【从零开始学Mask RCNN】四，RPN锚框生成和Proposal生成

1. Mask RCNN Anchor框 生成

Mask RCNN的锚框生成和SSD的锚框生成策略类似（SSD的锚框生成策略见：【资源分享】从零开始学习SSD教程） ，都遵循以下规则：

• Anchor的中心点的个数等于特征图像素个数
• Anchor的生成是围绕中心点的
• Anchor框的坐标最终需要归一化到0-1之间，即相对输入图像的大小

我们知道Faster RCNN只是在一个特征图上铺设Anchor，而Mask RCNN引入了FPN之后使用了多层特征，这样和SSD类似都是在多个特征图上铺设Anchor，不过SSD的Anchor铺设策略相对较为复杂，每一个尺度的特征都有不同的Anchor策略设计，如下图所示：

Mask RCNN相对于SSD的Anchor生成策略有以下不同之处：

• SSD的不同特征图的Anchor长宽比设置不同，而Mask RCNN的不同特征图的Anchor比例则完全相同
• Mask RCNN每一层设置固定个数的Anchor数（3个）
• SSD中心点为每个像素偏移0.5步长；Mask RCNN中心点直接选为像素位置

另外，Anchor的基本生成方式和SSD一样，即Anchor框的长度等于$h$乘$\sqrt{anchor-ratios}$，Anchor框的宽度等于$w$除以$\sqrt{anchor-ratios}$，其中$h,w$的初始值是给定的参考尺寸。具体设置如下：

anchor_sizes=[(21., 45.),
              (45., 99.),
              (99., 153.),
              (153., 207.),
              (207., 261.),
              (261., 315.)]
anchor_ratios=[[2, .5],
               [2, .5, 3, 1./3],
               [2, .5, 3, 1./3],
               [2, .5, 3, 1./3],
               [2, .5],
               [2, .5]]

在mrcnn/config,py中相关的设置如下：

self.config.BACKBONE_STRIDES  = [4, 8, 16, 32, 64]      # 特征层的下采样倍数，中心点计算使用
self.config.RPN_ANCHOR_RATIOS = [0.5, 1, 2]             # 特征层锚框生成参数
self.config.RPN_ANCHOR_SCALES = [32, 64, 128, 256, 512] # 特征层锚框感受野

Anchor框的生成函数为model.py中的get_anchors函数，参数为image_shape，只需要保证里面有[h,w]即可，也可以是[h,w,c]，代码如下：

# 锚框生成函数
    def get_anchors(self, image_shape):
        """Returns anchor pyramid for the given image size."""
        backbone_shapes = compute_backbone_shapes(self.config, image_shape)
        # 如果图片的尺寸相同就用缓存的Anchor信息回复
        # Cache anchors and reuse if image shape is the same
        if not hasattr(self, "_anchor_cache"):
            self._anchor_cache = {}
        # 如果对应尺寸图像的Anchors区域没有被缓存，那么就产生Anchors
        if not tuple(image_shape) in self._anchor_cache:
            # 产生Anchor区域
            a = utils.generate_pyramid_anchors(
                self.config.RPN_ANCHOR_SCALES,
                self.config.RPN_ANCHOR_RATIOS,
                backbone_shapes,
                self.config.BACKBONE_STRIDES,
                self.config.RPN_ANCHOR_STRIDE)
            # Keep a copy of the latest anchors in pixel coordinates because
            # it's used in inspect_model notebooks.
            # TODO: Remove this after the notebook are refactored to not use it
            self.anchors = a
            # Normalize coordinates
            self._anchor_cache[tuple(image_shape)] = utils.norm_boxes(a, image_shape[:2])
        return self._anchor_cache[tuple(image_shape)]

这个函数首先调用compute_backbone_shapes计算各个特征层的shape：

def compute_backbone_shapes(config, image_shape):
    """ 计算骨干网络的每个阶段特征图的长宽
    Returns:
        [N, (height, width)]. N代表阶段数
    """
    # 可调用返回 True，否则返回 False。
    if callable(config.BACKBONE):
        return config.COMPUTE_BACKBONE_SHAPE(image_shape)

