mirror of
https://github.com/qurator-spk/eynollah.git
synced 2025-10-31 01:34:17 +01:00
dilation of textregions and marginals are accomplished
This commit is contained in:
vahidrezanezhad
parent
95effe54a0
commit
133091137d
1 changed files with 313 additions and 139 deletions
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@ -252,7 +252,7 @@ class Eynollah:
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self.model_region_dir_p_ens = dir_models + "/eynollah-main-regions-ensembled_20210425"
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self.model_region_dir_p_ens_light = dir_models + "/eynollah-main-regions_20220314"
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self.model_reading_order_machine_dir = dir_models + "/model_ens_reading_order_machine_based"
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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"
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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"
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##self.model_region_dir_fully_new = dir_models + "/model_2_full_layout_new_trans"
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self.model_region_dir_fully = dir_models + "/modelens_full_layout_24_till_28"#"/model_2_full_layout_new_trans"
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if self.textline_light:
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@ -1050,7 +1050,7 @@ class Eynollah:
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#del model
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#gc.collect()
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return prediction_true
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def do_prediction_new_concept(self, patches, img, model, marginal_of_patch_percent=0.1):
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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):
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self.logger.debug("enter do_prediction")
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img_height_model = model.layers[len(model.layers) - 1].output_shape[1]
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@ -1064,14 +1064,14 @@ class Eynollah:
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label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]), verbose=0)
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seg_not_base = label_p_pred[0,:,:,4]
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#seg_not_base = label_p_pred[0,:,:,4]
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seg_not_base[seg_not_base>0.4] =1
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seg_not_base[seg_not_base<1] =0
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#seg_not_base[seg_not_base>0.4] =1
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#seg_not_base[seg_not_base<1] =0
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seg = np.argmax(label_p_pred, axis=3)[0]
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seg[seg_not_base==1]=4
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#seg[seg_not_base==1]=4
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seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
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prediction_true = resize_image(seg_color, img_h_page, img_w_page)
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@ -1099,6 +1099,16 @@ class Eynollah:
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nyf = img_h / float(height_mid)
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nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf)
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nyf = int(nyf) + 1 if nyf > int(nyf) else int(nyf)
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list_i_s = []
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list_j_s = []
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list_x_u = []
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list_x_d = []
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list_y_u = []
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list_y_d = []
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batch_indexer = 0
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img_patch = np.zeros((n_batch_inference, img_height_model, img_width_model, 3))
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for i in range(nxf):
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for j in range(nyf):
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@ -1120,44 +1130,57 @@ class Eynollah:
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if index_y_u > img_h:
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index_y_u = img_h
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index_y_d = img_h - img_height_model
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list_i_s.append(i)
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list_j_s.append(j)
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list_x_u.append(index_x_u)
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list_x_d.append(index_x_d)
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list_y_d.append(index_y_d)
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list_y_u.append(index_y_u)
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img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
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label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]),
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verbose=0)
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seg = np.argmax(label_p_pred, axis=3)[0]
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img_patch[batch_indexer,:,:,:] = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
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batch_indexer = batch_indexer + 1
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#img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
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#label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]),
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#verbose=0)
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#seg = np.argmax(label_p_pred, axis=3)[0]
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seg_not_base = label_p_pred[0,:,:,4]
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##seg2 = -label_p_pred[0,:,:,2]
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######seg_not_base = label_p_pred[0,:,:,4]
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########seg2 = -label_p_pred[0,:,:,2]
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seg_not_base[seg_not_base>0.03] =1
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seg_not_base[seg_not_base<1] =0
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######seg_not_base[seg_not_base>0.03] =1
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######seg_not_base[seg_not_base<1] =0
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seg_test = label_p_pred[0,:,:,1]
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##seg2 = -label_p_pred[0,:,:,2]
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######seg_test = label_p_pred[0,:,:,1]
