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eynollah
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dilation of textregions and marginals are accomplished

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pull/138/head^2
vahidrezanezhad 3 months ago
parent 95effe54a0
commit 133091137d
1 changed files with 314 additions and 140 deletions
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qurator/eynollah/eynollah.py
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@ -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 = dir_models + "/eynollah-main-regions-ensembled_20210425"
self.model_region_dir_p_ens_light = dir_models + "/eynollah-main-regions_20220314" 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_reading_order_machine_dir = dir_models + "/model_ens_reading_order_machine_based"
self.model_region_dir_p_1_2_sp_np = dir_models + "/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_earlylayout_12spaltige_2_3_5_6_7_8"#"/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_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" self.model_region_dir_fully = dir_models + "/modelens_full_layout_24_till_28"#"/model_2_full_layout_new_trans"
if self.textline_light: if self.textline_light:
@ -1050,7 +1050,7 @@ class Eynollah:
#del model #del model
#gc.collect() #gc.collect()
return prediction_true return prediction_true
def do_prediction_new_concept(self, patches, img, model, marginal_of_patch_percent=0.1): 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") self.logger.debug("enter do_prediction")
img_height_model = model.layers[len(model.layers) - 1].output_shape[1] img_height_model = model.layers[len(model.layers) - 1].output_shape[1]
@ -1064,14 +1064,14 @@ class Eynollah:
label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]), verbose=0) label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]), verbose=0)
seg_not_base = label_p_pred[0,:,:,4] #seg_not_base = label_p_pred[0,:,:,4]
seg_not_base[seg_not_base>0.4] =1 #seg_not_base[seg_not_base>0.4] =1
seg_not_base[seg_not_base<1] =0 #seg_not_base[seg_not_base<1] =0
seg = np.argmax(label_p_pred, axis=3)[0] seg = np.argmax(label_p_pred, axis=3)[0]
seg[seg_not_base==1]=4 #seg[seg_not_base==1]=4
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
prediction_true = resize_image(seg_color, img_h_page, img_w_page) prediction_true = resize_image(seg_color, img_h_page, img_w_page)
@ -1100,6 +1100,16 @@ class Eynollah:
nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf) nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf)
nyf = int(nyf) + 1 if nyf > int(nyf) else int(nyf) nyf = int(nyf) + 1 if nyf > int(nyf) else int(nyf)
list_i_s = []
list_j_s = []
list_x_u = []
list_x_d = []
list_y_u = []
list_y_d = []
batch_indexer = 0
img_patch = np.zeros((n_batch_inference, img_height_model, img_width_model, 3))
for i in range(nxf): for i in range(nxf):
for j in range(nyf): for j in range(nyf):
if i == 0: if i == 0:
@ -1121,43 +1131,56 @@ class Eynollah:
index_y_u = img_h index_y_u = img_h
index_y_d = img_h - img_height_model index_y_d = img_h - img_height_model
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]),
verbose=0)
seg = np.argmax(label_p_pred, axis=3)[0]
list_i_s.append(i)
list_j_s.append(j)
list_x_u.append(index_x_u)
list_x_d.append(index_x_d)
list_y_d.append(index_y_d)
list_y_u.append(index_y_u)
seg_not_base = label_p_pred[0,:,:,4]
##seg2 = -label_p_pred[0,:,:,2]
img_patch[batch_indexer,:,:,:] = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
seg_not_base[seg_not_base>0.03] =1 batch_indexer = batch_indexer + 1
seg_not_base[seg_not_base<1] =0
#img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
#label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]),
#verbose=0)
#seg = np.argmax(label_p_pred, axis=3)[0]
seg_test = label_p_pred[0,:,:,1] ######seg_not_base = label_p_pred[0,:,:,4]
##seg2 = -label_p_pred[0,:,:,2] ########seg2 = -label_p_pred[0,:,:,2]
seg_test[seg_test>0.75] =1 ######seg_not_base[seg_not_base>0.03] =1
seg_test[seg_test<1] =0 ######seg_not_base[seg_not_base<1] =0
seg_line = label_p_pred[0,:,:,3]
