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eynollah
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making light version faster for 1 and 2 columns images

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pull/138/head^2
vahidrezanezhad 4 months ago
parent c10a525675
commit 04e79002b3
2 changed files with 75 additions and 29 deletions
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88
qurator/eynollah/eynollah.py
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@ -28,6 +28,7 @@ from scipy.signal import find_peaks
from scipy.ndimage import gaussian_filter1d
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
#os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
stderr = sys.stderr
sys.stderr = open(os.devnull, "w")
import tensorflow as tf
@ -299,17 +300,25 @@ class Eynollah:
def _cache_images(self, image_filename=None, image_pil=None):
ret = {}
t_c0 = time.time()
if image_filename:
ret['img'] = cv2.imread(image_filename)
self.dpi = check_dpi(image_filename)
if self.light_version:
self.dpi = 100
else:
self.dpi = check_dpi(image_filename)
else:
ret['img'] = pil2cv(image_pil)
self.dpi = check_dpi(image_pil)
if self.light_version:
self.dpi = 100
else:
self.dpi = check_dpi(image_pil)
ret['img_grayscale'] = cv2.cvtColor(ret['img'], cv2.COLOR_BGR2GRAY)
for prefix in ('', '_grayscale'):
ret[f'img{prefix}_uint8'] = ret[f'img{prefix}'].astype(np.uint8)
return ret
def reset_file_name_dir(self, image_filename):
t_c = time.time()
self._imgs = self._cache_images(image_filename=image_filename)
self.image_filename = image_filename
@ -491,6 +500,27 @@ class Eynollah:
num_column_is_classified = True
return img_new, num_column_is_classified
def calculate_width_height_by_columns_1_2(self, img, num_col, width_early, label_p_pred):
self.logger.debug("enter calculate_width_height_by_columns")
if num_col == 1:
img_w_new = 1300
img_h_new = int(img.shape[0] / float(img.shape[1]) * 1300)
else:
img_w_new = 1500
img_h_new = int(img.shape[0] / float(img.shape[1]) * 1500)
if label_p_pred[0][int(num_col - 1)] < 0.9 and img_w_new < width_early:
img_new = np.copy(img)
num_column_is_classified = False
elif label_p_pred[0][int(num_col - 1)] < 0.8 and img_h_new >= 8000:
img_new = np.copy(img)
num_column_is_classified = False
else:
img_new = resize_image(img, img_h_new, img_w_new)
num_column_is_classified = True
return img_new, num_column_is_classified
def resize_image_with_column_classifier(self, is_image_enhanced, img_bin):
self.logger.debug("enter resize_image_with_column_classifier")
@ -600,16 +630,24 @@ class Eynollah:
self.logger.info("Found %d columns (%s)", num_col, np.around(label_p_pred, decimals=5))
if dpi < DPI_THRESHOLD:
img_new, num_column_is_classified = self.calculate_width_height_by_columns(img, num_col, width_early, label_p_pred)
if light_version and num_col in (1,2):
img_new, num_column_is_classified = self.calculate_width_height_by_columns_1_2(img, num_col, width_early, label_p_pred)
else:
img_new, num_column_is_classified = self.calculate_width_height_by_columns(img, num_col, width_early, label_p_pred)
if light_version:
image_res = np.copy(img_new)
else:
image_res = self.predict_enhancement(img_new)
is_image_enhanced = True
else:
num_column_is_classified = True
image_res = np.copy(img)
is_image_enhanced = False
if light_version and num_col in (1,2):
img_new, num_column_is_classified = self.calculate_width_height_by_columns_1_2(img, num_col, width_early, label_p_pred)
image_res = np.copy(img_new)
is_image_enhanced = True
else:
num_column_is_classified = True
image_res = np.copy(img)
is_image_enhanced = False
self.logger.debug("exit resize_and_enhance_image_with_column_classifier")
return is_image_enhanced, img, image_res, num_col, num_column_is_classified, img_bin
@ -1175,7 +1213,7 @@ class Eynollah:
marginal_of_patch_percent = 0.1
prediction_regions = self.do_prediction(patches, img, model_region, marginal_of_patch_percent=marginal_of_patch_percent)
prediction_regions = self.do_prediction(patches, img, model_region, marginal_of_patch_percent=marginal_of_patch_percent, n_batch_inference=4)
