libavfilter: Code style fixes for pointers in DNN module and sr filter.
authorSergey Lavrushkin <dualfal@gmail.com>
Fri, 27 Jul 2018 16:34:02 +0000 (19:34 +0300)
committerPedro Arthur <bygrandao@gmail.com>
Tue, 7 Aug 2018 14:58:34 +0000 (11:58 -0300)
Signed-off-by: Pedro Arthur <bygrandao@gmail.com>
libavfilter/dnn_backend_native.c
libavfilter/dnn_backend_native.h
libavfilter/dnn_backend_tf.c
libavfilter/dnn_backend_tf.h
libavfilter/dnn_espcn.h
libavfilter/dnn_interface.c
libavfilter/dnn_interface.h
libavfilter/dnn_srcnn.h
libavfilter/vf_sr.c

index 3e6b862..baefea7 100644 (file)
@@ -34,15 +34,15 @@ typedef enum {RELU, TANH, SIGMOID} ActivationFunc;
 
 typedef struct Layer{
     LayerType type;
-    floatoutput;
-    voidparams;
+    float *output;
+    void *params;
 } Layer;
 
 typedef struct ConvolutionalParams{
     int32_t input_num, output_num, kernel_size;
     ActivationFunc activation;
-    floatkernel;
-    floatbiases;
+    float *kernel;
+    float *biases;
 } ConvolutionalParams;
 
 typedef struct InputParams{
@@ -55,16 +55,16 @@ typedef struct DepthToSpaceParams{
 
 // Represents simple feed-forward convolutional network.
 typedef struct ConvolutionalNetwork{
-    Layerlayers;
+    Layer *layers;
     int32_t layers_num;
 } ConvolutionalNetwork;
 
-static DNNReturnType set_input_output_native(void* model, DNNData* input, DNNData* output)
+static DNNReturnType set_input_output_native(void *model, DNNData *input, DNNData *output)
 {
-    ConvolutionalNetwork* network = (ConvolutionalNetwork*)model;
-    InputParamsinput_params;
-    ConvolutionalParamsconv_params;
-    DepthToSpaceParamsdepth_to_space_params;
+    ConvolutionalNetwork *network = (ConvolutionalNetwork *)model;
+    InputParams *input_params;
+    ConvolutionalParams *conv_params;
+    DepthToSpaceParams *depth_to_space_params;
     int cur_width, cur_height, cur_channels;
     int32_t layer;
 
@@ -72,7 +72,7 @@ static DNNReturnType set_input_output_native(void* model, DNNData* input, DNNDat
         return DNN_ERROR;
     }
     else{
-        input_params = (InputParams*)network->layers[0].params;
+        input_params = (InputParams *)network->layers[0].params;
         input_params->width = cur_width = input->width;
         input_params->height = cur_height = input->height;
         input_params->channels = cur_channels = input->channels;
@@ -88,14 +88,14 @@ static DNNReturnType set_input_output_native(void* model, DNNData* input, DNNDat
     for (layer = 1; layer < network->layers_num; ++layer){
         switch (network->layers[layer].type){
         case CONV:
-            conv_params = (ConvolutionalParams*)network->layers[layer].params;
+            conv_params = (ConvolutionalParams *)network->layers[layer].params;
             if (conv_params->input_num != cur_channels){
                 return DNN_ERROR;
             }
             cur_channels = conv_params->output_num;
             break;
         case DEPTH_TO_SPACE:
-            depth_to_space_params = (DepthToSpaceParams*)network->layers[layer].params;
+            depth_to_space_params = (DepthToSpaceParams *)network->layers[layer].params;
             if (cur_channels % (depth_to_space_params->block_size * depth_to_space_params->block_size) != 0){
                 return DNN_ERROR;
             }
@@ -127,16 +127,16 @@ static DNNReturnType set_input_output_native(void* model, DNNData* input, DNNDat
 // layers_num,layer_type,layer_parameterss,layer_type,layer_parameters...
 // For CONV layer: activation_function, input_num, output_num, kernel_size, kernel, biases
 // For DEPTH_TO_SPACE layer: block_size
-DNNModel* ff_dnn_load_model_native(const char* model_filename)
+DNNModel *ff_dnn_load_model_native(const char *model_filename)
 {
-    DNNModelmodel = NULL;
-    ConvolutionalNetworknetwork = NULL;
-    AVIOContextmodel_file_context;
+    DNNModel *model = NULL;
+    ConvolutionalNetwork *network = NULL;
+    AVIOContext *model_file_context;
     int file_size, dnn_size, kernel_size, i;
     int32_t layer;
     LayerType layer_type;
-    ConvolutionalParamsconv_params;
-    DepthToSpaceParamsdepth_to_space_params;
+    ConvolutionalParams *conv_params;
+    DepthToSpaceParams *depth_to_space_params;
 