    # 当前只支持ResNet
    assert config.BACKBONE in ["resnet50", "resnet101"]
    return np.array(
        [[int(math.ceil(image_shape[0] / stride)),
            int(math.ceil(image_shape[1] / stride))]
            for stride in config.BACKBONE_STRIDES])

然后再调用utils.generate_pyramid_anchors函数生成所有的Anchor框，这个函数在mrcnn/utils.py中，代码如下：

def generate_pyramid_anchors(scales, ratios, feature_shapes, feature_strides,
                             anchor_stride):
    """在特征金字塔的不同层级产生anchors，每个尺度都
    和金字塔的层级有关，但是长宽比例都是相同的

    返回:
    anchors: [N, (y1, x1, y2, x2)]. 所有产生的Anchors放在一个数组中.
    按照scales给定的顺序进行排序. 所以scale[0]是第一个,然后scale[1]第二个,等等.
    """
    # Anchors
    # [anchor_count, (y1, x1, y2, x2)]
    anchors = []
    for i in range(len(scales)):
        anchors.append(generate_anchors(scales[i], ratios, feature_shapes[i],
                                        feature_strides[i], anchor_stride))
    return np.concatenate(anchors, axis=0)

这里又调用了utils.generate_anchors函数来生成每一个特征层的Anchors，这个函数大量应用了np.meshgrid函数，这个函数是用来生成网格坐标，一个很好的说明图如下：

虽然xy双坐标比较常用，但实际上输入可以是任意多的数组，输出数组数目等于输出数组数目，且彼此间shape一致。

如果输入数组不是一维的，会拉伸为1维进行计算。

输出维度是：[len(x2), len(x1), len(x3)……]

例子如下：

utils.generate_anchors函数解释如下：

############################################################
#  Anchors
############################################################

def generate_anchors(scales, ratios, shape, feature_stride, anchor_stride):
    """
    scales: 1D array of anchor sizes in pixels. Example: [32, 64, 128]
    ratios: 1D array of anchor ratios of width/height. Example: [0.5, 1, 2]
    shape: [height, width] spatial shape of the feature map over which
            to generate anchors.
    feature_stride: Stride of the feature map relative to the image in pixels.
    anchor_stride: Stride of anchors on the feature map. For example, if the
        value is 2 then generate anchors for every other feature map pixel.
    """
    # Get all combinations of scales and ratios
    scales, ratios = np.meshgrid(np.array(scales), np.array(ratios))
    scales = scales.flatten()
    ratios = ratios.flatten()

    # Enumerate heights and widths from scales and ratios
    heights = scales / np.sqrt(ratios)
    widths = scales * np.sqrt(ratios)

    # Enumerate shifts in feature space
    shifts_y = np.arange(0, shape[0], anchor_stride) * feature_stride
    shifts_x = np.arange(0, shape[1], anchor_stride) * feature_stride
    shifts_x, shifts_y = np.meshgrid(shifts_x, shifts_y)

    # Enumerate combinations of shifts, widths, and heights
    box_widths, box_centers_x = np.meshgrid(widths, shifts_x)
    box_heights, box_centers_y = np.meshgrid(heights, shifts_y)

    # Reshape to get a list of (y, x) and a list of (h, w)
    box_centers = np.stack(
        [box_centers_y, box_centers_x], axis=2).reshape([-1, 2])
    box_sizes = np.stack([box_heights, box_widths], axis=2).reshape([-1, 2])

    # Convert to corner coordinates (y1, x1, y2, x2)
    # 框体信息是相对于原图的, [N, (y1, x1, y2, x2)]
    boxes = np.concatenate([box_centers - 0.5 * box_sizes,
                            box_centers + 0.5 * box_sizes], axis=1)
    return boxes

最后再回到model.py中的get_anchors函数，再调用utils.norm_boxes进行Anchor坐标的归一化，即utils.norm_boxes函数，代码如下：

# 坐标归一化
def norm_boxes(boxes, shape):
    """Converts boxes from pixel coordinates to normalized coordinates.
    boxes: [N, (y1, x1, y2, x2)] in pixel coordinates
    shape: [..., (height, width)] in pixels

    Note: In pixel coordinates (y2, x2) is outside the box. But in normalized
    coordinates it's inside the box.

