Python utils.blob.get_image_blob() Examples

The following are 15 code examples of utils.blob.get_image_blob(). 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.blob , or try the search function .
Example #1
Source File: test.py    From Detectron.pytorch with MIT License 5 votes vote down vote up 
def _get_blobs(im, rois, target_scale, target_max_size):
    """Convert an image and RoIs within that image into network inputs."""
    blobs = {}
    blobs['data'], im_scale, blobs['im_info'] = \
        blob_utils.get_image_blob(im, target_scale, target_max_size)
    if rois is not None:
        blobs['rois'] = _get_rois_blob(rois, im_scale)
    return blobs, im_scale
Example #2
Source File: test.py    From FPN-Pytorch with MIT License 5 votes vote down vote up 
def _get_blobs(im, rois, target_scale, target_max_size):
    """Convert an image and RoIs within that image into network inputs."""
    blobs = {}
    blobs['data'], im_scale, blobs['im_info'] = \
        blob_utils.get_image_blob(im, target_scale, target_max_size)
    if rois is not None:
        blobs['rois'] = _get_rois_blob(rois, im_scale)
    return blobs, im_scale
Example #3
Source File: test.py    From pcl.pytorch with MIT License 5 votes vote down vote up 
def _get_blobs(im, rois, target_scale, target_max_size):
    """Convert an image and RoIs within that image into network inputs."""
    blobs = {}
    blobs['data'], im_scale = \
        blob_utils.get_image_blob(im, target_scale, target_max_size)
    if rois is not None:
        blobs['rois'] = _get_rois_blob(rois, im_scale)
    blobs['labels'] = np.zeros((1, cfg.MODEL.NUM_CLASSES), dtype=np.int32)
    return blobs, im_scale
Example #4
Source File: test.py    From Detectron.pytorch with MIT License 5 votes vote down vote up 
def _get_blobs(im, rois, target_scale, target_max_size):
    """Convert an image and RoIs within that image into network inputs."""
    blobs = {}
    blobs['data'], im_scale, blobs['im_info'] = \
        blob_utils.get_image_blob(im, target_scale, target_max_size)
    if rois is not None:
        blobs['rois'] = _get_rois_blob(rois, im_scale)
    return blobs, im_scale
Example #5
Source File: test.py    From Context-aware-ZSR with MIT License 5 votes vote down vote up 
def _get_blobs(im, rois, target_scale, target_max_size):
    """Convert an image and RoIs within that image into network inputs."""
    blobs = {}
    blobs['data'], im_scale, blobs['im_info'] = \
        blob_utils.get_image_blob(im, target_scale, target_max_size)
    if rois is not None:
        blobs['rois'] = _get_rois_blob(rois, im_scale)
    return blobs, im_scale
Example #6
Source File: rpn_generator.py    From Context-aware-ZSR with MIT License 5 votes vote down vote up 
def im_proposals(model, im, roidb=None):
    """Generate RPN proposals on a single image."""
    inputs = {}
    inputs['data'], im_scale, inputs['im_info'] = \
        blob_utils.get_image_blob(im, cfg.TEST.SCALE, cfg.TEST.MAX_SIZE)
    inputs['data'] = [torch.from_numpy(inputs['data'])]
    inputs['im_info'] = [torch.from_numpy(inputs['im_info'])]
    if roidb is not None:
        inputs['roidb'] = [[roidb]]
    return_dict = model(**inputs)

    if cfg.FPN.FPN_ON and cfg.FPN.MULTILEVEL_RPN:
        k_max = cfg.FPN.RPN_MAX_LEVEL
        k_min = cfg.FPN.RPN_MIN_LEVEL
        rois = [
            return_dict['rpn_rois_fpn' + str(l)]
            for l in range(k_min, k_max + 1)
        ]
        scores = [
            return_dict['rpn_rois_prob_fpn' + str(l)]
            for l in range(k_min, k_max + 1)
        ]
        # Combine predictions across all levels and retain the top scoring
        boxes = np.concatenate(rois)
        scores = np.concatenate(scores).squeeze()
        # Discussion: one could do NMS again after combining predictions from
        # the different FPN levels. Conceptually, it's probably the right thing
        # to do. For arbitrary reasons, the original FPN RPN implementation did
        # not do another round of NMS.
        inds = np.argsort(-scores)[:cfg.TEST.RPN_POST_NMS_TOP_N]
        scores = scores[inds]
        boxes = boxes[inds, :]
    else:
        boxes = return_dict['rpn_rois'].data.cpu().numpy()
        scores = return_dict['rpn_roi_probs'].data.cpu().numpy().squeeze()