     model = av_malloc(sizeof(DNNModel));
     if (!model){
@@ -155,7 +155,7 @@ DNNModel* ff_dnn_load_model_native(const char* model_filename)
         av_freep(&model);
         return NULL;
     }
-    model->model = (void*)network;
+    model->model = (void *)network;
 
     network->layers_num = 1 + (int32_t)avio_rl32(model_file_context);
     dnn_size = 4;
@@ -251,10 +251,10 @@ DNNModel* ff_dnn_load_model_native(const char* model_filename)
     return model;
 }
 
-static int set_up_conv_layer(Layer* layer, const float* kernel, const float* biases, ActivationFunc activation,
+static int set_up_conv_layer(Layer *layer, const float *kernel, const float *biases, ActivationFunc activation,
                              int32_t input_num, int32_t output_num, int32_t size)
 {
-    ConvolutionalParamsconv_params;
+    ConvolutionalParams *conv_params;
     int kernel_size;
 
     conv_params = av_malloc(sizeof(ConvolutionalParams));
@@ -282,11 +282,11 @@ static int set_up_conv_layer(Layer* layer, const float* kernel, const float* bia
     return DNN_SUCCESS;
 }
 
-DNNModelff_dnn_load_default_model_native(DNNDefaultModel model_type)
+DNNModel *ff_dnn_load_default_model_native(DNNDefaultModel model_type)
 {
-    DNNModelmodel = NULL;
-    ConvolutionalNetworknetwork = NULL;
-    DepthToSpaceParamsdepth_to_space_params;
+    DNNModel *model = NULL;
+    ConvolutionalNetwork *network = NULL;
+    DepthToSpaceParams *depth_to_space_params;
     int32_t layer;
 
     model = av_malloc(sizeof(DNNModel));
@@ -299,7 +299,7 @@ DNNModel* ff_dnn_load_default_model_native(DNNDefaultModel model_type)
         av_freep(&model);
         return NULL;
     }
-    model->model = (void*)network;
+    model->model = (void *)network;
 
     switch (model_type){
     case DNN_SRCNN:
@@ -365,7 +365,7 @@ DNNModel* ff_dnn_load_default_model_native(DNNDefaultModel model_type)
 
 #define CLAMP_TO_EDGE(x, w) ((x) < 0 ? 0 : ((x) >= (w) ? (w - 1) : (x)))
 
-static void convolve(const float* input, float* output, const ConvolutionalParams* conv_params, int width, int height)
+static void convolve(const float *input, float *output, const ConvolutionalParams *conv_params, int width, int height)
 {
     int y, x, n_filter, ch, kernel_y, kernel_x;
     int radius = conv_params->kernel_size >> 1;
@@ -403,7 +403,7 @@ static void convolve(const float* input, float* output, const ConvolutionalParam
     }
 }
 
-static void depth_to_space(const float* input, float* output, int block_size, int width, int height, int channels)
+static void depth_to_space(const float *input, float *output, int block_size, int width, int height, int channels)
 {
     int y, x, by, bx, ch;
     int new_channels = channels / (block_size * block_size);
@@ -426,20 +426,20 @@ static void depth_to_space(const float* input, float* output, int block_size, in
     }
 }
 
