Python boxes.nms方法代码示例

# 需要导入模块: from utils import boxes [as 别名]
# 或者: from utils.boxes import nms [as 别名]
def im_detect_all(model, im, box_proposals=None, timers=None):
    """Process the outputs of model for testing
    Args:
      model: the network module
      im_data: Pytorch variable. Input batch to the model.
      im_info: Pytorch variable. Input batch to the model.
      gt_boxes: Pytorch variable. Input batch to the model.
      num_boxes: Pytorch variable. Input batch to the model.
      args: arguments from command line.
      timer: record the cost of time for different steps
    The rest of inputs are of type pytorch Variables and either input to or output from the model.
    """
    if timers is None:
        timers = defaultdict(Timer)

    timers['im_detect_bbox'].tic()
    if cfg.TEST.BBOX_AUG.ENABLED:
        scores, boxes, im_scale, blob_conv = im_detect_bbox_aug(
            model, im, box_proposals)
    else:
        scores, boxes, im_scale, blob_conv = im_detect_bbox(
            model, im, cfg.TEST.SCALE, cfg.TEST.MAX_SIZE, box_proposals)
    timers['im_detect_bbox'].toc()

    # score and boxes are from the whole image after score thresholding and nms
    # (they are not separated by class) (numpy.ndarray)
    # cls_boxes boxes and scores are separated by class and in the format used
    # for evaluating results
    # timers['misc_bbox'].tic()
    # scores, boxes, cls_boxes = box_results_with_nms_and_limit(scores, boxes)
    # timers['misc_bbox'].toc()

    return {'scores': scores, 'boxes' : boxes} 

# 需要导入模块: from utils import boxes [as 别名]
# 或者: from utils.boxes import nms [as 别名]
def im_detect_all(model, im, box_proposals=None, timers=None):
    """Process the outputs of model for testing
    Args:
      model: the network module
      im_data: Pytorch variable. Input batch to the model.
      im_info: Pytorch variable. Input batch to the model.
      gt_boxes: Pytorch variable. Input batch to the model.
      num_boxes: Pytorch variable. Input batch to the model.
      args: arguments from command line.
      timer: record the cost of time for different steps
    The rest of inputs are of type pytorch Variables and either input to or output from the model.
    """
    if timers is None:
        timers = defaultdict(Timer)

    timers['im_detect_bbox'].tic()
    if cfg.TEST.BBOX_AUG.ENABLED:
        scores, boxes, im_scale, blob_conv = im_detect_bbox_aug(
            model, im, box_proposals)
    else:
        scores, boxes, im_scale, blob_conv = im_detect_bbox(
            model, im, cfg.TEST.SCALE, cfg.TEST.MAX_SIZE, box_proposals)
    timers['im_detect_bbox'].toc()

    # score and boxes are from the whole image after score thresholding and nms
    # (they are not separated by class) (numpy.ndarray)
    # cls_boxes boxes and scores are separated by class and in the format used
    # for evaluating results
    timers['misc_bbox'].tic()
    scores, boxes, cls_boxes = box_results_with_nms_and_limit(scores, boxes)
    timers['misc_bbox'].toc()

    cls_segms = None
    cls_keyps = None

    return cls_boxes, cls_segms, cls_keyps 

# 需要导入模块: from utils import boxes [as 别名]
# 或者: from utils.boxes import nms [as 别名]
def im_detect_all(model, im, box_proposals=None, timers=None):
    """Process the outputs of model for testing
    Args:
      model: the network module
      im_data: Pytorch variable. Input batch to the model.
      im_info: Pytorch variable. Input batch to the model.
      gt_boxes: Pytorch variable. Input batch to the model.
      num_boxes: Pytorch variable. Input batch to the model.
      args: arguments from command line.
      timer: record the cost of time for different steps
    The rest of inputs are of type pytorch Variables and either input to or output from the model.
    """
    if timers is None:
        timers = defaultdict(Timer)

    timers['im_detect_bbox'].tic()
    if cfg.TEST.BBOX_AUG.ENABLED:
        scores, boxes, im_scale, blob_conv = im_detect_bbox_aug(
            model, im, box_proposals)
    else:
        scores, boxes, im_scale, blob_conv = im_detect_bbox(
            model, im, cfg.TEST.SCALE, cfg.TEST.MAX_SIZE, box_proposals)
    timers['im_detect_bbox'].toc()