    Returns:
        [N, (y1, x1, y2, x2)] in normalized coordinates
    """
    h, w = shape
    scale = np.array([h - 1, w - 1, h - 1, w - 1])
    shift = np.array([0, 0, 1, 1])
    return np.divide((boxes - shift), scale).astype(np.float32)

2. RPN锚框生成

在第三个推文中我们获得了rpn的每个特征图信息即：

rpn_feature_maps = [P2, P3, P4, P5, P6]

然后在每个特征图上产生Anchor框，不过这里是分训练还是测试，训练的话使用刚刚介绍的方法来产生Anchor，如果是测试的话则使用定义好的Anchor，代码如下：

# Anchors
        if mode == "training":
            anchors = self.get_anchors(config.IMAGE_SHAPE)
            # Duplicate across the batch dimension because Keras requires it
            # TODO: can this be optimized to avoid duplicating the anchors?
            anchors = np.broadcast_to(anchors, (config.BATCH_SIZE,) + anchors.shape)
            # A hack to get around Keras's bad support for constants
            anchors = KL.Lambda(lambda x: tf.Variable(anchors), name="anchors")(input_image)
        else:
            anchors = input_anchors

然后基于RPN的各个特征图和定义好的Anchor框信息我们生成每个Anchor框的前景/背景得分信息及每个Anchor框的坐标修正信息。 接前文主函数，我们初始化rpn model class的对象，并应用于各层特征：

# RPN Model, 返回的是keras的Module对象, 注意keras中的Module对象是可call的
        rpn = build_rpn_model(config.RPN_ANCHOR_STRIDE,
                              len(config.RPN_ANCHOR_RATIOS), config.TOP_DOWN_PYRAMID_SIZE)
        # Loop through pyramid layers
        layer_outputs = []  # list of lists
        # 保存各pyramid特征经过RPN之后的结果
        for p in rpn_feature_maps:
            layer_outputs.append(rpn([p]))

其中build_rpn_model的代码实现如下：

def build_rpn_model(anchor_stride, anchors_per_location, depth):
    """建立RPN网络的Keras模型。它包装了RPN图，可以共享权重。

    anchors_per_location: 特征图中每个像素的Anchor个数。
    anchor_stride: 控制Anchor框的密度，通常为1或者2。
    depth: 骨干网络特征图的深度。

    返回一个Keras的Model对象。当被调用时，model的输出是 :
    rpn_class_logits: [batch, H * W * anchors_per_location, 2] Anchor classifier logits (before softmax)
    rpn_probs: [batch, H * W * anchors_per_location, 2] Anchor classifier probabilities.
    rpn_bbox: [batch, H * W * anchors_per_location, (dy, dx, log(dh), log(dw))] Deltas to be
                applied to anchors.
    """
    input_feature_map = KL.Input(shape=[None, None, depth],
                                 name="input_rpn_feature_map")
    outputs = rpn_graph(input_feature_map, anchors_per_location, anchor_stride)
    return KM.Model([input_feature_map], outputs, name="rpn_model")

它接收一个输入特征图，然后调用rpn_graph这个函数完成RPN模块的实现，这个函数的代码解析如下：

############################################################
#  Region Proposal Network (RPN)
############################################################

def rpn_graph(feature_map, anchors_per_location, anchor_stride):
    """建立RPN网络的计算图。

    feature_map: 骨干特征 [batch, height, width, depth]
    anchors_per_location: 特征图中每个像素的Anchor个数
    anchor_stride: 控制Anchors的密度，比如设置成1或者2