-DNNReturnType ff_dnn_execute_model_native(const DNNModelmodel)
+DNNReturnType ff_dnn_execute_model_native(const DNNModel *model)
 {
-    ConvolutionalNetwork* network = (ConvolutionalNetwork*)model->model;
+    ConvolutionalNetwork *network = (ConvolutionalNetwork *)model->model;
     int cur_width, cur_height, cur_channels;
     int32_t layer;
-    InputParamsinput_params;
-    ConvolutionalParamsconv_params;
-    DepthToSpaceParamsdepth_to_space_params;
+    InputParams *input_params;
+    ConvolutionalParams *conv_params;
+    DepthToSpaceParams *depth_to_space_params;
 
     if (network->layers_num <= 0 || network->layers[0].type != INPUT || !network->layers[0].output){
         return DNN_ERROR;
     }
     else{
-        input_params = (InputParams*)network->layers[0].params;
+        input_params = (InputParams *)network->layers[0].params;
         cur_width = input_params->width;
         cur_height = input_params->height;
         cur_channels = input_params->channels;
@@ -451,12 +451,12 @@ DNNReturnType ff_dnn_execute_model_native(const DNNModel* model)
         }
         switch (network->layers[layer].type){
         case CONV:
-            conv_params = (ConvolutionalParams*)network->layers[layer].params;
+            conv_params = (ConvolutionalParams *)network->layers[layer].params;
             convolve(network->layers[layer - 1].output, network->layers[layer].output, conv_params, cur_width, cur_height);
             cur_channels = conv_params->output_num;
             break;
         case DEPTH_TO_SPACE:
-            depth_to_space_params = (DepthToSpaceParams*)network->layers[layer].params;
+            depth_to_space_params = (DepthToSpaceParams *)network->layers[layer].params;
             depth_to_space(network->layers[layer - 1].output, network->layers[layer].output,
                            depth_to_space_params->block_size, cur_width, cur_height, cur_channels);
             cur_height *= depth_to_space_params->block_size;
@@ -471,19 +471,19 @@ DNNReturnType ff_dnn_execute_model_native(const DNNModel* model)
     return DNN_SUCCESS;
 }
 
-void ff_dnn_free_model_native(DNNModel** model)
+void ff_dnn_free_model_native(DNNModel **model)
 {
-    ConvolutionalNetworknetwork;
-    ConvolutionalParamsconv_params;
+    ConvolutionalNetwork *network;
+    ConvolutionalParams *conv_params;
     int32_t layer;
 
     if (*model)
     {
-        network = (ConvolutionalNetwork*)(*model)->model;
+        network = (ConvolutionalNetwork *)(*model)->model;
         for (layer = 0; layer < network->layers_num; ++layer){
             av_freep(&network->layers[layer].output);
             if (network->layers[layer].type == CONV){
-                conv_params = (ConvolutionalParams*)network->layers[layer].params;
+                conv_params = (ConvolutionalParams *)network->layers[layer].params;
                 av_freep(&conv_params->kernel);
                 av_freep(&conv_params->biases);
             }
index 599c130..adbb708 100644 (file)
 
 #include "dnn_interface.h"
 
-DNNModel* ff_dnn_load_model_native(const char* model_filename);
+DNNModel *ff_dnn_load_model_native(const char *model_filename);
 
-DNNModelff_dnn_load_default_model_native(DNNDefaultModel model_type);
+DNNModel *ff_dnn_load_default_model_native(DNNDefaultModel model_type);
 
-DNNReturnType ff_dnn_execute_model_native(const DNNModelmodel);
+DNNReturnType ff_dnn_execute_model_native(const DNNModel *model);
 
-void ff_dnn_free_model_native(DNNModel** model);
+void ff_dnn_free_model_native(DNNModel **model);
 
 #endif
index 51608c7..6528a2a 100644 (file)
 #include <tensorflow/c/c_api.h>
 
 typedef struct TFModel{
-    TF_Graphgraph;
-    TF_Sessionsession;
-    TF_Statusstatus;
+    TF_Graph *graph;
+    TF_Session *session;
+    TF_Status *status;
     TF_Output input, output;
-    TF_Tensorinput_tensor;
-    DNNDataoutput_data;
+    TF_Tensor *input_tensor;
+    DNNData *output_data;
 } TFModel;
 
-static void free_buffer(voiddata, size_t length)
+static void free_buffer(void *data, size_t length)
 {
     av_freep(&data);
 }
 
