mirror of
https://github.com/qurator-spk/eynollah.git
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return_deskew_slop: move to utils
This commit is contained in:
Konstantin Baierer
parent
340b48ab4b
commit
1fa123fcf8
2 changed files with 444 additions and 443 deletions
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@ -77,6 +77,7 @@ from .utils import (
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seperate_lines_vertical_cont,
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delete_seperator_around,
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return_regions_without_seperators,
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return_deskew_slop,
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)
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@ -1300,7 +1301,7 @@ class eynollah:
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img_int_p[img_int_p > 0] = 1
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# slope_for_all=self.return_deskew_slope_new(img_int_p,sigma_des)
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slope_for_all = self.return_deskew_slop(img_int_p, sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name)
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slope_for_all = return_deskew_slop(img_int_p, sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name)
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if abs(slope_for_all) < 0.5:
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slope_for_all = [slope_deskew][0]
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@ -1311,7 +1312,7 @@ class eynollah:
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except:
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slope_for_all = 999
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##slope_for_all=self.return_deskew_slop(img_int_p,sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name)
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##slope_for_all=return_deskew_slop(img_int_p,sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name)
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if slope_for_all == 999:
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slope_for_all = [slope_deskew][0]
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@ -1457,7 +1458,7 @@ class eynollah:
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img_int_p[img_int_p > 0] = 1
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# slope_for_all=self.return_deskew_slope_new(img_int_p,sigma_des)
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slope_for_all = self.return_deskew_slop(img_int_p, sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name)
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slope_for_all = return_deskew_slop(img_int_p, sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name)
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if abs(slope_for_all) <= 0.5:
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slope_for_all = [slope_deskew][0]
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@ -1465,7 +1466,7 @@ class eynollah:
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except:
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slope_for_all = 999
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##slope_for_all=self.return_deskew_slop(img_int_p,sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name)
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##slope_for_all=return_deskew_slop(img_int_p,sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name)
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if slope_for_all == 999:
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slope_for_all = [slope_deskew][0]
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@ -1726,7 +1727,7 @@ class eynollah:
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sigma = 2
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try:
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slope_xline = self.return_deskew_slop(img_xline, sigma, dir_of_all=self.dir_of_all, f_name=self.f_name)
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slope_xline = return_deskew_slop(img_xline, sigma, dir_of_all=self.dir_of_all, f_name=self.f_name)
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except:
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slope_xline = 0
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slopes_tile_wise.append(slope_xline)
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@ -1748,7 +1749,7 @@ class eynollah:
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sigma=3
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try:
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slope_xline=self.return_deskew_slop(img_xline,sigma, dir_of_all=self.dir_of_all, f_name=self.f_name)
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slope_xline=return_deskew_slop(img_xline,sigma, dir_of_all=self.dir_of_all, f_name=self.f_name)
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except:
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slope_xline=0
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slopes_tile_wise.append(slope_xline)
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@ -1891,7 +1892,7 @@ class eynollah:
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sigma = 2
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try:
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slope_xline = self.return_deskew_slop(img_xline, sigma, dir_of_all=self.dir_of_all, f_name=self.f_name)
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slope_xline = return_deskew_slop(img_xline, sigma, dir_of_all=self.dir_of_all, f_name=self.f_name)
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except:
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slope_xline = 0
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@ -2106,438 +2107,6 @@ class eynollah:
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return slope
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def return_deskew_slop(self, img_patch_org, sigma_des, main_page=False, dir_of_all=None, f_name=None):
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if main_page and dir_of_all is not None:
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plt.figure(figsize=(70, 40))
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plt.rcParams["font.size"] = "50"
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plt.subplot(1, 2, 1)
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plt.imshow(img_patch_org)
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plt.subplot(1, 2, 2)
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plt.plot(gaussian_filter1d(img_patch_org.sum(axis=1), 3), np.array(range(len(gaussian_filter1d(img_patch_org.sum(axis=1), 3)))), linewidth=8)
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plt.xlabel("Density of textline prediction in direction of X axis", fontsize=60)
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plt.ylabel("Height", fontsize=60)
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plt.yticks([0, len(gaussian_filter1d(img_patch_org.sum(axis=1), 3))])
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plt.gca().invert_yaxis()
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plt.savefig(os.path.join(dir_of_all, f_name + "_density_of_textline.png"))
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# print(np.max(img_patch_org.sum(axis=0)) ,np.max(img_patch_org.sum(axis=1)),'axislar')
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# img_patch_org=resize_image(img_patch_org,int(img_patch_org.shape[0]*2.5),int(img_patch_org.shape[1]/2.5))
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# print(np.max(img_patch_org.sum(axis=0)) ,np.max(img_patch_org.sum(axis=1)),'axislar2')
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img_int = np.zeros((img_patch_org.shape[0], img_patch_org.shape[1]))
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img_int[:, :] = img_patch_org[:, :] # img_patch_org[:,:,0]
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img_resized = np.zeros((int(img_int.shape[0] * (1.8)), int(img_int.shape[1] * (2.6))))
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img_resized[int(img_int.shape[0] * (0.4)) : int(img_int.shape[0] * (0.4)) + img_int.shape[0], int(img_int.shape[1] * (0.8)) : int(img_int.shape[1] * (0.8)) + img_int.shape[1]] = img_int[:, :]
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if main_page and img_patch_org.shape[1] > img_patch_org.shape[0]:
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# plt.imshow(img_resized)
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# plt.show()
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angels = np.array(
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[
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-45,
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0,
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45,
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90,
