Python utils.image.get_image() Examples

The following are 30 code examples of utils.image.get_image(). 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 utils.image , or try the search function .
Example #1
Source File: rcnn.py    From Accel with MIT License 6 votes vote down vote up 
def get_rcnn_testbatch(roidb, cfg):
    """
    return a dict of testbatch
    :param roidb: ['image', 'flipped'] + ['boxes']
    :return: data, label, im_info
    """
    # assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs
    im_info = [np.array([roidb[i]['im_info']], dtype=np.float32) for i in range(len(roidb))]

    im_rois = [roidb[i]['boxes'] for i in range(len(roidb))]
    rois = im_rois
    rois_array = [np.hstack((0 * np.ones((rois[i].shape[0], 1)), rois[i])) for i in range(len(rois))]

    data = [{'data': im_array[i],
             'rois': rois_array[i]} for i in range(len(roidb))]
    label = {}

    return data, label, im_info
Example #2
Source File: rcnn.py    From Deformable-ConvNets with MIT License 6 votes vote down vote up 
def get_rcnn_testbatch(roidb, cfg):
    """
    return a dict of testbatch
    :param roidb: ['image', 'flipped'] + ['boxes']
    :return: data, label, im_info
    """
    # assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs
    im_info = [np.array([roidb[i]['im_info']], dtype=np.float32) for i in range(len(roidb))]

    im_rois = [roidb[i]['boxes'] for i in range(len(roidb))]
    rois = im_rois
    rois_array = [np.hstack((0 * np.ones((rois[i].shape[0], 1)), rois[i])) for i in range(len(rois))]

    data = [{'data': im_array[i],
             'rois': rois_array[i]} for i in range(len(roidb))]
    label = {}

    return data, label, im_info
Example #3
Source File: rpn.py    From Deep-Feature-Flow-Segmentation with MIT License 6 votes vote down vote up 
def get_rpn_testbatch(roidb, cfg):
    """
    return a dict of testbatch
    :param roidb: ['image', 'flipped']
    :return: data, label, im_info
    """
    # assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs
    im_info = [np.array([roidb[i]['im_info']], dtype=np.float32) for i in range(len(roidb))]

    data = [{'data': im_array[i],
            'im_info': im_info[i]} for i in range(len(roidb))]
    label = {}

    return data, label, im_info
Example #4
Source File: rcnn.py    From RoITransformer_DOTA with MIT License 6 votes vote down vote up 
def get_rcnn_testbatch(roidb, cfg):
    """
    return a dict of testbatch
    :param roidb: ['image', 'flipped'] + ['boxes']
    :return: data, label, im_info
    """
    # assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs
    im_info = [np.array([roidb[i]['im_info']], dtype=np.float32) for i in range(len(roidb))]

    im_rois = [roidb[i]['boxes'] for i in range(len(roidb))]
    rois = im_rois
    rois_array = [np.hstack((0 * np.ones((rois[i].shape[0], 1)), rois[i])) for i in range(len(rois))]

    data = [{'data': im_array[i],
             'rois': rois_array[i]} for i in range(len(roidb))]
    label = {}

    return data, label, im_info
Example #5
Source File: rpn.py    From RoITransformer_DOTA with MIT License 6 votes vote down vote up 
def get_rpn_batch(roidb, cfg):
    """
    prototype for rpn batch: data, im_info, gt_boxes
    :param roidb: ['image', 'flipped'] + ['gt_boxes', 'boxes', 'gt_classes']
    :return: data, label
    """
    assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs[0]
    im_info = np.array([roidb[0]['im_info']], dtype=np.float32)

    # gt boxes: (x1, y1, x2, y2, cls)
    if roidb[0]['gt_classes'].size > 0:
        gt_inds = np.where(roidb[0]['gt_classes'] != 0)[0]
        gt_boxes = np.empty((roidb[0]['boxes'].shape[0], 5), dtype=np.float32)
        gt_boxes[:, 0:4] = roidb[0]['boxes'][gt_inds, :]
        gt_boxes[:, 4] = roidb[0]['gt_classes'][gt_inds]
    else:
        gt_boxes = np.empty((0, 5), dtype=np.float32)

    data = {'data': im_array,
            'im_info': im_info}
    label = {'gt_boxes': gt_boxes}

