From 168af40fe9a3cc81c6ee16b3e81f154780c36bdb Mon Sep 17 00:00:00 2001
From: Scheaven <xuepengqiang>
Date: 星期四, 03 六月 2021 15:03:27 +0800
Subject: [PATCH] up new v4
---
lib/detecter_tools/darknet/voxel.c | 328 +++++++++++++++++++++++++++---------------------------
1 files changed, 164 insertions(+), 164 deletions(-)
diff --git a/lib/detecter_tools/darknet/voxel.c b/lib/detecter_tools/darknet/voxel.c
index 805f3d4..9f50112 100644
--- a/lib/detecter_tools/darknet/voxel.c
+++ b/lib/detecter_tools/darknet/voxel.c
@@ -1,164 +1,164 @@
-#include "network.h"
-#include "cost_layer.h"
-#include "utils.h"
-#include "parser.h"
-
-void extract_voxel(char *lfile, char *rfile, char *prefix)
-{
-#ifdef OPENCV
- int w = 1920;
- int h = 1080;
- int shift = 0;
- int count = 0;
- cap_cv *lcap = get_capture_video_stream(lfile);
- cap_cv *rcap = get_capture_video_stream(rfile);
- while(1){
- image l = get_image_from_stream_cpp(lcap);
- image r = get_image_from_stream_cpp(rcap);
- if(!l.w || !r.w) break;
- if(count%100 == 0) {
- shift = best_3d_shift_r(l, r, -l.h/100, l.h/100);
- printf("%d\n", shift);
- }
- image ls = crop_image(l, (l.w - w)/2, (l.h - h)/2, w, h);
- image rs = crop_image(r, 105 + (r.w - w)/2, (r.h - h)/2 + shift, w, h);
- char buff[256];
- sprintf(buff, "%s_%05d_l", prefix, count);
- save_image(ls, buff);
- sprintf(buff, "%s_%05d_r", prefix, count);
- save_image(rs, buff);
- free_image(l);
- free_image(r);
- free_image(ls);
- free_image(rs);
- ++count;
- }
-
-#else
- printf("need OpenCV for extraction\n");
-#endif
-}
-
-void train_voxel(char *cfgfile, char *weightfile)
-{
- char* train_images = "data/imagenet/imagenet1k.train.list";
- char* backup_directory = "backup/";
- srand(time(0));
- char *base = basecfg(cfgfile);
- printf("%s\n", base);
- float avg_loss = -1;
- network net = parse_network_cfg(cfgfile);
- if(weightfile){
- load_weights(&net, weightfile);
- }
- printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
- int imgs = net.batch*net.subdivisions;
- int i = *net.seen/imgs;
- data train, buffer;
-
-
- list *plist = get_paths(train_images);
- //int N = plist->size;
- char **paths = (char **)list_to_array(plist);
-
- load_args args = {0};
- args.w = net.w;
- args.h = net.h;
- args.scale = 4;
- args.paths = paths;
- args.n = imgs;
- args.m = plist->size;
- args.d = &buffer;
- args.type = SUPER_DATA;
-
- pthread_t load_thread = load_data_in_thread(args);
- clock_t time;
- //while(i*imgs < N*120){
- while(get_current_batch(net) < net.max_batches){
- i += 1;
- time=clock();
- pthread_join(load_thread, 0);
- train = buffer;
- load_thread = load_data_in_thread(args);
-
- printf("Loaded: %lf seconds\n", sec(clock()-time));
-
- time=clock();
- float loss = train_network(net, train);
- if (avg_loss < 0) avg_loss = loss;
- avg_loss = avg_loss*.9 + loss*.1;
-
- printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
- if(i%1000==0){
- char buff[256];
- sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
- save_weights(net, buff);
- }
- if(i%100==0){
- char buff[256];
- sprintf(buff, "%s/%s.backup", backup_directory, base);
- save_weights(net, buff);
- }
- free_data(train);
- }
- char buff[256];
- sprintf(buff, "%s/%s_final.weights", backup_directory, base);
- save_weights(net, buff);
-}
-
-void test_voxel(char *cfgfile, char *weightfile, char *filename)
-{
- network net = parse_network_cfg(cfgfile);
- if(weightfile){
- load_weights(&net, weightfile);
- }
- set_batch_network(&net, 1);
- srand(2222222);
-
- clock_t time;
- char buff[256];
- char *input = buff;
- while(1){
- if(filename){
- strncpy(input, filename, 256);
- }else{
- printf("Enter Image Path: ");
- fflush(stdout);
- input = fgets(input, 256, stdin);
- if(!input) return;
- strtok(input, "\n");
- }
- image im = load_image_color(input, 0, 0);
- resize_network(&net, im.w, im.h);
- printf("%d %d\n", im.w, im.h);
-
- float *X = im.data;
- time=clock();
- network_predict(net, X);
- image out = get_network_image(net);
- printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
- save_image(out, "out");
-
- free_image(im);
- if (filename) break;
- }
-}
-
-
-void run_voxel(int argc, char **argv)
-{
- if(argc < 4){
- fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
- return;
- }
-
- char *cfg = argv[3];
- char *weights = (argc > 4) ? argv[4] : 0;
- char *filename = (argc > 5) ? argv[5] : 0;
