libavfilter: Add derain filter

Remove the rain in the input image/video by applying the derain
methods based on convolutional neural networks. Training scripts
as well as scripts for model generation are provided in the
repository at https://github.com/XueweiMeng/derain_filter.git.

Signed-off-by: Xuewei Meng <xwmeng96@gmail.com>

doc/filters.texi

@@ -8264,6 +8264,40 @@ delogo=x=0:y=0:w=100:h=77:band=10
 
 @end itemize
 
+@section derain
+
+Remove the rain in the input image/video by applying the derain methods based on
+convolutional neural networks. Supported models:
+
+@itemize
+@item
+Recurrent Squeeze-and-Excitation Context Aggregation Net (RESCAN).
+See @url{http://openaccess.thecvf.com/content_ECCV_2018/papers/Xia_Li_Recurrent_Squeeze-and-Excitation_Context_ECCV_2018_paper.pdf}.
+@end itemize
+
+Training scripts as well as scripts for model generation are provided in
+the repository at @url{https://github.com/XueweiMeng/derain_filter.git}.
+
+The filter accepts the following options:
+
+@table @option
+@item dnn_backend
+Specify which DNN backend to use for model loading and execution. This option accepts
+the following values:
+
+@table @samp
+@item native
+Native implementation of DNN loading and execution.
+@end table
+Default value is @samp{native}.
+
+@item model
+Set path to model file specifying network architecture and its parameters.
+Note that different backends use different file formats. TensorFlow backend
+can load files for both formats, while native backend can load files for only
+its format.
+@end table
+
 @section deshake
 
 Attempt to fix small changes in horizontal and/or vertical shift. This

libavfilter/Makefile

@@ -200,6 +200,7 @@ OBJS-$(CONFIG_DCTDNOIZ_FILTER)               += vf_dctdnoiz.o
 OBJS-$(CONFIG_DEBAND_FILTER)                 += vf_deband.o
 OBJS-$(CONFIG_DEBLOCK_FILTER)                += vf_deblock.o
 OBJS-$(CONFIG_DECIMATE_FILTER)               += vf_decimate.o
+OBJS-$(CONFIG_DERAIN_FILTER)                 += vf_derain.o
 OBJS-$(CONFIG_DECONVOLVE_FILTER)             += vf_convolve.o framesync.o
 OBJS-$(CONFIG_DEDOT_FILTER)                  += vf_dedot.o
 OBJS-$(CONFIG_DEFLATE_FILTER)                += vf_neighbor.o

libavfilter/allfilters.c

@@ -196,6 +196,7 @@ extern AVFilter ff_vf_deinterlace_vaapi;
 extern AVFilter ff_vf_dejudder;
 extern AVFilter ff_vf_delogo;
 extern AVFilter ff_vf_denoise_vaapi;
+extern AVFilter ff_vf_derain;
 extern AVFilter ff_vf_deshake;
 extern AVFilter ff_vf_despill;
 extern AVFilter ff_vf_detelecine;

