mirror of
https://github.com/qurator-spk/eynollah.git
synced 2025-07-05 08:59:57 +02:00
more outsourcing of utils
This commit is contained in:
Konstantin Baierer
parent
8d2227b04b
commit
87ef313502
6 changed files with 979 additions and 967 deletions
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@ -1,3 +1,4 @@
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import matplotlib.pyplot as plt
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import numpy as np
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import cv2
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from scipy.signal import find_peaks
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@ -1366,3 +1367,456 @@ def textline_contours_postprocessing(textline_mask, slope, contour_text_interest
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return contours_rotated_clean
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def seperate_lines_new2(img_path, thetha, num_col, slope_region, dir_of_all, f_name):
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if num_col == 1:
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num_patches = int(img_path.shape[1] / 200.0)
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else:
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num_patches = int(img_path.shape[1] / 140.0)
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# num_patches=int(img_path.shape[1]/200.)
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if num_patches == 0:
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num_patches = 1
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img_patch_ineterst = img_path[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[15]+dis_down ,:]
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# plt.imshow(img_patch_ineterst)
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# plt.show()
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length_x = int(img_path.shape[1] / float(num_patches))
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# margin = int(0.04 * length_x) just recently this was changed because it break lines into 2
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margin = int(0.04 * length_x)
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# print(margin,'margin')
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# if margin<=4:
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# margin = int(0.08 * length_x)
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# margin=0
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width_mid = length_x - 2 * margin
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nxf = img_path.shape[1] / float(width_mid)
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if nxf > int(nxf):
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nxf = int(nxf) + 1
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else:
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nxf = int(nxf)
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slopes_tile_wise = []
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for i in range(nxf):
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if i == 0:
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index_x_d = i * width_mid
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index_x_u = index_x_d + length_x
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elif i > 0:
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index_x_d = i * width_mid
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index_x_u = index_x_d + length_x
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if index_x_u > img_path.shape[1]:
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index_x_u = img_path.shape[1]
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index_x_d = img_path.shape[1] - length_x
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# img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
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img_xline = img_patch_ineterst[:, index_x_d:index_x_u]
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sigma = 2
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try:
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slope_xline = return_deskew_slop(img_xline, sigma, dir_of_all=dir_of_all, f_name=f_name)
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except:
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slope_xline = 0
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if abs(slope_region) < 25 and abs(slope_xline) > 25:
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slope_xline = [slope_region][0]
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# if abs(slope_region)>70 and abs(slope_xline)<25:
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# slope_xline=[slope_region][0]
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slopes_tile_wise.append(slope_xline)
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# print(slope_xline,'xlineeee')
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img_line_rotated = rotate_image(img_xline, slope_xline)
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img_line_rotated[:, :][img_line_rotated[:, :] != 0] = 1
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# print(slopes_tile_wise,'slopes_tile_wise')
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img_patch_ineterst = img_path[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[14]+dis_down ,:]
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img_patch_ineterst_revised = np.zeros(img_patch_ineterst.shape)
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for i in range(nxf):
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if i == 0:
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index_x_d = i * width_mid
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index_x_u = index_x_d + length_x
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elif i > 0:
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index_x_d = i * width_mid
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index_x_u = index_x_d + length_x
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if index_x_u > img_path.shape[1]:
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index_x_u = img_path.shape[1]
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index_x_d = img_path.shape[1] - length_x
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img_xline = img_patch_ineterst[:, index_x_d:index_x_u]
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img_int = np.zeros((img_xline.shape[0], img_xline.shape[1]))
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img_int[:, :] = img_xline[:, :] # img_patch_org[:,:,0]
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img_resized = np.zeros((int(img_int.shape[0] * (1.2)), int(img_int.shape[1] * (3))))
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img_resized[int(img_int.shape[0] * (0.1)) : int(img_int.shape[0] * (0.1)) + img_int.shape[0], int(img_int.shape[1] * (1)) : int(img_int.shape[1] * (1)) + img_int.shape[1]] = img_int[:, :]
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# plt.imshow(img_xline)
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# plt.show()
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img_line_rotated = rotate_image(img_resized, slopes_tile_wise[i])
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img_line_rotated[:, :][img_line_rotated[:, :] != 0] = 1
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img_patch_seperated = seperate_lines_new_inside_teils2(img_line_rotated, 0)
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img_patch_seperated_returned = rotate_image(img_patch_seperated, -slopes_tile_wise[i])
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img_patch_seperated_returned[:, :][img_patch_seperated_returned[:, :] != 0] = 1
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img_patch_seperated_returned_true_size = img_patch_seperated_returned[int(img_int.shape[0] * (0.1)) : int(img_int.shape[0] * (0.1)) + img_int.shape[0], int(img_int.shape[1] * (1)) : int(img_int.shape[1] * (1)) + img_int.shape[1]]
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img_patch_seperated_returned_true_size = img_patch_seperated_returned_true_size[:, margin : length_x - margin]
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img_patch_ineterst_revised[:, index_x_d + margin : index_x_u - margin] = img_patch_seperated_returned_true_size
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# plt.imshow(img_patch_ineterst_revised)
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# plt.show()
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return img_patch_ineterst_revised
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def return_deskew_slop(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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max_shape=np.max(img_int.shape)
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img_resized=np.zeros((int( max_shape*(1.1) ) , int( max_shape*(1.1) ) ))
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onset_x=int((img_resized.shape[1]-img_int.shape[1])/2.)
