qurator-spk/eynollah
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-light version need -tll to be enabled otherwise the process will be ended.

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vahidrezanezhad 2024-10-02 14:09:13 +02:00
parent ab63d5ba40
commit 543ed4bc38
2 changed files with 65 additions and 1 deletions
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3
qurator/eynollah/cli.py
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@ -227,6 +227,9 @@ def layout(image, out, dir_in, model, save_images, save_layout, save_deskewed, s
if textline_light and not light_version:
print('Error: You used -tll to enable light textline detection but -light is not enabled')
sys.exit(1)
if light_version and not textline_light:
print('Error: You used -light without -tll. Light version need light textline to be enabled.')
sys.exit(1)
eynollah = Eynollah(
image_filename=image,
dir_out=out,

63
qurator/eynollah/eynollah.py
View file

@ -252,7 +252,7 @@ class Eynollah:
self.model_region_dir_p_ens = dir_models + "/eynollah-main-regions-ensembled_20210425"
self.model_region_dir_p_ens_light = dir_models + "/eynollah-main-regions_20220314"
self.model_reading_order_machine_dir = dir_models + "/model_ens_reading_order_machine_based"
self.model_region_dir_p_1_2_sp_np = dir_models + "/modelens_earlylay12sp_0_2"#"/modelens_earlylayout_12spaltige_2_3_5_6_7_8"#"/modelens_early12_sp_2_3_5_6_7_8_9_10_12_14_15_16_18"#"/modelens_1_2_4_5_early_lay_1_2_spaltige"#"/model_3_eraly_layout_no_patches_1_2_spaltige"
self.model_region_dir_p_1_2_sp_np = dir_models + "/modelens_earlyla_12_0_2_con_18_22"#"/modelens_earlylayout_12spaltige_2_3_5_6_7_8"#"/modelens_early12_sp_2_3_5_6_7_8_9_10_12_14_15_16_18"#"/modelens_1_2_4_5_early_lay_1_2_spaltige"#"/model_3_eraly_layout_no_patches_1_2_spaltige"
##self.model_region_dir_fully_new = dir_models + "/model_2_full_layout_new_trans"
self.model_region_dir_fully = dir_models + "/modelens_full_layout_24_till_28"#"/model_2_full_layout_new_trans"
if self.textline_light:
@ -1055,6 +1055,35 @@ class Eynollah:
#del model
#gc.collect()
return prediction_true
def do_padding_with_scale(self,img, scale):
h_n = int(img.shape[0]*scale)
w_n = int(img.shape[1]*scale)
channel0_avg = int( np.mean(img[:,:,0]) )
channel1_avg = int( np.mean(img[:,:,1]) )
channel2_avg = int( np.mean(img[:,:,2]) )
h_diff = img.shape[0] - h_n
w_diff = img.shape[1] - w_n
h_start = int(h_diff / 2.)
w_start = int(w_diff / 2.)
img_res = resize_image(img, h_n, w_n)
#label_res = resize_image(label, h_n, w_n)
img_scaled_padded = np.copy(img)
#label_scaled_padded = np.zeros(label.shape)
img_scaled_padded[:,:,0] = channel0_avg
img_scaled_padded[:,:,1] = channel1_avg
img_scaled_padded[:,:,2] = channel2_avg
img_scaled_padded[h_start:h_start+h_n, w_start:w_start+w_n,:] = img_res[:,:,:]
#label_scaled_padded[h_start:h_start+h_n, w_start:w_start+w_n,:] = label_res[:,:,:]
return img_scaled_padded#, label_scaled_padded
def do_prediction_new_concept(self, patches, img, model, n_batch_inference=1, marginal_of_patch_percent=0.1, thresholding_for_some_classes_in_light_version=False, thresholding_for_artificial_class_in_light_version=False):
self.logger.debug("enter do_prediction")
@ -4349,6 +4378,38 @@ class Eynollah:
con_scaled[:,0, 1][con_scaled[:,0, 1]<0] = 0
con_scaled[:,0, 0][con_scaled[:,0, 0]<0] = 0
con_ind = con_ind.astype(np.int32)
results = [cv2.pointPolygonTest(con_ind, (con_scaled[ind,0, 0], con_scaled[ind,0, 1]), False) for ind in range(len(con_scaled[:,0, 1])) ]
results = np.array(results)
results[results==0] = 1
diff_result = np.diff(results)
indices_2 = [ind for ind in range(len(diff_result)) if diff_result[ind]==2]
indices_m2 = [ind for ind in range(len(diff_result)) if diff_result[ind]==-2]
if results[0]==1:
con_scaled[:indices_m2[0]+1,0, 1] = con_ind[:indices_m2[0]+1,0,1]
con_scaled[:indices_m2[0]+1,0, 0] = con_ind[:indices_m2[0]+1,0,0]
indices_m2 = indices_m2[1:]
if len(indices_2)>len(indices_m2):
con_scaled[indices_2[-1]+1:,0, 1] = con_ind[indices_2[-1]+1:,0,1]
con_scaled[indices_2[-1]+1:,0, 0] = con_ind[indices_2[-1]+1:,0,0]
indices_2 = indices_2[:-1]
for ii in range(len(indices_2)):
con_scaled[indices_2[ii]+1:indices_m2[ii]+1,0, 1] = con_scaled[indices_2[ii],0, 1]
con_scaled[indices_2[ii]+1:indices_m2[ii]+1,0, 0] = con_scaled[indices_2[ii],0, 0]
all_found_textline_polygons[j][ij][:,0,1] = con_scaled[:,0, 1]
all_found_textline_polygons[j][ij][:,0,0] = con_scaled[:,0, 0]
return all_found_textline_polygons