    Returns:
        rpn_class_logits: [batch, H * W * anchors_per_location, 2] Anchor classifier logits (before softmax)
        rpn_probs: [batch, H * W * anchors_per_location, 2] Anchor classifier probabilities.
        rpn_bbox: [batch, H * W * anchors_per_location, (dy, dx, log(dh), log(dw))] Deltas to be
                  applied to anchors.
    """
    # TODO: check if stride of 2 causes alignment issues if the feature map
    # is not even.
    # Shared convolutional base of the RPN
    shared = KL.Conv2D(512, (3, 3), padding='same', activation='relu',
                       strides=anchor_stride,
                       name='rpn_conv_shared')(feature_map)

    # Anchor的分数
    # Anchor Score. [batch, height, width, anchors per location * 2].
    x = KL.Conv2D(2 * anchors_per_location, (1, 1), padding='valid',
                  activation='linear', name='rpn_class_raw')(shared)

    # Reshape to [batch, anchors, 2]
    rpn_class_logits = KL.Lambda(
        lambda t: tf.reshape(t, [tf.shape(t)[0], -1, 2]))(x)

    # Softmax on last dimension of BG/FG.
    rpn_probs = KL.Activation(
        "softmax", name="rpn_class_xxx")(rpn_class_logits)

    # Bounding box refinement. [batch, H, W, anchors per location * depth]
    # where depth is [x, y, log(w), log(h)]
    x = KL.Conv2D(anchors_per_location * 4, (1, 1), padding="valid",
                  activation='linear', name='rpn_bbox_pred')(shared)

    # Reshape to [batch, anchors, 4]
    rpn_bbox = KL.Lambda(lambda t: tf.reshape(t, [tf.shape(t)[0], -1, 4]))(x)

    return [rpn_class_logits, rpn_probs, rpn_bbox]

紧接着前文的主函数，我们需要将获取的list形式的各层Anchor框进行拼接和重组，代码如下：

# Concatenate layer outputs
        # Convert from list of lists of level outputs to list of lists
        # of outputs across levels.
        # e.g. [[a1, b1, c1], [a2, b2, c2]] => [[a1, a2], [b1, b2], [c1, c2]]
        output_names = ["rpn_class_logits", "rpn_class", "rpn_bbox"]
        outputs = list(zip(*layer_outputs))
        outputs = [KL.Concatenate(axis=1, name=n)(list(o))
                   for o, n in zip(outputs, output_names)]
        # [batch, num_anchors, 2/4]
        # 其中num_anchors指的是全部特征层上的anchors总数
        rpn_class_logits, rpn_class, rpn_bbox = outputs

这段代码的目的就是将原始的$[(logits2,class2,bbox2),(logits3,class3,bbox3),\dots \dots 6]$，这种排布的返回值转换为$[(logits2,logits3\dots \dots 6),(class2,class3\dots \dots 6),(bbox2,bbox3\dots \dots 6)]$这种形式（通过zip函数转换），然后将每个小list中的tensor按照第一维度（即anchors维度）拼接，得到三个tensor，每个tensor表明batch中图片对应$5$个特征层的全部anchors的分类回归信息，即：[batch, anchors, 2分类结果 或者 (dy, dx, log(dh), log(dw))]。

3. Proposal生成

3.1 初始化Proposal Layer

现在我们已经获取了所有的Anchor框的信息，这一步的目的是从中挑选指定个数的可能包含目标的Anchor框作为候选区域，即在上一步的二分类中的前景类别得分更高的框，同时由于Anchor框是比较密集并且重叠严重，所以这里还会执行一个NMS来去重。这部分的代码如下，其中，proposal_count是一个整数，用于指定生成proposal数目，不足时会生成坐标为[0,0,0,0]的空值进行补全：

# 产生候选框
# Proposals are [batch, N, (y1, x1, y2, x2)] in normalized coordinates
# and zero padded.
# POST_NMS_ROIS_INFERENCE = 1000
# POST_NMS_ROIS_TRAINING = 2000
proposal_count = config.POST_NMS_ROIS_TRAINING if mode == "training"\
    else config.POST_NMS_ROIS_INFERENCE
# [IMAGES_PER_GPU, num_rois, (y1, x1, y2, x2)]
# IMAGES_PER_GPU取代了batch，之后说的batch都是IMAGES_PER_GPU
rpn_rois = ProposalLayer(
    proposal_count=proposal_count, 
    nms_threshold=config.RPN_NMS_THRESHOLD, #0.7
    name="ROI",
    config=config)([rpn_class, rpn_bbox, anchors])

接下来我们跟进ProposalLayer，在初始部分获取[rpn_class, rpn_bbox, anchors]三个张量作为参数，然后获取前景分数最大的PRE_NMS_LIMITPRE_NMS_LIMIT这么多个候选框，这部分的代码如下：

# 在初始部分获取[rpn_class, rpn_bbox, anchors]三个张量作为参数，
class ProposalLayer(KE.Layer):
    """接收Anchor框的分数并选择一个子集传入到第二个阶段. 
    根据Anchor框分数和非最大抑制进行过滤，以消除一些重叠框.
    并且将bbox调整值应用于Anchor上。
    Inputs:
        rpn_probs: [batch, num_anchors, (bg prob, fg prob)]
        rpn_bbox: [batch, num_anchors, (dy, dx, log(dh), log(dw))]
        anchors: [batch, num_anchors, (y1, x1, y2, x2)] anchors in normalized coordinates

    Returns:
        Proposals in normalized coordinates [batch, rois, (y1, x1, y2, x2)]
    """

    def __init__(self, proposal_count, nms_threshold, config=None, **kwargs):
        super(ProposalLayer, self).__init__(**kwargs)
        self.config = config
        self.proposal_count = proposal_count
        self.nms_threshold = nms_threshold

    def call(self, inputs):
        # Box Scores. Use the foreground class confidence. [Batch, num_rois, 1]
        # 获得Anchor框的前景分数
        scores = inputs[0][:, :, 1]
        # Box deltas [batch, num_rois, 4]
        # Box deltas. 记录坐标修正信息：(dy, dx, log(dh), log(dw)). [batch, num_rois, 4]
        deltas = inputs[1]
        deltas = deltas * np.reshape(self.config.RPN_BBOX_STD_DEV, [1, 1, 4]) # [ 0.1  0.1  0.2  0.2]
        # Anchors 记录坐标信息：(y1, x1, y2, x2). [batch, num_rois, 4]
        anchors = inputs[2]

        # Improve performance by trimming to top anchors by score
        # and doing the rest on the smaller subset.
        # 然后我们获取前景得分最大的n个候选框，
        pre_nms_limit = tf.minimum(self.config.PRE_NMS_LIMIT, tf.shape(anchors)[1])
        # 输入矩阵时输出每一行的top k. [batch, top_k]
        ix = tf.nn.top_k(scores, pre_nms_limit, sorted=True,
                         name="top_anchors").indices

3.2 获取Top K Anchor框

注意上面的代码结尾只是获取了要保留的Anchor框的索引，接下来还需要对三个输入分别进行提取，接上面代码：

# 提取top k锚框，同时对三个输入进行了提取
        # batch_slice函数：
        # #   将batch特征拆分为单张
        # #   然后提取指定的张数
        # #   使用单张特征处理函数处理，并合并（此时返回的第一维不是输入时的batch，而是上步指定的张数）
        scores = utils.batch_slice([scores, ix], lambda x, y: tf.gather(x, y),
                                   self.config.IMAGES_PER_GPU)
        deltas = utils.batch_slice([deltas, ix], lambda x, y: tf.gather(x, y),
                                   self.config.IMAGES_PER_GPU)
        pre_nms_anchors = utils.batch_slice([anchors, ix], lambda a, x: tf.gather(a, x),
                                    self.config.IMAGES_PER_GPU,
                                    names=["pre_nms_anchors"])

这里主要使用了一个utils.batch_slice函数，这个函数把只支持batch=1的函数进行了扩展，由于tf.gather函数只能进行一维数组的切片，而scores的维度现在是[batch,num_rois]，相对的ix也是二维即[batch,top_k]，因此这里要将两者应用tf.gather函数切片后将结果重新拼接，代码如下（mrnn/utils.py）：