-static TF_Buffer* read_graph(const char* model_filename)
+static TF_Buffer *read_graph(const char *model_filename)
 {
-    TF_Buffergraph_buf;
-    unsigned chargraph_data = NULL;
-    AVIOContextmodel_file_context;
+    TF_Buffer *graph_buf;
+    unsigned char *graph_data = NULL;
+    AVIOContext *model_file_context;
     long size, bytes_read;
 
     if (avio_open(&model_file_context, model_filename, AVIO_FLAG_READ) < 0){
@@ -70,20 +70,20 @@ static TF_Buffer* read_graph(const char* model_filename)
     }
 
     graph_buf = TF_NewBuffer();
-    graph_buf->data = (void*)graph_data;
+    graph_buf->data = (void *)graph_data;
     graph_buf->length = size;
     graph_buf->data_deallocator = free_buffer;
 
     return graph_buf;
 }
 
-static DNNReturnType set_input_output_tf(void* model, DNNData* input, DNNData* output)
+static DNNReturnType set_input_output_tf(void *model, DNNData *input, DNNData *output)
 {
-    TFModel* tf_model = (TFModel*)model;
+    TFModel *tf_model = (TFModel *)model;
     int64_t input_dims[] = {1, input->height, input->width, input->channels};
-    TF_SessionOptionssess_opts;
-    const TF_Operationinit_op = TF_GraphOperationByName(tf_model->graph, "init");
-    TF_Tensoroutput_tensor;
+    TF_SessionOptions *sess_opts;
+    const TF_Operation *init_op = TF_GraphOperationByName(tf_model->graph, "init");
+    TF_Tensor *output_tensor;
 
     // Input operation should be named 'x'
     tf_model->input.oper = TF_GraphOperationByName(tf_model->graph, "x");
@@ -99,7 +99,7 @@ static DNNReturnType set_input_output_tf(void* model, DNNData* input, DNNData* o
     if (!tf_model->input_tensor){
         return DNN_ERROR;
     }
-    input->data = (float*)TF_TensorData(tf_model->input_tensor);
+    input->data = (float *)TF_TensorData(tf_model->input_tensor);
 
     // Output operation should be named 'y'
     tf_model->output.oper = TF_GraphOperationByName(tf_model->graph, "y");
@@ -156,12 +156,12 @@ static DNNReturnType set_input_output_tf(void* model, DNNData* input, DNNData* o
     return DNN_SUCCESS;
 }
 
-DNNModel* ff_dnn_load_model_tf(const char* model_filename)
+DNNModel *ff_dnn_load_model_tf(const char *model_filename)
 {
-    DNNModelmodel = NULL;
-    TFModeltf_model = NULL;
-    TF_Buffergraph_def;
-    TF_ImportGraphDefOptionsgraph_opts;
+    DNNModel *model = NULL;
+    TFModel *tf_model = NULL;
+    TF_Buffer *graph_def;
+    TF_ImportGraphDefOptions *graph_opts;
 
     model = av_malloc(sizeof(DNNModel));
     if (!model){
@@ -197,25 +197,25 @@ DNNModel* ff_dnn_load_model_tf(const char* model_filename)
         return NULL;
     }
 
-    model->model = (void*)tf_model;
+    model->model = (void *)tf_model;
     model->set_input_output = &set_input_output_tf;
 
     return model;
 }
 
-static TF_Operation* add_pad_op(TFModel* tf_model, TF_Operation* input_op, int32_t pad)
+static TF_Operation *add_pad_op(TFModel *tf_model, TF_Operation *input_op, int32_t pad)
 {
-    TF_OperationDescriptionop_desc;
-    TF_Operationop;
-    TF_Tensortensor;
+    TF_OperationDescription *op_desc;
+    TF_Operation *op;
+    TF_Tensor *tensor;
     TF_Output input;
-    int32_tpads;
+    int32_t *pads;
     int64_t pads_shape[] = {4, 2};
 
     op_desc = TF_NewOperation(tf_model->graph, "Const", "pads");
     TF_SetAttrType(op_desc, "dtype", TF_INT32);
     tensor = TF_AllocateTensor(TF_INT32, pads_shape, 2, 4 * 2 * sizeof(int32_t));
-    pads = (int32_t*)TF_TensorData(tensor);
+    pads = (int32_t *)TF_TensorData(tensor);
     pads[0] = 0;   pads[1] = 0;
     pads[2] = pad; pads[3] = pad;
     pads[4] = pad; pads[5] = pad;
@@ -246,11 +246,11 @@ static TF_Operation* add_pad_op(TFModel* tf_model, TF_Operation* input_op, int32
     return op;
 }
 