    return data, label
Example #6
Source File: rcnn.py    From RoITransformer_DOTA with MIT License 6 votes vote down vote up 
def get_rcnn_testbatch(roidb, cfg):
    """
    return a dict of testbatch
    :param roidb: ['image', 'flipped'] + ['boxes']
    :return: data, label, im_info
    """
    # assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs
    im_info = [np.array([roidb[i]['im_info']], dtype=np.float32) for i in range(len(roidb))]

    im_rois = [roidb[i]['boxes'] for i in range(len(roidb))]
    rois = im_rois
    rois_array = [np.hstack((0 * np.ones((rois[i].shape[0], 1)), rois[i])) for i in range(len(rois))]

    data = [{'data': im_array[i],
             'rois': rois_array[i]} for i in range(len(roidb))]
    label = {}

    return data, label, im_info
Example #7
Source File: rpn.py    From Relation-Networks-for-Object-Detection with MIT License 6 votes vote down vote up 
def get_rpn_testbatch(roidb, cfg):
    """
    return a dict of testbatch
    :param roidb: ['image', 'flipped']
    :return: data, label, im_info
    """
    # assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs
    im_info = [np.array([roidb[i]['im_info']], dtype=np.float32) for i in range(len(roidb))]

    data = [{'data': im_array[i],
            'im_info': im_info[i]} for i in range(len(roidb))]
    label = {}

    return data, label, im_info
Example #8
Source File: rpn.py    From Relation-Networks-for-Object-Detection with MIT License 6 votes vote down vote up 
def get_rpn_batch(roidb, cfg):
    """
    prototype for rpn batch: data, im_info, gt_boxes
    :param roidb: ['image', 'flipped'] + ['gt_boxes', 'boxes', 'gt_classes']
    :return: data, label
    """
    assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs[0]
    im_info = np.array([roidb[0]['im_info']], dtype=np.float32)

    # gt boxes: (x1, y1, x2, y2, cls)
    if roidb[0]['gt_classes'].size > 0:
        gt_inds = np.where(roidb[0]['gt_classes'] != 0)[0]
        gt_boxes = np.empty((roidb[0]['boxes'].shape[0], 5), dtype=np.float32)
        gt_boxes[:, 0:4] = roidb[0]['boxes'][gt_inds, :]
        gt_boxes[:, 4] = roidb[0]['gt_classes'][gt_inds]
    else:
        gt_boxes = np.empty((0, 5), dtype=np.float32)

    data = {'data': im_array,
            'im_info': im_info}
    label = {'gt_boxes': gt_boxes}

    return data, label
Example #9
Source File: rcnn.py    From Accel with MIT License 6 votes vote down vote up 
def get_rcnn_testbatch(roidb, cfg):
    """
    return a dict of testbatch
    :param roidb: ['image', 'flipped'] + ['boxes']
    :return: data, label, im_info
    """
    # assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs
    im_info = [np.array([roidb[i]['im_info']], dtype=np.float32) for i in range(len(roidb))]

    im_rois = [roidb[i]['boxes'] for i in range(len(roidb))]
    rois = im_rois
    rois_array = [np.hstack((0 * np.ones((rois[i].shape[0], 1)), rois[i])) for i in range(len(rois))]

    data = [{'data': im_array[i],
             'rois': rois_array[i]} for i in range(len(roidb))]
    label = {}

    return data, label, im_info
Example #10
Source File: rpn.py    From Accel with MIT License 6 votes vote down vote up 
def get_rpn_testbatch(roidb, cfg):
    """
    return a dict of testbatch
    :param roidb: ['image', 'flipped']
    :return: data, label, im_info
    """
    # assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs
    im_info = [np.array([roidb[i]['im_info']], dtype=np.float32) for i in range(len(roidb))]

    data = [{'data': im_array[i],
            'im_info': im_info[i]} for i in range(len(roidb))]
    label = {}

    return data, label, im_info
Example #11
Source File: rpn.py    From Accel with MIT License 6 votes vote down vote up 
def get_rpn_batch(roidb, cfg):
    """
    prototype for rpn batch: data, im_info, gt_boxes
    :param roidb: ['image', 'flipped'] + ['gt_boxes', 'boxes', 'gt_classes']
    :return: data, label
    """
    assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs[0]
    im_info = np.array([roidb[0]['im_info']], dtype=np.float32)

