Multiple class detection through text file containing list of models

.gitignore

@@ -3,7 +3,6 @@
 *.swp
 tags
 gh-pages
-site
 *.tests
 *.pyc
 lib/.CC
@@ -31,3 +30,4 @@ data/*
 tool/*
 config.mk
 build
+site/_site

.travis.yml

@@ -0,0 +1,6 @@
+language: c
+compiler: clang
+before_install:
+  - sudo apt-get update -qq
+  - sudo apt-get install -qq libpng-dev libjpeg-dev libblas-dev libgsl0-dev
+script: cd lib && ./configure && make && cd ../bin && make && cd ../test && make test

COPYING

@@ -1,3 +1,5 @@
+Files in directories ./doc, ./samples and ./site are licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
+
 Copyright (c) 2010, Liu Liu
 All rights reserved.
 

README.md

@@ -1,6 +1,8 @@
 Intro
 =====
 
+[![Build Status](https://travis-ci.org/liuliu/ccv.png?branch=unstable)](https://travis-ci.org/liuliu/ccv)
+
 Around 2010, when Lian and I was working on our gesture recognition demo, out
 of the frustration to abstract redundant image preprocessing operations into a
 set of clean and concise functions, I started to consider moving away from the

bin/bbfdetect.c

@@ -25,7 +25,7 @@ int main(int argc, char** argv)
 		for (i = 0; i < seq->rnum; i++)
 		{
 			ccv_comp_t* comp = (ccv_comp_t*)ccv_array_get(seq, i);
-			printf("%d %d %d %d %f\n", comp->rect.x, comp->rect.y, comp->rect.width, comp->rect.height, comp->confidence);
+			printf("%d %d %d %d %f\n", comp->rect.x, comp->rect.y, comp->rect.width, comp->rect.height, comp->classification.confidence);
 		}
 		printf("total : %d in time %dms\n", seq->rnum, elapsed_time);
 		ccv_array_free(seq);
@@ -51,7 +51,7 @@ int main(int argc, char** argv)
 				for (i = 0; i < seq->rnum; i++)
 				{
 					ccv_comp_t* comp = (ccv_comp_t*)ccv_array_get(seq, i);
-					printf("%s %d %d %d %d %f\n", file, comp->rect.x, comp->rect.y, comp->rect.width, comp->rect.height, comp->confidence);
+					printf("%s %d %d %d %d %f\n", file, comp->rect.x, comp->rect.y, comp->rect.width, comp->rect.height, comp->classification.confidence);
 				}
 				ccv_array_free(seq);
 				ccv_matrix_free(image);