# ## Batch Slicing
# Some custom layers support a batch size of 1 only, and require a lot of work
# to support batches greater than 1. This function slices an input tensor
# across the batch dimension and feeds batches of size 1. Effectively,
# an easy way to support batches > 1 quickly with little code modification.
# In the long run, it's more efficient to modify the code to support large
# batches and getting rid of this function. Consider this a temporary solution
def batch_slice(inputs, graph_fn, batch_size, names=None):
    """Splits inputs into slices and feeds each slice to a copy of the given
    computation graph and then combines the results. It allows you to run a
    graph on a batch of inputs even if the graph is written to support one
    instance only.
 
    inputs: list of tensors. All must have the same first dimension length
    graph_fn: A function that returns a TF tensor that's part of a graph.
    batch_size: number of slices to divide the data into.
    names: If provided, assigns names to the resulting tensors.
    """
    if not isinstance(inputs, list):
        inputs = [inputs]
 
    outputs = []
    for i in range(batch_size):
        inputs_slice = [x[i] for x in inputs]
        output_slice = graph_fn(*inputs_slice)
        if not isinstance(output_slice, (tuple, list)):
            output_slice = [output_slice]
        outputs.append(output_slice)
 
    # 使用tf.while_loop实现循环体代码如下：
    # import tensorflow as tf
    # i = 0
    # outputs = []
    #
    # def cond(index):
    #     return index < batch_size  # 返回bool值
    #
    # def body(index):
    #     index += 1
    #     inputs_slice = [x[i] for x in inputs]
    #     output_slice = graph_fn(*inputs_slice)
    #     if not isinstance(output_slice, (tuple, list)):
    #         output_slice = [output_slice]
    #     outputs.append(output_slice)
    #     return index  # 返回cond需要的判断参数进行下一次判断
    #
    # tf.while_loop(cond, body, [i])
 
    # Change outputs from a list of slices where each is
    # a list of outputs to a list of outputs and each has
    # a list of slices
    # 下面示意中假设每次graph_fn返回两个tensor
    # [[tensor11, tensor12], [tensor21, tensor22], ……]
    # ——> [(tensor11, tensor21, ……), (tensor12, tensor22, ……)]  zip返回的是多个tuple
    outputs = list(zip(*outputs))
 
    if names is None:
        names = [None] * len(outputs)
 
    # 一般来讲就是batch维度合并回去（上面的for循环实际是将batch拆分了）
    result = [tf.stack(o, axis=0, name=n)
              for o, n in zip(outputs, names)]
    if len(result) == 1:
        result = result[0]
 
    return result

3.3 Anchor框坐标修正

我们在第二节中利用RPN网络获取了全部Anchor框的坐标回归结果，即rpn_bbox: [batch, anchors, (dy, dx, log(dh), log(dw))]，然后我们在上一个小节（3.2）获得了top k Anchor框的坐标信息以及top k的Anchor框回归偏移量，现在将其合并，即利用RPN的回归结果修正top k Anchor框的坐标。代码如下：

		# Anchor框修正
        # Apply deltas to anchors to get refined anchors.
        # 现在的N代表Top K的K
        # [batch, N, (y1, x1, y2, x2)]
        boxes = utils.batch_slice([pre_nms_anchors, deltas],
                                  lambda x, y: apply_box_deltas_graph(x, y),
                                  self.config.IMAGES_PER_GPU,
                                  names=["refined_anchors"])

可以看到这里主要调用了apply_box_deltas_graph这个函数结合上面介绍的batch_slice函数完成了RPN回归结果修正，我们来看一下apply_box_deltas_graph这个函数的代码实现，如下：