    # gt boxes: (x1, y1, x2, y2, cls)
    if roidb[0]['gt_classes'].size > 0:
        gt_inds = np.where(roidb[0]['gt_classes'] != 0)[0]
        gt_boxes = np.empty((roidb[0]['boxes'].shape[0], 5), dtype=np.float32)
        gt_boxes[:, 0:4] = roidb[0]['boxes'][gt_inds, :]
        gt_boxes[:, 4] = roidb[0]['gt_classes'][gt_inds]
    else:
        gt_boxes = np.empty((0, 5), dtype=np.float32)

    data = {'data': im_array,
            'im_info': im_info}
    label = {'gt_boxes': gt_boxes}

    return data, label
Example #12
Source File: rcnn.py    From Deformable-ConvNets with MIT License 6 votes vote down vote up 
def get_rcnn_testbatch(roidb, cfg):
    """
    return a dict of testbatch
    :param roidb: ['image', 'flipped'] + ['boxes']
    :return: data, label, im_info
    """
    # assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs
    im_info = [np.array([roidb[i]['im_info']], dtype=np.float32) for i in range(len(roidb))]

    im_rois = [roidb[i]['boxes'] for i in range(len(roidb))]
    rois = im_rois
    rois_array = [np.hstack((0 * np.ones((rois[i].shape[0], 1)), rois[i])) for i in range(len(rois))]

    data = [{'data': im_array[i],
             'rois': rois_array[i]} for i in range(len(roidb))]
    label = {}

    return data, label, im_info
Example #13
Source File: rcnn.py    From Accel with MIT License 6 votes vote down vote up 
def get_rcnn_testbatch(roidb, cfg):
    """
    return a dict of testbatch
    :param roidb: ['image', 'flipped'] + ['boxes']
    :return: data, label, im_info
    """
    # assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs
    im_info = [np.array([roidb[i]['im_info']], dtype=np.float32) for i in range(len(roidb))]

    im_rois = [roidb[i]['boxes'] for i in range(len(roidb))]
    rois = im_rois
    rois_array = [np.hstack((0 * np.ones((rois[i].shape[0], 1)), rois[i])) for i in range(len(rois))]

    data = [{'data': im_array[i],
             'rois': rois_array[i]} for i in range(len(roidb))]
    label = {}

    return data, label, im_info
Example #14
Source File: rcnn.py    From Faster_RCNN_for_DOTA with Apache License 2.0 6 votes vote down vote up 
def get_rcnn_testbatch(roidb, cfg):
    """
    return a dict of testbatch
    :param roidb: ['image', 'flipped'] + ['boxes']
    :return: data, label, im_info
    """
    # assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs
    im_info = [np.array([roidb[i]['im_info']], dtype=np.float32) for i in range(len(roidb))]

    im_rois = [roidb[i]['boxes'] for i in range(len(roidb))]
    rois = im_rois
    rois_array = [np.hstack((0 * np.ones((rois[i].shape[0], 1)), rois[i])) for i in range(len(rois))]

    data = [{'data': im_array[i],
             'rois': rois_array[i]} for i in range(len(roidb))]
    label = {}

    return data, label, im_info
Example #15
Source File: rpn.py    From Sequence-Level-Semantics-Aggregation with Apache License 2.0 6 votes vote down vote up 
def get_rpn_testbatch(roidb, cfg):
    """
    return a dict of testbatch
    :param roidb: ['image', 'flipped']
    :return: data, label, im_info
    """
    # assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs
    im_info = [np.array([roidb[i]['im_info']], dtype=np.float32) for i in range(len(roidb))]

    data = [{'data': im_array[i],
            'im_info': im_info[i]} for i in range(len(roidb))]
    label = {}

    return data, label, im_info
Example #16
Source File: rpn.py    From Sequence-Level-Semantics-Aggregation with Apache License 2.0 6 votes vote down vote up 
def get_rpn_batch(roidb, cfg):
    """
    prototype for rpn batch: data, im_info, gt_boxes
    :param roidb: ['image', 'flipped'] + ['gt_boxes', 'boxes', 'gt_classes']
    :return: data, label
    """
    assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs[0]
    im_info = np.array([roidb[0]['im_info']], dtype=np.float32)

