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eynollah
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Merge branch 'main' into 420-namespace-package

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pull/97/head
Konstantin Baierer 1 year ago
parent 7cd07dd550 ea792d1e4a
commit 6c8f60993f
5 changed files with 92 additions and 150 deletions
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12
CHANGELOG.md
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@ -5,6 +5,17 @@ Versioned according to [Semantic Versioning](http://semver.org/).
## Unreleased
## [0.2.0] - 2023-03-24
Changed:
* Convert default model from HDFS to TF SavedModel, #91
Added:
* parmeter `tables` to toggle table detectino, #91
* default model described in ocrd-tool.json, #91
## [0.1.0] - 2023-03-22
Fixed:
@ -85,6 +96,7 @@ Fixed:
Initial release
<!-- link-labels -->
[0.2.0]: ../../compare/v0.2.0...v0.1.0
[0.1.0]: ../../compare/v0.1.0...v0.0.11
[0.0.11]: ../../compare/v0.0.11...v0.0.10
[0.0.10]: ../../compare/v0.0.10...v0.0.9

208
qurator/eynollah/eynollah.py
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@ -145,6 +145,8 @@ class Eynollah:
self.model_region_dir_p_ens = dir_models + "/model_ensemble_s.h5"
self.model_textline_dir = dir_models + "/model_textline_newspapers.h5"
self.model_tables = dir_models + "/model_tables_ens_mixed_new_2.h5"
self.models = {}
def _cache_images(self, image_filename=None, image_pil=None):
ret = {}
@ -220,7 +222,8 @@ class Eynollah:
index_y_d = img_h - img_height_model
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
label_p_pred = model_enhancement.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
label_p_pred = model_enhancement.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]),
verbose=0)
seg = label_p_pred[0, :, :, :]
seg = seg * 255
@ -254,11 +257,6 @@ 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
def calculate_width_height_by_columns(self, img, num_col, width_early, label_p_pred):
@ -355,21 +353,11 @@ class Eynollah:
img_in[0, :, :, 1] = img_1ch[:, :]
img_in[0, :, :, 2] = img_1ch[:, :]
label_p_pred = model_num_classifier.predict(img_in)
label_p_pred = model_num_classifier.predict(img_in, verbose=0)
num_col = np.argmax(label_p_pred[0]) + 1
self.logger.info("Found %s columns (%s)", num_col, label_p_pred)
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)
if img_new.shape[1] > img.shape[1]:
@ -393,11 +381,6 @@ class Eynollah:
prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
session_bin.close()
del model_bin
del session_bin
gc.collect()
prediction_bin = prediction_bin.astype(np.uint8)
img= np.copy(prediction_bin)
img_bin = np.copy(prediction_bin)
@ -405,6 +388,7 @@ class Eynollah:
img = self.imread()
img_bin = None
t1 = time.time()
_, page_coord = self.early_page_for_num_of_column_classification(img_bin)
model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)
@ -427,12 +411,10 @@ class Eynollah:
img_in[0, :, :, 2] = img_1ch[:, :]
label_p_pred = model_num_classifier.predict(img_in)
label_p_pred = model_num_classifier.predict(img_in, verbose=0)
num_col = np.argmax(label_p_pred[0]) + 1
self.logger.info("Found %s columns (%s)", num_col, label_p_pred)
session_col_classifier.close()
K.clear_session()
self.logger.info("Found %d columns (%s)", num_col, np.around(label_p_pred, decimals=5))
self.logger.info("detecting columns took %.1fs", time.time() - t1)
if dpi < DPI_THRESHOLD:
img_new, num_column_is_classified = self.calculate_width_height_by_columns(img, num_col, width_early, label_p_pred)
@ -443,9 +425,6 @@ class Eynollah:
image_res = np.copy(img)
is_image_enhanced = False
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, img_bin
@ -512,12 +491,24 @@ class Eynollah:
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(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)
#session = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(gpu_options=gpu_options))
physical_devices = tf.config.list_physical_devices('GPU')
try:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
except:
self.logger.warning("no GPU device available")
if model_dir.endswith('.h5') and Path(model_dir[:-3]).exists():
# prefer SavedModel over HDF5 format if it exists
model_dir = model_dir[:-3]
if model_dir in self.models:
model = self.models[model_dir]
else:
model = load_model(model_dir, compile=False)
self.models[model_dir] = model
return model, session
return model, None
def do_prediction(self, patches, img, model, marginal_of_patch_percent=0.1):
self.logger.debug("enter do_prediction")
@ -531,7 +522,8 @@ class Eynollah:
img = img / float(255.0)
img = resize_image(img, img_height_model, img_width_model)
label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]))
label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]),
verbose=0)
seg = np.argmax(label_p_pred, axis=3)[0]
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
@ -583,7 +575,8 @@ class Eynollah:
index_y_d = img_h - img_height_model
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
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_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
@ -634,8 +627,6 @@ 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,img_bin):
@ -662,19 +653,15 @@ class Eynollah:
else:
box = [0, 0, img.shape[1], img.shape[0]]
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
def extract_page(self):
self.logger.debug("enter extract_page")
cont_page = []
