From 63fe7ac763f3e2b6e6914089eb45a93f40b52689 Mon Sep 17 00:00:00 2001
From: Konstantin Baierer <unixprog@gmail.com>
Date: Tue, 24 Nov 2020 12:43:40 +0100
Subject: [PATCH] more extraction of util/unused functions

---
 sbb_newspapers_org_image/eynollah.py | 142 ++++-----------------------
 sbb_newspapers_org_image/unused.py   |   6 ++
 sbb_newspapers_org_image/utils.py    |  97 ++++++++++++++++++
 3 files changed, 123 insertions(+), 122 deletions(-)

diff --git a/sbb_newspapers_org_image/eynollah.py b/sbb_newspapers_org_image/eynollah.py
index 8748dd6..2e5ae40 100644
--- a/sbb_newspapers_org_image/eynollah.py
+++ b/sbb_newspapers_org_image/eynollah.py
@@ -60,6 +60,7 @@ from .utils import (
     seperate_lines,
     seperate_lines_new_inside_teils2,
     filter_small_drop_capitals_from_no_patch_layout,
+    find_num_col_deskew,
 )
 
 
@@ -1542,12 +1543,6 @@ class eynollah:
 
         return main_contours
 
-    def get_all_image_patches_coordination(self, image_page):
-        self.all_box_coord = []
-        for jk in range(len(self.boxes)):
-            _, crop_coor = crop_image_inside_box(self.boxes[jk], image_page)
-            self.all_box_coord.append(crop_coor)
-
     def textline_contours(self, img, patches, scaler_h, scaler_w):
 
         if patches:
@@ -2341,103 +2336,6 @@ class eynollah:
 
         return interest_neg_fin
 
-    def find_num_col_deskew(self, regions_without_seperators, sigma_, multiplier=3.8):
-        regions_without_seperators_0 = regions_without_seperators[:, :].sum(axis=1)
-
-        meda_n_updown = regions_without_seperators_0[len(regions_without_seperators_0) :: -1]
-
-        first_nonzero = next((i for i, x in enumerate(regions_without_seperators_0) if x), 0)
-        last_nonzero = next((i for i, x in enumerate(meda_n_updown) if x), 0)
-
-        last_nonzero = len(regions_without_seperators_0) - last_nonzero
-
-        y = regions_without_seperators_0  # [first_nonzero:last_nonzero]
-
-        y_help = np.zeros(len(y) + 20)
-
-        y_help[10 : len(y) + 10] = y
-
-        x = np.array(range(len(y)))
-
-        zneg_rev = -y_help + np.max(y_help)
-
-        zneg = np.zeros(len(zneg_rev) + 20)
-
-        zneg[10 : len(zneg_rev) + 10] = zneg_rev
-
-        z = gaussian_filter1d(y, sigma_)
-        zneg = gaussian_filter1d(zneg, sigma_)
-
-        peaks_neg, _ = find_peaks(zneg, height=0)
-        peaks, _ = find_peaks(z, height=0)
-
-        peaks_neg = peaks_neg - 10 - 10
-
-        # print(np.std(z),'np.std(z)np.std(z)np.std(z)')
-
-        ##plt.plot(z)
-        ##plt.show()
-
-        ##plt.imshow(regions_without_seperators)
-        ##plt.show()
-        """
-        last_nonzero=last_nonzero-0#100
-        first_nonzero=first_nonzero+0#+100
-
-        peaks_neg=peaks_neg[(peaks_neg>first_nonzero) & (peaks_neg<last_nonzero)]
-
-        peaks=peaks[(peaks>.06*regions_without_seperators.shape[1]) & (peaks<0.94*regions_without_seperators.shape[1])]
-        """
-        interest_pos = z[peaks]
-
-        interest_pos = interest_pos[interest_pos > 10]
-
-        interest_neg = z[peaks_neg]
-
-        min_peaks_pos = np.mean(interest_pos)
-        min_peaks_neg = 0  # np.min(interest_neg)
-
-        dis_talaei = (min_peaks_pos - min_peaks_neg) / multiplier
-        # print(interest_pos)
-        grenze = min_peaks_pos - dis_talaei  # np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
-
-        interest_neg_fin = interest_neg[(interest_neg < grenze)]
-        peaks_neg_fin = peaks_neg[(interest_neg < grenze)]
-        interest_neg_fin = interest_neg[(interest_neg < grenze)]
-
-        """
-        if interest_neg[0]<0.1:
-            interest_neg=interest_neg[1:]
-        if interest_neg[len(interest_neg)-1]<0.1:
-            interest_neg=interest_neg[:len(interest_neg)-1]
-
-
-
-        min_peaks_pos=np.min(interest_pos)
-        min_peaks_neg=0#np.min(interest_neg)
-
-
-        dis_talaei=(min_peaks_pos-min_peaks_neg)/multiplier
-        grenze=min_peaks_pos-dis_talaei#np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
-        """
-        # interest_neg_fin=interest_neg#[(interest_neg<grenze)]
-        # peaks_neg_fin=peaks_neg#[(interest_neg<grenze)]
-        # interest_neg_fin=interest_neg#[(interest_neg<grenze)]
-
-        num_col = (len(interest_neg_fin)) + 1
-
-        p_l = 0
-        p_u = len(y) - 1
-        p_m = int(len(y) / 2.0)
-        p_g_l = int(len(y) / 3.0)
-        p_g_u = len(y) - int(len(y) / 3.0)
-
-        diff_peaks = np.abs(np.diff(peaks_neg_fin))
-        diff_peaks_annormal = diff_peaks[diff_peaks < 30]
-
-        # print(len(interest_neg_fin),np.mean(interest_neg_fin))
-        return interest_neg_fin, np.std(z)
-
     def return_deskew_slop(self, img_patch_org, sigma_des, main_page=False):
 
