def db_name_for_hog(self, hog):
hog_name = utils.name_from_hog_descriptor(hog)
# Note: MongoDB limits database names to 64 characters, making hog_name too long.
# Its hash value encoded in base64 is used instead.
import base64
return base64.urlsafe_b64encode(repr(hash(hog_name)))
def db_name_for_hog(self, hog):
hog_name = utils.name_from_hog_descriptor(hog)
# Note: MongoDB limits database names to 64 characters, making hog_name too long.
# Its hash value encoded in base64 is used instead.
import base64
return base64.urlsafe_b64encode(repr(hash(hog_name)))
def create_or_load_descriptors(classifier_yaml, hog, window_dims):
pos_img_dir = classifier_yaml["dataset"]["directory"]["positive"]
pos_num = int(classifier_yaml["training"]["svm"]["pos_num"])
bbinfo_dir = classifier_yaml["dataset"]["directory"]["bbinfo"]
bak_img_dir = classifier_yaml["dataset"]["directory"]["background"]
neg_num = int(classifier_yaml["training"]["svm"]["neg_num"])
# pos_descriptor_file = 'output/pos_descriptors.pickle'
# neg_descriptor_file = 'output/neg_descriptors.pickle'
# if not (os.path.isfile(pos_descriptor_file) and os.path.isfile(neg_descriptor_file)):
store = DataStore()
print "db_name:", store.db_name_for_hog(hog)
print "hog_name:", utils.name_from_hog_descriptor(hog)
# if not store.has_region_descriptors_for_hog(hog):
# Preprocess samples:
# - generate/select sample regions
# - compute and descriptors for all samples
# - save the regions and descriptors to a file
curr_pos_num = store.num_region_descriptors(hog, 1)
req_pos_num = pos_num if curr_pos_num is None else pos_num - curr_pos_num
print "pos_num:", pos_num
print "curr_pos_num:", curr_pos_num
print "req_pos_num:", req_pos_num
if req_pos_num > 0:
# if not os.path.isfile(pos_descriptor_file):
# pos_reg_generator = generate_positive_regions(pos_img_dir, bbinfo_dir, classifier_yaml['dataset']['modifiers'], 0.5*np.array(window_dims))
pos_reg_generator = generate_samples.generate_positive_regions(
pos_img_dir, bbinfo_dir, classifier_yaml["dataset"]["modifiers"], window_dims
)
# view_image_regions(pos_reg_generator, window_dims, display_scale=3)
pos_regions = list(itertools.islice(pos_reg_generator, 0, req_pos_num))
pos_reg_descriptors = compute_hog_descriptors(hog, pos_regions, window_dims, 1)
# save_region_descriptors(hog, pos_regions, pos_descriptors, 'pos_descriptors')
store.save_region_descriptors(pos_reg_descriptors, hog)
curr_neg_num = store.num_region_descriptors(hog, -1)
req_neg_num = neg_num if curr_neg_num is None else neg_num - curr_neg_num
print "neg_num:", neg_num
print "curr_neg_num:", curr_neg_num
print "req_neg_num:", req_neg_num
if req_neg_num > 0:
# if not os.path.isfile(neg_descriptor_file):
# neg_regions = generate_negative_regions(bak_img_dir, req_neg_num, window_dims)
exl_info_map = utils.load_opencv_bounding_box_info_directory(bbinfo_dir, suffix="exclusion")
neg_reg_generator = generate_samples.generate_negative_regions_with_exclusions(
bak_img_dir, exl_info_map, window_dims, classifier_yaml["dataset"]["modifiers"]
)
neg_regions = list(itertools.islice(neg_reg_generator, 0, req_neg_num))
neg_reg_descriptors = compute_hog_descriptors(hog, neg_regions, window_dims, -1)
# save_region_descriptors(hog, neg_regions, neg_descriptors, 'neg_descriptors')
store.save_region_descriptors(neg_reg_descriptors, hog)