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]
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) # np.linspace(-12,12,100)#np.array([0 , 45 , 90 , -45])
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res = []
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num_of_peaks = []
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index_cor = []
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var_res = []
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indexer = 0
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for rot in angels:
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img_rot = rotate_image(img_resized, rot)
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# plt.imshow(img_rot)
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# plt.show()
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img_rot[img_rot != 0] = 1
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# res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0 ))
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# neg_peaks,var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
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# print(var_spectrum,'var_spectrum')
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try:
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neg_peaks, var_spectrum = find_num_col_deskew(img_rot, sigma_des, 20.3)
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# print(rot,var_spectrum,'var_spectrum')
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res_me = np.mean(neg_peaks)
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if res_me == 0:
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res_me = VERY_LARGE_NUMBER
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else:
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pass
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res_num = len(neg_peaks)
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except:
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res_me = VERY_LARGE_NUMBER
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res_num = 0
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var_spectrum = 0
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if isNaN(res_me):
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pass
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else:
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res.append(res_me)
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var_res.append(var_spectrum)
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num_of_peaks.append(res_num)
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index_cor.append(indexer)
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indexer = indexer + 1
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try:
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var_res = np.array(var_res)
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ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
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except:
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ang_int = 0
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angels = np.linspace(ang_int - 22.5, ang_int + 22.5, 100)
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res = []
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num_of_peaks = []
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index_cor = []
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var_res = []
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indexer = 0
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for rot in angels:
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img_rot = rotate_image(img_resized, rot)
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##plt.imshow(img_rot)
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##plt.show()
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img_rot[img_rot != 0] = 1
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# res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0 ))
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try:
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neg_peaks, var_spectrum = find_num_col_deskew(img_rot, sigma_des, 20.3)
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# print(indexer,'indexer')
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res_me = np.mean(neg_peaks)
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if res_me == 0:
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res_me = VERY_LARGE_NUMBER
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else:
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pass
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res_num = len(neg_peaks)
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except:
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res_me = VERY_LARGE_NUMBER
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res_num = 0
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var_spectrum = 0
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if isNaN(res_me):
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pass
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else:
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res.append(res_me)
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var_res.append(var_spectrum)
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num_of_peaks.append(res_num)
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index_cor.append(indexer)
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indexer = indexer + 1
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try:
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var_res = np.array(var_res)
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ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
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except:
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ang_int = 0
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elif main_page and img_patch_org.shape[1] <= img_patch_org.shape[0]:
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# plt.imshow(img_resized)
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# plt.show()
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angels = np.linspace(-12, 12, 100) # np.array([0 , 45 , 90 , -45])
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res = []
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num_of_peaks = []
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index_cor = []
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var_res = []
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indexer = 0
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for rot in angels:
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img_rot = rotate_image(img_resized, rot)
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# plt.imshow(img_rot)
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# plt.show()
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img_rot[img_rot != 0] = 1
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# res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0 ))
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# neg_peaks,var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
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# print(var_spectrum,'var_spectrum')
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try:
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neg_peaks, var_spectrum = find_num_col_deskew(img_rot, sigma_des, 20.3)
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# print(rot,var_spectrum,'var_spectrum')
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res_me = np.mean(neg_peaks)
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if res_me == 0:
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res_me = VERY_LARGE_NUMBER
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else:
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pass
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res_num = len(neg_peaks)
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except:
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res_me = VERY_LARGE_NUMBER
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res_num = 0
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var_spectrum = 0
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if isNaN(res_me):
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pass
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else:
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res.append(res_me)
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var_res.append(var_spectrum)
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num_of_peaks.append(res_num)
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index_cor.append(indexer)
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indexer = indexer + 1
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if dir_of_all is not None:
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print("galdi?")
