Python cfg.DATA_DIR属性代码示例

# 需要导入模块: from fast_rcnn.config import cfg [as 别名]
# 或者: from fast_rcnn.config.cfg import DATA_DIR [as 别名]
def _load_selective_search_roidb(self, gt_roidb):
        filename = os.path.abspath(os.path.join(cfg.DATA_DIR,
                                                'selective_search_data',
                                                self.name + '.mat'))
        assert os.path.exists(filename), \
               'Selective search data not found at: {}'.format(filename)
        raw_data = sio.loadmat(filename)['boxes'].ravel()

        box_list = []
        for i in xrange(raw_data.shape[0]):
            boxes = raw_data[i][:, (1, 0, 3, 2)] - 1
            keep = ds_utils.unique_boxes(boxes)
            boxes = boxes[keep, :]
            keep = ds_utils.filter_small_boxes(boxes, self.config['min_size'])
            boxes = boxes[keep, :]
            box_list.append(boxes)

        return self.create_roidb_from_box_list(box_list, gt_roidb) 

# 需要导入模块: from fast_rcnn.config import cfg [as 别名]
# 或者: from fast_rcnn.config.cfg import DATA_DIR [as 别名]
def _get_default_path(self):
        """
        Return the default path where PASCAL VOC is expected to be installed.
        """
        return os.path.join(cfg.DATA_DIR, 'VOCdevkit' + self._year) 

# 需要导入模块: from fast_rcnn.config import cfg [as 别名]
# 或者: from fast_rcnn.config.cfg import DATA_DIR [as 别名]
def __init__(self, image_set, year):
        imdb.__init__(self, 'coco_' + year + '_' + image_set)
        # COCO specific config options
        self.config = {'top_k' : 2000,
                       'use_salt' : True,
                       'cleanup' : True,
                       'crowd_thresh' : 0.7,
                       'min_size' : 2}
        # name, paths
        self._year = year
        self._image_set = image_set
        self._data_path = osp.join(cfg.DATA_DIR, 'coco')
        # load COCO API, classes, class <-> id mappings
        self._COCO = COCO(self._get_ann_file())
        cats = self._COCO.loadCats(self._COCO.getCatIds())
        self._classes = tuple(['__background__'] + [c['name'] for c in cats])
        self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))
        self._class_to_coco_cat_id = dict(zip([c['name'] for c in cats],
                                              self._COCO.getCatIds()))
        self._image_index = self._load_image_set_index()
        # Default to roidb handler
        self.set_proposal_method('selective_search')
        self.competition_mode(False)

        # Some image sets are "views" (i.e. subsets) into others.
        # For example, minival2014 is a random 5000 image subset of val2014.
        # This mapping tells us where the view's images and proposals come from.
        self._view_map = {
            'minival2014' : 'val2014',          # 5k val2014 subset
            'valminusminival2014' : 'val2014',  # val2014 \setminus minival2014
        }
        coco_name = image_set + year  # e.g., "val2014"
        self._data_name = (self._view_map[coco_name]
                           if self._view_map.has_key(coco_name)
                           else coco_name)
        # Dataset splits that have ground-truth annotations (test splits
        # do not have gt annotations)
        self._gt_splits = ('train', 'val', 'minival') 

# 需要导入模块: from fast_rcnn.config import cfg [as 别名]
# 或者: from fast_rcnn.config.cfg import DATA_DIR [as 别名]
def cache_path(self):
        cache_path = osp.abspath(osp.join(cfg.DATA_DIR, 'cache'))
        if not os.path.exists(cache_path):
            os.makedirs(cache_path)
        return cache_path 

# 需要导入模块: from fast_rcnn.config import cfg [as 别名]
# 或者: from fast_rcnn.config.cfg import DATA_DIR [as 别名]
def _get_default_path(self):
        """
        Return the default path where RGZ is expected to be installed.
        """
        return os.path.join(cfg.DATA_DIR, 'RGZdevkit' + self._year) 

# 需要导入模块: from fast_rcnn.config import cfg [as 别名]
# 或者: from fast_rcnn.config.cfg import DATA_DIR [as 别名]
def demo(net, image_name):
    """Detect object classes in an image using pre-computed object proposals."""

    # Load the demo image
    im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)
    im = cv2.imread(im_file)

    # Detect all object classes and regress object bounds
    timer = Timer()
    timer.tic()
    scores, boxes = im_detect(net, im)
    timer.toc()
    print ('Detection took {:.3f}s for '
           '{:d} object proposals').format(timer.total_time, boxes.shape[0])

    # Visualize detections for each class
    CONF_THRESH = 0.8
    NMS_THRESH = 0.3
    for cls_ind, cls in enumerate(CLASSES[1:]):
        cls_ind += 1 # because we skipped background
        cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
        cls_scores = scores[:, cls_ind]
        dets = np.hstack((cls_boxes,
                          cls_scores[:, np.newaxis])).astype(np.float32)
        keep = nms(dets, NMS_THRESH)
        dets = dets[keep, :]
        vis_detections(im, cls, dets, thresh=CONF_THRESH) 

# 需要导入模块: from fast_rcnn.config import cfg [as 别名]
# 或者: from fast_rcnn.config.cfg import DATA_DIR [as 别名]
def demo(net, image_name):
    """Detect object classes in an image using pre-computed object proposals."""

    # Load the demo image
    im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)
    im = cv2.imread(im_file)

    # Detect all object classes and regress object bounds
    timer = Timer()
    timer.tic()
    scores, boxes = im_detect(net, im)
    timer.toc()
    print ('Detection took {:.3f}s for '
           '{:d} object proposals').format(timer.total_time, boxes.shape[0])

    # Visualize detections for each class
    CONF_THRESH = 0.8
    NMS_THRESH = 0.3
    for cls_ind, cls in enumerate(CLASSES[1:]):
        cls_ind += 1 # because we skipped background
        cls_boxes = boxes[:, 4:8]
        cls_scores = scores[:, cls_ind]
        dets = np.hstack((cls_boxes,
                          cls_scores[:, np.newaxis])).astype(np.float32)
        keep = nms(dets, NMS_THRESH)
        dets = dets[keep, :]
        vis_detections(im, cls, dets, thresh=CONF_THRESH) 

# 需要导入模块: from fast_rcnn.config import cfg [as 别名]
# 或者: from fast_rcnn.config.cfg import DATA_DIR [as 别名]
def _get_default_path(self):
        return os.path.join(cfg.DATA_DIR, 'wider')