def generate_all_anchors(self, im_c, size):
if self.image_center == im_c and self.size == size:
return False
self.image_center = im_c
self.size = size
a0x = im_c - size // 2 * self.stride
ori = np.array([a0x] * 4, dtype=np.float32)
zero_anchors = self.anchors + ori
x1 = zero_anchors[:, 0]
y1 = zero_anchors[:, 1]
x2 = zero_anchors[:, 2]
y2 = zero_anchors[:, 3]
x1, y1, x2, y2 = map(lambda x: x.reshape(self.anchor_num, 1, 1),
[x1, y1, x2, y2])
cx, cy, w, h = corner2center([x1, y1, x2, y2])
disp_x = np.arange(0, size).reshape(1, 1, -1) * self.stride
disp_y = np.arange(0, size).reshape(1, -1, 1) * self.stride
cx = cx + disp_x
cy = cy + disp_y
# broadcast
zero = np.zeros((self.anchor_num, size, size), dtype=np.float32)
cx, cy, w, h = map(lambda x: x + zero, [cx, cy, w, h])
x1, y1, x2, y2 = center2corner([cx, cy, w, h])
self.all_anchors = np.stack([x1, y1, x2, y2]), np.stack([cx, cy, w, h])
return True
def generate_all_anchors(self, im_c, size):
"""
生成整幅图像的anchors
:param im_c:图像的中心点
:param size:图像的尺寸
:return:
"""
if self.image_center == im_c and self.size == size:
return False
# 更新config中的内容
self.image_center = im_c
self.size = size
# anchor0 的xy 坐标，即 x 和 y 对称。
a0x = im_c - size // 2 * self.stride
# 生成anchor0的坐标
ori = np.array([a0x] * 4, dtype=np.float32)
# 以图像中心点为中心点的anchor
zero_anchors = self.anchors + ori
# 获取anchor0的坐标
x1 = zero_anchors[:, 0]
y1 = zero_anchors[:, 1]
x2 = zero_anchors[:, 2]
y2 = zero_anchors[:, 3]
x1, y1, x2, y2 = map(lambda x: x.reshape(self.anchor_num, 1, 1),
[x1, y1, x2, y2])
cx, cy, w, h = corner2center([x1, y1, x2, y2])
# disp_x是[1, 1, size]，disp_y是[1, size, 1]
disp_x = np.arange(0, size).reshape(1, 1, -1) * self.stride
disp_y = np.arange(0, size).reshape(1, -1, 1) * self.stride
# 得到整幅图像中anchor中心点的坐标
cx = cx + disp_x
cy = cy + disp_y
# 通过广播生成整幅图像的anchor broadcast
zero = np.zeros((self.anchor_num, size, size), dtype=np.float32)
cx, cy, w, h = map(lambda x: x + zero, [cx, cy, w, h])
x1, y1, x2, y2 = center2corner([cx, cy, w, h])
# 以中心点坐标,宽高和左上角、右下角坐标两种方式存储anchors
self.all_anchors = np.stack([x1, y1, x2, y2]), np.stack([cx, cy, w, h])
return True
def generate_all_anchors(self, im_c, size):
if self.image_center == im_c and self.size == size:
return False
self.image_center = im_c
self.size = size # size到底代表啥
# 下面的一行是说：size是search image经过孪生网络后、再经过depth-wise的xcorr后的feature map的大小(17x17)，从论文中可以看出stride=16(相当于经过四次下采样)
# 而feature map左上角 第一个点 对应的在输入孪生网络前的search image里的横/纵坐标值就是下面的公式计算的值
# 而计算出来的a0x就是一个锚点，一个锚点就可以产生一系列的anchors
a0x = im_c - size // 2 * self.stride
ori = np.array([a0x] * 4, dtype=np.float32)
zero_anchors = self.anchors + ori # 这里的加法存在广播机制; generate_anchors里面产生的是相对坐标，作为中心是0；
# 这里我们要给出锚点的绝对中心，才能得到绝对坐标
x1 = zero_anchors[:, 0]
y1 = zero_anchors[:, 1]
x2 = zero_anchors[:, 2]
y2 = zero_anchors[:, 3]
x1, y1, x2, y2 = map(lambda x: x.reshape(self.anchor_num, 1, 1),
[x1, y1, x2, y2])
cx, cy, w, h = corner2center([x1, y1, x2, y2])
disp_x = np.arange(0, size).reshape(1, 1, -1) * self.stride
disp_y = np.arange(0, size).reshape(1, -1, 1) * self.stride
cx = cx + disp_x
cy = cy + disp_y
# broadcast
zero = np.zeros((self.anchor_num, size, size), dtype=np.float32)
# 从这里我们可以看到，zero的第零个维度方向代表的是anchor的种类(种类包括不同的横纵比，尺度scale，密度)
# 第一、二个维度方向代表的是每个锚点(即feature map点，要区别于anchor)针对于第零维度上某一类anchor的具体的横纵坐标值
cx, cy, w, h = map(
lambda x: x + zero,
[cx, cy, w, h
]) # 这四个值的每一个都需要一个shape=(self.anchor_num, size, size)的ndarray来描述
x1, y1, x2, y2 = center2corner([cx, cy, w, h])
self.all_anchors = np.stack([x1, y1, x2, y2]), np.stack(
[cx, cy, w, h]) # np.stack就是摞起来了，多了一个维度方向；注意和np.cat的区别;
return True # 返回来的是tuple，第一个元素是两坐标表示法的anchor信息；第二个是中心做标加h,w