) { > + case AV_PIX_FMT_RGB24: > + case AV_PIX_FMT_BGR24: > + if (dnn_output->dt == DNN_FLOAT) { > + float *dnn_output_data = dnn_output->data; > + for (int i = 0; i < frame->height; i++) { > + for(int j = 0; j < frame->width * 3; j++) { > + int k = i * frame->linesize[0] + j; > + int t = i * frame->width * 3 + j; > + frame->data[0][k] = av_clip_uintp2((int)(dnn_output_data[t] * 255.0f), 8); > + } > + } > + } else { > + uint8_t *dnn_output_data = dnn_output->data; > + av_assert0(dnn_output->dt == DNN_UINT8); > + for (int i = 0; i < frame->height; i++) { > + for(int j = 0; j < frame->width * 3; j++) { > + int k = i * frame->linesize[0] + j; > + int t = i * frame->width * 3 + j; > + frame->data[0][k] = dnn_output_data[t]; > + } > + } > + } > + return 0; > + case AV_PIX_FMT_GRAY8: > + { > + uint8_t *dnn_output_data = dnn_output->data; > + av_assert0(dnn_output->dt == DNN_UINT8); > + for (int i = 0; i < frame->height; i++) { > + for(int j = 0; j < frame->width; j++) { > + int k = i * frame->linesize[0] + j; > + int t = i * frame->width + j; > + frame->data[0][k] = dnn_output_data[t]; > + } > } > } > - } else { > - uint8_t *dnn_output_data = dnn_output->data; > - av_assert0(dnn_output->dt == DNN_UINT8); > - for (int i = 0; i < frame->height; i++) { > - for(int j = 0; j < frame->width * 3; j++) { > - int k = i * frame->linesize[0] + j; > - int t = i * frame->width * 3 + j; > - frame->data[0][k] = dnn_output_data[t]; > + return 0; > + case AV_PIX_FMT_GRAYF32: > + { > + float *dnn_output_data = dnn_output->data; > + av_assert0(dnn_output->dt == DNN_FLOAT); > + for (int i = 0; i < frame->height; i++) { > + for(int j = 0; j < frame->width; j++) { > + int k = i * frame->linesize[0] + j * sizeof(float); > + int t = i * frame->width + j; > + *(float*)(frame->data[0] + k) = dnn_output_data[t]; > + } > } > } > + return 0; > + default: > + return AVERROR(EIO); > } > > return 0; > @@ -278,7 +363,6 @@ static int filter_frame(AVFilterLink *inlink, AVFrame *in) > av_frame_free(&in); > return AVERROR(EIO); > } > - av_assert0(ctx->output.channels == 3); > > out = ff_get_video_buffer(outlink, outlink->w, outlink->h); > if (!out) { > -- > 2.7.4 > LGTM, pushed thanks. > _______________________________________________ > ffmpeg-devel mailing list > ffmpeg-devel@ffmpeg.org > https://ffmpeg.org/mailman/listinfo/ffmpeg-devel > > To unsubscribe, visit link above, or email > ffmpeg-devel-request@ffmpeg.org with subject "unsubscribe".
diff --git a/doc/filters.texi b/doc/filters.texi index f467378..57a129d 100644 --- a/doc/filters.texi +++ b/doc/filters.texi @@ -9075,6 +9075,12 @@ Halve the red channle of the frame with format rgb24: ffmpeg -i input.jpg -vf format=rgb24,dnn_processing=model=halve_first_channel.model:input=dnn_in:output=dnn_out:dnn_backend=native out.native.png @end example +@item +Halve the pixel value of the frame with format gray32f: +@example +ffmpeg -i input.jpg -vf format=grayf32,dnn_processing=model=halve_gray_float.model:input=dnn_in:output=dnn_out:dnn_backend=native -y out.native.png +@end example + @end itemize @section drawbox diff --git a/libavfilter/vf_dnn_processing.c b/libavfilter/vf_dnn_processing.c index 4a6b900..13273f2 100644 --- a/libavfilter/vf_dnn_processing.c +++ b/libavfilter/vf_dnn_processing.c @@ -104,12 +104,20 @@ static int query_formats(AVFilterContext *context) { 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_NONE }; AVFilterFormats *fmts_list = ff_make_format_list(pix_fmts); return ff_set_common_formats(context, fmts_list); } +#define LOG_FORMAT_CHANNEL_MISMATCH() \ + av_log(ctx, AV_LOG_ERROR, \ + "the frame's format %s does not match " \ + "the model input channel %d\n", \ + av_get_pix_fmt_name(fmt), \ + model_input->channels); + static int check_modelinput_inlink(const DNNData *model_input, const AVFilterLink *inlink) { AVFilterContext *ctx = inlink->dst; @@ -131,17 +139,34 @@ static int check_modelinput_inlink(const DNNData *model_input, const AVFilterLin case AV_PIX_FMT_RGB24: case AV_PIX_FMT_BGR24: if (model_input->channels != 3) { - av_log(ctx, AV_LOG_ERROR, "the frame's input format %s does not match " - "the model input channels %d\n", - av_get_pix_fmt_name(fmt), - model_input->channels); + LOG_FORMAT_CHANNEL_MISMATCH(); return AVERROR(EIO); } if (model_input->dt != DNN_FLOAT && model_input->dt != DNN_UINT8) { av_log(ctx, AV_LOG_ERROR, "only support dnn models with input data type as float32 and uint8.