mask rcnn 笔记weixin39849839的博客-

image, window, scale, padding, crop = utils.resize_image(…)
Returns:
image: the resized image
window: (y1, x1, y2, x2). If max_dim is provided, padding might
be inserted in the returned image. If so, this window is the
coordinates of the image part of the full image (excluding
the padding). The x2, y2 pixels are not included.
scale: The scale factor used to resize the image
padding: Padding added to the image [(top, bottom), (left, right), (0, 0)]

extract_bboxes(mask):
Compute bounding boxes from masks.
mask: [height, width, num_instances]. Mask pixels are either 1 or 0.
Returns: bbox array [num_instances, (y1, x1, y2, x2)].

# Note that some boxes might be all zeros if the corresponding mask got cropped out. # and here is to filter them out 

***active_class_ids：***：
one-hot: classes supported in the dataset of this image.

***compute_overlaps(anchors, gt_boxes)***：
retrun [n_anchors,n_box] each item denotes IOU

# Match anchors to GT Boxes # If an anchor overlaps a GT box with IoU >= 0.7 then it's positive. # If an anchor overlaps a GT box with IoU < 0.3 then it's negative. # Neutral anchors are those that don't match the conditions above, # and they don't influence the loss function. # However, don't keep any GT box unmatched (rare, but happens). Instead, # match it to the closest anchor (even if its max IoU is < 0.3). # 

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