bin/cifar-10.c

@@ -14,6 +14,7 @@ int main(int argc, char** argv)
 					.rows = 31,
 					.cols = 31,
 					.channels = 3,
+					.partition = 1,
 				},
 			},
 			.output = {
@@ -24,41 +25,44 @@ int main(int argc, char** argv)
 					.border = 2,
 					.strides = 1,
 					.count = 32,
+					.partition = 1,
 				},
 			},
 		},
 		{
-			.type = CCV_CONVNET_MAX_POOL,
+			.type = CCV_CONVNET_LOCAL_RESPONSE_NORM,
 			.input = {
 				.matrix = {
 					.rows = 31,
 					.cols = 31,
 					.channels = 32,
+					.partition = 1,
 				},
 			},
 			.output = {
-				.pool = {
+				.rnorm = {
 					.size = 3,
-					.strides = 2,
-					.border = 0,
+					.kappa = 1,
+					.alpha = 1e-4,
+					.beta = 0.75,
 				},
 			},
 		},
 		{
-			.type = CCV_CONVNET_LOCAL_RESPONSE_NORM,
+			.type = CCV_CONVNET_MAX_POOL,
 			.input = {
 				.matrix = {
-					.rows = 15,
-					.cols = 15,
+					.rows = 31,
+					.cols = 31,
 					.channels = 32,
+					.partition = 1,
 				},
 			},
 			.output = {
-				.rnorm = {
+				.pool = {
 					.size = 3,
-					.kappa = 1,
-					.alpha = 0.0001,
-					.beta = 0.75,
+					.strides = 2,
+					.border = 0,
 				},
 			},
 		},
@@ -71,6 +75,7 @@ int main(int argc, char** argv)
 					.rows = 15,
 					.cols = 15,
 					.channels = 32,
+					.partition = 1,
 				},
 			},
 			.output = {
@@ -81,41 +86,44 @@ int main(int argc, char** argv)
 					.border = 2,
 					.strides = 1,
 					.count = 32,
+					.partition = 1,
 				},
 			},
 		},
 		{
-			.type = CCV_CONVNET_AVERAGE_POOL,
+			.type = CCV_CONVNET_LOCAL_RESPONSE_NORM,
 			.input = {
 				.matrix = {
 					.rows = 15,
 					.cols = 15,
 					.channels = 32,
+					.partition = 1,
 				},
 			},
 			.output = {
-				.pool = {
+				.rnorm = {
 					.size = 3,
-					.strides = 2,
-					.border = 0,
+					.kappa = 1,
+					.alpha = 1e-4,
+					.beta = 0.75,
 				},
 			},
 		},
 		{
-			.type = CCV_CONVNET_LOCAL_RESPONSE_NORM,
+			.type = CCV_CONVNET_AVERAGE_POOL,
 			.input = {
 				.matrix = {
-					.rows = 7,
-					.cols = 7,
+					.rows = 15,
+					.cols = 15,
 					.channels = 32,
+					.partition = 1,
 				},
 			},
 			.output = {
-				.rnorm = {
+				.pool = {
 					.size = 3,
-					.kappa = 1,
-					.alpha = 0.0001,
-					.beta = 0.75,
+					.strides = 2,
+					.border = 0,
 				},
 			},
 		},
@@ -128,6 +136,7 @@ int main(int argc, char** argv)
 					.rows = 7,
 					.cols = 7,
 					.channels = 32,
+					.partition = 1,
 				},
 			},
 			.output = {
@@ -138,6 +147,7 @@ int main(int argc, char** argv)
 					.border = 2,
 					.strides = 1,
 					.count = 64,
+					.partition = 1,
 				},
 			},
 		},
@@ -148,6 +158,7 @@ int main(int argc, char** argv)
 					.rows = 7,
 					.cols = 7,
 					.channels = 64,
+					.partition = 1,
 				},
 			},
 			.output = {
@@ -167,19 +178,21 @@ int main(int argc, char** argv)
 					.rows = 3,
 					.cols = 3,
 					.channels = 64,
+					.partition = 1,
 				},
 				.node = {
 					.count = 3 * 3 * 64,
 				},
 			},
 			.output = {
 				.full_connect = {
+					.relu = 0,
 					.count = 10,
 				},
 			},
 		},
 	};
-	ccv_convnet_t* convnet = ccv_convnet_new(1, params, sizeof(params) / sizeof(ccv_convnet_layer_param_t));
+	ccv_convnet_t* convnet = ccv_convnet_new(1, ccv_size(32, 32), params, sizeof(params) / sizeof(ccv_convnet_layer_param_t));
 	assert(ccv_convnet_verify(convnet, 10) == 0);
 	assert(argc == 5);
 	int num1 = atoi(argv[2]);
@@ -195,18 +208,18 @@ int main(int argc, char** argv)
 		{
 			fread(bytes, 32 * 32 + 1, 1, r1);
 			int c = bytes[0];
-			ccv_dense_matrix_t* a = ccv_dense_matrix_new(31, 31, CCV_32F | CCV_C3, 0, 0);
-			for (i = 0; i < 31; i++)
-				for (j = 0; j < 31; j++)
-					a->data.f32[(j + i * 31) * 3] = bytes[j + i * 32 + 1] / 255.0 * 2 - 1;
+			ccv_dense_matrix_t* a = ccv_dense_matrix_new(32, 32, CCV_32F | CCV_C3, 0, 0);
+			for (i = 0; i < 32; i++)
+				for (j = 0; j < 32; j++)
+					a->data.f32[(j + i * 32) * 3] = bytes[j + i * 32 + 1];
 			fread(bytes, 32 * 32, 1, r1);
-			for (i = 0; i < 31; i++)
-				for (j = 0; j < 31; j++)
-					a->data.f32[(j + i * 31) * 3 + 1] = bytes[j + i * 32] / 255.0 * 2 - 1;
+			for (i = 0; i < 32; i++)
+				for (j = 0; j < 32; j++)
+					a->data.f32[(j + i * 32) * 3 + 1] = bytes[j + i * 32];
 			fread(bytes, 32 * 32, 1, r1);
-			for (i = 0; i < 31; i++)
-				for (j = 0; j < 31; j++)
-					a->data.f32[(j + i * 31) * 3 + 2] = bytes[j + i * 32] / 255.0 * 2 - 1;
+			for (i = 0; i < 32; i++)
+				for (j = 0; j < 32; j++)
+					a->data.f32[(j + i * 32) * 3 + 2] = bytes[j + i * 32];
 			ccv_categorized_t categorized = ccv_categorized(c, a, 0);
 			ccv_array_push(categorizeds, &categorized);
 		}
@@ -215,47 +228,47 @@ int main(int argc, char** argv)
 		{
 			fread(bytes, 32 * 32 + 1, 1, r2);
 			int c = bytes[0];
-			ccv_dense_matrix_t* a = ccv_dense_matrix_new(31, 31, CCV_32F | CCV_C3, 0, 0);
-			for (i = 0; i < 31; i++)
-				for (j = 0; j < 31; j++)
-					a->data.f32[(j + i * 31) * 3] = bytes[j + i * 32 + 1] / 255.0 * 2 - 1;
+			ccv_dense_matrix_t* a = ccv_dense_matrix_new(32, 32, CCV_32F | CCV_C3, 0, 0);
+			for (i = 0; i < 32; i++)
+				for (j = 0; j < 32; j++)
+					a->data.f32[(j + i * 32) * 3] = bytes[j + i * 32 + 1];
 			fread(bytes, 32 * 32, 1, r2);
-			for (i = 0; i < 31; i++)
-				for (j = 0; j < 31; j++)
-					a->data.f32[(j + i * 31) * 3 + 1] = bytes[j + i * 32] / 255.0 * 2 - 1;
+			for (i = 0; i < 32; i++)
+				for (j = 0; j < 32; j++)
+					a->data.f32[(j + i * 32) * 3 + 1] = bytes[j + i * 32];
 			fread(bytes, 32 * 32, 1, r2);
-			for (i = 0; i < 31; i++)
-				for (j = 0; j < 31; j++)
-					a->data.f32[(j + i * 31) * 3 + 2] = bytes[j + i * 32] / 255.0 * 2 - 1;
+			for (i = 0; i < 32; i++)
+				for (j = 0; j < 32; j++)
+					a->data.f32[(j + i * 32) * 3 + 2] = bytes[j + i * 32];
 			ccv_categorized_t categorized = ccv_categorized(c, a, 0);
 			ccv_array_push(tests, &categorized);
 		}
 		ccv_convnet_layer_train_param_t layer_params[9];
 		memset(layer_params, 0, sizeof(layer_params));
 
