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eynollah
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back on track- freezing problem , memory error and issues with reading order by drop capitals and marginals are resolved

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pull/28/head
vahidrezanezhad 4 years ago committed by GitHub
parent 3d9da4feaa
commit 43b8759acf
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1 changed files with 50 additions and 15 deletions
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qurator/eynollah/eynollah.py
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@ -13,11 +13,10 @@ import time
import warnings
from pathlib import Path
from multiprocessing import Process, Queue, cpu_count
import gc
from ocrd_utils import getLogger
import cv2
import numpy as np
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
stderr = sys.stderr
sys.stderr = open(os.devnull, "w")
@ -149,7 +148,7 @@ class Eynollah:
def predict_enhancement(self, img):
self.logger.debug("enter predict_enhancement")
model_enhancement, _ = self.start_new_session_and_model(self.model_dir_of_enhancement)
model_enhancement, session_enhancement = self.start_new_session_and_model(self.model_dir_of_enhancement)
img_height_model = model_enhancement.layers[len(model_enhancement.layers) - 1].output_shape[1]
img_width_model = model_enhancement.layers[len(model_enhancement.layers) - 1].output_shape[2]
@ -230,6 +229,10 @@ class Eynollah:
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg
prediction_true = prediction_true.astype(int)
session_enhancement.close()
del model_enhancement
del session_enhancement
gc.collect()
return prediction_true
@ -325,7 +328,13 @@ class Eynollah:
session_col_classifier.close()
del model_num_classifier
del session_col_classifier
K.clear_session()
gc.collect()
img_new, _ = self.calculate_width_height_by_columns(img, num_col, width_early, label_p_pred)
@ -376,6 +385,9 @@ class Eynollah:
num_column_is_classified = True
image_res = np.copy(img)
session_col_classifier.close()
self.logger.debug("exit resize_and_enhance_image_with_column_classifier")
return is_image_enhanced, img, image_res, num_col, num_column_is_classified
@ -429,7 +441,7 @@ class Eynollah:
self.writer.height_org = self.height_org
self.writer.width_org = self.width_org
def start_new_session_and_model(self, model_dir):
def start_new_session_and_model_old(self, model_dir):
self.logger.debug("enter start_new_session_and_model (model_dir=%s)", model_dir)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
@ -439,6 +451,15 @@ class Eynollah:
return model, session
def start_new_session_and_model(self, model_dir):
self.logger.debug("enter start_new_session_and_model (model_dir=%s)", model_dir)
gpu_options = tf.compat.v1.GPUOptions(allow_growth=True)
#gpu_options = tf.compat.v1.GPUOptions(per_process_gpu_memory_fraction=7.7, allow_growth=True)
session = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(gpu_options=gpu_options))
model = load_model(model_dir, compile=False)
return model, session
def do_prediction(self, patches, img, model, marginal_of_patch_percent=0.1):
self.logger.debug("enter do_prediction")
@ -554,6 +575,8 @@ class Eynollah:
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
prediction_true = prediction_true.astype(np.uint8)
del model
gc.collect()
return prediction_true
def early_page_for_num_of_column_classification(self):
@ -574,7 +597,10 @@ class Eynollah:
box = [x, y, w, h]
croped_page, page_coord = crop_image_inside_box(box, img)
session_page.close()
del model_page
del session_page
gc.collect()
K.clear_session()
self.logger.debug("exit early_page_for_num_of_column_classification")
return croped_page, page_coord
@ -606,7 +632,9 @@ class Eynollah:
croped_page, page_coord = crop_image_inside_box(box, self.image)
cont_page.append(np.array([[page_coord[2], page_coord[0]], [page_coord[3], page_coord[0]], [page_coord[3], page_coord[1]], [page_coord[2], page_coord[1]]]))
session_page.close()
del model_page
del session_page
gc.collect()
K.clear_session()
self.logger.debug("exit extract_page")
return croped_page, page_coord, cont_page
@ -704,6 +732,10 @@ class Eynollah:
prediction_regions = resize_image(prediction_regions, img_height_h, img_width_h)
session_region.close()
del model_region
del session_region
gc.collect()
self.logger.debug("exit extract_text_regions")
return prediction_regions, prediction_regions2
@ -1000,11 +1032,10 @@ class Eynollah:
prediction_textline = resize_image(prediction_textline, img_h, img_w)
prediction_textline_longshot = self.do_prediction(False, img, model_textline)
prediction_textline_longshot_true_size = resize_image(prediction_textline_longshot, img_h, img_w)
##plt.imshow(prediction_textline_streched[:,:,0])
##plt.show()
session_textline.close()
return prediction_textline[:, :, 0], prediction_textline_longshot_true_size[:, :, 0]
def do_work_of_slopes(self, q, poly, box_sub, boxes_per_process, textline_mask_tot, contours_per_process):
@ -1071,18 +1102,22 @@ class Eynollah:
##plt.show()
prediction_regions_org=prediction_regions_org[:,:,0]
prediction_regions_org[(prediction_regions_org[:,:]==1) & (mask_zeros_y[:,:]==1)]=0
session_region.close()
del model_region
del session_region
gc.collect()
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]))
prediction_regions_org2 = self.do_prediction(True, img, model_region, 0.2)
prediction_regions_org2=resize_image(prediction_regions_org2, img_height_h, img_width_h )
#plt.imshow(prediction_regions_org2[:,:,0])
#plt.show()
##prediction_regions_org=prediction_regions_org[:,:,0]
session_region.close()
del model_region
del session_region
gc.collect()
mask_zeros2 = (prediction_regions_org2[:,:,0] == 0)
mask_lines2 = (prediction_regions_org2[:,:,0] == 3)
@ -1303,7 +1338,7 @@ class Eynollah:
arg_order_v = indexes_sorted_main[zahler]
order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = np.where(indexes_sorted == arg_order_v)[0][0] + ref_point
for jji, _ in range(len(id_of_texts)):
for jji, _ in enumerate(id_of_texts):
order_of_texts_tot.append(order_of_texts[jji] + ref_point)
id_of_texts_tot.append(id_of_texts[jji])
ref_point += len(id_of_texts)

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