-static TF_Operation* add_const_op(TFModel* tf_model, const float* values, const int64_t* dims, int dims_len, const char* name)
+static TF_Operation *add_const_op(TFModel *tf_model, const float *values, const int64_t *dims, int dims_len, const char *name)
 {
     int dim;
-    TF_OperationDescriptionop_desc;
-    TF_Tensortensor;
+    TF_OperationDescription *op_desc;
+    TF_Tensor *tensor;
     size_t len;
 
     op_desc = TF_NewOperation(tf_model->graph, "Const", name);
@@ -269,18 +269,18 @@ static TF_Operation* add_const_op(TFModel* tf_model, const float* values, const
     return TF_FinishOperation(op_desc, tf_model->status);
 }
 
-static TF_Operation* add_conv_layers(TFModel* tf_model, const float** consts, const int64_t** consts_dims,
-                                     const int* consts_dims_len, const char** activations,
-                                     TF_Operationinput_op, int layers_num)
+static TF_Operation* add_conv_layers(TFModel *tf_model, const float **consts, const int64_t **consts_dims,
+                                     const int *consts_dims_len, const char **activations,
+                                     TF_Operation *input_op, int layers_num)
 {
     int i;
-    TF_OperationDescriptionop_desc;
-    TF_Operationop;
-    TF_Operationtranspose_op;
+    TF_OperationDescription *op_desc;
+    TF_Operation *op;
+    TF_Operation *transpose_op;
     TF_Output input;
     int64_t strides[] = {1, 1, 1, 1};
-    int32_ttranspose_perm;
-    TF_Tensortensor;
+    int32_t *transpose_perm;
+    TF_Tensor *tensor;
     int64_t transpose_perm_shape[] = {4};
     #define NAME_BUFF_SIZE 256
     char name_buffer[NAME_BUFF_SIZE];
@@ -288,7 +288,7 @@ static TF_Operation* add_conv_layers(TFModel* tf_model, const float** consts, co
     op_desc = TF_NewOperation(tf_model->graph, "Const", "transpose_perm");
     TF_SetAttrType(op_desc, "dtype", TF_INT32);
     tensor = TF_AllocateTensor(TF_INT32, transpose_perm_shape, 1, 4 * sizeof(int32_t));
-    transpose_perm = (int32_t*)TF_TensorData(tensor);
+    transpose_perm = (int32_t *)TF_TensorData(tensor);
     transpose_perm[0] = 1;
     transpose_perm[1] = 2;
     transpose_perm[2] = 3;
@@ -369,13 +369,13 @@ static TF_Operation* add_conv_layers(TFModel* tf_model, const float** consts, co
     return input_op;
 }
 
-DNNModelff_dnn_load_default_model_tf(DNNDefaultModel model_type)
+DNNModel *ff_dnn_load_default_model_tf(DNNDefaultModel model_type)
 {
-    DNNModelmodel = NULL;
-    TFModeltf_model = NULL;
-    TF_OperationDescriptionop_desc;
-    TF_Operationop;
-    TF_Operationconst_ops_buffer[6];
+    DNNModel *model = NULL;
+    TFModel *tf_model = NULL;
+    TF_OperationDescription *op_desc;
+    TF_Operation *op;
+    TF_Operation *const_ops_buffer[6];
     TF_Output input;
     int64_t input_shape[] = {1, -1, -1, 1};
 
@@ -461,16 +461,16 @@ DNNModel* ff_dnn_load_default_model_tf(DNNDefaultModel model_type)
         CLEANUP_ON_ERROR(tf_model, model);
     }
 