    # Column 0 is the batch index in the (batch ind, x1, y1, x2, y2) encoding,
    # so we remove it since we just want to return boxes
    # Scale proposals back to the original input image scale
    boxes = boxes[:, 1:] / im_scale
    return boxes, scores
Example #7
Source File: test.py    From PANet with MIT License 5 votes vote down vote up 
def _get_blobs(im, rois, target_scale, target_max_size):
    """Convert an image and RoIs within that image into network inputs."""
    blobs = {}
    blobs['data'], im_scale, blobs['im_info'] = \
        blob_utils.get_image_blob(im, target_scale, target_max_size)
    if rois is not None:
        blobs['rois'] = _get_rois_blob(rois, im_scale)
    return blobs, im_scale
Example #8
Source File: test.py    From seg_every_thing with Apache License 2.0 5 votes vote down vote up 
def im_conv_body_only(model, im, target_scale, target_max_size):
    """Runs `model.conv_body_net` on the given image `im`."""
    im_blob, im_scale, _im_info = blob_utils.get_image_blob(
        im, target_scale, target_max_size
    )
    workspace.FeedBlob(core.ScopedName('data'), im_blob)
    workspace.RunNet(model.conv_body_net.Proto().name)
    return im_scale
Example #9
Source File: test.py    From seg_every_thing with Apache License 2.0 5 votes vote down vote up 
def _get_blobs(im, rois, target_scale, target_max_size):
    """Convert an image and RoIs within that image into network inputs."""
    blobs = {}
    blobs['data'], im_scale, blobs['im_info'] = \
        blob_utils.get_image_blob(im, target_scale, target_max_size)
    if rois is not None:
        blobs['rois'] = _get_rois_blob(rois, im_scale)
    return blobs, im_scale
Example #10
Source File: test.py    From PMFNet with MIT License 5 votes vote down vote up 
def _get_blobs(im, rois, target_scale, target_max_size):
    """Convert an image and RoIs within that image into network inputs."""
    blobs = {}
    blobs['data'], im_scale, blobs['im_info'] = \
        blob_utils.get_image_blob(im, target_scale, target_max_size)
    if rois is not None:
        blobs['rois'] = _get_rois_blob(rois, im_scale)
    return blobs, im_scale


# -------------------------- HOI ----------------------------
Example #11
Source File: test.py    From Large-Scale-VRD.pytorch with MIT License 5 votes vote down vote up 
def _get_blobs(im, rois, target_scale, target_max_size):
    """Convert an image and RoIs within that image into network inputs."""
    blobs = {}
    blobs['data'], im_scale, blobs['im_info'] = \
        blob_utils.get_image_blob(im, target_scale, target_max_size)
    if rois is not None:
        blobs['rois'] = _get_rois_blob(rois, im_scale)
    return blobs, im_scale
Example #12
Source File: test_rel.py    From Large-Scale-VRD.pytorch with MIT License 5 votes vote down vote up 
def _get_blobs(im, rois, target_scale, target_max_size):
    """Convert an image and RoIs within that image into network inputs."""
    blobs = {}
    blobs['data'], im_scale, blobs['im_info'] = \
        blob_utils.get_image_blob(im, target_scale, target_max_size)
    if rois is not None:
        blobs['rois'] = _get_rois_blob(rois, im_scale)
    return blobs, im_scale
Example #13
Source File: test.py    From detectron-self-train with MIT License 5 votes vote down vote up 
def _get_blobs(im, rois, target_scale, target_max_size):
    """Convert an image and RoIs within that image into network inputs."""
    blobs = {}
    blobs['data'], im_scale, blobs['im_info'] = \
        blob_utils.get_image_blob(im, target_scale, target_max_size)
    if rois is not None:
        blobs['rois'] = _get_rois_blob(rois, im_scale)
    return blobs, im_scale
Example #14
Source File: test.py    From DIoU-pytorch-detectron with GNU General Public License v3.0 5 votes vote down vote up 
def _get_blobs(im, rois, target_scale, target_max_size):
    """Convert an image and RoIs within that image into network inputs."""
    blobs = {}
    blobs['data'], im_scale, blobs['im_info'] = \
        blob_utils.get_image_blob(im, target_scale, target_max_size)
    if rois is not None:
        blobs['rois'] = _get_rois_blob(rois, im_scale)
    return blobs, im_scale
Example #15
Source File: rpn_generator.py    From seg_every_thing with Apache License 2.0 4 votes vote down vote up 
def im_proposals(model, im):
    """Generate RPN proposals on a single image."""
    inputs = {}
    inputs['data'], im_scale, inputs['im_info'] = \
        blob_utils.get_image_blob(im, cfg.TEST.SCALE, cfg.TEST.MAX_SIZE)
    for k, v in inputs.items():
        workspace.FeedBlob(core.ScopedName(k), v.astype(np.float32, copy=False))
    workspace.RunNet(model.net.Proto().name)

    if cfg.FPN.FPN_ON and cfg.FPN.MULTILEVEL_RPN:
        k_max = cfg.FPN.RPN_MAX_LEVEL
        k_min = cfg.FPN.RPN_MIN_LEVEL
        rois_names = [
            core.ScopedName('rpn_rois_fpn' + str(l))
            for l in range(k_min, k_max + 1)
        ]
        score_names = [
            core.ScopedName('rpn_roi_probs_fpn' + str(l))
            for l in range(k_min, k_max + 1)
        ]
        blobs = workspace.FetchBlobs(rois_names + score_names)
        # Combine predictions across all levels and retain the top scoring
        boxes = np.concatenate(blobs[:len(rois_names)])
        scores = np.concatenate(blobs[len(rois_names):]).squeeze()
        # Discussion: one could do NMS again after combining predictions from
        # the different FPN levels. Conceptually, it's probably the right thing
        # to do. For arbitrary reasons, the original FPN RPN implementation did
        # not do another round of NMS.
        inds = np.argsort(-scores)[:cfg.TEST.RPN_POST_NMS_TOP_N]
        scores = scores[inds]
        boxes = boxes[inds, :]
    else:
        boxes, scores = workspace.FetchBlobs(
            [core.ScopedName('rpn_rois'),
             core.ScopedName('rpn_roi_probs')]
        )
        scores = scores.squeeze()

    # Column 0 is the batch index in the (batch ind, x1, y1, x2, y2) encoding,
    # so we remove it since we just want to return boxes
    # Scale proposals back to the original input image scale
    boxes = boxes[:, 1:] / im_scale
    return boxes, scores