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plt.figure(figsize=(60, 30))
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plt.rcParams["font.size"] = "50"
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plt.plot(angels, np.array(var_res), "-o", markersize=25, linewidth=4)
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plt.xlabel("angle", fontsize=50)
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plt.ylabel("variance of sum of rotated textline in direction of x axis", fontsize=50)
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plt.plot(angels[np.argmax(var_res)], var_res[np.argmax(np.array(var_res))], "*", markersize=50, label="Angle of deskewing=" + str("{:.2f}".format(angels[np.argmax(var_res)])) + r"$\degree$")
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plt.legend(loc="best")
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plt.savefig(os.path.join(dir_of_all, f_name + "_rotation_angle.png"))
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try:
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var_res = np.array(var_res)
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ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
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except:
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ang_int = 0
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early_slope_edge = 11
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if abs(ang_int) > early_slope_edge and ang_int < 0:
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angels = np.linspace(-90, -12, 100)
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res = []
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num_of_peaks = []
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index_cor = []
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var_res = []
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indexer = 0
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for rot in angels:
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img_rot = rotate_image(img_resized, rot)
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##plt.imshow(img_rot)
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##plt.show()
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img_rot[img_rot != 0] = 1
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# res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0 ))
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try:
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neg_peaks, var_spectrum = find_num_col_deskew(img_rot, sigma_des, 20.3)
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# print(indexer,'indexer')
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res_me = np.mean(neg_peaks)
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if res_me == 0:
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res_me = VERY_LARGE_NUMBER
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else:
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pass
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res_num = len(neg_peaks)
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except:
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res_me = VERY_LARGE_NUMBER
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res_num = 0
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var_spectrum = 0
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if isNaN(res_me):
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pass
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else:
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res.append(res_me)
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var_res.append(var_spectrum)
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num_of_peaks.append(res_num)
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index_cor.append(indexer)
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indexer = indexer + 1
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try:
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var_res = np.array(var_res)
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ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
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except:
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ang_int = 0
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elif abs(ang_int) > early_slope_edge and ang_int > 0:
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angels = np.linspace(90, 12, 100)
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res = []
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num_of_peaks = []
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index_cor = []
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var_res = []
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indexer = 0
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for rot in angels:
