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Merge 61be0a80c3 into f6afaabcdf

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Jeferson Ferreira 2022-07-31 09:13:08 +05:30 committed by GitHub
parent f6afaabcdf 61be0a80c3
commit 3eb97aa12b
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GPG Key ID: 4AEE18F83AFDEB23
6 changed files with 207 additions and 4 deletions
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2
Makefile
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@ -58,7 +58,7 @@ CFLAGS+= -DCUDNN
LDFLAGS+= -lcudnn
endif
OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o detection_layer.o route_layer.o upsample_layer.o box.o normalization_layer.o avgpool_layer.o layer.o local_layer.o shortcut_layer.o logistic_layer.o activation_layer.o rnn_layer.o gru_layer.o crnn_layer.o demo.o batchnorm_layer.o region_layer.o reorg_layer.o tree.o lstm_layer.o l2norm_layer.o yolo_layer.o iseg_layer.o image_opencv.o
OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o detection_layer.o route_layer.o upsample_layer.o box.o normalization_layer.o avgpool_layer.o layer.o local_layer.o shortcut_layer.o logistic_layer.o activation_layer.o rnn_layer.o gru_layer.o crnn_layer.o demo.o batchnorm_layer.o region_layer.o reorg_layer.o table.o tree.o lstm_layer.o l2norm_layer.o yolo_layer.o iseg_layer.o image_opencv.o
EXECOBJA=captcha.o lsd.o super.o art.o tag.o cifar.o go.o rnn.o segmenter.o regressor.o classifier.o coco.o yolo.o detector.o nightmare.o instance-segmenter.o darknet.o
ifeq ($(GPU), 1)
LDFLAGS+= -lstdc++

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darknet19_448.conv.23 Normal file
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91
examples/classifier.c
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@ -1,5 +1,7 @@
#include "darknet.h"
#include <table.h>
#include <dirent.h>
#include <sys/time.h>
#include <assert.h>
@ -557,7 +559,7 @@ void try_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filena
}
}
void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top)
void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top, char *folder, char *csv_file)
{
network *net = load_network(cfgfile, weightfile, 0);
set_batch_network(net, 1);
@ -573,8 +575,89 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi
char **names = get_labels(name_list);
clock_t time;
int *indexes = calloc(top, sizeof(int));
char buff[256];
char buff[1024];
char *input = buff;
if (folder) {
printf("\nUsing input images from %s\n", folder);
int sz = strlen(folder);
if (folder[sz-1] != '/') {
char* temp = malloc(sz + 1);
strcpy(temp, folder);
strcat(temp, "/");
folder = temp;
}
DIR *d;
struct dirent *dir;
d = opendir(folder);
if (d) {
int img_count = 0;
while ((dir = readdir(d)) != NULL) {
if (dir->d_name[0] == '.') continue;
img_count++;
}
table t_predictions = make_table(top + 1, img_count);
table t_classes = make_table(top + 1, img_count);
int j = 0;
d = opendir(folder);
while ((dir = readdir(d)) != NULL) {
if (dir->d_name[0] == '.') continue;
char* abs_filename = malloc(strlen(folder) + strlen(dir->d_name) + 1);
strcpy(abs_filename, folder);
strcat(abs_filename, dir->d_name);
strncpy(input, abs_filename, 1024);
image im = load_image_color(input, 0, 0);
image r = letterbox_image(im, net->w, net->h);
float *X = r.data;
time = clock();
float *predictions = network_predict(net, X);
if (net->hierarchy) hierarchy_predictions(predictions, net->outputs, net->hierarchy, 1, 1);
top_k(predictions, net->outputs, top, indexes);
fprintf(stderr, "%s: Predicted in %f seconds.\n", input, sec(clock()-time));
t_predictions.vals[j] = (char *) malloc(1024);
strcpy(t_predictions.vals[j], abs_filename + 1);
t_classes.vals[j] = (char *) malloc(1024);
strcpy(t_classes.vals[j], abs_filename + 1);
char bf[1024];
for (i = 0; i < top; ++i) {
int index = indexes[i];
printf("%5.2f%%: %s\n", predictions[index]*100, names[index]);
int pos = (i + 1)*t_predictions.cols + j;
t_predictions.vals[pos] = (char *) malloc(1024);
sprintf(bf, "%.50f", predictions[index]);
strcpy(t_predictions.vals[pos], bf);
t_classes.vals[pos] = (char *) malloc(1024);
sprintf(bf, "%s", names[index]);
strcpy(t_classes.vals[pos], bf);
}
if (r.data != im.data) free_image(r);
free_image(im);
j++;
}
closedir(d);
table_to_csv(t_predictions, t_classes, csv_file);
} else {
fprintf(stderr, "%s folder does not exist!\n", folder);
}
return;
}
while(1){
if(filename){
strncpy(input, filename, 256);
@ -1072,13 +1155,15 @@ void run_classifier(int argc, char **argv)
int cam_index = find_int_arg(argc, argv, "-c", 0);
int top = find_int_arg(argc, argv, "-t", 0);
int clear = find_arg(argc, argv, "-clear");
char *dataset_folder = find_char_arg(argc, argv, "-folder", 0);
char *csv_file = find_char_arg(argc, argv, "-csv", 0);
char *data = argv[3];
char *cfg = argv[4];
char *weights = (argc > 5) ? argv[5] : 0;
char *filename = (argc > 6) ? argv[6]: 0;
char *layer_s = (argc > 7) ? argv[7]: 0;
int layer = layer_s ? atoi(layer_s) : -1;
if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename, top);
if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename, top, dataset_folder, csv_file);
else if(0==strcmp(argv[2], "fout")) file_output_classifier(data, cfg, weights, filename);
else if(0==strcmp(argv[2], "try")) try_classifier(data, cfg, weights, filename, atoi(layer_s));
else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights, gpus, ngpus, clear);

