continue tune some parameters for release

.gitignore

@@ -3,6 +3,7 @@
 *.swp
 tags
 gh-pages
+site
 *.tests
 test/*_test
 test/*.jpg

bin/dpmcreate.c

@@ -17,12 +17,12 @@ void exit_with_help()
 	"\t--symmetric : 0 or 1, whether to exploit symmetric property of the object\n\n"
 	"\033[1mOTHER OPTIONS\033[0m\n"
 	"\t--base-dir : change the base directory so that the program can read images from there\n"
-	"\t--iterations : how many iterations needed for stochastic gradient descent [DEFAULT TO 10]\n"
+	"\t--iterations : how many iterations needed for stochastic gradient descent [DEFAULT TO 15]\n"
 	"\t--relabels : how many relabel procedure needed [DEFAULT TO 5]\n"
 	"\t--alpha : the step size for stochastic gradient descent [DEFAULT TO 0.1]\n"
-	"\t--alpha-ratio : decrease the step size for each iteration [DEFAULT TO 0.85]\n"
+	"\t--alpha-ratio : decrease the step size for each iteration [DEFAULT TO 0.9]\n"
 	"\t--margin-c : the famous C in SVM [DEFAULT TO 0.002]\n"
-	"\t--balance : to balance the weight of positive examples and negative examples [DEFAULT TO 1.75]\n"
+	"\t--balance : to balance the weight of positive examples and negative examples [DEFAULT TO 1.5]\n"
 	"\t--negative-cache-size : the cache size for negative examples it should be smaller than negative-count and larger than 100 [DEFAULT TO 1000]\n"
 	"\t--include-overlap : the percentage of overlap between expected bounding box and the bounding box from detection. Beyond this threshold, it is ensured to be the same object [DEFAULT TO 0.7]\n"
 	"\t--exclude-overlap : the percentage of overlap between expected bounding box and the bounding box from detection. Below this threshold, it is ensured to not be the same object [DEFAULT TO 0.5]\n"
@@ -73,9 +73,9 @@ int main(int argc, char** argv)
 								   .max_area = 5000,
 								   .symmetric = 1,
 								   .alpha = 0.1,
-								   .balance = 1.75,
-								   .alpha_ratio = 0.85,
-								   .iterations = 10,
+								   .balance = 1.5,
+								   .alpha_ratio = 0.9,
+								   .iterations = 15,
 								   .relabels = 5,
 								   .negative_cache_size = 1000,
 								   .C = 0.002,
@@ -217,7 +217,7 @@ int main(int argc, char** argv)
 		free(posfiles[i]);
 	ccfree(posfiles);
 	ccfree(bboxes);
-	for (i = 0; i < posnum; i++)
+	for (i = 0; i < bgnum; i++)
 		free(bgfiles[i]);
 	ccfree(bgfiles);
 	ccv_disable_cache();

bin/dpmdetect.c

@@ -17,7 +17,7 @@ int main(int argc, char** argv)
 	ccv_dense_matrix_t* image = 0;
 	ccv_read(argv[1], &image, CCV_IO_ANY_FILE);
 	ccv_dpm_mixture_model_t* model = ccv_load_dpm_mixture_model(argv[2]);
-	ccv_dpm_param_t params = { .interval = 8, .min_neighbors = 1, .flags = 0, .threshold = 0.5 };
+	ccv_dpm_param_t params = { .interval = 8, .min_neighbors = 1, .flags = 0, .threshold = -0.3 };
 	if (image != 0)
 	{
 		unsigned int elapsed_time = get_current_time();

bin/dpmvldtr.rb

@@ -44,7 +44,7 @@
 			opx_max = [obj[:x] + obj[:width], x + width].min
 			opy_max = [obj[:y] + obj[:height], y + height].min
 			r0 = [opx_max - opx_min, 0].max * [opy_max - opy_min, 0].max
-			r1 = obj[:width] * obj[:height] * 0.5
+			r1 = obj[:width] * obj[:height] * 0.6
 			if r0 > r1
 				outlier = false
 				break

doc/bbf.md

@@ -78,7 +78,7 @@ information, for me, it is:
 
 Suppose you have copied the ground truth to truth.txt file, run the validator:
 
-	./validator.rb truth.txt result.txt
+	./bbfvldr.rb truth.txt result.txt
 
 My result for bbfdetect is:
 

doc/dpm.md

@@ -22,10 +22,33 @@ Checkout output.png, see what happens?
 What about performance?
 -----------------------
 