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img_rot = rotate_image(img_resized, rot)
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##plt.imshow(img_rot)
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##plt.show()
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img_rot[img_rot != 0] = 1
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# res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0 ))
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try:
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neg_peaks, var_spectrum = find_num_col_deskew(img_rot, sigma_des, 20.3)
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# print(indexer,'indexer')
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res_me = np.mean(neg_peaks)
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if res_me == 0:
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res_me = VERY_LARGE_NUMBER
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else:
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pass
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res_num = len(neg_peaks)
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except:
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res_me = VERY_LARGE_NUMBER
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res_num = 0
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var_spectrum = 0
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if isNaN(res_me):
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pass
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else:
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res.append(res_me)
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var_res.append(var_spectrum)
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num_of_peaks.append(res_num)
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index_cor.append(indexer)
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indexer = indexer + 1
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try:
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var_res = np.array(var_res)
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ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
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except:
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ang_int = 0
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else:
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angels = np.linspace(-25, 25, 60)
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res = []
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num_of_peaks = []
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index_cor = []
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var_res = []
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indexer = 0
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for rot in angels:
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img_rot = rotate_image(img_resized, rot)
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# plt.imshow(img_rot)
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# plt.show()
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img_rot[img_rot != 0] = 1
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# res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0 ))
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# neg_peaks,var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
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# print(var_spectrum,'var_spectrum')
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try:
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neg_peaks, var_spectrum = find_num_col_deskew(img_rot, sigma_des, 20.3)
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# print(rot,var_spectrum,'var_spectrum')
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res_me = np.mean(neg_peaks)
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if res_me == 0:
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res_me = VERY_LARGE_NUMBER
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else:
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pass
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res_num = len(neg_peaks)
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except:
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res_me = VERY_LARGE_NUMBER
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res_num = 0
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var_spectrum = 0
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if isNaN(res_me):
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pass
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else:
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res.append(res_me)
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var_res.append(var_spectrum)
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num_of_peaks.append(res_num)
