write image: automatically detect padding

src/cnn/cnn.c

@@ -1,5 +1,6 @@
 #include <stdbool.h>
 #include <stdlib.h>
+#include <assert.h>
 #include <stdio.h>
 #include <float.h>
 #include <math.h>
@@ -17,8 +18,6 @@
 
 #include "include/cnn.h"
 
-// Augmente les dimensions de l'image d'entrée
-#define PADDING_INPUT 2
 
 int indice_max(float* tab, int n) {
     int indice = -1;
@@ -131,25 +130,27 @@ void write_image_in_network_32(int** image, int height, int width, float** input
     }
 }
 
-void write_image_in_network_260(unsigned char* image, int height, int width, float*** input) {
-    int size_input = 260;
-    int padding = (size_input - height)/2;
+void write_256_image_in_network(unsigned char* image, int img_width, int img_depth, int input_width, float*** input) {
+    assert(img_width <= input_width);
+    assert((input_width - img_width)%2 == 0);
+
+    int padding = (input_width - img_width)/2;
 
     for (int i=0; i < padding; i++) {
-        for (int j=0; j < size_input; j++) {
-            for (int composante=0; composante < 3; composante++) {
+        for (int j=0; j < input_width; j++) {
+            for (int composante=0; composante < img_depth; composante++) {
                 input[composante][i][j] = 0.;
-                input[composante][size_input-1-i][j] = 0.;
+                input[composante][input_width-1-i][j] = 0.;
                 input[composante][j][i] = 0.;
-                input[composante][j][size_input-1-i] = 0.;
+                input[composante][j][input_width-1-i] = 0.;
             }
         }
     }
 
-    for (int i=0; i < width; i++) {
-        for (int j=0; j < height; j++) {
-            for (int composante=0; composante < 3; composante++) {
-                input[composante][i+2][j+2] = (float)image[(i*height+j)*3 + composante] / 255.0f;
+    for (int i=0; i < img_width; i++) {
+        for (int j=0; j < img_width; j++) {
+            for (int composante=0; composante < img_depth; composante++) {
+                input[composante][i+padding][j+padding] = (float)image[(i*img_width+j)*img_depth + composante] / 255.0f;
             }
         }
     }
@@ -219,7 +220,7 @@ void forward_propagation(Network* network) {
                     make_max_pooling(input, output, kernel_size, output_depth, output_width, stride, padding);
                 } 
                 else {
-                    printf_error("Impossible de reconnaître le type de couche de pooling: ");
+                    printf_error((char*)"Impossible de reconnaître le type de couche de pooling: ");
                     printf("identifiant: %d, position: %d\n", pooling, i);
                 }
             }

src/cnn/export.c

@@ -126,7 +126,7 @@ void visual_propagation(char* modele_file, char* mnist_images_file, char* out_ba
         free(mnist_parameters);
 
         if (numero < 0 || numero >= nb_elem) {
-            printf_error("Numéro d'image spécifié invalide.");
+            printf_error((char*)"Numéro d'image spécifié invalide.");
             printf(" Le fichier contient %d images.\n", nb_elem);
             exit(1);
         }
@@ -145,7 +145,7 @@ void visual_propagation(char* modele_file, char* mnist_images_file, char* out_ba
     } else {
         imgRawImage* image = loadJpegImageFile(jpeg_file);
 
-        write_image_in_network_260(image->lpData, image->height, image->width, network->input[0]);
+        write_256_image_in_network(image->lpData, image->width, image->numComponents, network->width[0], network->input[0]);
 
         // Free allocated memory from image reading
         free(image->lpData);

src/cnn/include/cnn.h

@@ -20,9 +20,12 @@ int will_be_drop(int dropout_prob);
 void write_image_in_network_32(int** image, int height, int width, float** input, bool random_offset);
 
 /*
-* Écrit une image linéarisée de 256*256*3 pixels dans un tableau de taille 260*260*3
+* Écrit une image linéarisée de img_width*img_width*img_depth pixels dans un tableau de taille size_input*size_input*3
+* Les conditions suivantes doivent être respectées:
+* - l'image est au plus de la même taille que input
+* - la différence de taille entre input et l'image doit être un multiple de 2 (pour centrer l'image)
 */
-void write_image_in_network_260(unsigned char* image, int height, int width, float*** input);
+void write_256_image_in_network(unsigned char* image, int img_width, int img_depth, int input_width, float*** input);
 
 /*
 * Propage en avant le cnn. Le dropout est actif que si le réseau est en phase d'apprentissage.

src/cnn/test_network.c

@@ -79,7 +79,7 @@ float* test_network_jpg(Network* network, char* data_dir, bool preview_fails, bo
             printf("Avancement: %.1f%%\r",  1000*i/(float)dataset->numImages);
             fflush(stdout);
         }
-        write_image_in_network_260(dataset->images[i], dataset->height, dataset->height, network->input[0]);
+        write_256_image_in_network(dataset->images[i], dataset->height, dataset->numComponents, network->width[0], network->input[0]);
         forward_propagation(network);
         maxi = indice_max(network->input[network->size-1][0][0], 50);
 
@@ -196,7 +196,7 @@ void recognize_jpg(Network* network, char* input_file, char* out) {
     }
 
     // Load image in the first layer of the Network
-    write_image_in_network_260(image->lpData, height, width, network->input[0]);
+    write_256_image_in_network(image->lpData, width, image->numComponents, network->width[0], network->input[0]);
     forward_propagation(network);
 
 

src/cnn/train.c

@@ -103,7 +103,7 @@ void* train_thread(void* parameters) {
                 load_image_param->index = index[i+1];
                 pthread_create(&tid, NULL, load_image, (void*) load_image_param);
             }
-            write_image_in_network_260(param->dataset->images[index[i]], height, width, network->input[0]);
+            write_256_image_in_network(param->dataset->images[index[i]], width, param->dataset->numComponents, network->width[0], network->input[0]);
             forward_propagation(network);
             maxi = indice_max(network->input[network->size-1][0][0], param->dataset->numCategories);
             backward_propagation(network, param->dataset->labels[index[i]]);