# else:
# Load all descriptors and the hog object used to generate them:
# hog, reg_descriptors = store.load_region_descriptors_for_hog(hog)
hog, pos_reg_descriptors = store.load_region_descriptors_for_hog(hog, pos_num, 1)
hog, neg_reg_descriptors = store.load_region_descriptors_for_hog(hog, neg_num, -1)
# hog, reg_descriptors = store.load_region_descriptors('hog_Llht0,2_n64_gcFalse_hnt0_bs8x8_cs8x8_ws4,0_da1')
# hog, pos_regions, pos_descriptors = load_region_descriptors(pos_descriptor_file)
# hog, neg_regions, neg_descriptors = load_region_descriptors(neg_descriptor_file)
reg_descriptors = list(pos_reg_descriptors) + list(neg_reg_descriptors)
return reg_descriptors, hog
# # all_images = utils.list_images_in_directory(bak_img_dir)
# # neg_reg_generator = generate_negative_regions_in_image_with_exclusions(all_images[0], exl_info_map, window_dims)
# mosaic_gen = utils.mosaic_generator(pos_reg_generator, (4, 6), (window_dims[1], window_dims[0]))
# # mosaic_gen = utils.mosaic_generator(pos_reg_generator, (20, 30), (40, 60))
# for mosaic in mosaic_gen:
# cv2.imshow('mosaic', mosaic)
# while True:
# key = cv2.waitKey(1) & 0xFF
# if key != 255:
# break
# view_image_regions(neg_reg_generator, window_dims, display_scale=2)
# Create the hog object with which to compute the descriptors:
hog = get_hog_object(window_dims)
db_name = utils.name_from_hog_descriptor(hog)
svm_name = strutils.safe_name_from_info_dict(classifier_yaml["training"]["svm"], "svm_")
svm_save_path = "output/car_detector_{}_{}.yaml".format(svm_name, db_name)
if not os.path.isfile(svm_save_path):
reg_descriptors, hog = create_or_load_descriptors(classifier_yaml, hog, window_dims)
# Create lists of samples and labels:
descriptors = [rd.descriptor for rd in reg_descriptors]
labels = [rd.label for rd in reg_descriptors]
# Train the classifier:
train_svm(svm_save_path, descriptors, labels)
# Test the classifier:
test_classifier(classifier_yaml, svm_save_path, window_dims)
def create_or_load_descriptors(classifier_yaml, hog, window_dims):
pos_img_dir = classifier_yaml['dataset']['directory']['positive']
pos_num = int(classifier_yaml['training']['svm']['pos_num'])
bbinfo_dir = classifier_yaml['dataset']['directory']['bbinfo']
bak_img_dir = classifier_yaml['dataset']['directory']['background']
neg_num = int(classifier_yaml['training']['svm']['neg_num'])
# pos_descriptor_file = 'output/pos_descriptors.pickle'
# neg_descriptor_file = 'output/neg_descriptors.pickle'
# if not (os.path.isfile(pos_descriptor_file) and os.path.isfile(neg_descriptor_file)):
store = DataStore()
print 'db_name:', store.db_name_for_hog(hog)
print 'hog_name:', utils.name_from_hog_descriptor(hog)
# if not store.has_region_descriptors_for_hog(hog):
# Preprocess samples:
# - generate/select sample regions
# - compute and descriptors for all samples
# - save the regions and descriptors to a file
curr_pos_num = store.num_region_descriptors(hog, 1)
req_pos_num = pos_num if curr_pos_num is None else pos_num - curr_pos_num
print 'pos_num:', pos_num
print 'curr_pos_num:', curr_pos_num
print 'req_pos_num:', req_pos_num
if req_pos_num > 0:
# if not os.path.isfile(pos_descriptor_file):
# pos_reg_generator = generate_positive_regions(pos_img_dir, bbinfo_dir, classifier_yaml['dataset']['modifiers'], 0.5*np.array(window_dims))
pos_reg_generator = generate_samples.generate_positive_regions(