-    model->model = (void*)tf_model;
+    model->model = (void *)tf_model;
     model->set_input_output = &set_input_output_tf;
 
     return model;
 }
 
-DNNReturnType ff_dnn_execute_model_tf(const DNNModelmodel)
+DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model)
 {
-    TFModel* tf_model = (TFModel*)model->model;
-    TF_Tensoroutput_tensor;
+    TFModel *tf_model = (TFModel *)model->model;
+    TF_Tensor *output_tensor;
 
     TF_SessionRun(tf_model->session, NULL,
                   &tf_model->input, &tf_model->input_tensor, 1,
@@ -490,12 +490,12 @@ DNNReturnType ff_dnn_execute_model_tf(const DNNModel* model)
     }
 }
 
-void ff_dnn_free_model_tf(DNNModel** model)
+void ff_dnn_free_model_tf(DNNModel **model)
 {
-    TFModeltf_model;
+    TFModel *tf_model;
 
     if (*model){
-        tf_model = (TFModel*)(*model)->model;
+        tf_model = (TFModel *)(*model)->model;
         if (tf_model->graph){
             TF_DeleteGraph(tf_model->graph);
         }
index 08e4a56..357a82d 100644 (file)
 
 #include "dnn_interface.h"
 
-DNNModel* ff_dnn_load_model_tf(const char* model_filename);
+DNNModel *ff_dnn_load_model_tf(const char *model_filename);
 
-DNNModelff_dnn_load_default_model_tf(DNNDefaultModel model_type);
+DNNModel *ff_dnn_load_default_model_tf(DNNDefaultModel model_type);
 
-DNNReturnType ff_dnn_execute_model_tf(const DNNModelmodel);
+DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model);
 
-void ff_dnn_free_model_tf(DNNModel** model);
+void ff_dnn_free_model_tf(DNNModel **model);
 
 #endif
index 315ecf0..a0dd61c 100644 (file)
@@ -5398,7 +5398,7 @@ static const long int espcn_conv3_bias_dims[] = {
     4
 };
 
-static const floatespcn_consts[] = {
+static const float *espcn_consts[] = {
     espcn_conv1_kernel,
     espcn_conv1_bias,
     espcn_conv2_kernel,
@@ -5407,7 +5407,7 @@ static const float* espcn_consts[] = {
     espcn_conv3_bias
 };
 
-static const long intespcn_consts_dims[] = {
+static const long int *espcn_consts_dims[] = {
     espcn_conv1_kernel_dims,
     espcn_conv1_bias_dims,
     espcn_conv2_kernel_dims,
@@ -5429,7 +5429,7 @@ static const char espcn_tanh[] = "Tanh";
 
 static const char espcn_sigmoid[] = "Sigmoid";
 
-static const charespcn_activations[] = {
+static const char *espcn_activations[] = {
     espcn_tanh,
     espcn_tanh,
     espcn_sigmoid
index 87c9052..ca7d6d1 100644 (file)
@@ -28,9 +28,9 @@
 #include "dnn_backend_tf.h"
 #include "libavutil/mem.h"
 
-DNNModuleff_get_dnn_module(DNNBackendType backend_type)
+DNNModule *ff_get_dnn_module(DNNBackendType backend_type)
 {
-    DNNModulednn_module;
+    DNNModule *dnn_module;
 
     dnn_module = av_malloc(sizeof(DNNModule));
     if(!dnn_module){
index 6b820d1..a69717a 100644 (file)
@@ -33,31 +33,31 @@ typedef enum {DNN_NATIVE, DNN_TF} DNNBackendType;
 typedef enum {DNN_SRCNN, DNN_ESPCN} DNNDefaultModel;
 
 typedef struct DNNData{
-    floatdata;
+    float *data;
     int width, height, channels;
 } DNNData;
 
 typedef struct DNNModel{
     // Stores model that can be different for different backends.
-    voidmodel;
+    void *model;
     // Sets model input and output, while allocating additional memory for intermediate calculations.
     // Should be called at least once before model execution.
-    DNNReturnType (*set_input_output)(void* model, DNNData* input, DNNData* output);
+    DNNReturnType (*set_input_output)(void *model, DNNData *input, DNNData *output);
 } DNNModel;
 