- if(0==strcmp(argv[2], "train")) train_voxel(cfg, weights);
- else if(0==strcmp(argv[2], "test")) test_voxel(cfg, weights, filename);
- else if(0==strcmp(argv[2], "extract")) extract_voxel(argv[3], argv[4], argv[5]);
- /*
- else if(0==strcmp(argv[2], "valid")) validate_voxel(cfg, weights);
- */
-}
+#include "network.h"
+#include "cost_layer.h"
+#include "utils.h"
+#include "parser.h"
+
+void extract_voxel(char *lfile, char *rfile, char *prefix)
+{
+#ifdef OPENCV
+ int w = 1920;
+ int h = 1080;
+ int shift = 0;
+ int count = 0;
+ cap_cv *lcap = get_capture_video_stream(lfile);
+ cap_cv *rcap = get_capture_video_stream(rfile);
+ while(1){
+ image l = get_image_from_stream_cpp(lcap);
+ image r = get_image_from_stream_cpp(rcap);
+ if(!l.w || !r.w) break;
+ if(count%100 == 0) {
+ shift = best_3d_shift_r(l, r, -l.h/100, l.h/100);
+ printf("%d\n", shift);
+ }
+ image ls = crop_image(l, (l.w - w)/2, (l.h - h)/2, w, h);
+ image rs = crop_image(r, 105 + (r.w - w)/2, (r.h - h)/2 + shift, w, h);
+ char buff[256];
+ sprintf(buff, "%s_%05d_l", prefix, count);
+ save_image(ls, buff);
+ sprintf(buff, "%s_%05d_r", prefix, count);
+ save_image(rs, buff);
+ free_image(l);
+ free_image(r);
+ free_image(ls);
+ free_image(rs);
+ ++count;
+ }
+
+#else
+ printf("need OpenCV for extraction\n");
+#endif
+}
+
+void train_voxel(char *cfgfile, char *weightfile)
+{
+ char* train_images = "data/imagenet/imagenet1k.train.list";
+ char* backup_directory = "backup/";
+ srand(time(0));
+ char *base = basecfg(cfgfile);
+ printf("%s\n", base);
+ float avg_loss = -1;
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
+ int imgs = net.batch*net.subdivisions;
+ int i = *net.seen/imgs;
+ data train, buffer;
+
+
+ list *plist = get_paths(train_images);
+ //int N = plist->size;
+ char **paths = (char **)list_to_array(plist);
+
+ load_args args = {0};
+ args.w = net.w;
+ args.h = net.h;
+ args.scale = 4;
+ args.paths = paths;
+ args.n = imgs;
+ args.m = plist->size;
+ args.d = &buffer;
+ args.type = SUPER_DATA;
+
+ pthread_t load_thread = load_data_in_thread(args);
+ clock_t time;
+ //while(i*imgs < N*120){
+ while(get_current_batch(net) < net.max_batches){
+ i += 1;
+ time=clock();
+ pthread_join(load_thread, 0);
+ train = buffer;
+ load_thread = load_data_in_thread(args);
+
+ printf("Loaded: %lf seconds\n", sec(clock()-time));
+
+ time=clock();
+ float loss = train_network(net, train);
+ if (avg_loss < 0) avg_loss = loss;
+ avg_loss = avg_loss*.9 + loss*.1;
+
+ printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
+ if(i%1000==0){
+ char buff[256];
+ sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
+ save_weights(net, buff);
+ }
+ if(i%100==0){
+ char buff[256];
+ sprintf(buff, "%s/%s.backup", backup_directory, base);
+ save_weights(net, buff);
+ }
+ free_data(train);
+ }
+ char buff[256];
+ sprintf(buff, "%s/%s_final.weights", backup_directory, base);
+ save_weights(net, buff);
+}
+
+void test_voxel(char *cfgfile, char *weightfile, char *filename)
+{
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ set_batch_network(&net, 1);
+ srand(2222222);
+
+ clock_t time;
+ char buff[256];
+ char *input = buff;
+ while(1){
+ if(filename){
+ strncpy(input, filename, 256);
+ }else{
+ printf("Enter Image Path: ");
+ fflush(stdout);
+ input = fgets(input, 256, stdin);
+ if(!input) return;
+ strtok(input, "\n");
+ }
+ image im = load_image_color(input, 0, 0);
+ resize_network(&net, im.w, im.h);
+ printf("%d %d\n", im.w, im.h);
+
+ float *X = im.data;
+ time=clock();
+ network_predict(net, X);
+ image out = get_network_image(net);
+ printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
+ save_image(out, "out");
+
+ free_image(im);
+ if (filename) break;
+ }
+}
+
+
+void run_voxel(int argc, char **argv)
+{
+ if(argc < 4){
+ fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+ return;
+ }
+
+ char *cfg = argv[3];
+ char *weights = (argc > 4) ? argv[4] : 0;
+ char *filename = (argc > 5) ? argv[5] : 0;
+ if(0==strcmp(argv[2], "train")) train_voxel(cfg, weights);
+ else if(0==strcmp(argv[2], "test")) test_voxel(cfg, weights, filename);
+ else if(0==strcmp(argv[2], "extract")) extract_voxel(argv[3], argv[4], argv[5]);
+ /*
+ else if(0==strcmp(argv[2], "valid")) validate_voxel(cfg, weights);
+ */
+}
--
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