Python fast_rcnn.config.cfg.DATA_DIR Examples

The following are 30 code examples of fast_rcnn.config.cfg.DATA_DIR(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module fast_rcnn.config.cfg , or try the search function .
Example #1
Source File: pascal_voc.py    From caffe-faster-rcnn-resnet-fpn with MIT License 6 votes vote down vote up 
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)
Example #2
Source File: pascal_voc.py    From uai-sdk with Apache License 2.0 6 votes vote down vote up 
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)
Example #3
Source File: pascal_voc.py    From Faster-RCNN_TF with MIT License 6 votes vote down vote up 
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)
Example #4
Source File: pascal_voc.py    From rgz_rcnn with MIT License 6 votes vote down vote up 
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)
Example #5
Source File: pascal_voc.py    From faster-rcnn-resnet with MIT License 6 votes vote down vote up 
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)
Example #6
Source File: pascal_voc.py    From py-R-FCN with MIT License 6 votes vote down vote up 
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)
Example #7
Source File: pascal_voc.py    From face-py-faster-rcnn with MIT License 6 votes vote down vote up 
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)
Example #8
Source File: coco.py    From py-R-FCN with MIT License 5 votes vote down vote up 
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,
                       'rpn_file': None,
                       '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')
Example #9
Source File: pascal_voc.py    From py-R-FCN with MIT License 5 votes vote down vote up 
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)
Example #10
Source File: imdb.py    From caffe-faster-rcnn-resnet-fpn with MIT License 5 votes vote down vote up 
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
Example #11
Source File: coco.py    From caffe-faster-rcnn-resnet-fpn with MIT License 5 votes vote down vote up 
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')
Example #12
Source File: demo_rfcn.py    From py-R-FCN with MIT License 5 votes vote down vote up 
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)
Example #13
Source File: imdb.py    From py-R-FCN with MIT License 5 votes vote down vote up 
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
Example #14
Source File: pascal_voc.py    From caffe-faster-rcnn-resnet-fpn with MIT License 5 votes vote down vote up 
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)
Example #15
Source File: imdb.py    From face-magnet with Apache License 2.0 5 votes vote down vote up 
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
Example #16
Source File: wider.py    From face-magnet with Apache License 2.0 5 votes vote down vote up 
def _get_default_path(self):
        return os.path.join(cfg.DATA_DIR, 'wider')
Example #17
Source File: pascal_voc.py    From face-py-faster-rcnn with MIT License 5 votes vote down vote up 
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)
Example #18
Source File: demo_rfcn.py    From uai-sdk with Apache License 2.0 5 votes vote down vote up 
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)
Example #19
Source File: demo.py    From uai-sdk with Apache License 2.0 5 votes vote down vote up 
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)
Example #20
Source File: imdb.py    From uai-sdk with Apache License 2.0 5 votes vote down vote up 
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
Example #21
Source File: coco.py    From uai-sdk with Apache License 2.0 5 votes vote down vote up 
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')
Example #22
Source File: imdb.py    From faster-rcnn-resnet with MIT License 5 votes vote down vote up 
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
Example #23
Source File: coco.py    From face-py-faster-rcnn with MIT License 5 votes vote down vote up 
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')
Example #24
Source File: imdb.py    From face-py-faster-rcnn with MIT License 5 votes vote down vote up 
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
Example #25
Source File: pascal_voc.py    From faster-rcnn-resnet with MIT License 5 votes vote down vote up 
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)
Example #26
Source File: coco.py    From faster-rcnn-resnet with MIT License 5 votes vote down vote up 
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')
Example #27
Source File: pascal_voc.py    From uai-sdk with Apache License 2.0 5 votes vote down vote up 
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)
Example #28
Source File: pascal_voc.py    From rgz_rcnn with MIT License 5 votes vote down vote up 
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)
Example #29
Source File: imdb.py    From rgz_rcnn with MIT License 5 votes vote down vote up 
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
Example #30
Source File: rgz.py    From rgz_rcnn with MIT License 5 votes vote down vote up 
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)