    # score and boxes are from the whole image after score thresholding and nms
    # (they are not separated by class) (numpy.ndarray)
    # cls_boxes boxes and scores are separated by class and in the format used
    # for evaluating results
    timers['misc_bbox'].tic()
    scores, boxes, cls_boxes = box_results_with_nms_and_limit(scores, boxes)
    timers['misc_bbox'].toc()

    if cfg.MODEL.MASK_ON and boxes.shape[0] > 0:
        timers['im_detect_mask'].tic()
        if cfg.TEST.MASK_AUG.ENABLED:
            masks = im_detect_mask_aug(model, im, boxes, im_scale, blob_conv)
        else:
            masks = im_detect_mask(model, im_scale, boxes, blob_conv)
        timers['im_detect_mask'].toc()

        timers['misc_mask'].tic()
        cls_segms = segm_results(cls_boxes, masks, boxes, im.shape[0], im.shape[1])
        timers['misc_mask'].toc()
    else:
        cls_segms = None

    if cfg.MODEL.KEYPOINTS_ON and boxes.shape[0] > 0:
        timers['im_detect_keypoints'].tic()
        if cfg.TEST.KPS_AUG.ENABLED:
            heatmaps = im_detect_keypoints_aug(model, im, boxes, im_scale, blob_conv)
        else:
            heatmaps = im_detect_keypoints(model, im_scale, boxes, blob_conv)
        timers['im_detect_keypoints'].toc()

        timers['misc_keypoints'].tic()
        cls_keyps = keypoint_results(cls_boxes, heatmaps, boxes)
        timers['misc_keypoints'].toc()
    else:
        cls_keyps = None

    return cls_boxes, cls_segms, cls_keyps 

# 需要导入模块: from utils import boxes [as 别名]
# 或者: from utils.boxes import nms [as 别名]
def box_results_with_nms_and_limit(scores, boxes):  # NOTE: support single-batch
    """Returns bounding-box detection results by thresholding on scores and
    applying non-maximum suppression (NMS).

    `boxes` has shape (#detections, 4 * #classes), where each row represents
    a list of predicted bounding boxes for each of the object classes in the
    dataset (including the background class). The detections in each row
    originate from the same object proposal.

    `scores` has shape (#detection, #classes), where each row represents a list
    of object detection confidence scores for each of the object classes in the
    dataset (including the background class). `scores[i, j]`` corresponds to the
    box at `boxes[i, j * 4:(j + 1) * 4]`.
    """
    num_classes = cfg.MODEL.NUM_CLASSES
    cls_boxes = [[] for _ in range(num_classes)]
    # Apply threshold on detection probabilities and apply NMS
    # Skip j = 0, because it's the background class
    for j in range(1, num_classes):
        inds = np.where(scores[:, j] > cfg.TEST.SCORE_THRESH)[0]
        scores_j = scores[inds, j]
        boxes_j = boxes[inds, j * 4:(j + 1) * 4]
        dets_j = np.hstack((boxes_j, scores_j[:, np.newaxis])).astype(np.float32, copy=False)
        if cfg.TEST.SOFT_NMS.ENABLED:
            nms_dets, _ = box_utils.soft_nms(
                dets_j,
                sigma=cfg.TEST.SOFT_NMS.SIGMA,
                overlap_thresh=cfg.TEST.NMS,
                score_thresh=0.0001,
                method=cfg.TEST.SOFT_NMS.METHOD
            )
        else:
            keep = box_utils.nms(dets_j, cfg.TEST.NMS)
            nms_dets = dets_j[keep, :]
        # Refine the post-NMS boxes using bounding-box voting
        if cfg.TEST.BBOX_VOTE.ENABLED:
            nms_dets = box_utils.box_voting(
                nms_dets,
                dets_j,
                cfg.TEST.BBOX_VOTE.VOTE_TH,
                scoring_method=cfg.TEST.BBOX_VOTE.SCORING_METHOD
            )
        cls_boxes[j] = nms_dets