 // Stores pointers to functions for loading, executing, freeing DNN models for one of the backends.
 typedef struct DNNModule{
     // Loads model and parameters from given file. Returns NULL if it is not possible.
-    DNNModel* (*load_model)(const char* model_filename);
+    DNNModel *(*load_model)(const char *model_filename);
     // Loads one of the default models
-    DNNModel(*load_default_model)(DNNDefaultModel model_type);
+    DNNModel *(*load_default_model)(DNNDefaultModel model_type);
     // Executes model with specified input and output. Returns DNN_ERROR otherwise.
-    DNNReturnType (*execute_model)(const DNNModelmodel);
+    DNNReturnType (*execute_model)(const DNNModel *model);
     // Frees memory allocated for model.
-    void (*free_model)(DNNModel** model);
+    void (*free_model)(DNNModel **model);
 } DNNModule;
 
 // Initializes DNNModule depending on chosen backend.
-DNNModuleff_get_dnn_module(DNNBackendType backend_type);
+DNNModule *ff_get_dnn_module(DNNBackendType backend_type);
 
 #endif
index 7ec1165..2614365 100644 (file)
@@ -2110,7 +2110,7 @@ static const long int srcnn_conv3_bias_dims[] = {
     1
 };
 
-static const floatsrcnn_consts[] = {
+static const float *srcnn_consts[] = {
     srcnn_conv1_kernel,
     srcnn_conv1_bias,
     srcnn_conv2_kernel,
@@ -2119,7 +2119,7 @@ static const float* srcnn_consts[] = {
     srcnn_conv3_bias
 };
 
-static const long intsrcnn_consts_dims[] = {
+static const long int *srcnn_consts_dims[] = {
     srcnn_conv1_kernel_dims,
     srcnn_conv1_bias_dims,
     srcnn_conv2_kernel_dims,
@@ -2139,7 +2139,7 @@ static const int srcnn_consts_dims_len[] = {
 
 static const char srcnn_relu[] = "Relu";
 
-static const charsrcnn_activations[] = {
+static const char *srcnn_activations[] = {
     srcnn_relu,
     srcnn_relu,
     srcnn_relu
index f3ca9a0..944a0e2 100644 (file)
@@ -39,13 +39,13 @@ typedef struct SRContext {
     const AVClass *class;
 
     SRModel model_type;
-    charmodel_filename;
+    char *model_filename;
     DNNBackendType backend_type;
-    DNNModulednn_module;
-    DNNModelmodel;
+    DNNModule *dnn_module;
+    DNNModel *model;
     DNNData input, output;
     int scale_factor;
-    struct SwsContextsws_context;
+    struct SwsContext *sws_context;
     int sws_slice_h;
 } SRContext;
 
@@ -67,9 +67,9 @@ static const AVOption sr_options[] = {
 
 AVFILTER_DEFINE_CLASS(sr);
 
-static av_cold int init(AVFilterContextcontext)
+static av_cold int init(AVFilterContext *context)
 {
-    SRContextsr_context = context->priv;
+    SRContext *sr_context = context->priv;
 
     sr_context->dnn_module = ff_get_dnn_module(sr_context->backend_type);
     if (!sr_context->dnn_module){
@@ -98,12 +98,12 @@ static av_cold int init(AVFilterContext* context)
     return 0;
 }
 
-static int query_formats(AVFilterContextcontext)
+static int query_formats(AVFilterContext *context)
 {
     const enum AVPixelFormat pixel_formats[] = {AV_PIX_FMT_YUV420P, AV_PIX_FMT_YUV422P, AV_PIX_FMT_YUV444P,
                                                 AV_PIX_FMT_YUV410P, AV_PIX_FMT_YUV411P, AV_PIX_FMT_GRAY8,
                                                 AV_PIX_FMT_NONE};
-    AVFilterFormatsformats_list;
+    AVFilterFormats *formats_list;
 
     formats_list = ff_make_format_list(pixel_formats);
     if (!formats_list){
@@ -113,11 +113,11 @@ static int query_formats(AVFilterContext* context)
     return ff_set_common_formats(context, formats_list);
 }
 