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onset_y=int((img_resized.shape[0]-img_int.shape[0])/2.)
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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]*(.4)):int( img_int.shape[0]*(.4))+img_int.shape[0] , int( img_int.shape[1]*(.8)):int( img_int.shape[1]*(.8))+img_int.shape[1] ]=img_int[:,:]
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img_resized[ onset_y:onset_y+img_int.shape[0] , onset_x:onset_x+img_int.shape[1] ]=img_int[:,:]
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#print(img_resized.shape,'img_resizedshape')
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#plt.imshow(img_resized)
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#plt.show()
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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([-45, 0 , 45 , 90 , ])#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=self.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(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
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#neg_peaks,var_spectrum=self.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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var_spectrum=self.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=1000000000000000000000
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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=1000000000000000000000
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#res_num=0
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var_spectrum=0
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#if self.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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var_res.append(var_spectrum)
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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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for rot in angels:
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img_rot=self.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(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
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try:
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var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
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except:
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var_spectrum=0
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var_res.append(var_spectrum)
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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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var_res=[]
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for rot in angels:
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img_rot=self.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(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
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#neg_peaks,var_spectrum=self.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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var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
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except:
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var_spectrum=0
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var_res.append(var_spectrum)
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if self.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(self.dir_of_all,self.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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var_res=[]
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for rot in angels:
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img_rot=self.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(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
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try:
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var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
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except:
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var_spectrum=0
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var_res.append(var_spectrum)
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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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var_res=[]
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for rot in angels:
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img_rot=self.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(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
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try:
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var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
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#print(indexer,'indexer')
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except:
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var_spectrum=0
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var_res.append(var_spectrum)
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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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var_res=[]
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indexer=0
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for rot in angels:
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img_rot=self.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(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
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#neg_peaks,var_spectrum=self.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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var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
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except:
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var_spectrum=0
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var_res.append(var_spectrum)
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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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#plt.plot(var_res)
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#plt.show()
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##plt.plot(mom3_res)
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##plt.show()
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#print(ang_int,'ang_int111')
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early_slope_edge=22
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if abs(ang_int)>early_slope_edge and ang_int<0:
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angels=np.linspace(-90,-25,60)
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var_res=[]
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for rot in angels:
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img_rot=self.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
|
||||
#res_me=np.mean(self.find_num_col_deskew(img_rot,sigma_des,2.0 ))
|
||||
try:
|
||||
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
|
||||
except:
|
||||
var_spectrum=0
|
||||
|
||||
var_res.append(var_spectrum)
|
||||
|
||||
|
||||
|
||||
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)
|
||||
|
||||
var_res=[]
|
||||
|
||||
indexer=0
|
||||
for rot in angels:
|
||||
img_rot=self.rotate_image(img_resized,rot)
|
||||
##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 ))
|
||||
try:
|
||||
var_spectrum=self.find_num_col_deskew(img_rot,sigma_des,20.3 )
|
||||
#print(indexer,'indexer')
|
||||
except:
|
||||
var_spectrum=0
|
||||
|
||||
var_res.append(var_spectrum)
|
||||
|
||||
|
||||
|
||||
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