5
include/darknet.h
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@ -530,6 +530,10 @@ typedef struct matrix{
float **vals;
} matrix;
typedef struct table{
int rows, cols;
char **vals;
} table;
typedef struct{
int w, h;
@ -753,6 +757,7 @@ void do_nms_obj(detection *dets, int total, int classes, float thresh);
void do_nms_sort(detection *dets, int total, int classes, float thresh);
matrix make_matrix(int rows, int cols);
table make_table(int rows, int cols);
#ifdef OPENCV
void *open_video_stream(const char *f, int c, int w, int h, int fps);

103
src/table.c Normal file
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@ -0,0 +1,103 @@
#include "table.h"
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <assert.h>
#include <math.h>
table make_table(int rows, int cols)
{
table t;
t.rows = rows;
t.cols = cols;
t.vals = malloc(t.rows * t.cols * sizeof(char *) * 1024);
return t;
}
table copy_table(table t)
{
table c = {0};
c.rows = t.rows;
c.cols = t.cols;
c.vals = malloc(t.rows * t.cols * sizeof(char *) * 1024);
int i, j, p;
for (i = 0; i < c.rows; ++i) {
for (j = 0; j < c.cols; ++j) {
p = i*c.rows + j;
c.vals[p] = t.vals[p];
}
}
return c;
}
void table_to_csv(table t1, table t2, char *filename)
{
char *default_csv = "classifier_predictions.csv";
char *csv_classes;
if (!filename) {
filename = default_csv;
csv_classes = "classifier_classes.csv";
} else {
int k = strlen(filename) - 4;
char *csv_ext = ".csv";
csv_classes = calloc(strlen(filename), sizeof(char));
if (k > 0) {
int c = 0;
char sub[5];
while (c < 4) {
sub[c] = filename[k + c];
c++;
}
sub[c] = '\0';
if (0!=strcmp(sub, csv_ext)) {
strcpy(csv_classes, filename);
strcat(filename, csv_ext);
} else {
strncpy(csv_classes, filename, k);
}
} else if (k == 0) {
strcpy(csv_classes, filename);
if (0!=strcmp(filename, csv_ext))
strcat(filename, csv_ext);
} else {
strcpy(csv_classes, filename);
strcat(filename, csv_ext);
}
strcat(csv_classes, "_classes.csv");
}
printf("\nCSV files...\n");
save_csv(t1, filename);
save_csv(t2, csv_classes);
}
void save_csv(table t, char *filename)
{
FILE *fp;
fp = fopen(filename, "w");
int i, j;
for (i = 0; i < t.rows; ++i) {
for (j = 0; j < t.cols; ++j) {
if (j > 0) fprintf(fp, ",");
fprintf(fp, "%s", t.vals[ i*t.cols + j ]);
}
fprintf(fp, "\n");
}
fclose(fp);
printf("%s file created!\n", filename);
}
void print_table(table t)
{
int i, j;
for (i = 0; i < t.rows; ++i){
for (j = 0; j < t.cols; ++j) {
printf(" %s", t.vals[ i*t.cols + j ]);
}
printf("\n");
}
}

10
src/table.h Normal file
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@ -0,0 +1,10 @@
#ifndef TABLE_H
#define TABLE_H
#include "darknet.h"
table copy_table(table t);
void table_to_csv(table t1, table t2, char *filename);
void print_table(table t);
#endif