/*
  * 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;
 

     int                filter_type;

     char              *model_filename;
     DNNBackendType     backend_type;
     DNNModule         *dnn_module;
     DNNModel          *model;

     DNNData            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[] = {

     { "filter_type", "filter type(derain/dehaze)",  OFFSET(filter_type),    AV_OPT_TYPE_INT,    { .i64 = 0 },    0, 1, FLAGS, "type" },
     { "derain",      "derain filter flag",          0,                      AV_OPT_TYPE_CONST,  { .i64 = 0 },    0, 0, FLAGS, "type" },
     { "dehaze",      "dehaze filter flag",          0,                      AV_OPT_TYPE_CONST,  { .i64 = 1 },    0, 0, FLAGS, "type" },

     { "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;
 
     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;
 
     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;

             out->data[0][k] = CLIP((int)((((float *)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,
 };