##seg2 = -label_p_pred[0,:,:,2]
######seg_test = label_p_pred[0,:,:,1]
########seg2 = -label_p_pred[0,:,:,2]
seg_line[seg_line>0.1] =1
seg_line[seg_line<1] =0
######seg_test[seg_test>0.75] =1
######seg_test[seg_test<1] =0
seg_background = label_p_pred[0,:,:,0]
##seg2 = -label_p_pred[0,:,:,2]
######seg_line = label_p_pred[0,:,:,3]
########seg2 = -label_p_pred[0,:,:,2]
seg_background[seg_background>0.25] =1
seg_background[seg_background<1] =0 ######seg_line[seg_line>0.1] =1
######seg_line[seg_line<1] =0
######seg_background = label_p_pred[0,:,:,0]
########seg2 = -label_p_pred[0,:,:,2]
######seg_background[seg_background>0.25] =1
######seg_background[seg_background<1] =0
##seg = seg+seg2 ##seg = seg+seg2
#seg = label_p_pred[0,:,:,2] #seg = label_p_pred[0,:,:,2]
#seg[seg>0.4] =1 #seg[seg>0.4] =1
@ -1170,56 +1193,221 @@ class Eynollah:
##plt.show() ##plt.show()
#seg[seg==1]=0 #seg[seg==1]=0
#seg[seg_test==1]=1 #seg[seg_test==1]=1
######seg[seg_not_base==1]=4
######seg[seg_background==1]=0
######seg[(seg_line==1) & (seg==0)]=3
#seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
#if i == 0 and j == 0:
#seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
#seg = seg[0 : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
#mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
#prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg_color
#elif i == nxf - 1 and j == nyf - 1:
#seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
#seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - 0]
#mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0] = seg
#prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0, :] = seg_color
#elif i == 0 and j == nyf - 1:
#seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
#seg = seg[margin : seg.shape[0] - 0, 0 : seg.shape[1] - margin]
#mask_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin] = seg
#prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin, :] = seg_color
#elif i == nxf - 1 and j == 0:
#seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
#seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - 0]
#mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg
#prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg_color
#elif i == 0 and j != 0 and j != nyf - 1:
#seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
#seg = seg[margin : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
#mask_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
#prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg_color
#elif i == nxf - 1 and j != 0 and j != nyf - 1:
#seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
#seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - 0]
#mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg
#prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg_color
#elif i != 0 and i != nxf - 1 and j == 0:
#seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
#seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - margin]
#mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
#prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
#elif i != 0 and i != nxf - 1 and j == nyf - 1:
#seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
#seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - margin]
#mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin] = seg
#prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin, :] = seg_color
#else:
#seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
#seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin]
#mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
#prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
if batch_indexer == n_batch_inference:
label_p_pred = model.predict(img_patch,verbose=0)
seg = np.argmax(label_p_pred, axis=3)
if thresholding_for_some_classes_in_light_version:
seg_not_base = label_p_pred[:,:,:,4]
seg_not_base[seg_not_base>0.03] =1
seg_not_base[seg_not_base<1] =0
seg_line = label_p_pred[:,:,:,3]
seg_line[seg_line>0.1] =1
seg_line[seg_line<1] =0
seg_background = label_p_pred[:,:,:,0]
seg_background[seg_background>0.25] =1
seg_background[seg_background<1] =0
seg[seg_not_base==1]=4 seg[seg_not_base==1]=4
seg[seg_background==1]=0 seg[seg_background==1]=0
seg[(seg_line==1) & (seg==0)]=3 seg[(seg_line==1) & (seg==0)]=3
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) if thresholding_for_artificial_class_in_light_version:
seg_art = label_p_pred[:,:,:,2]
if i == 0 and j == 0: seg_art[seg_art<0.2] = 0
seg_art[seg_art>0] =1
seg[seg_art==1]=2
indexer_inside_batch = 0
for i_batch, j_batch in zip(list_i_s, list_j_s):
seg_in = seg[indexer_inside_batch,:,:]
seg_color = np.repeat(seg_in[:, :, np.newaxis], 3, axis=2)
index_y_u_in = list_y_u[indexer_inside_batch]
index_y_d_in = list_y_d[indexer_inside_batch]
index_x_u_in = list_x_u[indexer_inside_batch]
index_x_d_in = list_x_d[indexer_inside_batch]
if i_batch == 0 and j_batch == 0:
seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :] seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
seg = seg[0 : seg.shape[0] - margin, 0 : seg.shape[1] - margin] prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg elif i_batch == nxf - 1 and j_batch == nyf - 1:
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg_color
elif i == nxf - 1 and j == nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :] seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - 0] prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0] = seg elif i_batch == 0 and j_batch == nyf - 1:
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0, :] = seg_color
elif i == 0 and j == nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :] seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
seg = seg[margin : seg.shape[0] - 0, 0 : seg.shape[1] - margin] prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
mask_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin] = seg elif i_batch == nxf - 1 and j_batch == 0:
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin, :] = seg_color
elif i == nxf - 1 and j == 0:
seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :] seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - 0] prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1:
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg_color
elif i == 0 and j != 0 and j != nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :] seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
seg = seg[margin : seg.shape[0] - margin, 0 : seg.shape[1] - margin] prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
mask_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1:
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg_color
elif i == nxf - 1 and j != 0 and j != nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :] seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - 0] prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0:
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg_color
elif i != 0 and i != nxf - 1 and j == 0:
seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :] seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - margin] prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1:
prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
elif i != 0 and i != nxf - 1 and j == nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :] seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - margin] prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin] = seg
prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin, :] = seg_color
else: else:
seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :] seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin] prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color indexer_inside_batch = indexer_inside_batch +1
list_i_s = []
list_j_s = []
list_x_u = []
list_x_d = []
list_y_u = []
list_y_d = []
batch_indexer = 0
img_patch = np.zeros((n_batch_inference, img_height_model, img_width_model, 3))
elif i==(nxf-1) and j==(nyf-1):
label_p_pred = model.predict(img_patch,verbose=0)
seg = np.argmax(label_p_pred, axis=3)
if thresholding_for_some_classes_in_light_version:
seg_not_base = label_p_pred[:,:,:,4]
seg_not_base[seg_not_base>0.03] =1
seg_not_base[seg_not_base<1] =0
seg_line = label_p_pred[:,:,:,3]
seg_line[seg_line>0.1] =1
seg_line[seg_line<1] =0
seg_background = label_p_pred[:,:,:,0]
seg_background[seg_background>0.25] =1
seg_background[seg_background<1] =0
seg[seg_not_base==1]=4
seg[seg_background==1]=0
seg[(seg_line==1) & (seg==0)]=3
if thresholding_for_artificial_class_in_light_version:
seg_art = label_p_pred[:,:,:,2]
seg_art[seg_art<0.2] = 0
seg_art[seg_art>0] =1
seg[seg_art==1]=2
indexer_inside_batch = 0
for i_batch, j_batch in zip(list_i_s, list_j_s):
seg_in = seg[indexer_inside_batch,:,:]
seg_color = np.repeat(seg_in[:, :, np.newaxis], 3, axis=2)
index_y_u_in = list_y_u[indexer_inside_batch]
index_y_d_in = list_y_d[indexer_inside_batch]
index_x_u_in = list_x_u[indexer_inside_batch]
index_x_d_in = list_x_d[indexer_inside_batch]
if i_batch == 0 and j_batch == 0:
seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
elif i_batch == nxf - 1 and j_batch == nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
elif i_batch == 0 and j_batch == nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
elif i_batch == nxf - 1 and j_batch == 0:
seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0:
seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1:
seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
else:
seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
indexer_inside_batch = indexer_inside_batch +1
list_i_s = []
list_j_s = []
list_x_u = []
list_x_d = []
list_y_u = []
list_y_d = []
batch_indexer = 0
img_patch = np.zeros((n_batch_inference, img_height_model, img_width_model, 3))
prediction_true = prediction_true.astype(np.uint8) prediction_true = prediction_true.astype(np.uint8)
return prediction_true return prediction_true
@ -1963,7 +2151,7 @@ class Eynollah:
#print(num_col_classifier,'num_col_classifier') #print(num_col_classifier,'num_col_classifier')
if num_col_classifier == 1: if num_col_classifier == 1:
img_w_new = 800#1000 img_w_new = 1000
img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new) img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
elif num_col_classifier == 2: elif num_col_classifier == 2:
@ -1971,17 +2159,17 @@ class Eynollah:
img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new) img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
elif num_col_classifier == 3: elif num_col_classifier == 3:
img_w_new = 1600#2000 img_w_new = 2000
img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new) img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
elif num_col_classifier == 4: elif num_col_classifier == 4:
img_w_new = 1900#2500 img_w_new = 2500
img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new) img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
elif num_col_classifier == 5: elif num_col_classifier == 5:
img_w_new = 2300#3000 img_w_new = 3000
img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new) img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
else: else:
img_w_new = 3000#4000 img_w_new = 4000
img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new) img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
img_resized = resize_image(img,img_h_new, img_w_new ) img_resized = resize_image(img,img_h_new, img_w_new )
@ -2025,17 +2213,17 @@ class Eynollah:
if not self.dir_in: if not self.dir_in:
if num_col_classifier == 1 or num_col_classifier == 2: if num_col_classifier == 1 or num_col_classifier == 2:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_1_2_sp_np) model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_1_2_sp_np)
prediction_regions_org = self.do_prediction_new_concept(False, img_resized, model_region) prediction_regions_org = self.do_prediction_new_concept(False, img_resized, model_region, n_batch_inference=1)
else: else:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light) model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light, n_batch_inference=3)
prediction_regions_org = self.do_prediction_new_concept(True, img_bin, model_region) prediction_regions_org = self.do_prediction_new_concept(True, img_bin, model_region)
##model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light) ##model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light)
##prediction_regions_org = self.do_prediction(True, img_bin, model_region, n_batch_inference=3, thresholding_for_some_classes_in_light_version=True) ##prediction_regions_org = self.do_prediction(True, img_bin, model_region, n_batch_inference=3, thresholding_for_some_classes_in_light_version=True)
else: else:
if num_col_classifier == 1 or num_col_classifier == 2: if num_col_classifier == 1 or num_col_classifier == 2:
prediction_regions_org = self.do_prediction_new_concept(False, img_resized, self.model_region_1_2) prediction_regions_org = self.do_prediction_new_concept(False, img_resized, self.model_region_1_2, n_batch_inference=1)
else: else:
prediction_regions_org = self.do_prediction_new_concept(True, img_bin, self.model_region) prediction_regions_org = self.do_prediction_new_concept(True, img_bin, self.model_region, n_batch_inference=3)
###prediction_regions_org = self.do_prediction(True, img_bin, self.model_region, n_batch_inference=3, thresholding_for_some_classes_in_light_version=True) ###prediction_regions_org = self.do_prediction(True, img_bin, self.model_region, n_batch_inference=3, thresholding_for_some_classes_in_light_version=True)
#print("inside 3 ", time.time()-t_in) #print("inside 3 ", time.time()-t_in)
@ -2054,8 +2242,12 @@ class Eynollah:
mask_texts_only = mask_texts_only.astype('uint8') mask_texts_only = mask_texts_only.astype('uint8')
#mask_texts_only = cv2.erode(mask_texts_only, KERNEL, iterations=1) ##if num_col_classifier == 1 or num_col_classifier == 2:
mask_texts_only = cv2.dilate(mask_texts_only, KERNEL, iterations=1) ###mask_texts_only = cv2.erode(mask_texts_only, KERNEL, iterations=1)
##mask_texts_only = cv2.dilate(mask_texts_only, KERNEL, iterations=1)
mask_texts_only = cv2.dilate(mask_texts_only, kernel=np.ones((2,2), np.uint8), iterations=1)
mask_images_only=(prediction_regions_org[:,:] ==2)*1 mask_images_only=(prediction_regions_org[:,:] ==2)*1
@ -3150,6 +3342,13 @@ class Eynollah:
pixel_img = 4 pixel_img = 4
min_area_mar = 0.00001 min_area_mar = 0.00001
if self.light_version:
marginal_mask = (text_regions_p[:,:]==pixel_img)*1
marginal_mask = marginal_mask.astype('uint8')
marginal_mask = cv2.dilate(marginal_mask, KERNEL, iterations=2)
polygons_of_marginals = return_contours_of_interested_region(marginal_mask, 1, min_area_mar)
else:
polygons_of_marginals = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar) polygons_of_marginals = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
pixel_img = 10 pixel_img = 10
@ -3241,6 +3440,14 @@ class Eynollah:
pixel_img = 4 pixel_img = 4
min_area_mar = 0.00001 min_area_mar = 0.00001
if self.light_version:
marginal_mask = (text_regions_p[:,:]==pixel_img)*1
marginal_mask = marginal_mask.astype('uint8')
marginal_mask = cv2.dilate(marginal_mask, KERNEL, iterations=2)
polygons_of_marginals = return_contours_of_interested_region(marginal_mask, 1, min_area_mar)
else:
polygons_of_marginals = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar) polygons_of_marginals = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
pixel_img = 10 pixel_img = 10
@ -3850,18 +4057,19 @@ class Eynollah:
for j in range(len(all_found_textline_polygons)): for j in range(len(all_found_textline_polygons)):
con_ind = all_found_textline_polygons[j] con_ind = all_found_textline_polygons[j]
#print(len(con_ind[:,0,0]),'con_ind[:,0,0]')
area = cv2.contourArea(con_ind) area = cv2.contourArea(con_ind)
con_ind = con_ind.astype(np.float) con_ind = con_ind.astype(np.float)
con_ind[:,0,0] = gaussian_filter1d(con_ind[:,0,0], 0.1) #con_ind[:,0,0] = gaussian_filter1d(con_ind[:,0,0], 0.5)
con_ind[:,0,1] = gaussian_filter1d(con_ind[:,0,1], 0.1) #con_ind[:,0,1] = gaussian_filter1d(con_ind[:,0,1], 0.5)
x_differential = np.diff( con_ind[:,0,0]) x_differential = np.diff( con_ind[:,0,0])
y_differential = np.diff( con_ind[:,0,1]) y_differential = np.diff( con_ind[:,0,1])
x_differential = gaussian_filter1d(x_differential, .5) x_differential = gaussian_filter1d(x_differential, 0.1)