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index_cor.append(indexer)
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indexer = indexer + 1
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try:
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var_res = np.array(var_res)
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ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
||||
except:
|
||||
ang_int = 0
|
||||
|
||||
# print(ang_int,'ang_int')
|
||||
|
||||
early_slope_edge = 22
|
||||
if abs(ang_int) > early_slope_edge and ang_int < 0:
|
||||
|
||||
angels = np.linspace(-90, -25, 60)
|
||||
|
||||
res = []
|
||||
num_of_peaks = []
|
||||
index_cor = []
|
||||
var_res = []
|
||||
|
||||
indexer = 0
|
||||
for rot in angels:
|
||||
img_rot = rotate_image(img_resized, rot)
|
||||
##plt.imshow(img_rot)
|
||||
##plt.show()
|
||||
img_rot[img_rot != 0] = 1
|
||||
# res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0 ))
|
||||
try:
|
||||
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:
|
||||
res_me = VERY_LARGE_NUMBER
|
||||
else:
|
||||
pass
|
||||
|
||||
res_num = len(neg_peaks)
|
||||
except:
|
||||
res_me = VERY_LARGE_NUMBER
|
||||
res_num = 0
|
||||
var_spectrum = 0
|
||||
if isNaN(res_me):
|
||||
pass
|
||||
else:
|
||||
res.append(res_me)
|
||||
var_res.append(var_spectrum)
|
||||
num_of_peaks.append(res_num)
|
||||
index_cor.append(indexer)
|
||||
indexer = indexer + 1
|
||||
|
||||
try:
|
||||
var_res = np.array(var_res)
|
||||
|
||||
ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
||||
except:
|
||||
ang_int = 0
|
||||
|
||||
elif abs(ang_int) > early_slope_edge and ang_int > 0:
|
||||
|
||||
angels = np.linspace(90, 25, 60)
|
||||
|
||||
res = []
|
||||
num_of_peaks = []
|
||||
index_cor = []
|
||||
var_res = []
|
||||
|
||||
indexer = 0
|
||||
for rot in angels:
|
||||
img_rot = rotate_image(img_resized, rot)
|
||||
##plt.imshow(img_rot)
|
||||
##plt.show()
|
||||
img_rot[img_rot != 0] = 1
|
||||
# res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0 ))
|
||||
try:
|
||||
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:
|
||||
res_me = VERY_LARGE_NUMBER
|
||||
else:
|
||||
pass
|
||||
|
||||
res_num = len(neg_peaks)
|
||||
except:
|
||||
res_me = VERY_LARGE_NUMBER
|
||||
res_num = 0
|
||||
var_spectrum = 0
|
||||
if isNaN(res_me):
|
||||
pass
|
||||
else:
|
||||
res.append(res_me)
|
||||
var_res.append(var_spectrum)
|
||||
num_of_peaks.append(res_num)
|
||||
index_cor.append(indexer)
|
||||
indexer = indexer + 1
|
||||
|
||||
try:
|
||||
var_res = np.array(var_res)
|
||||
|
||||
ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
||||
except:
|
||||
ang_int = 0
|
||||
|
||||
return ang_int
|
||||
|
||||
def return_deskew_slope_new(self, img_patch, sigma_des):
|
||||
max_x_y = max(img_patch.shape[0], img_patch.shape[1])
|
||||
|
||||
|
|
@ -2674,7 +2243,7 @@ class eynollah:
|
|||
sigma_des = 1
|
||||
|
||||
crop_img[crop_img > 0] = 1
|
||||
slope_corresponding_textregion = self.return_deskew_slop(crop_img, sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name)
|
||||
slope_corresponding_textregion = return_deskew_slop(crop_img, sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name)
|
||||
|
||||
except:
|
||||
slope_corresponding_textregion = 999
|
||||
|
|
@ -2698,7 +2267,7 @@ class eynollah:
|
|||
|
||||
def get_slopes_and_deskew(self, contours, textline_mask_tot):
|
||||
|
||||
slope_biggest = 0 # self.return_deskew_slop(img_int_p,sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name)
|
||||
slope_biggest = 0 # return_deskew_slop(img_int_p,sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name)
|
||||
|
||||
num_cores = cpu_count()
|
||||
q = Queue()
|
||||
|
|
@ -8356,8 +7925,8 @@ class eynollah:
|
|||
|
||||
sigma = 2
|
||||
main_page_deskew = True
|
||||
slope_deskew = self.return_deskew_slop(cv2.erode(textline_mask_tot_ea, self.kernel, iterations=2), sigma, main_page_deskew, dir_of_all=self.dir_of_all, f_name=self.f_name)
|
||||
slope_first = 0 # self.return_deskew_slop(cv2.erode(textline_mask_tot_ea, self.kernel, iterations=2),sigma, dir_of_all=self.dir_of_all, f_name=self.f_name)
|
||||
slope_deskew = return_deskew_slop(cv2.erode(textline_mask_tot_ea, self.kernel, iterations=2), sigma, main_page_deskew, dir_of_all=self.dir_of_all, f_name=self.f_name)
|
||||
slope_first = 0 # return_deskew_slop(cv2.erode(textline_mask_tot_ea, self.kernel, iterations=2),sigma, dir_of_all=self.dir_of_all, f_name=self.f_name)
|
||||
|
||||
if self.dir_of_deskewed is not None:
|
||||
self.save_deskewed_image(slope_deskew)
|
||||
|
|
|
|||
|
|
@ -2341,3 +2341,435 @@ def return_regions_without_seperators(regions_pre):
|
|||
return regions_without_seperators
|
||||
|
||||
|
||||
def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=None, f_name=None):
|
||||
|
||||
if main_page and dir_of_all is not None:
|
||||
|
||||
plt.figure(figsize=(70, 40))
|
||||
plt.rcParams["font.size"] = "50"
|
||||
plt.subplot(1, 2, 1)
|