    # Limit to max_per_image detections **over all classes**
    if cfg.TEST.DETECTIONS_PER_IM > 0:
        image_scores = np.hstack(
            [cls_boxes[j][:, -1] for j in range(1, num_classes)]
        )
        if len(image_scores) > cfg.TEST.DETECTIONS_PER_IM:
            image_thresh = np.sort(image_scores)[-cfg.TEST.DETECTIONS_PER_IM]
            for j in range(1, num_classes):
                keep = np.where(cls_boxes[j][:, -1] >= image_thresh)[0]
                cls_boxes[j] = cls_boxes[j][keep, :]

    im_results = np.vstack([cls_boxes[j] for j in range(1, num_classes)])
    boxes = im_results[:, :-1]
    scores = im_results[:, -1]
    return scores, boxes, cls_boxes 

# 需要导入模块: from utils import boxes [as 别名]
# 或者: from utils.boxes import nms [as 别名]
def box_results_with_nms_and_limit(scores, boxes):  # NOTE: support single-batch
    """Returns bounding-box detection results by thresholding on scores and
    applying non-maximum suppression (NMS).

    `boxes` has shape (#detections, 4 * #classes), where each row represents
    a list of predicted bounding boxes for each of the object classes in the
    dataset (including the background class). The detections in each row
    originate from the same object proposal.

    `scores` has shape (#detection, #classes), where each row represents a list
    of object detection confidence scores for each of the object classes in the
    dataset (including the background class). `scores[i, j]`` corresponds to the
    box at `boxes[i, j * 4:(j + 1) * 4]`.
    """
    num_classes = cfg.MODEL.NUM_CLASSES + 1
    cls_boxes = [[] for _ in range(num_classes)]
    # Apply threshold on detection probabilities and apply NMS
    # Skip j = 0, because it's the background class
    for j in range(1, num_classes):
        inds = np.where(scores[:, j] > cfg.TEST.SCORE_THRESH)[0]
        scores_j = scores[inds, j]
        boxes_j = boxes[inds, :]
        dets_j = np.hstack((boxes_j, scores_j[:, np.newaxis])).astype(np.float32, copy=False)
        if cfg.TEST.SOFT_NMS.ENABLED:
            nms_dets, _ = box_utils.soft_nms(
                dets_j,
                sigma=cfg.TEST.SOFT_NMS.SIGMA,
                overlap_thresh=cfg.TEST.NMS,
                score_thresh=0.0001,
                method=cfg.TEST.SOFT_NMS.METHOD
            )
        else:
            keep = box_utils.nms(dets_j, cfg.TEST.NMS)
            nms_dets = dets_j[keep, :]
        # Refine the post-NMS boxes using bounding-box voting
        if cfg.TEST.BBOX_VOTE.ENABLED:
            nms_dets = box_utils.box_voting(
                nms_dets,
                dets_j,
                cfg.TEST.BBOX_VOTE.VOTE_TH,
                scoring_method=cfg.TEST.BBOX_VOTE.SCORING_METHOD
            )
        cls_boxes[j] = nms_dets

    # Limit to max_per_image detections **over all classes**
    if cfg.TEST.DETECTIONS_PER_IM > 0:
        image_scores = np.hstack(
            [cls_boxes[j][:, -1] for j in range(1, num_classes)]
        )
        if len(image_scores) > cfg.TEST.DETECTIONS_PER_IM:
            image_thresh = np.sort(image_scores)[-cfg.TEST.DETECTIONS_PER_IM]
            for j in range(1, num_classes):
                keep = np.where(cls_boxes[j][:, -1] >= image_thresh)[0]
                cls_boxes[j] = cls_boxes[j][keep, :]

    im_results = np.vstack([cls_boxes[j] for j in range(1, num_classes)])
    boxes = im_results[:, :-1]
    scores = im_results[:, -1]
    return scores, boxes, cls_boxes 

# 需要导入模块: from utils import boxes [as 别名]
# 或者: from utils.boxes import nms [as 别名]
def box_results_with_nms_and_limit(scores, boxes):  # NOTE: support single-batch
    """Returns bounding-box detection results by thresholding on scores and
    applying non-maximum suppression (NMS).

    `boxes` has shape (#detections, 4 * #classes), where each row represents
    a list of predicted bounding boxes for each of the object classes in the
    dataset (including the background class). The detections in each row
    originate from the same object proposal.

    `scores` has shape (#detection, #classes), where each row represents a list
    of object detection confidence scores for each of the object classes in the
    dataset (including the background class). `scores[i, j]`` corresponds to the
    box at `boxes[i, j * 4:(j + 1) * 4]`.
    """
    num_classes = cfg.MODEL.NUM_CLASSES
    cls_boxes = [[] for _ in range(num_classes)]
    # Apply threshold on detection probabilities and apply NMS
    # Skip j = 0, because it's the background class
    for j in range(1, num_classes):
        inds = np.where(scores[:, j] > cfg.TEST.SCORE_THRESH)[0]
        scores_j = scores[inds, j]
        boxes_j = boxes[inds, j * 4:(j + 1) * 4]
        dets_j = np.hstack((boxes_j, scores_j[:, np.newaxis])).astype(np.float32, copy=False)
        if cfg.TEST.USE_GT_PROPOSALS:
            nms_dets = dets_j
        elif cfg.TEST.SOFT_NMS.ENABLED:
            nms_dets, _ = box_utils.soft_nms(
                dets_j,
                sigma=cfg.TEST.SOFT_NMS.SIGMA,
                overlap_thresh=cfg.TEST.NMS,
                score_thresh=0.0001,
                method=cfg.TEST.SOFT_NMS.METHOD
            )
        else:
            keep = box_utils.nms(dets_j, cfg.TEST.NMS)
            nms_dets = dets_j[keep, :]
        # Refine the post-NMS boxes using bounding-box voting
        if cfg.TEST.BBOX_VOTE.ENABLED:
            nms_dets = box_utils.box_voting(
                nms_dets,
                dets_j,
                cfg.TEST.BBOX_VOTE.VOTE_TH,
                scoring_method=cfg.TEST.BBOX_VOTE.SCORING_METHOD
            )
        cls_boxes[j] = nms_dets

    # Limit to max_per_image detections **over all classes**
    if cfg.TEST.DETECTIONS_PER_IM > 0:
        image_scores = np.hstack(
            [cls_boxes[j][:, -1] for j in range(1, num_classes)]
        )
        if len(image_scores) > cfg.TEST.DETECTIONS_PER_IM:
            image_thresh = np.sort(image_scores)[-cfg.TEST.DETECTIONS_PER_IM]
            for j in range(1, num_classes):
                keep = np.where(cls_boxes[j][:, -1] >= image_thresh)[0]
                cls_boxes[j] = cls_boxes[j][keep, :]

    im_results = np.vstack([cls_boxes[j] for j in range(1, num_classes)])
    boxes = im_results[:, :-1]
    scores = im_results[:, -1]
    return scores, boxes, cls_boxes 

# 需要导入模块: from utils import boxes [as 别名]
# 或者: from utils.boxes import nms [as 别名]
def im_detect_all(model, im, box_proposals, timers=None):
    if timers is None:
        timers = defaultdict(Timer)

    # Handle RetinaNet testing separately for now
    if cfg.RETINANET.RETINANET_ON:
        cls_boxes = test_retinanet.im_detect_bbox(model, im, timers)
        return cls_boxes, None, None

    timers['im_detect_bbox'].tic()
    if cfg.TEST.BBOX_AUG.ENABLED:
        scores, boxes, im_scale = im_detect_bbox_aug(model, im, box_proposals)
    else:
        scores, boxes, im_scale = im_detect_bbox(
            model, im, cfg.TEST.SCALE, cfg.TEST.MAX_SIZE, boxes=box_proposals
        )
    timers['im_detect_bbox'].toc()

    # score and boxes are from the whole image after score thresholding and nms
    # (they are not separated by class)
    # cls_boxes boxes and scores are separated by class and in the format used
    # for evaluating results
    timers['misc_bbox'].tic()
    scores, boxes, cls_boxes = box_results_with_nms_and_limit(scores, boxes)
    timers['misc_bbox'].toc()

    if cfg.MODEL.MASK_ON and boxes.shape[0] > 0:
        timers['im_detect_mask'].tic()
        if cfg.TEST.MASK_AUG.ENABLED:
            masks = im_detect_mask_aug(model, im, boxes)
        else:
            masks = im_detect_mask(model, im_scale, boxes)
        timers['im_detect_mask'].toc()

        timers['misc_mask'].tic()
        cls_segms = segm_results(
            cls_boxes, masks, boxes, im.shape[0], im.shape[1]
        )
        timers['misc_mask'].toc()
    else:
        cls_segms = None

    if cfg.MODEL.KEYPOINTS_ON and boxes.shape[0] > 0:
        timers['im_detect_keypoints'].tic()
        if cfg.TEST.KPS_AUG.ENABLED:
            heatmaps = im_detect_keypoints_aug(model, im, boxes)
        else:
            heatmaps = im_detect_keypoints(model, im_scale, boxes)
        timers['im_detect_keypoints'].toc()

        timers['misc_keypoints'].tic()
        cls_keyps = keypoint_results(cls_boxes, heatmaps, boxes)
        timers['misc_keypoints'].toc()
    else:
        cls_keyps = None

    return cls_boxes, cls_segms, cls_keyps 

# 需要导入模块: from utils import boxes [as 别名]
# 或者: from utils.boxes import nms [as 别名]
def box_results_with_nms_and_limit(scores, boxes):
    """Returns bounding-box detection results by thresholding on scores and
    applying non-maximum suppression (NMS).

    `boxes` has shape (#detections, 4 * #classes), where each row represents
    a list of predicted bounding boxes for each of the object classes in the
    dataset (including the background class). The detections in each row
    originate from the same object proposal.

    `scores` has shape (#detection, #classes), where each row represents a list
    of object detection confidence scores for each of the object classes in the
    dataset (including the background class). `scores[i, j]`` corresponds to the
    box at `boxes[i, j * 4:(j + 1) * 4]`.
    """
    num_classes = cfg.MODEL.NUM_CLASSES
    cls_boxes = [[] for _ in range(num_classes)]
    # Apply threshold on detection probabilities and apply NMS
    # Skip j = 0, because it's the background class
    for j in range(1, num_classes):
        inds = np.where(scores[:, j] > cfg.TEST.SCORE_THRESH)[0]
        scores_j = scores[inds, j]
        boxes_j = boxes[inds, j * 4:(j + 1) * 4]
        dets_j = np.hstack((boxes_j, scores_j[:, np.newaxis])).astype(
            np.float32, copy=False
        )
        if cfg.TEST.SOFT_NMS.ENABLED:
            nms_dets, _ = box_utils.soft_nms(
                dets_j,
                sigma=cfg.TEST.SOFT_NMS.SIGMA,
                overlap_thresh=cfg.TEST.NMS,
                score_thresh=0.0001,
                method=cfg.TEST.SOFT_NMS.METHOD
            )
        else:
            keep = box_utils.nms(dets_j, cfg.TEST.NMS)
            nms_dets = dets_j[keep, :]
        # Refine the post-NMS boxes using bounding-box voting
        if cfg.TEST.BBOX_VOTE.ENABLED:
            nms_dets = box_utils.box_voting(
                nms_dets,
                dets_j,
                cfg.TEST.BBOX_VOTE.VOTE_TH,
                scoring_method=cfg.TEST.BBOX_VOTE.SCORING_METHOD
            )
        cls_boxes[j] = nms_dets