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########seg2 = -label_p_pred[0,:,:,2]
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seg_test[seg_test>0.75] =1
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seg_test[seg_test<1] =0
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######seg_test[seg_test>0.75] =1
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######seg_test[seg_test<1] =0
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seg_line = label_p_pred[0,:,:,3]
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##seg2 = -label_p_pred[0,:,:,2]
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######seg_line = label_p_pred[0,:,:,3]
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########seg2 = -label_p_pred[0,:,:,2]
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seg_line[seg_line>0.1] =1
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seg_line[seg_line<1] =0
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######seg_line[seg_line>0.1] =1
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######seg_line[seg_line<1] =0
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seg_background = label_p_pred[0,:,:,0]
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##seg2 = -label_p_pred[0,:,:,2]
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######seg_background = label_p_pred[0,:,:,0]
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########seg2 = -label_p_pred[0,:,:,2]
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seg_background[seg_background>0.25] =1
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seg_background[seg_background<1] =0
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######seg_background[seg_background>0.25] =1
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######seg_background[seg_background<1] =0
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##seg = seg+seg2
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#seg = label_p_pred[0,:,:,2]
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#seg[seg>0.4] =1
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@ -1170,56 +1193,221 @@ class Eynollah:
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##plt.show()
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#seg[seg==1]=0
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#seg[seg_test==1]=1
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seg[seg_not_base==1]=4
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seg[seg_background==1]=0
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seg[(seg_line==1) & (seg==0)]=3
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seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
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######seg[seg_not_base==1]=4
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######seg[seg_background==1]=0
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######seg[(seg_line==1) & (seg==0)]=3
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#seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
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if i == 0 and j == 0:
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seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
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seg = seg[0 : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
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mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
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prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg_color
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elif i == nxf - 1 and j == nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
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seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - 0]
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mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0] = seg
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prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0, :] = seg_color
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elif i == 0 and j == nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
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seg = seg[margin : seg.shape[0] - 0, 0 : seg.shape[1] - margin]
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mask_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin] = seg
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prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin, :] = seg_color
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elif i == nxf - 1 and j == 0:
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seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
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seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - 0]
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mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg
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prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg_color
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elif i == 0 and j != 0 and j != nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
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seg = seg[margin : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
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mask_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
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prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg_color
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elif i == nxf - 1 and j != 0 and j != nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
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seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - 0]
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mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg
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prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg_color
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elif i != 0 and i != nxf - 1 and j == 0:
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seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
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seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - margin]
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mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
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prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
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elif i != 0 and i != nxf - 1 and j == nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
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seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - margin]
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mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin] = seg
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prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin, :] = seg_color
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else:
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seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