 		layer_params[0].w.decay = 0.005;
-		layer_params[0].w.learn_rate = 0.0005;
+		layer_params[0].w.learn_rate = 0.001;
 		layer_params[0].w.momentum = 0.9;
 		layer_params[0].bias.decay = 0;
 		layer_params[0].bias.learn_rate = 0.001;
 		layer_params[0].bias.momentum = 0.9;
 
 		layer_params[3].w.decay = 0.005;
-		layer_params[3].w.learn_rate = 0.0005;
+		layer_params[3].w.learn_rate = 0.001;
 		layer_params[3].w.momentum = 0.9;
 		layer_params[3].bias.decay = 0;
 		layer_params[3].bias.learn_rate = 0.001;
 		layer_params[3].bias.momentum = 0.9;
 
 		layer_params[6].w.decay = 0.005;
-		layer_params[6].w.learn_rate = 0.0005;
+		layer_params[6].w.learn_rate = 0.001;
 		layer_params[6].w.momentum = 0.9;
 		layer_params[6].bias.decay = 0;
 		layer_params[6].bias.learn_rate = 0.001;
 		layer_params[6].bias.momentum = 0.9;
 
 		layer_params[8].w.decay = 0.01;
-		layer_params[8].w.learn_rate = 0.0005;
+		layer_params[8].w.learn_rate = 0.001;
 		layer_params[8].w.momentum = 0.9;
 		layer_params[8].bias.decay = 0;
 		layer_params[8].bias.learn_rate = 0.001;
@@ -265,6 +278,8 @@ int main(int argc, char** argv)
 			.max_epoch = 999,
 			.mini_batch = 128,
 			.iterations = 500,
+			.symmetric = 1,
+			.color_gain = 0,
 			.layer_params = layer_params,
 		};
 		ccv_convnet_supervised_train(convnet, categorizeds, tests, "cifar-10.sqlite3", params);