         if main_page and self.dir_of_all is not None:
@@ -2491,12 +2389,12 @@ class eynollah:
                 # plt.imshow(img_rot)
                 # plt.show()
                 img_rot[img_rot != 0] = 1
-                # res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0  ))
+                # res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0  ))
 
-                # neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3  )
+                # neg_peaks,var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3  )
                 # print(var_spectrum,'var_spectrum')
                 try:
-                    neg_peaks, var_spectrum = self.find_num_col_deskew(img_rot, sigma_des, 20.3)
+                    neg_peaks, var_spectrum = find_num_col_deskew(img_rot, sigma_des, 20.3)
                     # print(rot,var_spectrum,'var_spectrum')
                     res_me = np.mean(neg_peaks)
                     if res_me == 0:
@@ -2538,9 +2436,9 @@ class eynollah:
                 ##plt.imshow(img_rot)
                 ##plt.show()
                 img_rot[img_rot != 0] = 1
-                # res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0  ))
+                # res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0  ))
                 try:
-                    neg_peaks, var_spectrum = self.find_num_col_deskew(img_rot, sigma_des, 20.3)
+                    neg_peaks, var_spectrum = find_num_col_deskew(img_rot, sigma_des, 20.3)
                     # print(indexer,'indexer')
                     res_me = np.mean(neg_peaks)
                     if res_me == 0:
@@ -2586,12 +2484,12 @@ class eynollah:
                 # plt.imshow(img_rot)
                 # plt.show()
                 img_rot[img_rot != 0] = 1
-                # res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0  ))
+                # res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0  ))
 
-                # neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3  )
+                # neg_peaks,var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3  )
                 # print(var_spectrum,'var_spectrum')
                 try:
-                    neg_peaks, var_spectrum = self.find_num_col_deskew(img_rot, sigma_des, 20.3)
+                    neg_peaks, var_spectrum = find_num_col_deskew(img_rot, sigma_des, 20.3)
                     # print(rot,var_spectrum,'var_spectrum')
                     res_me = np.mean(neg_peaks)
                     if res_me == 0:
@@ -2648,9 +2546,9 @@ class eynollah:
                     ##plt.imshow(img_rot)
                     ##plt.show()
                     img_rot[img_rot != 0] = 1
-                    # res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0  ))
+                    # res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0  ))
                     try:
-                        neg_peaks, var_spectrum = self.find_num_col_deskew(img_rot, sigma_des, 20.3)
+                        neg_peaks, var_spectrum = find_num_col_deskew(img_rot, sigma_des, 20.3)
                         # print(indexer,'indexer')
                         res_me = np.mean(neg_peaks)
                         if res_me == 0:
@@ -2694,9 +2592,9 @@ class eynollah:
                     ##plt.imshow(img_rot)
                     ##plt.show()
                     img_rot[img_rot != 0] = 1
-                    # res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0  ))
+                    # res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0  ))
                     try:
-                        neg_peaks, var_spectrum = self.find_num_col_deskew(img_rot, sigma_des, 20.3)
+                        neg_peaks, var_spectrum = find_num_col_deskew(img_rot, sigma_des, 20.3)
                         # print(indexer,'indexer')
                         res_me = np.mean(neg_peaks)
                         if res_me == 0:
@@ -2739,12 +2637,12 @@ class eynollah:
                 # plt.imshow(img_rot)
                 # plt.show()
                 img_rot[img_rot != 0] = 1
-                # res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0  ))
+                # res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0  ))
 
-                # neg_peaks,var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3  )
+                # neg_peaks,var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3  )
                 # print(var_spectrum,'var_spectrum')
                 try:
-                    neg_peaks, var_spectrum = self.find_num_col_deskew(img_rot, sigma_des, 20.3)
+                    neg_peaks, var_spectrum = find_num_col_deskew(img_rot, sigma_des, 20.3)
                     # print(rot,var_spectrum,'var_spectrum')
                     res_me = np.mean(neg_peaks)
                     if res_me == 0:
@@ -2791,9 +2689,9 @@ class eynollah:
                     ##plt.imshow(img_rot)
                     ##plt.show()
                     img_rot[img_rot != 0] = 1
-                    # res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0  ))
+                    # res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0  ))
                     try:
-                        neg_peaks, var_spectrum = self.find_num_col_deskew(img_rot, sigma_des, 20.3)
+                        neg_peaks, var_spectrum = find_num_col_deskew(img_rot, sigma_des, 20.3)
                         # print(indexer,'indexer')
                         res_me = np.mean(neg_peaks)
                         if res_me == 0:
@@ -2837,9 +2735,9 @@ class eynollah:
                     ##plt.imshow(img_rot)
                     ##plt.show()
                     img_rot[img_rot != 0] = 1
-                    # res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0  ))
+                    # res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0  ))
                     try:
-                        neg_peaks, var_spectrum = self.find_num_col_deskew(img_rot, sigma_des, 20.3)
+                        neg_peaks, var_spectrum = find_num_col_deskew(img_rot, sigma_des, 20.3)
                         # print(indexer,'indexer')
                         res_me = np.mean(neg_peaks)
                         if res_me == 0:
diff --git a/sbb_newspapers_org_image/unused.py b/sbb_newspapers_org_image/unused.py
index 022ccce..c2c2a4c 100644
--- a/sbb_newspapers_org_image/unused.py
+++ b/sbb_newspapers_org_image/unused.py
@@ -272,3 +272,9 @@ def deskew_erarly(textline_mask):
         image_regions_deskewd = image_regions_deskewd + deskewed_new
     return image_regions_deskewd, slope_true
 
+def get_all_image_patches_coordination(self, image_page):
+    self.all_box_coord = []
+    for jk in range(len(self.boxes)):
+        _, crop_coor = crop_image_inside_box(self.boxes[jk], image_page)
+        self.all_box_coord.append(crop_coor)
+
diff --git a/sbb_newspapers_org_image/utils.py b/sbb_newspapers_org_image/utils.py
index b2d9bd7..f764bf8 100644
--- a/sbb_newspapers_org_image/utils.py
+++ b/sbb_newspapers_org_image/utils.py
@@ -1457,3 +1457,100 @@ def filter_small_drop_capitals_from_no_patch_layout(layout_no_patch, layout1):
 