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seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin]
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mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
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prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
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#if i == 0 and j == 0:
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#seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
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#seg = seg[0 : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
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#mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
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#prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg_color
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#elif i == nxf - 1 and j == nyf - 1:
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#seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
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#seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - 0]
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#mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0] = seg
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#prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - 0, :] = seg_color
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#elif i == 0 and j == nyf - 1:
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#seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
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#seg = seg[margin : seg.shape[0] - 0, 0 : seg.shape[1] - margin]
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#mask_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin] = seg
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#prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + 0 : index_x_u - margin, :] = seg_color
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#elif i == nxf - 1 and j == 0:
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#seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
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#seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - 0]
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#mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg
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#prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg_color
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#elif i == 0 and j != 0 and j != nyf - 1:
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#seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
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#seg = seg[margin : seg.shape[0] - margin, 0 : seg.shape[1] - margin]
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#mask_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin] = seg
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#prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + 0 : index_x_u - margin, :] = seg_color
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#elif i == nxf - 1 and j != 0 and j != nyf - 1:
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#seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
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#seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - 0]
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#mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0] = seg
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#prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - 0, :] = seg_color
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#elif i != 0 and i != nxf - 1 and j == 0:
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#seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
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#seg = seg[0 : seg.shape[0] - margin, margin : seg.shape[1] - margin]
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#mask_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
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#prediction_true[index_y_d + 0 : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
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#elif i != 0 and i != nxf - 1 and j == nyf - 1:
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#seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
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#seg = seg[margin : seg.shape[0] - 0, margin : seg.shape[1] - margin]
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#mask_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin] = seg
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#prediction_true[index_y_d + margin : index_y_u - 0, index_x_d + margin : index_x_u - margin, :] = seg_color
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#else:
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#seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
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#seg = seg[margin : seg.shape[0] - margin, margin : seg.shape[1] - margin]
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#mask_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin] = seg
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#prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
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if batch_indexer == n_batch_inference:
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label_p_pred = model.predict(img_patch,verbose=0)
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seg = np.argmax(label_p_pred, axis=3)
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if thresholding_for_some_classes_in_light_version:
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seg_not_base = label_p_pred[:,:,:,4]
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seg_not_base[seg_not_base>0.03] =1
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seg_not_base[seg_not_base<1] =0
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seg_line = label_p_pred[:,:,:,3]
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seg_line[seg_line>0.1] =1
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seg_line[seg_line<1] =0
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seg_background = label_p_pred[:,:,:,0]
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seg_background[seg_background>0.25] =1
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seg_background[seg_background<1] =0
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seg[seg_not_base==1]=4
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seg[seg_background==1]=0
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seg[(seg_line==1) & (seg==0)]=3
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if thresholding_for_artificial_class_in_light_version:
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seg_art = label_p_pred[:,:,:,2]
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seg_art[seg_art<0.2] = 0
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seg_art[seg_art>0] =1
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seg[seg_art==1]=2
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indexer_inside_batch = 0