||||
plt.imshow(img_patch_org)
|
||||
plt.subplot(1, 2, 2)
|
||||
plt.plot(gaussian_filter1d(img_patch_org.sum(axis=1), 3), np.array(range(len(gaussian_filter1d(img_patch_org.sum(axis=1), 3)))), linewidth=8)
|
||||
plt.xlabel("Density of textline prediction in direction of X axis", fontsize=60)
|
||||
plt.ylabel("Height", fontsize=60)
|
||||
plt.yticks([0, len(gaussian_filter1d(img_patch_org.sum(axis=1), 3))])
|
||||
plt.gca().invert_yaxis()
|
||||
|
||||
plt.savefig(os.path.join(dir_of_all, f_name + "_density_of_textline.png"))
|
||||
# print(np.max(img_patch_org.sum(axis=0)) ,np.max(img_patch_org.sum(axis=1)),'axislar')
|
||||
|
||||
# img_patch_org=resize_image(img_patch_org,int(img_patch_org.shape[0]*2.5),int(img_patch_org.shape[1]/2.5))
|
||||
|
||||
# print(np.max(img_patch_org.sum(axis=0)) ,np.max(img_patch_org.sum(axis=1)),'axislar2')
|
||||
|
||||
img_int = np.zeros((img_patch_org.shape[0], img_patch_org.shape[1]))
|
||||
img_int[:, :] = img_patch_org[:, :] # img_patch_org[:,:,0]
|
||||
|
||||
img_resized = np.zeros((int(img_int.shape[0] * (1.8)), int(img_int.shape[1] * (2.6))))
|
||||
|
||||
img_resized[int(img_int.shape[0] * (0.4)) : int(img_int.shape[0] * (0.4)) + img_int.shape[0], int(img_int.shape[1] * (0.8)) : int(img_int.shape[1] * (0.8)) + img_int.shape[1]] = img_int[:, :]
|
||||
|
||||
if main_page and img_patch_org.shape[1] > img_patch_org.shape[0]:
|
||||
|
||||
# plt.imshow(img_resized)
|
||||
# plt.show()
|
||||
angels = np.array(
|
||||
[
|
||||
-45,
|
||||
0,
|
||||
45,
|
||||
90,
|
||||
]
|
||||
) # np.linspace(-12,12,100)#np.array([0 , 45 , 90 , -45])
|
||||
|
||||
res = []
|
||||
num_of_peaks = []
|
||||
index_cor = []
|
||||
var_res = []
|
||||
|
||||
indexer = 0
|
||||
for rot in angels:
|
||||
img_rot = rotate_image(img_resized, rot)
|
||||
# plt.imshow(img_rot)
|
||||
# plt.show()
|
||||
img_rot[img_rot != 0] = 1
|
||||
# res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0 ))
|
||||
|
||||
# neg_peaks,var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
# print(var_spectrum,'var_spectrum')
|
||||
try:
|
||||
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:
|
||||
res_me = VERY_LARGE_NUMBER
|
||||
else:
|
||||
pass
|
||||
|
||||
res_num = len(neg_peaks)
|
||||
except:
|
||||
res_me = VERY_LARGE_NUMBER
|
||||
res_num = 0
|
||||
var_spectrum = 0
|
||||
if isNaN(res_me):
|
||||
pass
|
||||
else:
|
||||
res.append(res_me)
|
||||
var_res.append(var_spectrum)
|
||||
num_of_peaks.append(res_num)
|
||||
index_cor.append(indexer)
|
||||
indexer = indexer + 1
|
||||
|
||||
try:
|
||||
var_res = np.array(var_res)
|
||||
|
||||
ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
||||
except:
|
||||
ang_int = 0
|
||||
|
||||
angels = np.linspace(ang_int - 22.5, ang_int + 22.5, 100)
|
||||
|
||||
res = []
|
||||
num_of_peaks = []
|
||||
index_cor = []
|
||||
var_res = []
|
||||
|
||||
indexer = 0
|
||||
for rot in angels:
|
||||
img_rot = rotate_image(img_resized, rot)
|
||||
##plt.imshow(img_rot)
|
||||
##plt.show()
|
||||
img_rot[img_rot != 0] = 1
|
||||
# res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0 ))
|
||||
try:
|
||||
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:
|
||||
res_me = VERY_LARGE_NUMBER
|
||||
else:
|
||||
pass
|
||||
|
||||
res_num = len(neg_peaks)
|
||||
except:
|
||||
res_me = VERY_LARGE_NUMBER
|
||||
res_num = 0
|
||||
var_spectrum = 0
|
||||
if isNaN(res_me):
|
||||
pass
|
||||
else:
|
||||
res.append(res_me)
|
||||
var_res.append(var_spectrum)
|
||||
num_of_peaks.append(res_num)
|
||||
index_cor.append(indexer)
|
||||
indexer = indexer + 1
|
||||
|
||||
try:
|
||||
var_res = np.array(var_res)
|
||||
|
||||
ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
||||
except:
|
||||
ang_int = 0
|
||||
|
||||
elif main_page and img_patch_org.shape[1] <= img_patch_org.shape[0]:
|
||||
|
||||
# plt.imshow(img_resized)
|
||||
# plt.show()
|
||||
angels = np.linspace(-12, 12, 100) # np.array([0 , 45 , 90 , -45])
|
||||
|
||||
res = []
|
||||
num_of_peaks = []
|
||||
index_cor = []
|
||||
var_res = []
|
||||
|
||||
indexer = 0
|
||||
for rot in angels:
|
||||
img_rot = rotate_image(img_resized, rot)
|
||||
# plt.imshow(img_rot)
|
||||
# plt.show()
|
||||
img_rot[img_rot != 0] = 1
|
||||
# res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0 ))
|
||||
|
||||
# neg_peaks,var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
# print(var_spectrum,'var_spectrum')
|
||||
try:
|
||||
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:
|
||||
res_me = VERY_LARGE_NUMBER
|
||||
else:
|
||||
pass
|
||||
|
||||
res_num = len(neg_peaks)
|
||||
except:
|
||||
res_me = VERY_LARGE_NUMBER
|
||||
res_num = 0
|
||||
var_spectrum = 0
|
||||
if isNaN(res_me):
|
||||
pass
|
||||
else:
|
||||
res.append(res_me)
|
||||
var_res.append(var_spectrum)
|
||||
num_of_peaks.append(res_num)
|
||||
index_cor.append(indexer)
|
||||
indexer = indexer + 1
|
||||
|
||||
if dir_of_all is not None:
|
||||
print("galdi?")