    # Limit to max_per_image detections **over all classes**
    if cfg.TEST.DETECTIONS_PER_IM > 0:
        image_scores = np.hstack(
            [cls_boxes[j][:, -1] for j in range(1, num_classes)]
        )
        if len(image_scores) > cfg.TEST.DETECTIONS_PER_IM:
            image_thresh = np.sort(image_scores)[-cfg.TEST.DETECTIONS_PER_IM]
            for j in range(1, num_classes):
                keep = np.where(cls_boxes[j][:, -1] >= image_thresh)[0]
                cls_boxes[j] = cls_boxes[j][keep, :]

    im_results = np.vstack([cls_boxes[j] for j in range(1, num_classes)])
    boxes = im_results[:, :-1]
    scores = im_results[:, -1]
    return scores, boxes, cls_boxes 

# 需要导入模块: from utils import boxes [as 别名]
# 或者: from utils.boxes import nms [as 别名]
def box_results_with_nms_and_limit(scores, boxes):  # NOTE: support single-batch
    """Returns bounding-box detection results by thresholding on scores and
    applying non-maximum suppression (NMS).

    `boxes` has shape (#detections, 4 * #classes), where each row represents
    a list of predicted bounding boxes for each of the object classes in the
    dataset (including the background class). The detections in each row
    originate from the same object proposal.

    `scores` has shape (#detection, #classes), where each row represents a list
    of object detection confidence scores for each of the object classes in the
    dataset (including the background class). `scores[i, j]`` corresponds to the
    box at `boxes[i, j * 4:(j + 1) * 4]`.
    """
    num_classes = cfg.MODEL.NUM_CLASSES
    cls_boxes = [[] for _ in range(num_classes)]
    # Apply threshold on detection probabilities and apply NMS
    # Skip j = 0, because it's the background class
    for j in range(1, num_classes):
        inds = np.where(scores[:, j] > cfg.TEST.SCORE_THRESH)[0]
        scores_j = scores[inds, j]
        boxes_j = boxes[inds, j * 4:(j + 1) * 4]
        dets_j = np.hstack((boxes_j, scores_j[:, np.newaxis])).astype(np.float32, copy=False)
        if cfg.TEST.SOFT_NMS.ENABLED:
            nms_dets, _ = box_utils.soft_nms(
                dets_j,
                sigma=cfg.TEST.SOFT_NMS.SIGMA,
                overlap_thresh=cfg.TEST.NMS,
                score_thresh=0.05,
                # score_thresh=0.0001,
                method=cfg.TEST.SOFT_NMS.METHOD
            )
        else:
            keep = box_utils.nms(dets_j, cfg.TEST.NMS)
            nms_dets = dets_j[keep, :]
        # Refine the post-NMS boxes using bounding-box voting
        if cfg.TEST.BBOX_VOTE.ENABLED:
            nms_dets = box_utils.box_voting(
                nms_dets,
                dets_j,
                cfg.TEST.BBOX_VOTE.VOTE_TH,
                scoring_method=cfg.TEST.BBOX_VOTE.SCORING_METHOD
            )
        cls_boxes[j] = nms_dets

    # Limit to max_per_image detections **over all classes**
    if cfg.TEST.DETECTIONS_PER_IM > 0:
        image_scores = np.hstack(
            [cls_boxes[j][:, -1] for j in range(1, num_classes)]
        )
        if len(image_scores) > cfg.TEST.DETECTIONS_PER_IM:
            image_thresh = np.sort(image_scores)[-cfg.TEST.DETECTIONS_PER_IM]
            for j in range(1, num_classes):
                keep = np.where(cls_boxes[j][:, -1] >= image_thresh)[0]
                cls_boxes[j] = cls_boxes[j][keep, :]

    im_results = np.vstack([cls_boxes[j] for j in range(1, num_classes)])
    boxes = im_results[:, :-1]
    scores = im_results[:, -1]
    return scores, boxes, cls_boxes 

# 需要导入模块: from utils import boxes [as 别名]
# 或者: from utils.boxes import nms [as 别名]
def box_results_with_nms_and_limit(scores, boxes, thresh=0.0001):
    """Returns bounding-box detection results by thresholding on scores and
    applying non-maximum suppression (NMS).

    `boxes` has shape (#detections, 4 * #classes), where each row represents
    a list of predicted bounding boxes for each of the object classes in the
    dataset (including the background class). The detections in each row
    originate from the same object proposal.

    `scores` has shape (#detection, #classes), where each row represents a list
    of object detection confidence scores for each of the object classes in the
    dataset (including the background class). `scores[i, j]`` corresponds to the
    box at `boxes[i, j * 4:(j + 1) * 4]`.
    """
    num_classes = cfg.MODEL.NUM_CLASSES
    cls_boxes = [[] for _ in range(num_classes)]
    # Apply threshold on detection probabilities and apply NMS
    # Skip j = 0, because it's the background class
    for j in range(1, num_classes):
        inds = np.where(scores[:, j] > cfg.TEST.SCORE_THRESH)[0]
        scores_j = scores[inds, j]
        boxes_j = boxes[inds, j * 4:(j + 1) * 4]
        dets_j = np.hstack((boxes_j, scores_j[:, np.newaxis])).astype(
            np.float32, copy=False
        )
        if cfg.TEST.SOFT_NMS.ENABLED:
            nms_dets, _ = box_utils.soft_nms(
                dets_j,
                sigma=cfg.TEST.SOFT_NMS.SIGMA,
                overlap_thresh=cfg.TEST.NMS,
                score_thresh=0.0001,
                method=cfg.TEST.SOFT_NMS.METHOD
            )
        else:
            keep = box_utils.nms(dets_j, cfg.TEST.NMS)
            nms_dets = dets_j[keep, :]
        # Refine the post-NMS boxes using bounding-box voting
        if cfg.TEST.BBOX_VOTE.ENABLED:
            nms_dets = box_utils.box_voting(
                nms_dets,
                dets_j,
                cfg.TEST.BBOX_VOTE.VOTE_TH,
                scoring_method=cfg.TEST.BBOX_VOTE.SCORING_METHOD
            )
        cls_boxes[j] = nms_dets

    # Limit to max_per_image detections **over all classes**
    if cfg.TEST.DETECTIONS_PER_IM > 0:
        image_scores = np.hstack(
            [cls_boxes[j][:, -1] for j in range(1, num_classes)]
        )
        if len(image_scores) > cfg.TEST.DETECTIONS_PER_IM:
            image_thresh = np.sort(image_scores)[-cfg.TEST.DETECTIONS_PER_IM]
            for j in range(1, num_classes):
                keep = np.where(cls_boxes[j][:, -1] >= image_thresh)[0]
                cls_boxes[j] = cls_boxes[j][keep, :]

    im_results = np.vstack([cls_boxes[j] for j in range(1, num_classes)])
    boxes = im_results[:, :-1]
    scores = im_results[:, -1]
    return scores, boxes, cls_boxes 

# 需要导入模块: from utils import boxes [as 别名]
# 或者: from utils.boxes import nms [as 别名]
def box_results_with_nms_and_limit(self, scores, boxes, score_thresh=cfg.TEST.SCORE_THRESH):
        num_classes = cfg.MODEL.NUM_CLASSES
        cls_boxes = [[] for _ in range(num_classes)]
        # Apply threshold on detection probabilities and apply NMS
        # Skip j = 0, because it's the background class
        for j in range(1, num_classes):
            inds = np.where(scores[:, j] > score_thresh)[0]
            scores_j = scores[inds, j]
            boxes_j = boxes[inds, j * 4:(j + 1) * 4]
            dets_j = np.hstack((boxes_j, scores_j[:, np.newaxis])).astype(np.float32, copy=False)
            if cfg.TEST.SOFT_NMS.ENABLED:
                nms_dets, _ = box_utils.soft_nms(
                    dets_j,
                    sigma=cfg.TEST.SOFT_NMS.SIGMA,
                    overlap_thresh=cfg.TEST.NMS,
                    score_thresh=0.0001,
                    method=cfg.TEST.SOFT_NMS.METHOD
                )
            else:
                keep = box_utils.nms(dets_j, cfg.TEST.NMS)
                nms_dets = dets_j[keep, :]
            # add labels
            label_j = np.ones((nms_dets.shape[0], 1), dtype=np.float32) * j
            nms_dets = np.hstack((nms_dets, label_j))
            # Refine the post-NMS boxes using bounding-box voting
            if cfg.TEST.BBOX_VOTE.ENABLED:
                nms_dets = box_utils.box_voting(
                    nms_dets,
                    dets_j,
                    cfg.TEST.BBOX_VOTE.VOTE_TH,
                    scoring_method=cfg.TEST.BBOX_VOTE.SCORING_METHOD
                )
            cls_boxes[j] = nms_dets

