diff --git a/qurator/eynollah/eynollah.py b/qurator/eynollah/eynollah.py index 794ebe6..2fe7325 100644 --- a/qurator/eynollah/eynollah.py +++ b/qurator/eynollah/eynollah.py @@ -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_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 = dir_models + "/modelens_full_layout_24_till_28"#"/model_2_full_layout_new_trans" if self.textline_light: @@ -1050,7 +1050,7 @@ class Eynollah: #del model #gc.collect() 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") 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) - 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<1] =0 + #seg_not_base[seg_not_base>0.4] =1 + #seg_not_base[seg_not_base<1] =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) prediction_true = resize_image(seg_color, img_h_page, img_w_page) @@ -1099,6 +1099,16 @@ class Eynollah: nyf = img_h / float(height_mid) nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf) 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 j in range(nyf): @@ -1120,44 +1130,57 @@ class Eynollah: if index_y_u > img_h: index_y_u = img_h index_y_d = img_h - img_height_model + + + 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) + - 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] + img_patch[batch_indexer,:,:,:] = img[index_y_d:index_y_u, index_x_d:index_x_u, :] + + batch_indexer = batch_indexer + 1 + + #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_not_base = label_p_pred[0,:,:,4] - ##seg2 = -label_p_pred[0,:,:,2] + ######seg_not_base = label_p_pred[0,:,:,4] + ########seg2 = -label_p_pred[0,:,:,2] - seg_not_base[seg_not_base>0.03] =1 - seg_not_base[seg_not_base<1] =0 + ######seg_not_base[seg_not_base>0.03] =1 + ######seg_not_base[seg_not_base<1] =0 - seg_test = label_p_pred[0,:,:,1] - ##seg2 = -label_p_pred[0,:,:,2] + ######seg_test = label_p_pred[0,:,:,1] + ########seg2 = -label_p_pred[0,:,:,2] - seg_test[seg_test>0.75] =1 - seg_test[seg_test<1] =0 + ######seg_test[seg_test>0.75] =1 + ######seg_test[seg_test<1] =0 - seg_line = label_p_pred[0,:,:,3] - ##seg2 = -label_p_pred[0,:,:,2] + ######seg_line = label_p_pred[0,:,:,3] + ########seg2 = -label_p_pred[0,:,:,2] - seg_line[seg_line>0.1] =1 - seg_line[seg_line<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 = 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_background[seg_background>0.25] =1 + ######seg_background[seg_background<1] =0 ##seg = seg+seg2 #seg = label_p_pred[0,:,:,2] #seg[seg>0.4] =1 @@ -1170,56 +1193,221 @@ class Eynollah: ##plt.show() #seg[seg==1]=0 #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 + ######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_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)) + + 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)