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for i_batch, j_batch in zip(list_i_s, list_j_s):
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seg_in = seg[indexer_inside_batch,:,:]
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seg_color = np.repeat(seg_in[:, :, np.newaxis], 3, axis=2)
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index_y_u_in = list_y_u[indexer_inside_batch]
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index_y_d_in = list_y_d[indexer_inside_batch]
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index_x_u_in = list_x_u[indexer_inside_batch]
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index_x_d_in = list_x_d[indexer_inside_batch]
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if i_batch == 0 and j_batch == 0:
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seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
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elif i_batch == nxf - 1 and j_batch == nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
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||||
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))
|
||||
|
||||
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)
|
||||
return prediction_true
|
||||
|
|
@ -1963,7 +2151,7 @@ class Eynollah:
|
|||
#print(num_col_classifier,'num_col_classifier')
|
||||
|
||||
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)
|
||||
|
||||
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)
|
||||
|
||||
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)
|
||||
|
||||
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)
|
||||
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)
|
||||
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_resized = resize_image(img,img_h_new, img_w_new )
|
||||
|
||||
|
|
@ -2025,17 +2213,17 @@ class Eynollah:
|
|||
if not self.dir_in:
|
||||
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)
|
||||
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:
|
||||
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)
|
||||
##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)
|
||||
else:
|
||||
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:
|
||||
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)
|
||||
|
||||
#print("inside 3 ", time.time()-t_in)
|
||||
|
|
@ -2054,8 +2242,12 @@ class Eynollah:
|
|||
|
||||
mask_texts_only = mask_texts_only.astype('uint8')
|
||||
|
||||
#mask_texts_only = cv2.erode(mask_texts_only, KERNEL, iterations=1)
|
||||
mask_texts_only = cv2.dilate(mask_texts_only, KERNEL, iterations=1)
|
||||
##if num_col_classifier == 1 or num_col_classifier == 2:
|
||||
###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
|
||||
|
||||
|
|
@ -3150,7 +3342,14 @@ class Eynollah:
|
|||
|
||||
pixel_img = 4
|
||||
min_area_mar = 0.00001
|
||||
polygons_of_marginals = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
|
||||
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)
|
||||
|
||||
pixel_img = 10
|
||||
contours_tables = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
|
||||
|
|
@ -3241,7 +3440,15 @@ class Eynollah:
|
|||
|
||||
pixel_img = 4
|
||||
min_area_mar = 0.00001
|
||||
polygons_of_marginals = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
|
||||
|
||||
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)
|
||||
|
||||
pixel_img = 10
|
||||
contours_tables = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
|
||||
|
|
@ -3850,18 +4057,19 @@ class Eynollah:
|
|||
for j in range(len(all_found_textline_polygons)):
|
||||
|
||||
con_ind = all_found_textline_polygons[j]
|
||||
#print(len(con_ind[:,0,0]),'con_ind[:,0,0]')
|
||||
area = cv2.contourArea(con_ind)
|
||||
con_ind = con_ind.astype(np.float)
|
||||
|
||||
con_ind[:,0,0] = gaussian_filter1d(con_ind[:,0,0], 0.1)
|
||||
con_ind[:,0,1] = gaussian_filter1d(con_ind[:,0,1], 0.1)
|
||||
#con_ind[:,0,0] = gaussian_filter1d(con_ind[:,0,0], 0.5)
|
||||
#con_ind[:,0,1] = gaussian_filter1d(con_ind[:,0,1], 0.5)
|
||||
|
||||
x_differential = np.diff( con_ind[:,0,0])
|
||||
y_differential = np.diff( con_ind[:,0,1])
|
||||
|
||||
|
||||
x_differential = gaussian_filter1d(x_differential, .5)
|
||||
y_differential = gaussian_filter1d(y_differential, .5)
|
||||
x_differential = gaussian_filter1d(x_differential, 0.1)
|
||||
y_differential = gaussian_filter1d(y_differential, 0.1)
|
||||
|
||||
x_min = float(np.min( con_ind[:,0,0] ))
|
||||
y_min = float(np.min( con_ind[:,0,1] ))
|
||||
|
|
@ -3885,8 +4093,8 @@ class Eynollah:
|
|||
|
||||
if dilation_m1>8:
|
||||
dilation_m1 = 8
|
||||
if dilation_m1<5:
|
||||
dilation_m1 = 5
|
||||
if dilation_m1<6:
|
||||
dilation_m1 = 6
|
||||
#print(dilation_m1, 'dilation_m1')
|
||||
dilation_m2 = int(dilation_m1/2.) +1
|
||||
|
||||
|
|
@ -4002,7 +4210,6 @@ class Eynollah:
|
|||
#indices_m2 = np.array(indices_m2)[diff_neg_pos>1]
|
||||
|
||||
for ii in range(len(indices_2)):
|
||||
|
||||
#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]
|
||||
|
||||
|
|
@ -4030,11 +4237,12 @@ class Eynollah:
|
|||
#print(indices_m2,'-2')
|
||||
#print(diff_neg_pos,'diff_neg_pos')
|
||||
|
||||
#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, 1] = gaussian_filter1d(con_scaled[:,0, 1], 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, 0] = con_scaled[0,0, 0]
|
||||
#con_scaled[-1,0, 1] = con_scaled[0,0, 1]
|
||||
#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][:,0,0] = con_scaled[:,0, 0]
|
||||
return all_found_textline_polygons
|
||||
|
|
@ -4045,7 +4253,7 @@ class Eynollah:
|
|||
for ij in range(len(all_found_textline_polygons[j])):
|
||||
|
||||
con_ind = all_found_textline_polygons[j][ij]
|
||||
|
||||
print(len(con_ind[:,0,0]),'con_ind[:,0,0]')
|
||||
area = cv2.contourArea(con_ind)
|
||||
|
||||
con_ind = con_ind.astype(np.float)
|
||||
|
|
@ -4069,31 +4277,6 @@ class Eynollah:
|
|||
|
||||
inc_x = 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):
|
||||
dilation_m1 = round(area / (x_max-x_min) * 0.35)
|
||||
|
|
@ -4126,11 +4309,6 @@ class Eynollah:
|
|||
inc_x[i+1] = dilation_m2*(-1*y_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_y[0] = inc_y[-1]
|
||||
|
|
@ -4146,11 +4324,6 @@ class Eynollah:
|
|||
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
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def dilate_textlines(self,all_found_textline_polygons):
|
||||
for j in range(len(all_found_textline_polygons)):
|
||||
|
|
@ -4403,12 +4576,12 @@ class Eynollah:
|
|||
t1 = time.time()
|
||||
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_marginals = self.dilate_textregions_contours(polygons_of_marginals)
|
||||
###polygons_of_marginals = self.dilate_textregions_contours(polygons_of_marginals)
|
||||
if self.full_layout:
|
||||
if not self.light_version:
|
||||
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_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
|
||||
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
|
||||
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)
|
||||
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)
|
||||
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:
|
||||
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)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue