_load_simple_annotation.py

def _load_simple_annotation(self, index):
    """
    Load image and bounding boxes info from txt space separeted values where you have
    lines in the format of
    classification x1 y1 x2 y2
    """
    filename = os.path.join(self._data_path, 'Annotations', index + '.txt')
    # print 'Loading: {}'.format(filename)
    
    with open(filename) as f:
        lines = [l.split() for l in f if l]
            
    num_objs = len(lines)

    boxes = np.zeros((num_objs, 4), dtype=np.uint16)
    gt_classes = np.zeros((num_objs), dtype=np.int32)
    overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)
    
    # "Seg" area here is just the box area
    seg_areas = np.zeros((num_objs), dtype=np.float32)
    
    # Load object bounding boxes into a data frame.
    for ix, line in enumerate(lines):
        # Make pixel indexes 0-based
        x1 = float(line[1])
        y1 = float(line[2])
        x2 = float(line[3])
        y2 = float(line[4])        
        classification = int(line[0])
        boxes[ix, :] = [x1, y1, x2, y2]
        gt_classes[ix] = classification
        overlaps[ix, classification] = 1.0
        eg_areas[ix] = (x2 - x1 + 1) * (y2 - y1 + 1)

    overlaps = scipy.sparse.csr_matrix(overlaps)

    return {'boxes' : boxes,
            'gt_classes': gt_classes,
            'gt_overlaps' : overlaps,
            'flipped' : False}