qurator-spk
/
eynollah
mirror of https://github.com/qurator-spk/eynollah.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

return_deskew_slop: move to utils

Browse Source
pull/8/head
Konstantin Baierer 4 years ago
parent 340b48ab4b
commit 1fa123fcf8
2 changed files with 444 additions and 443 deletions
Show Stats Download Patch File Download Diff File

455
sbb_newspapers_org_image/eynollah.py
Unescape Escape View File

@ -77,6 +77,7 @@ from .utils import (
seperate_lines_vertical_cont, seperate_lines_vertical_cont,
delete_seperator_around, delete_seperator_around,
return_regions_without_seperators, return_regions_without_seperators,
return_deskew_slop,
) )
@ -1300,7 +1301,7 @@ class eynollah:
img_int_p[img_int_p > 0] = 1 img_int_p[img_int_p > 0] = 1
# slope_for_all=self.return_deskew_slope_new(img_int_p,sigma_des) # slope_for_all=self.return_deskew_slope_new(img_int_p,sigma_des)
slope_for_all = self.return_deskew_slop(img_int_p, sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name) slope_for_all = return_deskew_slop(img_int_p, sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name)
if abs(slope_for_all) < 0.5: if abs(slope_for_all) < 0.5:
slope_for_all = [slope_deskew][0] slope_for_all = [slope_deskew][0]
@ -1311,7 +1312,7 @@ class eynollah:
except: except:
slope_for_all = 999 slope_for_all = 999
##slope_for_all=self.return_deskew_slop(img_int_p,sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name) ##slope_for_all=return_deskew_slop(img_int_p,sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name)
if slope_for_all == 999: if slope_for_all == 999:
slope_for_all = [slope_deskew][0] slope_for_all = [slope_deskew][0]
@ -1457,7 +1458,7 @@ class eynollah:
img_int_p[img_int_p > 0] = 1 img_int_p[img_int_p > 0] = 1
# slope_for_all=self.return_deskew_slope_new(img_int_p,sigma_des) # slope_for_all=self.return_deskew_slope_new(img_int_p,sigma_des)
slope_for_all = self.return_deskew_slop(img_int_p, sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name) slope_for_all = return_deskew_slop(img_int_p, sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name)
if abs(slope_for_all) <= 0.5: if abs(slope_for_all) <= 0.5:
slope_for_all = [slope_deskew][0] slope_for_all = [slope_deskew][0]
@ -1465,7 +1466,7 @@ class eynollah:
except: except:
slope_for_all = 999 slope_for_all = 999
##slope_for_all=self.return_deskew_slop(img_int_p,sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name) ##slope_for_all=return_deskew_slop(img_int_p,sigma_des, dir_of_all=self.dir_of_all, f_name=self.f_name)
if slope_for_all == 999: if slope_for_all == 999:
slope_for_all = [slope_deskew][0] slope_for_all = [slope_deskew][0]
@ -1726,7 +1727,7 @@ class eynollah:
sigma = 2 sigma = 2
try: try:
slope_xline = self.return_deskew_slop(img_xline, sigma, dir_of_all=self.dir_of_all, f_name=self.f_name) slope_xline = return_deskew_slop(img_xline, sigma, dir_of_all=self.dir_of_all, f_name=self.f_name)
except: except:
slope_xline = 0 slope_xline = 0
slopes_tile_wise.append(slope_xline) slopes_tile_wise.append(slope_xline)
@ -1748,7 +1749,7 @@ class eynollah:
sigma=3 sigma=3
try: try:
slope_xline=self.return_deskew_slop(img_xline,sigma, dir_of_all=self.dir_of_all, f_name=self.f_name) slope_xline=return_deskew_slop(img_xline,sigma, dir_of_all=self.dir_of_all, f_name=self.f_name)
except: except:
slope_xline=0 slope_xline=0
slopes_tile_wise.append(slope_xline) slopes_tile_wise.append(slope_xline)
@ -1891,7 +1892,7 @@ class eynollah:
sigma = 2 sigma = 2
try: try:
slope_xline = self.return_deskew_slop(img_xline, sigma, dir_of_all=self.dir_of_all, f_name=self.f_name) slope_xline = return_deskew_slop(img_xline, sigma, dir_of_all=self.dir_of_all, f_name=self.f_name)
except: except:
slope_xline = 0 slope_xline = 0
@ -2106,438 +2107,6 @@ class eynollah:
return slope return slope
def return_deskew_slop(self, 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
def return_deskew_slope_new(self, img_patch, sigma_des): def return_deskew_slope_new(self, img_patch, sigma_des):
max_x_y = max(img_patch.shape[0], img_patch.shape[1]) max_x_y = max(img_patch.shape[0], img_patch.shape[1])
@ -2674,7 +2243,7 @@ class eynollah:
sigma_des = 1 sigma_des = 1
crop_img[crop_img > 0] = 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: except:
slope_corresponding_textregion = 999 slope_corresponding_textregion = 999
@ -2698,7 +2267,7 @@ class eynollah:
def get_slopes_and_deskew(self, contours, textline_mask_tot): 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() num_cores = cpu_count()
q = Queue() q = Queue()
@ -8356,8 +7925,8 @@ class eynollah:
sigma = 2 sigma = 2
main_page_deskew = True 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_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 # 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_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: if self.dir_of_deskewed is not None:
self.save_deskewed_image(slope_deskew) self.save_deskewed_image(slope_deskew)

432
sbb_newspapers_org_image/utils.py
Unescape Escape View File

@ -2341,3 +2341,435 @@ def return_regions_without_seperators(regions_pre):
return regions_without_seperators 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

Write Preview
Loading…
Cancel
Save