lib/ccv_bbf.c

This is open source implementation of object detection algorithm: brightness binary feature it is an extension/modification of original haar-like feature with adaboost, featured faster computation and higher accuracy (current highest accuracy close-source face detector is based on the same algorithm)

ccv_bbf_classifier_cascade_new

void ccv_bbf_classifier_cascade_new(ccv_dense_matrix_t **posimg, int posnum, char **bgfiles, int bgnum, int negnum, ccv_size_t size, const char *dir, ccv_bbf_new_param_t params)

Create a new BBF classifier cascade from given positive examples and background images. This function has a hard dependency on GSL.

posimg: An array of positive examples.
posnum: Number of positive examples.
bgfiles: An array of background images.
bgnum: Number of background images.
negnum: Number of negative examples that is harvested from background images.
size: The image size of positive examples.
dir: The working directory to store/retrieve intermediate data.
params: A ccv_bbf_new_param_t structure that defines various aspects of the training function.

ccv_bbf_new_param_t

balance_k: Weight positive examples differently from negative examples.
detector: A ccv_bbf_params_t structure that will be used to search negative examples from background images.
feature_number: The maximum feature number for each classifier.
layer: The maximum layer trained for the classifier cascade.
neg_crit: Negative criteria or the targeted reject ratio, BBF classifier tries to include more weak features until meet this criteria.
optimizer: CCV_BBF_GENETIC_OPT, using genetic algorithm to search the best weak feature; CCV_BBF_FLOAT_OPT, using float search to improve the found best weak feature.
pos_crit: Positive criteria or the targeted recall ratio, BBF classifier tries to adjust the constant to meet this criteria.

ccv_bbf_detect_objects

ccv_bbf_detect_objects(ccv_dense_matrix_t *a, ccv_bbf_classifier_cascade_t **cascade, int count, ccv_bbf_param_t params)

Using a BBF classifier cascade to detect objects in a given image. If you have several classifier cascades, it is better to use them in one method call. In this way, ccv will try to optimize the overall performance.

a: The input image.
cascade: An array of classifier cascades.
count: How many classifier cascades you’ve passed in.
params: A ccv_bbf_param_t structure that defines various aspects of the detector.

return: A ccv_array_t of ccv_comp_t for detection results.

ccv_bbf_param_t

accurate: BBF will generates 4 spatial scale variations for better accuracy. Set this parameter to 0 will reduce to 1 scale variation, and thus 3 times faster but lower the general accuracy of the detector.
flags: CCV_BBF_NO_NESTED, if one class of object is inside another class of object, this flag will reject the first object.
interval: Interval images between the full size image and the half size one. e.g. 2 will generate 2 images in between full size image and half size one: image with full size, image with 5/6 size, image with 2/3 size, image with 1/2 size.
min_neighbors: 0: no grouping afterwards. 1: group objects that intersects each other. > 1: group objects that intersects each other, and only passes these that have at least min_neighbors intersected objects.
size: The smallest object size that will be interesting to us.

ccv_bbf_read_classifier_cascade

ccv_bbf_read_classifier_cascade(const char *directory)

Read BBF classifier cascade from working directory.

directory: The working directory that trains a BBF classifier cascade.

return: A classifier cascade, 0 if no valid classifier cascade available.

ccv_bbf_classifier_cascade_free

void ccv_bbf_classifier_cascade_free(ccv_bbf_classifier_cascade_t *cascade)

Free up the memory of BBF classifier cascade.

cascade: The BBF classifier cascade.

ccv_bbf_classifier_cascade_read_binary

ccv_bbf_classifier_cascade_read_binary(char *s)

Load BBF classifier cascade from a memory region.

s: The memory region of binarized BBF classifier cascade.