prediction_regions = resize_image(prediction_regions, img_height_h, img_width_h)
self.logger.debug("exit extract_text_regions")
@ -1280,7 +1318,10 @@ class Eynollah:
def get_slopes_and_deskew_new_light(self, contours, contours_par, textline_mask_tot, image_page_rotated, boxes, slope_deskew):
self.logger.debug("enter get_slopes_and_deskew_new")
num_cores = cpu_count()
if len(contours)>15:
num_cores = cpu_count()
else:
num_cores = 1
queue_of_all_params = Queue()
processes = []
@ -1554,8 +1595,6 @@ class Eynollah:
mask_only_con_region = np.zeros(textline_mask_tot_ea.shape)
mask_only_con_region = cv2.fillPoly(mask_only_con_region, pts=[contours_par_per_process[mv]], color=(1, 1, 1))
# plt.imshow(mask_only_con_region)
# plt.show()
if self.textline_light:
all_text_region_raw = np.copy(textline_mask_tot_ea)
@ -1660,11 +1699,11 @@ class Eynollah:
img_h = img_org.shape[0]
img_w = img_org.shape[1]
img = resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w))
#print(img.shape,'bin shape')
#print(img.shape,'bin shape textline')
if not self.dir_in:
prediction_textline = self.do_prediction(patches, img, model_textline, n_batch_inference=4)
prediction_textline = self.do_prediction(patches, img, model_textline, n_batch_inference=3)
else:
prediction_textline = self.do_prediction(patches, img, self.model_textline, n_batch_inference=4)
prediction_textline = self.do_prediction(patches, img, self.model_textline, n_batch_inference=3)
prediction_textline = resize_image(prediction_textline, img_h, img_w)
if not self.dir_in:
prediction_textline_longshot = self.do_prediction(False, img, model_textline)
@ -1747,11 +1786,14 @@ class Eynollah:
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 )
t_bin = time.time()
if not self.dir_in:
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
prediction_bin = self.do_prediction(True, img_resized, model_bin, n_batch_inference=5)
prediction_bin = self.do_prediction(True, img_resized, model_bin, n_batch_inference=10)
else:
prediction_bin = self.do_prediction(True, img_resized, self.model_bin, n_batch_inference=5)
prediction_bin = self.do_prediction(True, img_resized, self.model_bin, n_batch_inference=10)
#print("inside bin ", time.time()-t_bin)
prediction_bin=prediction_bin[:,:,0]
prediction_bin = (prediction_bin[:,:]==0)*1
prediction_bin = prediction_bin*255
@ -2710,10 +2752,10 @@ class Eynollah:
return num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, text_regions_p_1, cont_page, table_prediction
def run_enhancement(self,light_version):
t_in = time.time()
self.logger.info("Resizing and enhancing image...")
is_image_enhanced, img_org, img_res, num_col_classifier, num_column_is_classified, img_bin = self.resize_and_enhance_image_with_column_classifier(light_version)
self.logger.info("Image was %senhanced.", '' if is_image_enhanced else 'not ')
scale = 1
if is_image_enhanced:
if self.allow_enhancement:
@ -2731,6 +2773,7 @@ class Eynollah:
if self.allow_scaling:
img_org, img_res, is_image_enhanced = self.resize_image_with_column_classifier(is_image_enhanced, img_bin)
self.get_image_and_scales_after_enhancing(img_org, img_res)
#print("enhancement in ", time.time()-t_in)
return img_res, is_image_enhanced, num_col_classifier, num_column_is_classified
def run_textline(self, image_page):
@ -2748,7 +2791,8 @@ class Eynollah:
#print(textline_mask_tot_ea.shape, 'textline_mask_tot_ea deskew')
sigma = 2
main_page_deskew = True
slope_deskew = return_deskew_slop(cv2.erode(textline_mask_tot_ea, KERNEL, iterations=2), sigma, main_page_deskew, plotter=self.plotter)
n_total_angles = 30
slope_deskew = return_deskew_slop(cv2.erode(textline_mask_tot_ea, KERNEL, iterations=2), sigma, n_total_angles, main_page_deskew, plotter=self.plotter)
slope_first = 0
if self.plotter:
@ -2871,7 +2915,7 @@ class Eynollah:
def run_boxes_full_layout(self, image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, img_only_regions, table_prediction, erosion_hurts, img_bin_light):
self.logger.debug('enter run_boxes_full_layout')
t_full0 = time.time()
if self.tables:
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
image_page_rotated_n,textline_mask_tot_d,text_regions_p_1_n , table_prediction_n = rotation_not_90_func(image_page, textline_mask_tot, text_regions_p, table_prediction, slope_deskew)
@ -2963,12 +3007,12 @@ class Eynollah:
text_regions_p[:, :][text_regions_p[:, :] == 4] = 8
image_page = image_page.astype(np.uint8)
#print("full inside 1", time.time()- t_full0)
if self.light_version:
regions_fully, regions_fully_only_drop = self.extract_text_regions_new(img_bin_light, True, cols=num_col_classifier)
else:
regions_fully, regions_fully_only_drop = self.extract_text_regions_new(image_page, True, cols=num_col_classifier)
#print("full inside 2", time.time()- t_full0)
# 6 is the separators lable in old full layout model
# 4 is the drop capital class in old full layout model
# in the new full layout drop capital is 3 and separators are 5
@ -3012,6 +3056,7 @@ class Eynollah:
img_revised_tab = np.copy(text_regions_p[:, :])
polygons_of_images = return_contours_of_interested_region(img_revised_tab, 5)
self.logger.debug('exit run_boxes_full_layout')
#print("full inside 3", time.time()- t_full0)
return 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
def our_load_model(self, model_file):
@ -3534,6 +3579,7 @@ class Eynollah:
t0 = time.time()
if self.dir_in:
self.reset_file_name_dir(os.path.join(self.dir_in,img_name))
#print("text region early -11 in %.1fs", time.time() - t0)
img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement(self.light_version)
self.logger.info("Enhancing took %.1fs ", time.time() - t0)
@ -3922,7 +3968,7 @@ class Eynollah:
if self.dir_in:
self.writer.write_pagexml(pcgts)
#self.logger.info("Job done in %.1fs", time.time() - t0)
#print("Job done in %.1fs", time.time() - t0)
print("Job done in %.1fs", time.time() - t0)
if self.dir_in:
self.logger.info("All jobs done in %.1fs", time.time() - t0_tot)

16
qurator/eynollah/utils/separate_lines.py
Unescape Escape View File

@ -1569,7 +1569,7 @@ def separate_lines_new2(img_path, thetha, num_col, slope_region, plotter=None):
# plt.show()
return img_patch_ineterst_revised
def return_deskew_slop(img_patch_org, sigma_des, main_page=False, plotter=None):
def return_deskew_slop(img_patch_org, sigma_des,n_tot_angles=100, main_page=False, plotter=None):
if main_page and plotter:
plotter.save_plot_of_textline_density(img_patch_org)
@ -1626,7 +1626,7 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, plotter=None):
ang_int=0
angels=np.linspace(ang_int-22.5,ang_int+22.5,100)
angels=np.linspace(ang_int-22.5,ang_int+22.5,n_tot_angles)
var_res=[]
for rot in angels:
@ -1649,7 +1649,7 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, plotter=None):
#plt.imshow(img_resized)
#plt.show()
angels=np.linspace(-12,12,100)#np.array([0 , 45 , 90 , -45])
angels=np.linspace(-12,12,n_tot_angles)#np.array([0 , 45 , 90 , -45])
var_res=[]
@ -1680,7 +1680,7 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, plotter=None):
early_slope_edge=11
if abs(ang_int)>early_slope_edge and ang_int<0:
angels=np.linspace(-90,-12,100)
angels=np.linspace(-90,-12,n_tot_angles)
var_res=[]
for rot in angels:
img_rot=rotate_image(img_resized,rot)
@ -1700,7 +1700,7 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, plotter=None):
elif abs(ang_int)>early_slope_edge and ang_int>0:
angels=np.linspace(90,12,100)
angels=np.linspace(90,12,n_tot_angles)
var_res=[]
for rot in angels:
img_rot=rotate_image(img_resized,rot)
@ -1719,7 +1719,7 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, plotter=None):
except:
ang_int=0
else:
angels=np.linspace(-25,25,60)
angels=np.linspace(-25,25,int(n_tot_angles/2.)+10)
var_res=[]
indexer=0
for rot in angels:
@ -1749,7 +1749,7 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, plotter=None):
early_slope_edge=22
if abs(ang_int)>early_slope_edge and ang_int<0:
angels=np.linspace(-90,-25,60)
angels=np.linspace(-90,-25,int(n_tot_angles/2.)+10)
var_res=[]
@ -1772,7 +1772,7 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, plotter=None):
elif abs(ang_int)>early_slope_edge and ang_int>0:
angels=np.linspace(90,25,60)
angels=np.linspace(90,25,int(n_tot_angles/2.)+10)
var_res=[]

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