|
||||
plt.figure(figsize=(60, 30))
|
||||
plt.rcParams["font.size"] = "50"
|
||||
plt.plot(angels, np.array(var_res), "-o", markersize=25, linewidth=4)
|
||||
plt.xlabel("angle", fontsize=50)
|
||||
plt.ylabel("variance of sum of rotated textline in direction of x axis", fontsize=50)
|
||||
|
||||
plt.plot(angels[np.argmax(var_res)], var_res[np.argmax(np.array(var_res))], "*", markersize=50, label="Angle of deskewing=" + str("{:.2f}".format(angels[np.argmax(var_res)])) + r"$\degree$")
|
||||
plt.legend(loc="best")
|
||||
plt.savefig(os.path.join(dir_of_all, f_name + "_rotation_angle.png"))
|
||||
|
||||
try:
|
||||
var_res = np.array(var_res)
|
||||
|
||||
ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
||||
except:
|
||||
ang_int = 0
|
||||
|
||||
early_slope_edge = 11
|
||||
if abs(ang_int) > early_slope_edge and ang_int < 0:
|
||||
|
||||
angels = np.linspace(-90, -12, 100)
|
||||
|
||||
res = []
|
||||
num_of_peaks = []
|
||||
index_cor = []
|
||||
var_res = []
|
||||
|
||||
indexer = 0
|
||||
for rot in angels:
|
||||
img_rot = rotate_image(img_resized, rot)
|
||||
##plt.imshow(img_rot)
|
||||
##plt.show()
|
||||
img_rot[img_rot != 0] = 1
|
||||
# res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0 ))
|
||||
try:
|
||||
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:
|
||||
res_me = VERY_LARGE_NUMBER
|
||||
else:
|
||||
pass
|
||||
|
||||
res_num = len(neg_peaks)
|
||||
except:
|
||||
res_me = VERY_LARGE_NUMBER
|
||||
res_num = 0
|
||||
var_spectrum = 0
|
||||
if isNaN(res_me):
|
||||
pass
|
||||
else:
|
||||
res.append(res_me)
|
||||
var_res.append(var_spectrum)
|
||||
num_of_peaks.append(res_num)
|
||||
index_cor.append(indexer)
|
||||
indexer = indexer + 1
|
||||
|
||||
try:
|
||||
var_res = np.array(var_res)
|
||||
|
||||
ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
||||
except:
|
||||
ang_int = 0
|
||||
|
||||
elif abs(ang_int) > early_slope_edge and ang_int > 0:
|
||||
|
||||
angels = np.linspace(90, 12, 100)
|
||||
|
||||
res = []
|
||||
num_of_peaks = []
|
||||
index_cor = []
|
||||
var_res = []
|
||||
|
||||
indexer = 0
|
||||
for rot in angels:
|
||||
img_rot = rotate_image(img_resized, rot)
|
||||
##plt.imshow(img_rot)
|
||||
##plt.show()
|
||||
img_rot[img_rot != 0] = 1
|
||||
# res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0 ))
|
||||
try:
|
||||
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:
|
||||
res_me = VERY_LARGE_NUMBER
|
||||
else:
|
||||
pass
|
||||
|
||||
res_num = len(neg_peaks)
|
||||
except:
|
||||
res_me = VERY_LARGE_NUMBER
|
||||
res_num = 0
|
||||
var_spectrum = 0
|
||||
if isNaN(res_me):
|
||||
pass
|
||||
else:
|
||||
res.append(res_me)
|
||||
var_res.append(var_spectrum)
|
||||
num_of_peaks.append(res_num)
|
||||
index_cor.append(indexer)
|
||||
indexer = indexer + 1
|
||||
|
||||
try:
|
||||
var_res = np.array(var_res)
|
||||
|
||||
ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
||||
except:
|
||||
ang_int = 0
|
||||
else:
|
||||
|
||||
angels = np.linspace(-25, 25, 60)
|
||||
|
||||
res = []
|
||||
num_of_peaks = []
|
||||
index_cor = []
|
||||
var_res = []
|
||||
|
||||
indexer = 0
|
||||
for rot in angels:
|
||||
img_rot = rotate_image(img_resized, rot)
|
||||
# plt.imshow(img_rot)
|
||||