libavfilter/vf_derain.c

@@ -0,0 +1,212 @@
+/*
+ * Copyright (c) 2019 Xuewei Meng
+ *
+ * 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
+ * Filter implementing image derain filter using deep convolutional networks.
+ * http://openaccess.thecvf.com/content_ECCV_2018/html/Xia_Li_Recurrent_Squeeze-and-Excitation_Context_ECCV_2018_paper.html
+ */
+
+#include "libavformat/avio.h"
+#include "libavutil/opt.h"
+#include "avfilter.h"
+#include "dnn_interface.h"
+#include "formats.h"
+#include "internal.h"
+
+typedef struct DRContext {
+    const AVClass *class;
+
+    char              *model_filename;
+    DNNBackendType     backend_type;
+    DNNModule         *dnn_module;
+    DNNModel          *model;
+    DNNInputData       input;
+    DNNData            output;
+} DRContext;
+
+#define CLIP(x, min, max) (x < min ? min : (x > max ? max : x))
+#define OFFSET(x) offsetof(DRContext, x)
+#define FLAGS AV_OPT_FLAG_FILTERING_PARAM | AV_OPT_FLAG_VIDEO_PARAM
+static const AVOption derain_options[] = {
+    { "dnn_backend", "DNN backend",             OFFSET(backend_type),   AV_OPT_TYPE_INT,    { .i64 = 0 },    0, 1, FLAGS, "backend" },
+    { "native",      "native backend flag",     0,                      AV_OPT_TYPE_CONST,  { .i64 = 0 },    0, 0, FLAGS, "backend" },
+#if (CONFIG_LIBTENSORFLOW == 1)
+    { "tensorflow",  "tensorflow backend flag", 0,                      AV_OPT_TYPE_CONST,  { .i64 = 1 },    0, 0, FLAGS, "backend" },
+#endif
+    { "model",       "path to model file",      OFFSET(model_filename), AV_OPT_TYPE_STRING, { .str = NULL }, 0, 0, FLAGS },
+    { NULL }
+};
+
+AVFILTER_DEFINE_CLASS(derain);
+
+static int query_formats(AVFilterContext *ctx)
+{
+    AVFilterFormats *formats;
+    const enum AVPixelFormat pixel_fmts[] = {
+        AV_PIX_FMT_RGB24,
+        AV_PIX_FMT_NONE
+    };
+
+    formats = ff_make_format_list(pixel_fmts);
+
+    return ff_set_common_formats(ctx, formats);
+}
+
+static int config_inputs(AVFilterLink *inlink)
+{
+    AVFilterContext *ctx          = inlink->dst;
+    DRContext *dr_context         = ctx->priv;
+    const char *model_output_name = "y";
+    DNNReturnType result;
+
+    dr_context->input.width    = inlink->w;
+    dr_context->input.height   = inlink->h;
+    dr_context->input.channels = 3;
+
+    result = (dr_context->model->set_input_output)(dr_context->model->model, &dr_context->input, "x", &model_output_name, 1);
+    if (result != DNN_SUCCESS) {
+        av_log(ctx, AV_LOG_ERROR, "could not set input and output for the model\n");
+        return AVERROR(EIO);
+    }
+
+    return 0;
+}
+
+static int filter_frame(AVFilterLink *inlink, AVFrame *in)
+{
+    AVFilterContext *ctx  = inlink->dst;
+    AVFilterLink *outlink = ctx->outputs[0];
+    DRContext *dr_context = ctx->priv;
+    DNNReturnType dnn_result;
+    int pad_size;
+
+    AVFrame *out = ff_get_video_buffer(outlink, outlink->w, outlink->h);
+    if (!out) {
+        av_log(ctx, AV_LOG_ERROR, "could not allocate memory for output frame\n");
+        av_frame_free(&in);
+        return AVERROR(ENOMEM);
+    }
+
+    av_frame_copy_props(out, in);
+
+    for (int i = 0; i < in->height; i++){
+        for(int j = 0; j < in->width * 3; j++){
+            int k = i * in->linesize[0] + j;
+            int t = i * in->width * 3 + j;
+            ((float *)dr_context->input.data)[t] = in->data[0][k] / 255.0;
+        }
+    }
+
+    dnn_result = (dr_context->dnn_module->execute_model)(dr_context->model, &dr_context->output, 1);
+    if (dnn_result != DNN_SUCCESS){
+        av_log(ctx, AV_LOG_ERROR, "failed to execute model\n");
+        return AVERROR(EIO);
+    }
+
+    out->height = dr_context->output.height;
+    out->width  = dr_context->output.width;
+    outlink->h  = dr_context->output.height;
+    outlink->w  = dr_context->output.width;
+    pad_size    = (in->height - out->height) >> 1;
+
+    for (int i = 0; i < out->height; i++){
+        for(int j = 0; j < out->width * 3; j++){
+            int k = i * out->linesize[0] + j;
+            int t = i * out->width * 3 + j;
+
+            int t_in =  (i + pad_size) * in->width * 3 + j + pad_size * 3;
+            out->data[0][k] = CLIP((int)((((float *)dr_context->input.data)[t_in] - dr_context->output.data[t]) * 255), 0, 255);
+        }
+    }
+
+    av_frame_free(&in);
+
+    return ff_filter_frame(outlink, out);
+}
+
+static av_cold int init(AVFilterContext *ctx)
+{
+    DRContext *dr_context = ctx->priv;
+
+    dr_context->input.dt = DNN_FLOAT;
+    dr_context->dnn_module = ff_get_dnn_module(dr_context->backend_type);
+    if (!dr_context->dnn_module) {
+        av_log(ctx, AV_LOG_ERROR, "could not create DNN module for requested backend\n");
+        return AVERROR(ENOMEM);
+    }
+    if (!dr_context->model_filename) {
+        av_log(ctx, AV_LOG_ERROR, "model file for network is not specified\n");
+        return AVERROR(EINVAL);
+    }
+    if (!dr_context->dnn_module->load_model) {
+        av_log(ctx, AV_LOG_ERROR, "load_model for network is not specified\n");
+        return AVERROR(EINVAL);
+    }
+
+    dr_context->model = (dr_context->dnn_module->load_model)(dr_context->model_filename);
+    if (!dr_context->model) {
+        av_log(ctx, AV_LOG_ERROR, "could not load DNN model\n");
+        return AVERROR(EINVAL);
+    }
+
+    return 0;
+}
+
+static av_cold void uninit(AVFilterContext *ctx)
+{
+    DRContext *dr_context = ctx->priv;
+
+    if (dr_context->dnn_module) {
+        (dr_context->dnn_module->free_model)(&dr_context->model);
+        av_freep(&dr_context->dnn_module);
+    }
+}
+
+static const AVFilterPad derain_inputs[] = {
+    {
+        .name         = "default",
+        .type         = AVMEDIA_TYPE_VIDEO,
+        .config_props = config_inputs,
+        .filter_frame = filter_frame,
+    },
+    { NULL }
+};
+
+static const AVFilterPad derain_outputs[] = {
+    {
+        .name = "default",
+        .type = AVMEDIA_TYPE_VIDEO,
+    },
+    { NULL }
+};
+
+AVFilter ff_vf_derain = {
+    .name          = "derain",
+    .description   = NULL_IF_CONFIG_SMALL("Apply derain filter to the input."),
+    .priv_size     = sizeof(DRContext),
+    .init          = init,
+    .uninit        = uninit,
+    .query_formats = query_formats,
+    .inputs        = derain_inputs,
+    .outputs       = derain_outputs,
+    .priv_class    = &derain_class,
+    .flags         = AVFILTER_FLAG_SUPPORT_TIMELINE_GENERIC,
+};