    # gt boxes: (x1, y1, x2, y2, cls)
    if roidb[0]['gt_classes'].size > 0:
        gt_inds = np.where(roidb[0]['gt_classes'] != 0)[0]
        gt_boxes = np.empty((roidb[0]['boxes'].shape[0], 5), dtype=np.float32)
        gt_boxes[:, 0:4] = roidb[0]['boxes'][gt_inds, :]
        gt_boxes[:, 4] = roidb[0]['gt_classes'][gt_inds]
    else:
        gt_boxes = np.empty((0, 5), dtype=np.float32)

    data = {'data': im_array,
            'im_info': im_info}
    label = {'gt_boxes': gt_boxes}

    return data, label
Example #17
Source File: rcnn.py    From Sequence-Level-Semantics-Aggregation with Apache License 2.0 6 votes vote down vote up 
def get_rcnn_testbatch(roidb, cfg):
    """
    return a dict of testbatch
    :param roidb: ['image', 'flipped'] + ['boxes']
    :return: data, label, im_info
    """
    # assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs
    im_info = [np.array([roidb[i]['im_info']], dtype=np.float32) for i in range(len(roidb))]

    im_rois = [roidb[i]['boxes'] for i in range(len(roidb))]
    rois = im_rois
    rois_array = [np.hstack((0 * np.ones((rois[i].shape[0], 1)), rois[i])) for i in range(len(rois))]

    data = [{'data': im_array[i],
             'rois': rois_array[i]} for i in range(len(roidb))]
    label = {}

    return data, label, im_info
Example #18
Source File: rcnn.py    From Decoupled-Classification-Refinement with MIT License 6 votes vote down vote up 
def get_rcnn_testbatch(roidb, cfg):
    """
    return a dict of testbatch
    :param roidb: ['image', 'flipped'] + ['boxes']
    :return: data, label, im_info
    """
    # assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs
    im_info = [np.array([roidb[i]['im_info']], dtype=np.float32) for i in range(len(roidb))]

    im_rois = [roidb[i]['boxes'] for i in range(len(roidb))]
    rois = im_rois
    rois_array = [np.hstack((0 * np.ones((rois[i].shape[0], 1)), rois[i])) for i in range(len(rois))]

    data = [{'data': im_array[i],
             'rois': rois_array[i]} for i in range(len(roidb))]
    label = {}

    return data, label, im_info
Example #19
Source File: rcnn.py    From Decoupled-Classification-Refinement with MIT License 6 votes vote down vote up 
def get_rcnn_testbatch(roidb, cfg):
    """
    return a dict of testbatch
    :param roidb: ['image', 'flipped'] + ['boxes']
    :return: data, label, im_info
    """
    # assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs
    im_info = [np.array([roidb[i]['im_info']], dtype=np.float32) for i in range(len(roidb))]

    im_rois = [roidb[i]['boxes'] for i in range(len(roidb))]
    rois = im_rois
    rois_array = [np.hstack((0 * np.ones((rois[i].shape[0], 1)), rois[i])) for i in range(len(rois))]

    data = [{'data': im_array[i],
             'rois': rois_array[i]} for i in range(len(roidb))]
    label = {}

    return data, label, im_info
Example #20
Source File: rcnn.py    From Decoupled-Classification-Refinement with MIT License 6 votes vote down vote up 
def get_rcnn_testbatch(roidb, cfg):
    """
    return a dict of testbatch
    :param roidb: ['image', 'flipped'] + ['boxes']
    :return: data, label, im_info
    """
    # assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs
    im_info = [np.array([roidb[i]['im_info']], dtype=np.float32) for i in range(len(roidb))]

    im_rois = [roidb[i]['boxes'] for i in range(len(roidb))]
    rois = im_rois
    rois_array = [np.hstack((0 * np.ones((rois[i].shape[0], 1)), rois[i])) for i in range(len(rois))]

    data = [{'data': im_array[i],
             'rois': rois_array[i]} for i in range(len(roidb))]
    label = {}

    return data, label, im_info
Example #21
Source File: rpn.py    From Decoupled-Classification-Refinement with MIT License 6 votes vote down vote up 
def get_rpn_testbatch(roidb, cfg):
    """
    return a dict of testbatch
    :param roidb: ['image', 'flipped']
    :return: data, label, im_info
    """
    # assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs
    im_info = [np.array([roidb[i]['im_info']], dtype=np.float32) for i in range(len(roidb))]