# plt.show()
|
||||
img_rot[img_rot != 0] = 1
|
||||
# res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0 ))
|
||||
|
||||
# neg_peaks,var_spectrum=find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
# print(var_spectrum,'var_spectrum')
|
||||
try:
|
||||
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:
|
||||
res_me = VERY_LARGE_NUMBER
|
||||
else:
|
||||
pass
|
||||
|
||||
res_num = len(neg_peaks)
|
||||
except:
|
||||
res_me = VERY_LARGE_NUMBER
|
||||
res_num = 0
|
||||
var_spectrum = 0
|
||||
if isNaN(res_me):
|
||||
pass
|
||||
else:
|
||||
res.append(res_me)
|
||||
var_res.append(var_spectrum)
|
||||
num_of_peaks.append(res_num)
|
||||
index_cor.append(indexer)
|
||||
indexer = indexer + 1
|
||||
|
||||
try:
|
||||
var_res = np.array(var_res)
|
||||
|
||||
ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
||||
except:
|
||||
ang_int = 0
|
||||
|
||||
# print(ang_int,'ang_int')
|
||||
|
||||
early_slope_edge = 22
|
||||
if abs(ang_int) > early_slope_edge and ang_int < 0:
|
||||
|
||||
angels = np.linspace(-90, -25, 60)
|
||||
|
||||
res = []
|
||||
num_of_peaks = []
|
||||
index_cor = []
|
||||
var_res = []
|
||||
|
||||
indexer = 0
|
||||
for rot in angels:
|
||||
img_rot = rotate_image(img_resized, rot)
|
||||
##plt.imshow(img_rot)
|
||||
##plt.show()
|
||||
img_rot[img_rot != 0] = 1
|
||||
# res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0 ))
|
||||
try:
|
||||
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:
|
||||
res_me = VERY_LARGE_NUMBER
|
||||
else:
|
||||
pass
|
||||
|
||||
res_num = len(neg_peaks)
|
||||
except:
|
||||
res_me = VERY_LARGE_NUMBER
|
||||
res_num = 0
|
||||
var_spectrum = 0
|
||||
if isNaN(res_me):
|
||||
pass
|
||||
else:
|
||||
res.append(res_me)
|
||||
var_res.append(var_spectrum)
|
||||
num_of_peaks.append(res_num)
|
||||
index_cor.append(indexer)
|
||||
indexer = indexer + 1
|
||||
|
||||
try:
|
||||
var_res = np.array(var_res)
|
||||
|
||||
ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
||||
except:
|
||||
ang_int = 0
|
||||
|
||||
elif abs(ang_int) > early_slope_edge and ang_int > 0:
|
||||
|
||||
angels = np.linspace(90, 25, 60)
|
||||
|
||||
res = []
|
||||
num_of_peaks = []
|
||||
index_cor = []
|
||||
var_res = []
|
||||
|
||||
indexer = 0
|
||||
for rot in angels:
|
||||
img_rot = rotate_image(img_resized, rot)
|
||||
##plt.imshow(img_rot)
|
||||
##plt.show()
|
||||
img_rot[img_rot != 0] = 1
|
||||
# res_me=np.mean(find_num_col_deskew(img_rot,sigma_des,2.0 ))
|
||||
try:
|
||||
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:
|
||||
res_me = VERY_LARGE_NUMBER
|
||||
else:
|
||||
pass
|
||||
|
||||
res_num = len(neg_peaks)
|
||||
except:
|
||||
res_me = VERY_LARGE_NUMBER
|
||||
res_num = 0
|
||||
var_spectrum = 0
|
||||
if isNaN(res_me):
|
||||
pass
|
||||
else:
|
||||
res.append(res_me)
|
||||
var_res.append(var_spectrum)
|
||||
num_of_peaks.append(res_num)
|
||||
index_cor.append(indexer)
|
||||
indexer = indexer + 1
|
||||
|
||||
try:
|
||||
var_res = np.array(var_res)
|
||||
|
||||
ang_int = angels[np.argmax(var_res)] # angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
|
||||
except:
|
||||
ang_int = 0
|
||||
|
||||
return ang_int
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue