Github地址：Mask_RCNN
『計算機視覺』Mask-RCNN_論文學習
『計算機視覺』Mask-RCNN_項目文檔翻譯
『計算機視覺』Mask-RCNN_推斷網絡其一：總覽
『計算機視覺』Mask-RCNN_推斷網絡其二：基於ReNet101的FPN共享網絡
『計算機視覺』Mask-RCNN_推斷網絡其三：RPN錨框處理和Proposal生成
『計算機視覺』Mask-RCNN_推斷網絡其四：FPN和ROIAlign的耦合
『計算機視覺』Mask-RCNN_推斷網絡其五：目標檢測結果精煉
『計算機視覺』Mask-RCNN_推斷網絡其六：Mask生成
『計算機視覺』Mask-RCNN_推斷網絡終篇：使用detect方法進行推斷
『計算機視覺』Mask-RCNN_錨框生成
『計算機視覺』Mask-RCNN_訓練網絡其一：數據集與Dataset類
『計算機視覺』Mask-RCNN_訓練網絡其二：train網絡結構&損失函數
『計算機視覺』Mask-RCNN_訓練網絡其三：訓練Model
一、和SSD錨框對比
Mask_RCNN的錨框本質上來說和SSD的是一樣的（『TensorFlow』SSD源碼學習_其三：錨框生成），
中心點的個數等於特征層像素數
框體生成是圍繞中心點的
最終的框體坐標需要歸一化到01之間，都是對於輸入圖片的相對大小
RCNN系列一般都是一個共享特征，但在Mask_RCNN結構引入了FPN結構后，和SSD一樣，使用了多層特征，這樣兩者的錨框生成算法可以說是如出一轍了，只不過是生成策略有所微調：
SSD中不同特征層對應着不同的網格增強比例參數；Mask_RCNN不通層的比例（anchor_ratios）則完全一致
SSD每一層每一個中心點生成該層ratio+2個框；Mask_RCNN生成固定3個框
SSD中心點為feat像素偏移0.5步長；Mask_RCNN中心點直接選為feat像素位置
而基本生成方式兩者完全一致：
- h乘anchor_ratios**0.5
- w除anchor_ratios**0.5
h、w初始值為給定的參考尺寸，即感受野控制實際依賴的參數為每一層的anchor_ratios和參考尺寸，對SSD：
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]]
對Mask_RCNN（h、w參考尺寸大小一致）:
self.config.BACKBONE_STRIDES = [4, 8, 16, 32, 64] # 特征層的下采樣倍數，中心點計算使用
self.config.RPN_ANCHOR_RATIOS = [0.5, 1, 2] # 特征層錨框生成參數
self.config.RPN_ANCHOR_SCALES = [32, 64, 128, 256, 512] # 特征層錨框感受野
二、錨框生成
錨框生成入口函數位於model.py中的get_anchor函數，需要參數image_shape，保證含有[h, w]即可，也可以包含[h, w, c]，
def get_anchors(self, image_shape):
"""Returns anchor pyramid for the given image size."""
# [N, (height, width)]
backbone_shapes = compute_backbone_shapes(self.config, image_shape)
# Cache anchors and reuse if image shape is the same
if not hasattr(self, "_anchor_cache"):
self._anchor_cache = {}
if not tuple(image_shape) in self._anchor_cache:
# Generate Anchors: [anchor_count, (y1, x1, y2, x2)]
a = utils.generate_pyramid_anchors(
self.config.RPN_ANCHOR_SCALES, # (32, 64, 128, 256, 512)
self.config.RPN_ANCHOR_RATIOS, # [0.5, 1, 2]
backbone_shapes, # with shape [N, (height, width)]
self.config.BACKBONE_STRIDES, # [4, 8, 16, 32, 64]
self.config.RPN_ANCHOR_STRIDE) # 1
# 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)]
調用函數compute_backbone_shapes計算各個特征層shape：
def compute_backbone_shapes(config, image_shape):
"""Computes the width and height of each stage of the backbone network.
Returns:
[N, (height, width)]. Where N is the number of stages
"""
if callable(config.BACKBONE):
return config.COMPUTE_BACKBONE_SHAPE(image_shape)
# Currently supports ResNet only
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]) # [4, 8, 16, 32, 64]
調用函數utils.generate_pyramid_anchors生成全部錨框：
def generate_pyramid_anchors(scales, ratios, feature_shapes, feature_strides,
anchor_stride):
"""Generate anchors at different levels of a feature pyramid. Each scale
is associated with a level of the pyramid, but each ratio is used in
all levels of the pyramid.
Returns:
anchors: [N, (y1, x1, y2, x2)]. All generated anchors in one array. Sorted
with the same order of the given scales. So, anchors of scale[0] come
first, then anchors of scale[1], and so on.
"""
# 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))
# [anchor_count, (y1, x1, y2, x2)]
return np.concatenate(anchors, axis=0)
utils.generate_pyramid_anchors會調用utils.generate_anchors來生成每一層的錨框（這一步較多的使用了函數meshgrid，介紹見『Numpy』np.meshgrid）：
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) # (n, 3) (n, 3)
box_heights, box_centers_y = np.meshgrid(heights, shifts_y) # (n, 3) (n, 3)
# Reshape to get a list of (y, x) and a list of (h, w)
# (n, 3, 2) -> (3n, 2)
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)
boxes = np.concatenate([box_centers - 0.5 * box_sizes,
box_centers + 0.5 * box_sizes], axis=1)
# 框體信息是相對於原圖的, [N, (y1, x1, y2, x2)]
return boxes
模擬某層的中心點分布
最后回到get_anchor，調用utils.norm_boxes將錨框坐標化為01之間：
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)
最終返回相對坐標下的錨框，shape：[anchor_count, (y1, x1, y2, x2)]。