        # Limit to max_per_image detections **over all classes**
        if cfg.TEST.DETECTIONS_PER_IM > 0:
            image_scores = np.hstack(
                [cls_boxes[j][:, -2] for j in range(1, num_classes)]
            )
            if len(image_scores) > cfg.TEST.DETECTIONS_PER_IM:
                image_thresh = np.sort(image_scores)[-cfg.TEST.DETECTIONS_PER_IM]
                for j in range(1, num_classes):
                    keep = np.where(cls_boxes[j][:, -2] >= image_thresh)[0]
                    cls_boxes[j] = cls_boxes[j][keep, :]

        im_results = np.vstack([cls_boxes[j] for j in range(1, num_classes)])
        boxes = im_results[:, :-2]
        scores = im_results[:, -2]
        labels = im_results[:, -1]

        return scores, boxes, labels 

# 需要导入模块: from utils import boxes [as 别名]
# 或者: from utils.boxes import nms [as 别名]
def im_detect_all(model, im, box_proposals, timers=None):
    if timers is None:
        timers = defaultdict(Timer)

    # Handle RetinaNet testing separately for now
    if cfg.RETINANET.RETINANET_ON:
        cls_boxes = test_retinanet.im_detect_bbox(model, im, timers)
        return cls_boxes, None, None

    timers['im_detect_bbox'].tic()
    if cfg.TEST.BBOX_AUG.ENABLED:
        scores, boxes, im_scales = im_detect_bbox_aug(model, im, box_proposals)
    else:
        scores, boxes, im_scales = im_detect_bbox(model, im, box_proposals)
    timers['im_detect_bbox'].toc()

    # score and boxes are from the whole image after score thresholding and nms
    # (they are not separated by class)
    # cls_boxes boxes and scores are separated by class and in the format used
    # for evaluating results
    timers['misc_bbox'].tic()
    scores, boxes, cls_boxes = box_results_with_nms_and_limit(scores, boxes)
    timers['misc_bbox'].toc()

    if cfg.MODEL.MASK_ON and boxes.shape[0] > 0:
        timers['im_detect_mask'].tic()
        if cfg.TEST.MASK_AUG.ENABLED:
            masks = im_detect_mask_aug(model, im, boxes)
        else:
            masks = im_detect_mask(model, im_scales, boxes)
        timers['im_detect_mask'].toc()

        timers['misc_mask'].tic()
        cls_segms = segm_results(
            cls_boxes, masks, boxes, im.shape[0], im.shape[1]
        )
        timers['misc_mask'].toc()
    else:
        cls_segms = None

    if cfg.MODEL.KEYPOINTS_ON and boxes.shape[0] > 0:
        timers['im_detect_keypoints'].tic()
        if cfg.TEST.KPS_AUG.ENABLED:
            heatmaps = im_detect_keypoints_aug(model, im, boxes)
        else:
            heatmaps = im_detect_keypoints(model, im_scales, boxes)
        timers['im_detect_keypoints'].toc()

        timers['misc_keypoints'].tic()
        cls_keyps = keypoint_results(cls_boxes, heatmaps, boxes)
        timers['misc_keypoints'].toc()
    else:
        cls_keyps = None

    return cls_boxes, cls_segms, cls_keyps