def __init__(self):
self._classifiers = [
(x, classifiers.loads(y))
for x, y in file_parse.load(os.environ['CLASSIFIERS_FN'])
]
self._feat = features.select_feature(os.environ['FEATURE'])
self._image_height, self._image_width = _parse_height_width()
def convert_leaves_all_probs_pred_old(image, leaves, all_probs, num_leaves, classifiers_fn=None):
global CLASSIFIERS, CLASSIFIER_FEATURE
if classifiers_fn is None:
classifiers_fn = os.environ['CLASSIFIERS_FN']
get_classifier_confidence = lambda x: x[0][0] * x[0][1]
if CLASSIFIERS is None:
all_classifiers = sorted(file_parse.load(classifiers_fn))
name_classifiers = []
for x in range(len(all_classifiers)):
if x < len(all_classifiers): # TODO(brandyn): Fix memory issue so that we can use the last classifier too
name_classifiers.append((all_classifiers[x][0],
classifiers.loads(all_classifiers[x][1])))
else:
name_classifiers.append((all_classifiers[x][0],
name_classifiers[-1][1]))
all_classifiers[x] = None # NOTE(Brandyn): This is done to save memory
print('ILP Classifiers %r' % ([x for x, _ in name_classifiers],))
CLASSIFIERS = [x for _, x in name_classifiers]
if CLASSIFIER_FEATURE is None:
CLASSIFIER_FEATURE = features.select_feature('bovw_hog')
feature = CLASSIFIER_FEATURE(np.ascontiguousarray(image[:, :, :3]))
preds = np.ascontiguousarray([get_classifier_confidence(classifier.predict(feature))
for classifier in CLASSIFIERS], dtype=np.float64)
out0 = imseg.convert_labels_to_integrals(leaves, num_leaves)
out1 = imseg.convert_all_probs_to_integrals(all_probs)
return preds, np.ascontiguousarray(np.dstack([out0, out1]))
def __init__(self):
self.output_images = int(os.environ.get('OUTPUT_IMAGES', 0))
path = 'haarcascade_frontalface_default.xml'
if os.path.exists(path):
self.cascade = cv.Load(path)
else:
raise ValueError("Can't find .xml file!")
classifier_name, classifier_ser = file_parse.load(os.environ['CLASSIFIERS_FN'])
self._classifiers = [(classifier_name, classifiers.loads(classifier_ser))]
self._feat = features.select_feature(os.environ['FEATURE'])
self._image_height, self._image_width = _parse_height_width()
self._max_frames = os.environ.get('MAX_FRAMES', float('inf'))
self._block_size = os.environ.get('BLOCK_SIZE', 900)
self._match_line_prob = os.environ.get('MATCH_LINE_PROB', 0)
self._frame_output_prob = os.environ.get('FRAME_OUTPUT_PROB', 0)
self.timer = Timer()
def __init__(self):
self.output_images = int(os.environ.get('OUTPUT_IMAGES', 0))
path = 'haarcascade_frontalface_default.xml'
if os.path.exists(path):
self.cascade = cv.Load(path)
else:
raise ValueError("Can't find .xml file!")
classifier_name, classifier_ser = file_parse.load(
os.environ['CLASSIFIERS_FN'])
self._classifiers = [(classifier_name,
classifiers.loads(classifier_ser))]
self._feat = features.select_feature(os.environ['FEATURE'])
self._image_height, self._image_width = _parse_height_width()
self._max_frames = os.environ.get('MAX_FRAMES', float('inf'))
self._block_size = os.environ.get('BLOCK_SIZE', 900)
self._match_line_prob = os.environ.get('MATCH_LINE_PROB', 0)
self._frame_output_prob = os.environ.get('FRAME_OUTPUT_PROB', 0)
self.timer = Timer()
def predict_classifiers(image, start_ind, stop_ind, classifiers_fn=None):
global CLASSIFIER_FEATURE, ALL_CLASSIFIERS, CLASSIFIERS
if classifiers_fn is None:
classifiers_fn = os.environ['CLASSIFIERS_FN']
get_classifier_confidence = lambda x: x[0][0] * x[0][1]
if ALL_CLASSIFIERS is None:
ALL_CLASSIFIERS = sorted(file_parse.load(classifiers_fn))
if CLASSIFIER_FEATURE is None:
CLASSIFIER_FEATURE = features.select_feature('bovw_hog')
feature = CLASSIFIER_FEATURE(np.ascontiguousarray(image[:, :, :3]))
preds = {}
for x in range(start_ind, stop_ind):
try:
classifier = CLASSIFIERS[x]
except KeyError:
classifier = classifiers.loads(ALL_CLASSIFIERS[x][1])
CLASSIFIERS[x] = classifier
preds[x] = get_classifier_confidence(classifier.predict(feature))
return preds
def convert_leaves_all_probs_pred(image, leaves, all_probs, num_leaves, classifiers_fn=None):
global CLASSIFIER_FEATURE, ALL_CLASSIFIERS, CLASSIFIERS
preds = []
if classifiers_fn:
get_classifier_confidence = lambda x: x[0][0] * x[0][1]
if ALL_CLASSIFIERS is None:
ALL_CLASSIFIERS = sorted(file_parse.load(classifiers_fn))
if CLASSIFIER_FEATURE is None:
CLASSIFIER_FEATURE = features.select_feature('bovw_hog')
feature = CLASSIFIER_FEATURE(np.ascontiguousarray(image[:, :, :3]))
for x in range(len(ALL_CLASSIFIERS)):
try:
classifier = CLASSIFIERS[x]
except KeyError:
classifier = classifiers.loads(ALL_CLASSIFIERS[x][1])
if x < 14:
CLASSIFIERS[x] = classifier
preds.append(get_classifier_confidence(classifier.predict(feature)))
preds = np.ascontiguousarray(preds, dtype=np.float64)
out0 = imseg.convert_labels_to_integrals(leaves, num_leaves)
out1 = imseg.convert_all_probs_to_integrals(all_probs)
return preds, np.ascontiguousarray(np.dstack([out0, out1]))
def __init__(self):
self._classifiers = [(x, classifiers.loads(y))
for x, y in file_parse.load(os.environ['CLASSIFIERS_FN'])]
self._feat = features.select_feature(os.environ['FEATURE'])
self._image_height, self._image_width = _parse_height_width()
def __init__(self):
feature = open(os.environ['FEATURE']).read()
try:
self._feat = features.select_feature(feature)
except KeyError:
self._feat = call_import(json.loads(zlib.decompress(feature)))
def __init__(self):
feature = open(os.environ["FEATURE"]).read()
try:
self._feat = features.select_feature(feature)
except KeyError:
self._feat = call_import(json.loads(zlib.decompress(feature)))