    data = [{'data': im_array[i],
            'im_info': im_info[i]} for i in range(len(roidb))]
    label = {}

    return data, label, im_info
Example #22
Source File: rpn.py    From Decoupled-Classification-Refinement with MIT License 6 votes vote down vote up 
def get_rpn_batch(roidb, cfg):
    """
    prototype for rpn batch: data, im_info, gt_boxes
    :param roidb: ['image', 'flipped'] + ['gt_boxes', 'boxes', 'gt_classes']
    :return: data, label
    """
    assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs[0]
    im_info = np.array([roidb[0]['im_info']], dtype=np.float32)

    # gt boxes: (x1, y1, x2, y2, cls)
    if roidb[0]['gt_classes'].size > 0:
        gt_inds = np.where(roidb[0]['gt_classes'] != 0)[0]
        gt_boxes = np.empty((roidb[0]['boxes'].shape[0], 5), dtype=np.float32)
        gt_boxes[:, 0:4] = roidb[0]['boxes'][gt_inds, :]
        gt_boxes[:, 4] = roidb[0]['gt_classes'][gt_inds]
    else:
        gt_boxes = np.empty((0, 5), dtype=np.float32)

    data = {'data': im_array,
            'im_info': im_info}
    label = {'gt_boxes': gt_boxes}

    return data, label
Example #23
Source File: rpn.py    From MANet_for_Video_Object_Detection with Apache License 2.0 6 votes vote down vote up 
def get_rpn_batch(roidb, cfg):
    """
    prototype for rpn batch: data, im_info, gt_boxes
    :param roidb: ['image', 'flipped'] + ['gt_boxes', 'boxes', 'gt_classes']
    :return: data, label
    """
    assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs[0]
    im_info = np.array([roidb[0]['im_info']], dtype=np.float32)

    # gt boxes: (x1, y1, x2, y2, cls)
    if roidb[0]['gt_classes'].size > 0:
        gt_inds = np.where(roidb[0]['gt_classes'] != 0)[0]
        gt_boxes = np.empty((roidb[0]['boxes'].shape[0], 5), dtype=np.float32)
        gt_boxes[:, 0:4] = roidb[0]['boxes'][gt_inds, :]
        gt_boxes[:, 4] = roidb[0]['gt_classes'][gt_inds]
    else:
        gt_boxes = np.empty((0, 5), dtype=np.float32)

    data = {'data': im_array,
            'im_info': im_info}
    label = {'gt_boxes': gt_boxes}

    return data, label
Example #24
Source File: rpn.py    From Deep-Feature-Flow-Segmentation with MIT License 6 votes vote down vote up 
def get_rpn_batch(roidb, cfg):
    """
    prototype for rpn batch: data, im_info, gt_boxes
    :param roidb: ['image', 'flipped'] + ['gt_boxes', 'boxes', 'gt_classes']
    :return: data, label
    """
    assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs[0]
    im_info = np.array([roidb[0]['im_info']], dtype=np.float32)

    # gt boxes: (x1, y1, x2, y2, cls)
    if roidb[0]['gt_classes'].size > 0:
        gt_inds = np.where(roidb[0]['gt_classes'] != 0)[0]
        gt_boxes = np.empty((roidb[0]['boxes'].shape[0], 5), dtype=np.float32)
        gt_boxes[:, 0:4] = roidb[0]['boxes'][gt_inds, :]
        gt_boxes[:, 4] = roidb[0]['gt_classes'][gt_inds]
    else:
        gt_boxes = np.empty((0, 5), dtype=np.float32)

    data = {'data': im_array,
            'im_info': im_info}
    label = {'gt_boxes': gt_boxes}

    return data, label
Example #25
Source File: rcnn.py    From kaggle-rsna18 with MIT License 6 votes vote down vote up 
def get_rcnn_testbatch(roidb, cfg):
    """
    return a dict of testbatch
    :param roidb: ['image', 'flipped'] + ['boxes']
    :return: data, label, im_info
    """
    # assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs
    im_info = [np.array([roidb[i]['im_info']], dtype=np.float32) for i in range(len(roidb))]