-static int config_props(AVFilterLinkinlink)
+static int config_props(AVFilterLink *inlink)
 {
-    AVFilterContextcontext = inlink->dst;
-    SRContextsr_context = context->priv;
-    AVFilterLinkoutlink = context->outputs[0];
+    AVFilterContext *context = inlink->dst;
+    SRContext *sr_context = context->priv;
+    AVFilterLink *outlink = context->outputs[0];
     DNNReturnType result;
     int sws_src_h, sws_src_w, sws_dst_h, sws_dst_w;
 
@@ -202,18 +202,18 @@ static int config_props(AVFilterLink* inlink)
 }
 
 typedef struct ThreadData{
-    uint8_tdata;
+    uint8_t *data;
     int data_linesize, height, width;
 } ThreadData;
 
-static int uint8_to_float(AVFilterContext* context, void* arg, int jobnr, int nb_jobs)
+static int uint8_to_float(AVFilterContext *context, void *arg, int jobnr, int nb_jobs)
 {
-    SRContextsr_context = context->priv;
-    const ThreadDatatd = arg;
+    SRContext *sr_context = context->priv;
+    const ThreadData *td = arg;
     const int slice_start = (td->height *  jobnr     ) / nb_jobs;
     const int slice_end   = (td->height * (jobnr + 1)) / nb_jobs;
-    const uint8_tsrc = td->data + slice_start * td->data_linesize;
-    floatdst = sr_context->input.data + slice_start * td->width;
+    const uint8_t *src = td->data + slice_start * td->data_linesize;
+    float *dst = sr_context->input.data + slice_start * td->width;
     int y, x;
 
     for (y = slice_start; y < slice_end; ++y){
@@ -227,14 +227,14 @@ static int uint8_to_float(AVFilterContext* context, void* arg, int jobnr, int nb
     return 0;
 }
 
-static int float_to_uint8(AVFilterContext* context, void* arg, int jobnr, int nb_jobs)
+static int float_to_uint8(AVFilterContext *context, void *arg, int jobnr, int nb_jobs)
 {
-    SRContextsr_context = context->priv;
-    const ThreadDatatd = arg;
+    SRContext *sr_context = context->priv;
+    const ThreadData *td = arg;
     const int slice_start = (td->height *  jobnr     ) / nb_jobs;
     const int slice_end   = (td->height * (jobnr + 1)) / nb_jobs;
-    const floatsrc = sr_context->output.data + slice_start * td->width;
-    uint8_tdst = td->data + slice_start * td->data_linesize;
+    const float *src = sr_context->output.data + slice_start * td->width;
+    uint8_t *dst = td->data + slice_start * td->data_linesize;
     int y, x;
 
     for (y = slice_start; y < slice_end; ++y){
@@ -248,12 +248,12 @@ static int float_to_uint8(AVFilterContext* context, void* arg, int jobnr, int nb
     return 0;
 }
 
-static int filter_frame(AVFilterLink* inlink, AVFrame* in)
+static int filter_frame(AVFilterLink *inlink, AVFrame *in)
 {
-    AVFilterContextcontext = inlink->dst;
-    SRContextsr_context = context->priv;
-    AVFilterLinkoutlink = context->outputs[0];
-    AVFrameout = ff_get_video_buffer(outlink, outlink->w, outlink->h);
+    AVFilterContext *context = inlink->dst;
+    SRContext *sr_context = context->priv;
+    AVFilterLink *outlink = context->outputs[0];
+    AVFrame *out = ff_get_video_buffer(outlink, outlink->w, outlink->h);
     ThreadData td;
     int nb_threads;
     DNNReturnType dnn_result;
@@ -307,9 +307,9 @@ static int filter_frame(AVFilterLink* inlink, AVFrame* in)
     return ff_filter_frame(outlink, out);
 }
 
-static av_cold void uninit(AVFilterContextcontext)
+static av_cold void uninit(AVFilterContext *context)
 {
-    SRContextsr_context = context->priv;
+    SRContext *sr_context = context->priv;
 
     if (sr_context->dnn_module){
         (sr_context->dnn_module->free_model)(&sr_context->model);