y_differential = gaussian_filter1d(y_differential, .5) y_differential = gaussian_filter1d(y_differential, 0.1)
x_min = float(np.min( con_ind[:,0,0] )) x_min = float(np.min( con_ind[:,0,0] ))
y_min = float(np.min( con_ind[:,0,1] )) y_min = float(np.min( con_ind[:,0,1] ))
@ -3885,8 +4093,8 @@ class Eynollah:
if dilation_m1>8: if dilation_m1>8:
dilation_m1 = 8 dilation_m1 = 8
if dilation_m1<5: if dilation_m1<6:
dilation_m1 = 5 dilation_m1 = 6
#print(dilation_m1, 'dilation_m1') #print(dilation_m1, 'dilation_m1')
dilation_m2 = int(dilation_m1/2.) +1 dilation_m2 = int(dilation_m1/2.) +1
@ -4002,7 +4210,6 @@ class Eynollah:
#indices_m2 = np.array(indices_m2)[diff_neg_pos>1] #indices_m2 = np.array(indices_m2)[diff_neg_pos>1]
for ii in range(len(indices_2)): for ii in range(len(indices_2)):
#x_inner = con_ind[indices_2[ii]+1:indices_m2[ii]+1,0, 0] #x_inner = con_ind[indices_2[ii]+1:indices_m2[ii]+1,0, 0]
#y_inner = con_ind[indices_2[ii]+1:indices_m2[ii]+1,0, 1] #y_inner = con_ind[indices_2[ii]+1:indices_m2[ii]+1,0, 1]
@ -4030,11 +4237,12 @@ class Eynollah:
#print(indices_m2,'-2') #print(indices_m2,'-2')
#print(diff_neg_pos,'diff_neg_pos') #print(diff_neg_pos,'diff_neg_pos')
#con_scaled[:,0, 1] = gaussian_filter1d(con_scaled[:,0, 1], 0.1) ##con_scaled[:,0, 1] = gaussian_filter1d(con_scaled[:,0, 1], 0.1)
#con_scaled[:,0, 0] = gaussian_filter1d(con_scaled[:,0, 0], 0.1) ##con_scaled[:,0, 0] = gaussian_filter1d(con_scaled[:,0, 0], 0.1)
con_scaled[-1,0, 1] = con_scaled[0,0, 1] #con_scaled[-1,0, 1] = con_scaled[0,0, 1]
con_scaled[-1,0, 0] = con_scaled[0,0, 0] #con_scaled[-1,0, 0] = con_scaled[0,0, 0]
##print(len(con_scaled[:,0,0]),'con_scaled[:,0,0]')
all_found_textline_polygons[j][:,0,1] = con_scaled[:,0, 1] all_found_textline_polygons[j][:,0,1] = con_scaled[:,0, 1]
all_found_textline_polygons[j][:,0,0] = con_scaled[:,0, 0] all_found_textline_polygons[j][:,0,0] = con_scaled[:,0, 0]
return all_found_textline_polygons return all_found_textline_polygons
@ -4045,7 +4253,7 @@ class Eynollah:
for ij in range(len(all_found_textline_polygons[j])): for ij in range(len(all_found_textline_polygons[j])):
con_ind = all_found_textline_polygons[j][ij] con_ind = all_found_textline_polygons[j][ij]
print(len(con_ind[:,0,0]),'con_ind[:,0,0]')
area = cv2.contourArea(con_ind) area = cv2.contourArea(con_ind)
con_ind = con_ind.astype(np.float) con_ind = con_ind.astype(np.float)
@ -4070,31 +4278,6 @@ class Eynollah:
inc_x = np.zeros(len(x_differential)+1) inc_x = np.zeros(len(x_differential)+1)
inc_y = np.zeros(len(x_differential)+1) inc_y = np.zeros(len(x_differential)+1)
#print(y_max-y_min, x_max-x_min,(y_max-y_min)/(x_max-x_min), (x_max-x_min)/(y_max-y_min) )
#print(area / (x_max-x_min))
##if (y_max-y_min)<40:
##dilation_m1 = 5
##dilation_m2 = int(dilation_m1/2.) +1
##else:
##dilation_m1 = 12
##dilation_m2 = int(dilation_m1/2.) +1
#########if (y_max-y_min) <= (x_max-x_min) and ((y_max-y_min)/(x_max-x_min))<0.15 and (x_max-x_min)>50:
#########dilation_m1 = int( (y_max-y_min) * 5/20.0 )
#########elif (y_max-y_min) <= (x_max-x_min) and ((y_max-y_min)/(x_max-x_min))>=0.15 and ((y_max-y_min)/(x_max-x_min))<0.3 and (x_max-x_min)>50:
#########dilation_m1 = int( (y_max-y_min) * 2/20.0 )
#########elif (y_max-y_min) <= (x_max-x_min) and ((y_max-y_min)/(x_max-x_min))>=0.3 and (x_max-x_min)>50:
#########dilation_m1 = int( (y_max-y_min) * 1/20.0 )
#########elif (x_max-x_min) < (y_max-y_min) and ((x_max-x_min)/(y_max-y_min))<0.15 and (y_max-y_min)>50:
#########dilation_m1 = int( (x_max-x_min) * 5/20.0 )
#########elif (x_max-x_min) < (y_max-y_min) and ((x_max-x_min)/(y_max-y_min))>=0.15 and ((x_max-x_min)/(y_max-y_min))<0.3 and (y_max-y_min)>50:
#########dilation_m1 = int( (x_max-x_min) * 2/20.0 )
#########elif (x_max-x_min) < (y_max-y_min) and ((x_max-x_min)/(y_max-y_min))>=0.3 and (y_max-y_min)>50:
#########dilation_m1 = int( (x_max-x_min) * 1/20.0 )
#########else:
#########dilation_m1 = int( (y_max-y_min) * 4/20.0 )
if (y_max-y_min) <= (x_max-x_min): if (y_max-y_min) <= (x_max-x_min):
dilation_m1 = round(area / (x_max-x_min) * 0.35) dilation_m1 = round(area / (x_max-x_min) * 0.35)
else: else:
