From 894cb13f501deb45ca1f30d926d9c8dae47f22b7 Mon Sep 17 00:00:00 2001 From: Olivier Debeir Date: Fri, 12 Oct 2012 17:47:51 +0200 Subject: [PATCH] rename n_se_n to num_se_n etc, removed commented code --- skimage/rank/_core16.pxd | 44 ++--- skimage/rank/_core16b.pxd | 44 ++--- skimage/rank/_core16p.pxd | 44 ++--- skimage/rank/_core8.pxd | 44 ++--- skimage/rank/_core8p.pxd | 44 ++--- skimage/rank/_crank16_bilateral.pyx | 261 +--------------------------- 6 files changed, 112 insertions(+), 369 deletions(-) diff --git a/skimage/rank/_core16.pxd b/skimage/rank/_core16.pxd index 791f5fb9..1a3194de 100644 --- a/skimage/rank/_core16.pxd +++ b/skimage/rank/_core16.pxd @@ -93,7 +93,7 @@ char shift_x, char shift_y,int bitdepth): cdef int max_se = srows*scols # number of element in each attack border - cdef int n_se_n, n_se_s, n_se_e, n_se_w + cdef int num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution cdef int* histo = malloc(maxbin * sizeof(int)) @@ -126,26 +126,26 @@ char shift_x, char shift_y,int bitdepth): t = np.vstack((np.zeros((1,selem.shape[1])),selem)) t_n = np.diff(t,axis=0)==1 - n_se_n = n_se_s = n_se_e = n_se_w = 0 + num_se_n = num_se_s = num_se_e = num_se_w = 0 for r in range(srows): for c in range(scols): if t_e[r,c]: - se_e_r[n_se_e] = r - centre_r - se_e_c[n_se_e] = c - centre_c - n_se_e += 1 + se_e_r[num_se_e] = r - centre_r + se_e_c[num_se_e] = c - centre_c + num_se_e += 1 if t_w[r,c]: - se_w_r[n_se_w] = r - centre_r - se_w_c[n_se_w] = c - centre_c - n_se_w += 1 + se_w_r[num_se_w] = r - centre_r + se_w_c[num_se_w] = c - centre_c + num_se_w += 1 if t_n[r,c]: - se_n_r[n_se_n] = r - centre_r - se_n_c[n_se_n] = c - centre_c - n_se_n += 1 + se_n_r[num_se_n] = r - centre_r + se_n_c[num_se_n] = c - centre_c + num_se_n += 1 if t_s[r,c]: - se_s_r[n_se_s] = r - centre_r - se_s_c[n_se_s] = c - centre_c - n_se_s += 1 + se_s_r[num_se_s] = r - centre_r + se_s_c[num_se_s] = c - centre_c + num_se_s += 1 # initial population and histogram for i in range(maxbin): @@ -175,14 +175,14 @@ char shift_x, char shift_y,int bitdepth): for even_row in range(0,rows,2): # ---> west to east for c in range(1,cols): - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c - 1 if emask_data[rr * ecols + cc]: @@ -200,14 +200,14 @@ char shift_x, char shift_y,int bitdepth): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: @@ -222,14 +222,14 @@ char shift_x, char shift_y,int bitdepth): # ---> east to west for c in range(cols-2,-1,-1): - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c + 1 if emask_data[rr * ecols + cc]: @@ -247,14 +247,14 @@ char shift_x, char shift_y,int bitdepth): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: diff --git a/skimage/rank/_core16b.pxd b/skimage/rank/_core16b.pxd index 3007a2fb..163662b5 100644 --- a/skimage/rank/_core16b.pxd +++ b/skimage/rank/_core16b.pxd @@ -94,7 +94,7 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): cdef int max_se = srows*scols # number of element in each attack border - cdef int n_se_n, n_se_s, n_se_e, n_se_w + cdef int num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution cdef int* histo = malloc(maxbin * sizeof(int)) @@ -127,26 +127,26 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): t = np.vstack((np.zeros((1,selem.shape[1])),selem)) t_n = np.diff(t,axis=0)==1 - n_se_n = n_se_s = n_se_e = n_se_w = 0 + num_se_n = num_se_s = num_se_e = num_se_w = 0 for r in range(srows): for c in range(scols): if t_e[r,c]: - se_e_r[n_se_e] = r - centre_r - se_e_c[n_se_e] = c - centre_c - n_se_e += 1 + se_e_r[num_se_e] = r - centre_r + se_e_c[num_se_e] = c - centre_c + num_se_e += 1 if t_w[r,c]: - se_w_r[n_se_w] = r - centre_r - se_w_c[n_se_w] = c - centre_c - n_se_w += 1 + se_w_r[num_se_w] = r - centre_r + se_w_c[num_se_w] = c - centre_c + num_se_w += 1 if t_n[r,c]: - se_n_r[n_se_n] = r - centre_r - se_n_c[n_se_n] = c - centre_c - n_se_n += 1 + se_n_r[num_se_n] = r - centre_r + se_n_c[num_se_n] = c - centre_c + num_se_n += 1 if t_s[r,c]: - se_s_r[n_se_s] = r - centre_r - se_s_c[n_se_s] = c - centre_c - n_se_s += 1 + se_s_r[num_se_s] = r - centre_r + se_s_c[num_se_s] = c - centre_c + num_se_s += 1 # initial population and histogram for i in range(maxbin): @@ -176,14 +176,14 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): for even_row in range(0,rows,2): # ---> west to east for c in range(1,cols): - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c - 1 if emask_data[rr * ecols + cc]: @@ -201,14 +201,14 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: @@ -223,14 +223,14 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): # ---> east to west for c in range(cols-2,-1,-1): - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c + 1 if emask_data[rr * ecols + cc]: @@ -248,14 +248,14 @@ char shift_x, char shift_y,int bitdepth, int s0, int s1): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: diff --git a/skimage/rank/_core16p.pxd b/skimage/rank/_core16p.pxd index 484a6a6b..9e992b6b 100644 --- a/skimage/rank/_core16p.pxd +++ b/skimage/rank/_core16p.pxd @@ -90,7 +90,7 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): cdef int max_se = srows*scols # number of element in each attack border - cdef int n_se_n, n_se_s, n_se_e, n_se_w + cdef int num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution cdef int* histo = malloc(maxbin * sizeof(int)) @@ -123,26 +123,26 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): t = np.vstack((np.zeros((1,selem.shape[1])),selem)) t_n = np.diff(t,axis=0)==1 - n_se_n = n_se_s = n_se_e = n_se_w = 0 + num_se_n = num_se_s = num_se_e = num_se_w = 0 for r in range(srows): for c in range(scols): if t_e[r,c]: - se_e_r[n_se_e] = r - centre_r - se_e_c[n_se_e] = c - centre_c - n_se_e += 1 + se_e_r[num_se_e] = r - centre_r + se_e_c[num_se_e] = c - centre_c + num_se_e += 1 if t_w[r,c]: - se_w_r[n_se_w] = r - centre_r - se_w_c[n_se_w] = c - centre_c - n_se_w += 1 + se_w_r[num_se_w] = r - centre_r + se_w_c[num_se_w] = c - centre_c + num_se_w += 1 if t_n[r,c]: - se_n_r[n_se_n] = r - centre_r - se_n_c[n_se_n] = c - centre_c - n_se_n += 1 + se_n_r[num_se_n] = r - centre_r + se_n_c[num_se_n] = c - centre_c + num_se_n += 1 if t_s[r,c]: - se_s_r[n_se_s] = r - centre_r - se_s_c[n_se_s] = c - centre_c - n_se_s += 1 + se_s_r[num_se_s] = r - centre_r + se_s_c[num_se_s] = c - centre_c + num_se_s += 1 # initial population and histogram for i in range(maxbin): @@ -172,14 +172,14 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): for even_row in range(0,rows,2): # ---> west to east for c in range(1,cols): - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c - 1 if emask_data[rr * ecols + cc]: @@ -197,14 +197,14 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: @@ -219,14 +219,14 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): # ---> east to west for c in range(cols-2,-1,-1): - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c + 1 if emask_data[rr * ecols + cc]: @@ -244,14 +244,14 @@ char shift_x, char shift_y,int bitdepth, float p0, float p1): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: diff --git a/skimage/rank/_core8.pxd b/skimage/rank/_core8.pxd index 325f779d..d24297cb 100644 --- a/skimage/rank/_core8.pxd +++ b/skimage/rank/_core8.pxd @@ -83,7 +83,7 @@ char shift_x, char shift_y): cdef int max_se = srows*scols # number of element in each attack border - cdef int n_se_n, n_se_s, n_se_e, n_se_w + cdef int num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution cdef int* histo = malloc(256 * sizeof(int)) @@ -116,26 +116,26 @@ char shift_x, char shift_y): t = np.vstack((np.zeros((1,selem.shape[1])),selem)) t_n = np.diff(t,axis=0)==1 - n_se_n = n_se_s = n_se_e = n_se_w = 0 + num_se_n = num_se_s = num_se_e = num_se_w = 0 for r in range(srows): for c in range(scols): if t_e[r,c]: - se_e_r[n_se_e] = r - centre_r - se_e_c[n_se_e] = c - centre_c - n_se_e += 1 + se_e_r[num_se_e] = r - centre_r + se_e_c[num_se_e] = c - centre_c + num_se_e += 1 if t_w[r,c]: - se_w_r[n_se_w] = r - centre_r - se_w_c[n_se_w] = c - centre_c - n_se_w += 1 + se_w_r[num_se_w] = r - centre_r + se_w_c[num_se_w] = c - centre_c + num_se_w += 1 if t_n[r,c]: - se_n_r[n_se_n] = r - centre_r - se_n_c[n_se_n] = c - centre_c - n_se_n += 1 + se_n_r[num_se_n] = r - centre_r + se_n_c[num_se_n] = c - centre_c + num_se_n += 1 if t_s[r,c]: - se_s_r[n_se_s] = r - centre_r - se_s_c[n_se_s] = c - centre_c - n_se_s += 1 + se_s_r[num_se_s] = r - centre_r + se_s_c[num_se_s] = c - centre_c + num_se_s += 1 # initial population and histogram for i in range(256): @@ -164,14 +164,14 @@ char shift_x, char shift_y): for even_row in range(0,rows,2): # ---> west to east for c in range(1,cols): - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c - 1 if emask_data[rr * ecols + cc]: @@ -188,14 +188,14 @@ char shift_x, char shift_y): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: @@ -209,14 +209,14 @@ char shift_x, char shift_y): # ---> east to west for c in range(cols-2,-1,-1): - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c + 1 if emask_data[rr * ecols + cc]: @@ -233,14 +233,14 @@ char shift_x, char shift_y): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: diff --git a/skimage/rank/_core8p.pxd b/skimage/rank/_core8p.pxd index 5fa278d5..b1adac70 100644 --- a/skimage/rank/_core8p.pxd +++ b/skimage/rank/_core8p.pxd @@ -80,7 +80,7 @@ char shift_x, char shift_y, float p0, float p1): cdef int max_se = srows*scols # number of element in each attack border - cdef int n_se_n, n_se_s, n_se_e, n_se_w + cdef int num_se_n, num_se_s, num_se_e, num_se_w # the current local histogram distribution cdef int* histo = malloc(256 * sizeof(int)) @@ -113,26 +113,26 @@ char shift_x, char shift_y, float p0, float p1): t = np.vstack((np.zeros((1,selem.shape[1])),selem)) t_n = np.diff(t,axis=0)==1 - n_se_n = n_se_s = n_se_e = n_se_w = 0 + num_se_n = num_se_s = num_se_e = num_se_w = 0 for r in range(srows): for c in range(scols): if t_e[r,c]: - se_e_r[n_se_e] = r - centre_r - se_e_c[n_se_e] = c - centre_c - n_se_e += 1 + se_e_r[num_se_e] = r - centre_r + se_e_c[num_se_e] = c - centre_c + num_se_e += 1 if t_w[r,c]: - se_w_r[n_se_w] = r - centre_r - se_w_c[n_se_w] = c - centre_c - n_se_w += 1 + se_w_r[num_se_w] = r - centre_r + se_w_c[num_se_w] = c - centre_c + num_se_w += 1 if t_n[r,c]: - se_n_r[n_se_n] = r - centre_r - se_n_c[n_se_n] = c - centre_c - n_se_n += 1 + se_n_r[num_se_n] = r - centre_r + se_n_c[num_se_n] = c - centre_c + num_se_n += 1 if t_s[r,c]: - se_s_r[n_se_s] = r - centre_r - se_s_c[n_se_s] = c - centre_c - n_se_s += 1 + se_s_r[num_se_s] = r - centre_r + se_s_c[num_se_s] = c - centre_c + num_se_s += 1 # initial population and histogram for i in range(256): @@ -161,14 +161,14 @@ char shift_x, char shift_y, float p0, float p1): for even_row in range(0,rows,2): # ---> west to east for c in range(1,cols): - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c - 1 if emask_data[rr * ecols + cc]: @@ -185,14 +185,14 @@ char shift_x, char shift_y, float p0, float p1): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: @@ -206,14 +206,14 @@ char shift_x, char shift_y, float p0, float p1): # ---> east to west for c in range(cols-2,-1,-1): - for s in range(n_se_w): + for s in range(num_se_w): rr = r + se_w_r[s] + centre_r cc = c + se_w_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_e): + for s in range(num_se_e): rr = r + se_e_r[s] + centre_r cc = c + se_e_c[s] + centre_c + 1 