\n"); return AVERROR(EIO); } - break; + return 0; + case AV_PIX_FMT_GRAY8: + if (model_input->channels != 1) { + LOG_FORMAT_CHANNEL_MISMATCH(); + return AVERROR(EIO); + } + if (model_input->dt != DNN_UINT8) { + av_log(ctx, AV_LOG_ERROR, "only support dnn models with input data type uint8.\n"); + return AVERROR(EIO); + } + return 0; + case AV_PIX_FMT_GRAYF32: + if (model_input->channels != 1) { + LOG_FORMAT_CHANNEL_MISMATCH(); + return AVERROR(EIO); + } + if (model_input->dt != DNN_FLOAT) { + av_log(ctx, AV_LOG_ERROR, "only support dnn models with input data type float32.\n"); + return AVERROR(EIO); + } + return 0; default: av_log(ctx, AV_LOG_ERROR, "%s not supported.\n", av_get_pix_fmt_name(fmt)); return AVERROR(EIO); @@ -206,28 +231,58 @@ static int config_output(AVFilterLink *outlink) static int copy_from_frame_to_dnn(DNNData *dnn_input, const AVFrame *frame) { - // extend this function to support more formats - av_assert0(frame->format == AV_PIX_FMT_RGB24 || frame->format == AV_PIX_FMT_BGR24); - - if (dnn_input->dt == DNN_FLOAT) { - float *dnn_input_data = dnn_input->data; - for (int i = 0; i < frame->height; i++) { - for(int j = 0; j < frame->width * 3; j++) { - int k = i * frame->linesize[0] + j; - int t = i * frame->width * 3 + j; - dnn_input_data[t] = frame->data[0][k] / 255.0f; + switch (frame->format) { + case AV_PIX_FMT_RGB24: + case AV_PIX_FMT_BGR24: + if (dnn_input->dt == DNN_FLOAT) { + float *dnn_input_data = dnn_input->data; + for (int i = 0; i < frame->height; i++) { + for(int j = 0; j < frame->width * 3; j++) { + int k = i * frame->linesize[0] + j; + int t = i * frame->width * 3 + j; + dnn_input_data[t] = frame->data[0][k] / 255.0f; + } + } + } else { + uint8_t *dnn_input_data = dnn_input->data; + av_assert0(dnn_input->dt == DNN_UINT8); + for (int i = 0; i < frame->height; i++) { + for(int j = 0; j < frame->width * 3; j++) { + int k = i * frame->linesize[0] + j; + int t = i * frame->width * 3 + j; + dnn_input_data[t] = frame->data[0][k]; + } } } - } else { - uint8_t *dnn_input_data = dnn_input->data; - av_assert0(dnn_input->dt == DNN_UINT8); - for (int i = 0; i < frame->height; i++) { - for(int j = 0; j < frame->width * 3; j++) { - int k = i * frame->linesize[0] + j; - int t = i * frame->width * 3 + j; - dnn_input_data[t] = frame->data[0][k]; + return 0; + case AV_PIX_FMT_GRAY8: + { + uint8_t *dnn_input_data = dnn_input->data; + av_assert0(dnn_input->dt == DNN_UINT8); + for (int i = 0; i < frame->height; i++) { + for(int j = 0; j < frame->width; j++) { + int k = i * frame->linesize[0] + j; + int t = i * frame->width + j; + dnn_input_data[t] = frame->data[0][k]; + } } } + return 0; + case AV_PIX_FMT_GRAYF32: + { + float *dnn_input_data = dnn_input->data; + av_assert0(dnn_input->dt == DNN_FLOAT); + for (int i = 0; i < frame->height; i++) { + for(int j = 0; j < frame->width; j++) { + int k = i * frame->linesize[0] + j * sizeof(float); + int t = i * frame->width + j; + dnn_input_data[t] = *(float*)(frame->data[0] + k); + } + } + } + return 0; + default: + return AVERROR(EIO); } return 0; @@ -235,28 +290,58 @@ static int copy_from_frame_to_dnn(DNNData *dnn_input, const AVFrame *frame) static int copy_from_dnn_to_frame(AVFrame *frame, const DNNData *dnn_output) { - // extend this function to support more formats - av_assert0(frame->format == AV_PIX_FMT_RGB24 || frame->format == AV_PIX_FMT_BGR24); - - if (dnn_output->dt == DNN_FLOAT) { - float *dnn_output_data = dnn_output->data; - for (int i = 0; i < frame->height; i++) { - for(int j = 0; j < frame->width * 3; j++) { - int k = i * frame->linesize[0] + j; - int t = i * frame->width * 3 + j; - frame->data[0][k] = av_clip_uintp2((int)(dnn_output_data[t] * 255.0f), 8); + switch (frame->format) { + case AV_PIX_FMT_RGB24: + case AV_PIX_FMT_BGR24: + if (dnn_output->dt == DNN_FLOAT) { + float *dnn_output_data = dnn_output->data; + for (int i = 0; i < frame->height; i++) { + for(int j = 0; j < frame->width * 3; j++) { + int k = i * frame->linesize[0] + j; + int t = i * frame->width * 3 + j; + frame->data[0][k] = av_clip_uintp2((int)(dnn_output_data[t] * 255.0f), 8); + } + } + } else { + uint8_t *dnn_output_data = dnn_output->data; + av_assert0(dnn_output->dt == DNN_UINT8); + for (int i = 0; i < frame->height; i++) { + for(int j = 0; j < frame->width * 3; j++) { + int k = i * frame->linesize[0] + j; + int t = i * frame->width * 3 + j; + frame->data[0][k] = dnn_output_data[t]; + } + } + } + return 0; + case AV_PIX_FMT_GRAY8: + { + uint8_t *dnn_output_data = dnn_output->data; + av_assert0(dnn_output->dt == DNN_UINT8); + for (int i = 0; i < frame->height; i++) { + for(int j = 0; j < frame->width; j++) { + int k = i * frame->linesize[0] + j; + int t = i * frame->width + j; + frame->data[0][k] = dnn_output_data[t]; + } } } - } else { - uint8_t *dnn_output_data = dnn_output->data; - av_assert0(dnn_output->dt == DNN_UINT8); - for (int i = 0; i < frame->height; i++) { - for(int j = 0; j < frame->width * 3; j++) { - int k = i * frame->linesize[0] + j; - int t = i * frame->width * 3 + j; - frame->data[0][k] = dnn_output_data[t]; + return 0; + case AV_PIX_FMT_GRAYF32: + { + float *dnn_output_data = dnn_output->data; + av_assert0(dnn_output->dt == DNN_FLOAT); + for (int i = 0; i < frame->height; i++) { + for(int j = 0; j < frame->width; j++) { + int k = i * frame->linesize[0] + j * sizeof(float); + int t = i * frame->width + j; + *(float*)(frame->data[0] + k) = dnn_output_data[t]; + } } } + return 0; + default: + return AVERROR(EIO); } return 0; @@ -278,7 +363,6 @@ static int filter_frame(AVFilterLink *inlink, AVFrame *in) av_frame_free(&in); return AVERROR(EIO); } - av_assert0(ctx->output.channels == 3); out = ff_get_video_buffer(outlink, outlink->w, outlink->h); if (!out) {
The following is a python script to halve the value of the gray image. It demos how to setup and execute dnn model with python+tensorflow. It also generates .pb file which will be used by ffmpeg. import tensorflow as tf import numpy as np from skimage import color from skimage import io in_img = io.imread('input.jpg') in_img = color.rgb2gray(in_img) io.imsave('ori_gray.jpg', np.squeeze(in_img)) in_data = np.expand_dims(in_img, axis=0) in_data = np.expand_dims(in_data, axis=3) filter_data = np.array([0.5]).reshape(1,1,1,1).astype(np.float32) filter = tf.Variable(filter_data) x = tf.placeholder(tf.float32, shape=[1, None, None, 1], name='dnn_in') y = tf.nn.conv2d(x, filter, strides=[1, 1, 1, 1], padding='VALID', name='dnn_out') sess=tf.Session() sess.run(tf.global_variables_initializer()) graph_def = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, ['dnn_out']) tf.train.write_graph(graph_def, '.', 'halve_gray_float.pb', as_text=False) print("halve_gray_float.pb generated, please use \ path_to_ffmpeg/tools/python/convert.py to generate halve_gray_float.model\n") output = sess.run(y, feed_dict={x: in_data}) output = output * 255.0 output = output.astype(np.uint8) io.imsave("out.jpg", np.squeeze(output)) To do the same thing with ffmpeg: - generate halve_gray_float.pb with the above script - generate halve_gray_float.model with tools/python/convert.py - try with following commands ./ffmpeg -i input.jpg -vf format=grayf32,dnn_processing=model=halve_gray_float.model:input=dnn_in:output=dnn_out:dnn_backend=native out.native.png ./ffmpeg -i input.jpg -vf format=grayf32,dnn_processing=model=halve_gray_float.pb:input=dnn_in:output=dnn_out:dnn_backend=tensorflow out.tf.png Signed-off-by: Guo, Yejun <yejun.guo@intel.com> --- doc/filters.texi | 6 ++ libavfilter/vf_dnn_processing.c | 168 ++++++++++++++++++++++++++++++---------- 2 files changed, 132 insertions(+), 42 deletions(-)