def build_gauss_pyr(self, base): #---- 建立高斯金字塔
intvls = self.intvls
# 影像金字塔每一層有幾個分頁 =3
sigma = self.sigma
# 影像最開始的 高斯平滑參數=1.6
octvs = self.octvs
# 高斯金字塔有幾層
k = 2**(1 / intvls)
# =2^(0.333)
sig = np.zeros(intvls + 3)
# 每一頁的 sigma
sig[0] = sigma
sig[1] = sigma * np.sqrt(k * k - 1)
for i in range(2, intvls + 3):
sig[i] = sig[i - 1] * k
gauss_pyr = []
# 輸出高斯金字塔
for o in range(0, octvs):
if o == 0:
pyramid = np.zeros((base.shape[0], base.shape[1], intvls + 3))
pyramid[:, :, 0] = base
else:
pyramid = self.downsample_by2(gauss_pyr[o - 1][:, :, intvls])
for i in range(1, intvls + 3):
pyramid[:, :, i] = CVP.Gauss_blur2D(pyramid[:, :, i - 1],
sigma=sig[i])
gauss_pyr.append(pyramid)
return gauss_pyr
def pre_processing(self, src_img):
# 執行以下動作: (1)轉灰階圖 (2)將資料改成[0~1] (3)影像放大2倍 (4)Gauss smooth
gray = CVP.rgb2gray(src_img)
# 灰階圖
if np.max(gray) > 2:
gray = gray / 256
# 影像強度正規化到 [0 ~ 1]
#doubled = CVP.resize(gray,ratio=2); # 雙倍影像圖
if self.doubled == True:
doubled = CVP.resize(gray, ratio=2)
# 雙倍影像圖
else:
doubled = gray
sig_diff = np.sqrt(self.sigma**2 - 4 * self.init_sigma**2)
# s=1.25
init_img = CVP.Gauss_blur2D(doubled, sigma=sig_diff)
return init_img