     return layout_no_patch
 
+
+def find_num_col_deskew(regions_without_seperators, sigma_, multiplier=3.8):
+    regions_without_seperators_0 = regions_without_seperators[:, :].sum(axis=1)
+
+    meda_n_updown = regions_without_seperators_0[len(regions_without_seperators_0) :: -1]
+
+    first_nonzero = next((i for i, x in enumerate(regions_without_seperators_0) if x), 0)
+    last_nonzero = next((i for i, x in enumerate(meda_n_updown) if x), 0)
+
+    last_nonzero = len(regions_without_seperators_0) - last_nonzero
+
+    y = regions_without_seperators_0  # [first_nonzero:last_nonzero]
+
+    y_help = np.zeros(len(y) + 20)
+
+    y_help[10 : len(y) + 10] = y
+
+    x = np.array(range(len(y)))
+
+    zneg_rev = -y_help + np.max(y_help)
+
+    zneg = np.zeros(len(zneg_rev) + 20)
+
+    zneg[10 : len(zneg_rev) + 10] = zneg_rev
+
+    z = gaussian_filter1d(y, sigma_)
+    zneg = gaussian_filter1d(zneg, sigma_)
+
+    peaks_neg, _ = find_peaks(zneg, height=0)
+    peaks, _ = find_peaks(z, height=0)
+
+    peaks_neg = peaks_neg - 10 - 10
+
+    # print(np.std(z),'np.std(z)np.std(z)np.std(z)')
+
+    ##plt.plot(z)
+    ##plt.show()
+
+    ##plt.imshow(regions_without_seperators)
+    ##plt.show()
+    """
+    last_nonzero=last_nonzero-0#100
+    first_nonzero=first_nonzero+0#+100
+
+    peaks_neg=peaks_neg[(peaks_neg>first_nonzero) & (peaks_neg<last_nonzero)]
+
+    peaks=peaks[(peaks>.06*regions_without_seperators.shape[1]) & (peaks<0.94*regions_without_seperators.shape[1])]
+    """
+    interest_pos = z[peaks]
+
+    interest_pos = interest_pos[interest_pos > 10]
+
+    interest_neg = z[peaks_neg]
+
+    min_peaks_pos = np.mean(interest_pos)
+    min_peaks_neg = 0  # np.min(interest_neg)
+
+    dis_talaei = (min_peaks_pos - min_peaks_neg) / multiplier
+    # print(interest_pos)
+    grenze = min_peaks_pos - dis_talaei  # np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
+
+    interest_neg_fin = interest_neg[(interest_neg < grenze)]
+    peaks_neg_fin = peaks_neg[(interest_neg < grenze)]
+    interest_neg_fin = interest_neg[(interest_neg < grenze)]
+
+    """
+    if interest_neg[0]<0.1:
+        interest_neg=interest_neg[1:]
+    if interest_neg[len(interest_neg)-1]<0.1:
+        interest_neg=interest_neg[:len(interest_neg)-1]
+
+
+
+    min_peaks_pos=np.min(interest_pos)
+    min_peaks_neg=0#np.min(interest_neg)
+
+
+    dis_talaei=(min_peaks_pos-min_peaks_neg)/multiplier
+    grenze=min_peaks_pos-dis_talaei#np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
+    """
+    # interest_neg_fin=interest_neg#[(interest_neg<grenze)]
+    # peaks_neg_fin=peaks_neg#[(interest_neg<grenze)]
+    # interest_neg_fin=interest_neg#[(interest_neg<grenze)]
+
+    num_col = (len(interest_neg_fin)) + 1
+
+    p_l = 0
+    p_u = len(y) - 1
+    p_m = int(len(y) / 2.0)
+    p_g_l = int(len(y) / 3.0)
+    p_g_u = len(y) - int(len(y) / 3.0)
+
+    diff_peaks = np.abs(np.diff(peaks_neg_fin))
+    diff_peaks_annormal = diff_peaks[diff_peaks < 30]
+
+    # print(len(interest_neg_fin),np.mean(interest_neg_fin))
+    return interest_neg_fin, np.std(z)