pos_img_dir, bbinfo_dir, classifier_yaml['dataset']['modifiers'],
window_dims)
# view_image_regions(pos_reg_generator, window_dims, display_scale=3)
pos_regions = list(itertools.islice(pos_reg_generator, 0, req_pos_num))
pos_reg_descriptors = compute_hog_descriptors(hog, pos_regions,
window_dims, 1)
# save_region_descriptors(hog, pos_regions, pos_descriptors, 'pos_descriptors')
store.save_region_descriptors(pos_reg_descriptors, hog)
curr_neg_num = store.num_region_descriptors(hog, -1)
req_neg_num = neg_num if curr_neg_num is None else neg_num - curr_neg_num
print 'neg_num:', neg_num
print 'curr_neg_num:', curr_neg_num
print 'req_neg_num:', req_neg_num
if req_neg_num > 0:
# if not os.path.isfile(neg_descriptor_file):
# neg_regions = generate_negative_regions(bak_img_dir, req_neg_num, window_dims)
exl_info_map = utils.load_opencv_bounding_box_info_directory(
bbinfo_dir, suffix='exclusion')
neg_reg_generator = generate_samples.generate_negative_regions_with_exclusions(
bak_img_dir, exl_info_map, window_dims,
classifier_yaml['dataset']['modifiers'])
neg_regions = list(itertools.islice(neg_reg_generator, 0, req_neg_num))
neg_reg_descriptors = compute_hog_descriptors(hog, neg_regions,
window_dims, -1)
# save_region_descriptors(hog, neg_regions, neg_descriptors, 'neg_descriptors')
store.save_region_descriptors(neg_reg_descriptors, hog)
# else:
# Load all descriptors and the hog object used to generate them:
# hog, reg_descriptors = store.load_region_descriptors_for_hog(hog)
hog, pos_reg_descriptors = store.load_region_descriptors_for_hog(
hog, pos_num, 1)
hog, neg_reg_descriptors = store.load_region_descriptors_for_hog(
hog, neg_num, -1)
# hog, reg_descriptors = store.load_region_descriptors('hog_Llht0,2_n64_gcFalse_hnt0_bs8x8_cs8x8_ws4,0_da1')
# hog, pos_regions, pos_descriptors = load_region_descriptors(pos_descriptor_file)
# hog, neg_regions, neg_descriptors = load_region_descriptors(neg_descriptor_file)
reg_descriptors = list(pos_reg_descriptors) + list(neg_reg_descriptors)
return reg_descriptors, hog
# # all_images = utils.list_images_in_directory(bak_img_dir)
# # neg_reg_generator = generate_negative_regions_in_image_with_exclusions(all_images[0], exl_info_map, window_dims)
# mosaic_gen = utils.mosaic_generator(pos_reg_generator, (4, 6), (window_dims[1], window_dims[0]))
# # mosaic_gen = utils.mosaic_generator(pos_reg_generator, (20, 30), (40, 60))
# for mosaic in mosaic_gen:
# cv2.imshow('mosaic', mosaic)
# while True:
# key = cv2.waitKey(1) & 0xFF
# if key != 255:
# break
# view_image_regions(neg_reg_generator, window_dims, display_scale=2)
# Create the hog object with which to compute the descriptors:
hog = get_hog_object(window_dims)
db_name = utils.name_from_hog_descriptor(hog)
svm_name = strutils.safe_name_from_info_dict(
classifier_yaml['training']['svm'], 'svm_')
svm_save_path = 'output/car_detector_{}_{}.yaml'.format(svm_name, db_name)
if not os.path.isfile(svm_save_path):
reg_descriptors, hog = create_or_load_descriptors(
classifier_yaml, hog, window_dims)
# Create lists of samples and labels:
descriptors = [rd.descriptor for rd in reg_descriptors]
labels = [rd.label for rd in reg_descriptors]
# Train the classifier:
train_svm(svm_save_path, descriptors, labels)