############################################################
#  Proposal Layer
############################################################

def apply_box_deltas_graph(boxes, deltas):
    """Applies the given deltas to the given boxes.
    boxes: [N, (y1, x1, y2, x2)] boxes to update
    deltas: [N, (dy, dx, log(dh), log(dw))] refinements to apply
    """
    # Convert to y, x, h, w
    height = boxes[:, 2] - boxes[:, 0]
    width = boxes[:, 3] - boxes[:, 1]
    center_y = boxes[:, 0] + 0.5 * height
    center_x = boxes[:, 1] + 0.5 * width
    # Apply deltas
    center_y += deltas[:, 0] * height
    center_x += deltas[:, 1] * width
    height *= tf.exp(deltas[:, 2])
    width *= tf.exp(deltas[:, 3])
    # Convert back to y1, x1, y2, x2
    y1 = center_y - 0.5 * height
    x1 = center_x - 0.5 * width
    y2 = y1 + height
    x2 = x1 + width
    result = tf.stack([y1, x1, y2, x2], axis=1, name="apply_box_deltas_out")
    return result

从这个代码可以推出，RPN回归出的4个坐标值的意义土如下：

$dy=(y_n-y_o)/h_o$

$dx=(x_n-x_o)/w_o$

$dh=h_n/h_o$

$dw=w_n/w_o$

此外，这里的Anchor框坐标实际上是在一个归一化的图上的（即所有的Anchor框位于一个长宽均为1的虚拟画布上），上一步执行了修正之后不再能够保证这一点，因此这里还需要切除Anchor框越界的部分，即只保留Anchor框和$[0,0,1,1]$画布的交集。代码如下：

# Clip to image boundaries. Since we're in normalized coordinates,
        # clip to 0..1 range. [batch, N, (y1, x1, y2, x2)]
        window = np.array([0, 0, 1, 1], dtype=np.float32)
        boxes = utils.batch_slice(boxes,
                                  lambda x: clip_boxes_graph(x, window),
                                  self.config.IMAGES_PER_GPU,
                                  names=["refined_anchors_clipped"])

其中clip_boxes_graph这个函数实现了保留交集的功能，代码如下：

# 保留2个框的交集部分
def clip_boxes_graph(boxes, window):
    """
    boxes: [N, (y1, x1, y2, x2)]
    window: [4] in the form y1, x1, y2, x2
    """
    # Split
    wy1, wx1, wy2, wx2 = tf.split(window, 4)
    y1, x1, y2, x2 = tf.split(boxes, 4, axis=1)
    # Clip
    y1 = tf.maximum(tf.minimum(y1, wy2), wy1)
    x1 = tf.maximum(tf.minimum(x1, wx2), wx1)
    y2 = tf.maximum(tf.minimum(y2, wy2), wy1)
    x2 = tf.maximum(tf.minimum(x2, wx2), wx1)
    clipped = tf.concat([y1, x1, y2, x2], axis=1, name="clipped_boxes")
    clipped.set_shape((clipped.shape[0], 4))
    return clipped

3.4 非极大值抑制(NMS)

最后再进行非极大值抑制，以保证不会出现重叠度非常大的候选框，代码如下：

# Non-max suppression
        def nms(boxes, scores):
            # 非极大值抑制子函数
            # :param boxes: [top_k, (y1, x1, y2, x2)]
            # :param scores: [top_k]
            # :return: 
            # self.proposal_count为最大返回数目
            indices = tf.image.non_max_suppression(
                boxes, scores, self.proposal_count,
                self.nms_threshold, name="rpn_non_max_suppression")
            proposals = tf.gather(boxes, indices)
            # Pad if needed, 一旦返回数目不足, 填充(0,0,0,0)直到数目达标
            padding = tf.maximum(self.proposal_count - tf.shape(proposals)[0], 0)
            # 在后面添加全0行
            proposals = tf.pad(proposals, [(0, padding), (0, 0)])
            return proposals
        proposals = utils.batch_slice([boxes, scores], nms,
                                      self.config.IMAGES_PER_GPU)
        # [IMAGES_PER_GPU, proposal_count, (y1, x1, y2, x2)]
        return proposals

4. 小节

到这里，我们就将Mask RCNN的Anchor框生成和Proposal生成讲完了，下一节我将讲解获取RPN区域后Mask RCNN是如何处理这些候选框的(ROI Align)。

5. 参考

• https://www.cnblogs.com/hellcat/p/9811301.html

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