if emask_data[rr * ecols + cc]: @@ -230,14 +230,14 @@ char shift_x, char shift_y, float p0, float p1): break # ---> north to south - for s in range(n_se_s): + for s in range(num_se_s): rr = r + se_s_r[s] + centre_r cc = c + se_s_c[s] + centre_c if emask_data[rr * ecols + cc]: value = eimage_data[rr * ecols + cc] histo[value] += 1 pop += 1. - for s in range(n_se_n): + for s in range(num_se_n): rr = r + se_n_r[s] + centre_r - 1 cc = c + se_n_c[s] + centre_c if emask_data[rr * ecols + cc]: diff --git a/skimage/rank/_crank16_bilateral.pyx b/skimage/rank/_crank16_bilateral.pyx index 440d28e3..e783d7ea 100644 --- a/skimage/rank/_crank16_bilateral.pyx +++ b/skimage/rank/_crank16_bilateral.pyx @@ -21,73 +21,6 @@ from _core16b cimport _core16b # kernels uint16 take extra parameter for defining the bitdepth # ----------------------------------------------------------------- -#cdef inline np.uint16_t kernel_autolevel(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i,imin,imax,delta -# -# if pop: -# for i in range(maxbin-1,-1,-1): -# if histo[i]: -# imax = i -# break -# for i in range(maxbin): -# if histo[i]: -# imin = i -# break -# delta = imax-imin -# if delta>0: -# return (maxbin*1.*(g-imin)/delta) -# else: -# return (imax-imin) -# -#cdef inline np.uint16_t kernel_bottomhat(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i -# -# for i in range(maxbin): -# if histo[i]: -# break -# -# return (g-i) -# -# -#cdef inline np.uint16_t kernel_egalise(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i -# cdef float sum = 0. -# -# if pop: -# for i in range(maxbin): -# sum += histo[i] -# if i>=g: -# break -# -# return ((maxbin*1.*sum)/pop) -# else: -# return (0) -# -#cdef inline np.uint16_t kernel_gradient(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i,imin,imax -# -# if pop: -# for i in range(maxbin-1,-1,-1): -# if histo[i]: -# imax = i -# break -# for i in range(maxbin): -# if histo[i]: -# imin = i -# break -# return (imax-imin) -# else: -# return (0) -# -#cdef inline np.uint16_t kernel_maximum(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i -# -# if pop: -# for i in range(maxbin-1,-1,-1): -# if histo[i]: -# return (i) -# -# return (0) cdef inline np.uint16_t kernel_mean(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, int s0, int s1): cdef int i,bilat_pop=0 @@ -105,71 +38,7 @@ cdef inline np.uint16_t kernel_mean(int* histo, float pop, np.uint16_t g,int bit else: return (0) -#cdef inline np.uint16_t kernel_meansubstraction(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i -# cdef float mean = 0. -# -# if pop: -# for i in range(maxbin): -# mean += histo[i]*i -# return ((g-mean/pop)/2.+midbin) -# else: -# return (0) -# -#cdef inline np.uint16_t kernel_median(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i -# cdef float sum = pop/2.0 -# -# if pop: -# for i in range(maxbin): -# if histo[i]: -# sum -= histo[i] -# if sum<0: -# return (i) -# -# return (0) -# -#cdef inline np.uint16_t kernel_minimum(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i -# -# if pop: -# for i in range(maxbin): -# if histo[i]: -# return (i) -# -# return (0) -# -#cdef inline np.uint16_t kernel_modal(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int hmax=0,imax=0 -# -# if pop: -# for i in range(maxbin): -# if histo[i]>hmax: -# hmax = histo[i] -# imax = i -# return (imax) -# -# return (0) -# -#cdef inline np.uint16_t kernel_morph_contr_enh(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i,imin,imax -# -# if pop: -# for i in range(maxbin-1,-1,-1): -# if histo[i]: -# imax = i -# break -# for i in range(maxbin): -# if histo[i]: -# imin = i -# break -# if imax-g < g-imin: -# return (imax) -# else: -# return (imin) -# else: -# return (0) -# + cdef inline np.uint16_t kernel_pop(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin, int