generated from freudenreichan/info2Praktikum-NeuronalesNetz
fertiges Programm
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imageInput.c
87
imageInput.c
@ -1,3 +1,4 @@
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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@ -6,17 +7,93 @@
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#define BUFFER_SIZE 100
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#define FILE_HEADER_STRING "__info2_image_file_format__"
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// TODO Implementieren Sie geeignete Hilfsfunktionen für das Lesen der Bildserie aus einer Datei
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// TODO Vervollständigen Sie die Funktion readImages unter Benutzung Ihrer Hilfsfunktionen
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GrayScaleImageSeries *readImages(const char *path)
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{
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GrayScaleImageSeries *series = NULL;
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if (path == NULL) return NULL;
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FILE *file = fopen(path, "rb");
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if (!file) return NULL;
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// Header prüfen
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size_t tagLen = strlen(FILE_HEADER_STRING);
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if (tagLen >= BUFFER_SIZE) { fclose(file); return NULL; }
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char tagBuf[BUFFER_SIZE];
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if (fread(tagBuf, 1, tagLen, file) != tagLen || memcmp(tagBuf, FILE_HEADER_STRING, tagLen) != 0) {
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fclose(file);
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return NULL;
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}
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// Anzahl, Breite, Höhe lesen
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unsigned short numberOfImages = 0, width = 0, height = 0;
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if (fread(&numberOfImages, sizeof(numberOfImages), 1, file) != 1 ||
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fread(&width, sizeof(width), 1, file) != 1 ||
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fread(&height, sizeof(height), 1, file) != 1) {
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fclose(file);
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return NULL;
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}
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if (numberOfImages == 0 || width == 0 || height == 0) {
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fclose(file);
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return NULL;
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}
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// Serie allokieren
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GrayScaleImageSeries *series = malloc(sizeof(GrayScaleImageSeries));
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if (!series) { fclose(file); return NULL; }
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series->count = numberOfImages;
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series->images = calloc(series->count, sizeof(GrayScaleImage));
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series->labels = calloc(series->count, sizeof(unsigned char));
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if (!series->images || !series->labels) {
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free(series->images);
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free(series->labels);
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free(series);
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fclose(file);
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return NULL;
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}
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size_t pixelsPerImage = (size_t)width * height;
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for (unsigned int i = 0; i < series->count; i++) {
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series->images[i].width = width;
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series->images[i].height = height;
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series->images[i].buffer = malloc(pixelsPerImage * sizeof(GrayScalePixelType));
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if (!series->images[i].buffer) {
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for (unsigned int j = 0; j < i; j++) free(series->images[j].buffer);
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free(series->images);
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free(series->labels);
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free(series);
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fclose(file);
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return NULL;
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}
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if (fread(series->images[i].buffer, sizeof(GrayScalePixelType), pixelsPerImage, file) != pixelsPerImage ||
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fread(&series->labels[i], sizeof(unsigned char), 1, file) != 1) {
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for (unsigned int j = 0; j <= i; j++) free(series->images[j].buffer);
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free(series->images);
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free(series->labels);
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free(series);
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fclose(file);
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return NULL;
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}
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}
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fclose(file);
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return series;
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}
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// TODO Vervollständigen Sie die Funktion clearSeries, welche eine Bildserie vollständig aus dem Speicher freigibt
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void clearSeries(GrayScaleImageSeries *series)
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{
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if (!series) return;
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if (series->images) {
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for (unsigned int i = 0; i < series->count; i++) {
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free(series->images[i].buffer);
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}
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free(series->images);
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}
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free(series->labels);
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free(series);
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}
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@ -8,7 +8,43 @@
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static void prepareNeuralNetworkFile(const char *path, const NeuralNetwork nn)
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{
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// TODO
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FILE *file = fopen(path, "wb");
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if (!file) return;
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const char *header = "__info2_neural_network_file_format__";
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fwrite(header, sizeof(char), strlen(header), file);
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if (nn.numberOfLayers > 0) {
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// Erste Schicht: inputDim und outputDim
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unsigned int inputDim = nn.layers[0].weights.cols;
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unsigned int outputDim = nn.layers[0].weights.rows;
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fwrite(&inputDim, sizeof(unsigned int), 1, file);
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fwrite(&outputDim, sizeof(unsigned int), 1, file);
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// Erste Schicht schreiben
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fwrite(nn.layers[0].weights.buffer, sizeof(MatrixType), inputDim * outputDim, file);
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fwrite(nn.layers[0].biases.buffer, sizeof(MatrixType), outputDim, file);
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// Weitere Schichten: nur outputDim + Matrizen
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for (unsigned int i = 1; i < nn.numberOfLayers; i++) {
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outputDim = nn.layers[i].weights.rows;
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fwrite(&outputDim, sizeof(unsigned int), 1, file);
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fwrite(nn.layers[i].weights.buffer, sizeof(MatrixType),
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nn.layers[i].weights.rows * nn.layers[i].weights.cols, file);
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fwrite(nn.layers[i].biases.buffer, sizeof(MatrixType),
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nn.layers[i].biases.rows, file);
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}
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}
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// Endmarker
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unsigned int zero = 0;
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fwrite(&zero, sizeof(unsigned int), 1, file);
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fclose(file);
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}
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void test_loadModelReturnsCorrectNumberOfLayers(void)
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