    im_rois = [roidb[i]['boxes'] for i in range(len(roidb))]
    rois = im_rois
    rois_array = [np.hstack((0 * np.ones((rois[i].shape[0], 1)), rois[i])) for i in range(len(rois))]

    data = [{'data': im_array[i],
             'rois': rois_array[i]} for i in range(len(roidb))]
    label = {}

    return data, label, im_info
Example #26
Source File: rpn.py    From kaggle-rsna18 with MIT License 6 votes vote down vote up 
def get_rpn_testbatch(roidb, cfg):
    """
    return a dict of testbatch
    :param roidb: ['image', 'flipped']
    :return: data, label, im_info
    """
    # assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs
    im_info = [np.array([roidb[i]['im_info']], dtype=np.float32) for i in range(len(roidb))]

    data = [{'data': im_array[i],
            'im_info': im_info[i]} for i in range(len(roidb))]
    label = {}

    return data, label, im_info
Example #27
Source File: rpn.py    From kaggle-rsna18 with MIT License 6 votes vote down vote up 
def get_rpn_batch(roidb, cfg):
    """
    prototype for rpn batch: data, im_info, gt_boxes
    :param roidb: ['image', 'flipped'] + ['gt_boxes', 'boxes', 'gt_classes']
    :return: data, label
    """
    assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs[0]
    im_info = np.array([roidb[0]['im_info']], dtype=np.float32)

    # gt boxes: (x1, y1, x2, y2, cls)
    if roidb[0]['gt_classes'].size > 0:
        gt_inds = np.where(roidb[0]['gt_classes'] != 0)[0]
        gt_boxes = np.empty((roidb[0]['boxes'].shape[0], 5), dtype=np.float32)
        gt_boxes[:, 0:4] = roidb[0]['boxes'][gt_inds, :]
        gt_boxes[:, 4] = roidb[0]['gt_classes'][gt_inds]
    else:
        gt_boxes = np.empty((0, 5), dtype=np.float32)

    data = {'data': im_array,
            'im_info': im_info}
    label = {'gt_boxes': gt_boxes}

    return data, label
Example #28
Source File: rcnn.py    From kaggle-rsna18 with MIT License 6 votes vote down vote up 
def get_rcnn_testbatch(roidb, cfg):
    """
    return a dict of testbatch
    :param roidb: ['image', 'flipped'] + ['boxes']
    :return: data, label, im_info
    """
    # assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs
    im_info = [np.array([roidb[i]['im_info']], dtype=np.float32) for i in range(len(roidb))]

    im_rois = [roidb[i]['boxes'] for i in range(len(roidb))]
    rois = im_rois
    rois_array = [np.hstack((0 * np.ones((rois[i].shape[0], 1)), rois[i])) for i in range(len(rois))]

    data = [{'data': im_array[i],
             'rois': rois_array[i]} for i in range(len(roidb))]
    label = {}

    return data, label, im_info
Example #29
Source File: rcnn.py    From kaggle-rsna18 with MIT License 6 votes vote down vote up 
def get_rcnn_testbatch(roidb, cfg):
    """
    return a dict of testbatch
    :param roidb: ['image', 'flipped'] + ['boxes']
    :return: data, label, im_info
    """
    # assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs
    im_info = [np.array([roidb[i]['im_info']], dtype=np.float32) for i in range(len(roidb))]

    im_rois = [roidb[i]['boxes'] for i in range(len(roidb))]
    rois = im_rois
    rois_array = [np.hstack((0 * np.ones((rois[i].shape[0], 1)), rois[i])) for i in range(len(rois))]

    data = [{'data': im_array[i],
             'rois': rois_array[i]} for i in range(len(roidb))]
    label = {}

    return data, label, im_info
Example #30
Source File: rpn.py    From kaggle-rsna18 with MIT License 6 votes vote down vote up 
def get_rpn_testbatch(roidb, cfg):
    """
    return a dict of testbatch
    :param roidb: ['image', 'flipped']
    :return: data, label, im_info
    """
    # assert len(roidb) == 1, 'Single batch only'
    imgs, roidb = get_image(roidb, cfg)
    im_array = imgs
    im_info = [np.array([roidb[i]['im_info']], dtype=np.float32) for i in range(len(roidb))]

    data = [{'data': im_array[i],
            'im_info': im_info[i]} for i in range(len(roidb))]
    label = {}

    return data, label, im_info