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): 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 __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): self._classifiers = [(x, classifiers.loads(y)) for x, y in file_parse.load(os.environ['CLASSIFIERS_FN'])]