-DPM is not known for its speed. Its ability to identify difficult objects, that's
-the winning point. However, this implementation tries to optimize for speed as
-well. For a 640x480 photo, this mplementation will be done in about one seconds,
-no multi-threading cheat.
+DPM is not known for its speed. Its ability to identify difficult objects, is
+the selling point. However, this implementation tries to optimize for speed as
+well. For a 640x480 photo, this implementation will be done in about one second,
+without multi-thread support.
+
+Accuracy-wise:
+
+There are two off-the-shelf implementations. One is the DPM in matlab from author,
+the other is HOG detector from OpenCV. Fo the task to detect pedestrians in a
+given image, we use INTRA 2008 dataset, and it provides both training and testing
+data. With OpenCV stock peopledetect sample program, we get:
+
+	65.7% (156)
+
+The former one is the detection rate (how many objects have been detected), the
+later is the number of false alarms (the detected region doesn't contain the
+expected object)
+
+Our implementation?
+
+	80.14% (74)
+
+Looks pretty good!
+
+Speed-wise:
+
+
 
 How to train my own detector?
 -----------------------------

lib/ccv_dpm.c

@@ -773,8 +773,8 @@ static ccv_dpm_feature_vector_t* _ccv_dpm_collect_best(ccv_dense_matrix_t* image
 		for (j = next; j < scale_upto + next * 2; j++)
 		{
 			ccv_size_t size = ccv_size((int)(root_classifier->root.w->cols * CCV_DPM_WINDOW_SIZE * scale_x + 0.5), (int)(root_classifier->root.w->rows * CCV_DPM_WINDOW_SIZE * scale_y + 0.5));
-			if ((double)(size.width * size.height) / (double)(bbox.width * bbox.height) < overlap || 
-				(double)(bbox.width * bbox.height) / (double)(size.width * size.height) < overlap)
+			if (ccv_min((double)(size.width * size.height), (double)(bbox.width * bbox.height)) / 
+				ccv_max((double)(bbox.width * bbox.height), (double)(size.width * size.height)) < overlap)
 			{
 				scale_x *= scale;
 				scale_y *= scale;
@@ -795,7 +795,7 @@ static ccv_dpm_feature_vector_t* _ccv_dpm_collect_best(ccv_dense_matrix_t* image
 					ccv_rect_t rect = ccv_rect((int)((x - rww) * CCV_DPM_WINDOW_SIZE * scale_x + 0.5), (int)((y - rwh) * CCV_DPM_WINDOW_SIZE * scale_y + 0.5), (int)(root_classifier->root.w->cols * CCV_DPM_WINDOW_SIZE * scale_x + 0.5), (int)(root_classifier->root.w->rows * CCV_DPM_WINDOW_SIZE * scale_y + 0.5));
 					if ((double)(ccv_max(0, ccv_min(rect.x + rect.width, bbox.x + bbox.width) - ccv_max(rect.x, bbox.x)) *
 								 ccv_max(0, ccv_min(rect.y + rect.height, bbox.y + bbox.height) - ccv_max(rect.y, bbox.y))) /
-						(double)ccv_min(rect.width * rect.height, bbox.width * bbox.height) >= overlap && f_ptr[x] > best)
+						(double)ccv_max(rect.width * rect.height, bbox.width * bbox.height) >= overlap && f_ptr[x] > best)
 					{
 						// initialize v
 						if (v == 0)
@@ -870,7 +870,7 @@ static ccv_array_t* _ccv_dpm_collect_all(gsl_rng* rng, ccv_dense_matrix_t* image
 						for (k = 0; k < bnum; k++)
 							if ((double)(ccv_max(0, ccv_min(rect.x + rect.width, bboxes[k].x + bboxes[k].width) - ccv_max(rect.x, bboxes[k].x)) *
 								ccv_max(0, ccv_min(rect.y + rect.height, bboxes[k].y + bboxes[k].height) - ccv_max(rect.y, bboxes[k].y))) /
-								(double)ccv_min(rect.width * rect.height, bboxes[k].width * bboxes[k].height) >= overlap)
+								(double)ccv_max(rect.width * rect.height, bboxes[k].width * bboxes[k].height) >= overlap)
 							{
 								flag = 1;
 								break;