@ -4126,11 +4309,6 @@ class Eynollah:
inc_x[i+1] = dilation_m2*(-1*y_differential_mask_nonzeros[i]) inc_x[i+1] = dilation_m2*(-1*y_differential_mask_nonzeros[i])
inc_y[i+1] = dilation_m2*(x_differential_mask_nonzeros[i]) inc_y[i+1] = dilation_m2*(x_differential_mask_nonzeros[i])
###inc_x =list(inc_x)
###inc_x.append(inc_x[0])
###inc_y =list(inc_y)
###inc_y.append(inc_y[0])
inc_x[0] = inc_x[-1] inc_x[0] = inc_x[-1]
inc_y[0] = inc_y[-1] inc_y[0] = inc_y[-1]
@ -4147,11 +4325,6 @@ class Eynollah:
all_found_textline_polygons[j][ij][:,0,0] = con_scaled[:,0, 0] all_found_textline_polygons[j][ij][:,0,0] = con_scaled[:,0, 0]
return all_found_textline_polygons return all_found_textline_polygons
def dilate_textlines(self,all_found_textline_polygons): def dilate_textlines(self,all_found_textline_polygons):
for j in range(len(all_found_textline_polygons)): for j in range(len(all_found_textline_polygons)):
for i in range(len(all_found_textline_polygons[j])): for i in range(len(all_found_textline_polygons[j])):
@ -4403,12 +4576,12 @@ class Eynollah:
t1 = time.time() t1 = time.time()
if not self.full_layout: if not self.full_layout:
polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, boxes, boxes_d, polygons_of_marginals, contours_tables = self.run_boxes_no_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, table_prediction, erosion_hurts) polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, boxes, boxes_d, polygons_of_marginals, contours_tables = self.run_boxes_no_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, table_prediction, erosion_hurts)
polygons_of_marginals = self.dilate_textregions_contours(polygons_of_marginals) ###polygons_of_marginals = self.dilate_textregions_contours(polygons_of_marginals)
if self.full_layout: if self.full_layout:
if not self.light_version: if not self.light_version:
img_bin_light = None img_bin_light = None
polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, regions_fully, regions_without_separators, polygons_of_marginals, contours_tables = self.run_boxes_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, img_only_regions, table_prediction, erosion_hurts, img_bin_light) polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, regions_fully, regions_without_separators, polygons_of_marginals, contours_tables = self.run_boxes_full_layout(image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, img_only_regions, table_prediction, erosion_hurts, img_bin_light)
polygons_of_marginals = self.dilate_textregions_contours(polygons_of_marginals) ###polygons_of_marginals = self.dilate_textregions_contours(polygons_of_marginals)
text_only = ((img_revised_tab[:, :] == 1)) * 1 text_only = ((img_revised_tab[:, :] == 1)) * 1
if np.abs(slope_deskew) >= SLOPE_THRESHOLD: if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
text_only_d = ((text_regions_p_1_n[:, :] == 1)) * 1 text_only_d = ((text_regions_p_1_n[:, :] == 1)) * 1
@ -4537,9 +4710,10 @@ class Eynollah:
#print("text region early 3 in %.1fs", time.time() - t0) #print("text region early 3 in %.1fs", time.time() - t0)
if self.light_version: if self.light_version:
txt_con_org = get_textregion_contours_in_org_image_light(contours_only_text_parent, self.image, slope_first)
txt_con_org = self.dilate_textregions_contours(txt_con_org)
contours_only_text_parent = self.dilate_textregions_contours(contours_only_text_parent) contours_only_text_parent = self.dilate_textregions_contours(contours_only_text_parent)
txt_con_org = get_textregion_contours_in_org_image_light(contours_only_text_parent, self.image, slope_first)
#txt_con_org = self.dilate_textregions_contours(txt_con_org)
#contours_only_text_parent = self.dilate_textregions_contours(contours_only_text_parent)
else: else:
txt_con_org = get_textregion_contours_in_org_image(contours_only_text_parent, self.image, slope_first) txt_con_org = get_textregion_contours_in_org_image(contours_only_text_parent, self.image, slope_first)
#print("text region early 4 in %.1fs", time.time() - t0) #print("text region early 4 in %.1fs", time.time() - t0)

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