s0, int s1): cdef int i,bilat_pop=0 @@ -181,75 +50,10 @@ cdef inline np.uint16_t kernel_pop(int* histo, float pop, np.uint16_t g,int bitd else: return (0) -# -#cdef inline np.uint16_t kernel_threshold(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i -# cdef float mean = 0. -# -# if pop: -# for i in range(maxbin): -# mean += histo[i]*i -# return (g>(mean/pop)) -# else: -# return (0) -# -#cdef inline np.uint16_t kernel_tophat(int* histo, float pop, np.uint16_t g,int bitdepth,int maxbin, int midbin): -# cdef int i -# -# for i in range(maxbin-1,-1,-1): -# if histo[i]: -# break -# -# return (i-g) # ----------------------------------------------------------------- # python wrappers # ----------------------------------------------------------------- -#def autolevel(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """bottom hat -# """ -# return rank16b(kernel_autolevel,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def bottomhat(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """bottom hat -# """ -# return rank16b(kernel_bottomhat,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def egalise(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """local egalisation of the gray level -# """ -# return rank16b(kernel_egalise,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def gradient(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """local maximum - local minimum gray level -# """ -# return rank16b(kernel_gradient,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def maximum(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """local maximum gray level -# """ -# return rank16b(kernel_maximum,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) - def mean(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, @@ -259,51 +63,7 @@ def mean(np.ndarray[np.uint16_t, ndim=2] image, """ return _core16b(kernel_mean,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -#def meansubstraction(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """(g - average gray level)/2+midbin (clipped on uint8) -# """ -# return rank16b(kernel_meansubstraction,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def median(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """local median -# """ -# return rank16b(kernel_median,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def minimum(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """local minimum gray level -# """ -# return rank16b(kernel_minimum,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def morph_contr_enh(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """morphological contrast enhancement -# """ -# return rank16b(kernel_morph_contr_enh,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def modal(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """local mode -# """ -# return rank16b(kernel_modal,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# + def pop(np.ndarray[np.uint16_t, ndim=2] image, np.ndarray[np.uint8_t, ndim=2] selem, np.ndarray[np.uint8_t, ndim=2] mask=None, @@ -313,20 +73,3 @@ def pop(np.ndarray[np.uint16_t, ndim=2] image, """ return _core16b(kernel_pop,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -#def threshold(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """returns maxbin-1 if gray level higher than local mean, 0 else -# """ -# return rank16b(kernel_threshold,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1) -# -#def tophat(np.ndarray[np.uint16_t, ndim=2] image, -# np.ndarray[np.uint8_t, ndim=2] selem, -# np.ndarray[np.uint8_t, ndim=2] mask=None, -# np.ndarray[np.uint16_t, ndim=2] out=None, -# char shift_x=0, char shift_y=0, int bitdepth=8, int s0=1, int s1=1): -# """top hat -# """ -# return rank16b(kernel_tophat,image,selem,mask,out,shift_x,shift_y,bitdepth,s0,s1)