def extract_features(vocab_path, image_and_features_paths):
with open(vocab_path, "rb") as f:
vocab = np.load(f)
# FLANN parameters
FLANN_INDEX_KDTREE = 0
# FLANN_INDEX_LSH = 6
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
# index_params = dict(algorithm=FLANN_INDEX_LSH,
# table_number=20,
# key_size=10,
# multi_probe_level=2)
search_params = dict(checks=50) # or pass empty dictionary
matcher = cv2.FlannBasedMatcher(index_params, search_params)
detector = cv2.SIFT()
extractor = cv2.DescriptorExtractor_create("SIFT")
bow_de = cv2.BOWImgDescriptorExtractor(extractor, matcher)
bow_de.setVocabulary(vocab)
for image_path, feature_path in image_and_features_paths:
screenshot = cv2.imread(image_path)
keypoints = detector.detect(screenshot)
descriptor = bow_de.compute(screenshot, keypoints)
with open(feature_path, "w") as f:
np.save(f, descriptor)
def __init__(self, type, image_set, trained_hash, testing_hash, trained_directory):
self.testing_hash = testing_hash
self.trained_directory = trained_directory
if trained_hash is not None:
self._hash = trained_hash + "_" + image_set[0].hash
super().__init__(type, image_set)
self.size = 0.01
self.bowsize = 4096
if self.type is FeatureType.FREAK:
self.size = 0.001
matcher = self.initMatcher(type)
self.initFeature(type)
self.dictionary = None
self.bow_extract = cv2.BOWImgDescriptorExtractor(self.extractor, matcher)
self.bow_train = cv2.BOWKMeansTrainer(self.bowsize)
#print(testing_hash)
try:
if(self.trained_directory is not None):
dictfile = "cache/" + str(self.name) + "/" + self.trained_directory + "/" + trained_hash + "_dict.npy"
else:
dictfile = self.directory + self.hash + "_dict.npy"
logging.debug(dictfile)
self.dictionary = np.load(dictfile)
#logging.debug("Loaded dict cache.")
except FileNotFoundError:
logging.debug("Could not load dict cache file for this feature.")
def computeBOWDictionary(BOW):
dictionary = BOW.cluster()
sift2 = cv2.xfeatures2d.SIFT_create()
BOWDiction = cv2.BOWImgDescriptorExtractor(sift2,
cv2.BFMatcher(cv2.NORM_L2))
BOWDiction.setVocabulary(dictionary)
return BOWDiction
def pen_detector():
pos, neg = "pos_", "neg_"
detect, extract = get_extract_detect()
matcher = get_flann_matcher()
print("building BOWKMEANSTraniner...")
bow_kmeans_trainer = cv2.BOWKMeansTrainer(27)
extract_bow = cv2.BOWImgDescriptorExtractor(extract, matcher)
print("adding features to trainer")
for i in range(4):
print(i)
bow_kmeans_trainer.add(extract_sift(path(pos, i), extract, detect))
bow_kmeans_trainer.add(extract_sift(path(neg, i), extract, detect))
voc = bow_kmeans_trainer.cluster()
extract_bow.setVocabulary(voc)
traindata, trainlabels = [], []
print("adding to train data")
for i in range(7):
print(i+1)
traindata.extend(bow_features(cv2.imread(path(pos, i), 0), extract_bow, detect))
trainlabels.append(1)
traindata.extend(bow_features(cv2.imread(path(neg, i), 0), extract_bow, detect))
trainlabels.append(-1)
svm = cv2.ml.SVM_create()
svm.setType(cv2.ml.SVM_C_SVC)
svm.setGamma(0.4)
svm.setC(60)
svm.setKernel(cv2.ml.SVM_LINEAR)
svm.train(np.array(traindata), cv2.ml.ROW_SAMPLE, np.array(trainlabels))
return svm, extract_bow
def __init__ (self,threshold,cluster_num):
self.image_paths = []
self.image_keypoints = {}
self.surf = cv2.xfeatures2d.SURF_create(threshold)
self.bow_kmeans = cv2.BOWKMeansTrainer(cluster_num)
self.bow_extractor = cv2.BOWImgDescriptorExtractor(self.surf, cv2.BFMatcher(cv2.NORM_L2))
self.empty_histogram = [0.0] * cluster_num
self.count = 0