model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
img = cv2.GaussianBlur(self.image, (5, 5), 0)
model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
img_page_prediction = self.do_prediction(False, img, model_page)
imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(imgray, 0, 255, 0)
@ -701,11 +688,6 @@ class Eynollah:
box = [0, 0, img.shape[1], img.shape[0]]
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
@ -801,11 +783,6 @@ class Eynollah:
prediction_regions = self.do_prediction(patches, img, model_region, marginal_of_patch_percent)
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
@ -1106,9 +1083,6 @@ class Eynollah:
prediction_textline_longshot = self.do_prediction(False, img, model_textline)
prediction_textline_longshot_true_size = resize_image(prediction_textline_longshot, img_h, img_w)
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):
@ -1185,11 +1159,6 @@ 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]))
@ -1197,11 +1166,6 @@ class Eynollah:
prediction_regions_org2=resize_image(prediction_regions_org2, img_height_h, img_width_h )
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)
text_sume_early = (prediction_regions_org[:,:] == 1).sum()
@ -1241,12 +1205,6 @@ class Eynollah:
prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
session_bin.close()
del model_bin
del session_bin
gc.collect()
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
ratio_y=1
@ -1260,11 +1218,6 @@ class Eynollah:
prediction_regions_org=prediction_regions_org[:,:,0]
mask_lines_only=(prediction_regions_org[:,:]==3)*1
session_region.close()
del model_region
del session_region
gc.collect()
mask_texts_only=(prediction_regions_org[:,:]==1)*1
mask_images_only=(prediction_regions_org[:,:]==2)*1
@ -1283,20 +1236,12 @@ class Eynollah:
text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_texts, color=(1,1,1))
K.clear_session()
return text_regions_p_true, erosion_hurts, polygons_lines_xml
except:
if self.input_binary:
prediction_bin = np.copy(img_org)
else:
session_region.close()
del model_region
del session_region
gc.collect()
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
prediction_bin = self.do_prediction(True, img_org, model_bin)
prediction_bin = resize_image(prediction_bin, img_height_h, img_width_h )
@ -1308,15 +1253,6 @@ class Eynollah:
prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
session_bin.close()
del model_bin
del session_bin
gc.collect()
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
ratio_y=1
ratio_x=1
@ -1329,11 +1265,6 @@ class Eynollah:
prediction_regions_org=prediction_regions_org[:,:,0]
#mask_lines_only=(prediction_regions_org[:,:]==3)*1
session_region.close()
del model_region
del session_region
gc.collect()
#img = resize_image(img_org, int(img_org.shape[0]*1), int(img_org.shape[1]*1))
#prediction_regions_org = self.do_prediction(True, img, model_region)
@ -1343,11 +1274,6 @@ class Eynollah:
#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()
@ -1375,7 +1301,7 @@ class Eynollah:
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_texts, color=(1,1,1))
erosion_hurts = True
K.clear_session()
return text_regions_p_true, erosion_hurts, polygons_lines_xml
def do_order_of_regions_full_layout(self, contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot):
@ -1867,9 +1793,8 @@ class Eynollah:
img_new =np.ones((height_new,width_new,img.shape[2])).astype(float)*0
img_new[h_start:h_start+img.shape[0] ,w_start: w_start+img.shape[1], : ] =img[:,:,:]
prediction_ext = self.do_prediction(patches, img_new, model_region)
pre_updown = self.do_prediction(patches, cv2.flip(img_new[:,:,:], -1), model_region)
pre_updown = cv2.flip(pre_updown, -1)
@ -1890,9 +1815,8 @@ class Eynollah:
img_new =np.ones((height_new,width_new,img.shape[2])).astype(float)*0
img_new[h_start:h_start+img.shape[0] ,w_start: w_start+img.shape[1], : ] =img[:,:,:]
prediction_ext = self.do_prediction(patches, img_new, model_region)
pre_updown = self.do_prediction(patches, cv2.flip(img_new[:,:,:], -1), model_region)
pre_updown = cv2.flip(pre_updown, -1)
@ -1905,12 +1829,10 @@ class Eynollah:
else:
prediction_table = np.zeros(img.shape)
img_w_half = int(img.shape[1]/2.)
pre1 = self.do_prediction(patches, img[:,0:img_w_half,:], model_region)
pre2 = self.do_prediction(patches, img[:,img_w_half:,:], model_region)
pre_full = self.do_prediction(patches, img[:,:,:], model_region)
pre_updown = self.do_prediction(patches, cv2.flip(img[:,:,:], -1), model_region)
pre_updown = cv2.flip(pre_updown, -1)
@ -1933,11 +1855,6 @@ class Eynollah:
prediction_table_erode = cv2.erode(prediction_table[:,:,0], KERNEL, iterations=20)
prediction_table_erode = cv2.dilate(prediction_table_erode, KERNEL, iterations=20)
del model_region
del session_region
gc.collect()
return prediction_table_erode.astype(np.int16)
def run_graphics_and_columns(self, text_regions_p_1, num_col_classifier, num_column_is_classified, erosion_hurts):
@ -1989,7 +1906,7 @@ class Eynollah:
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()
self.logger.info("Image was %senhanced.", '' if is_image_enhanced else 'not ')
K.clear_session()
scale = 1
if is_image_enhanced:
if self.allow_enhancement:
@ -2013,7 +1930,7 @@ class Eynollah:
scaler_h_textline = 1 # 1.2#1.2
scaler_w_textline = 1 # 0.9#1
textline_mask_tot_ea, _ = self.textline_contours(image_page, True, scaler_h_textline, scaler_w_textline)
K.clear_session()
if self.plotter:
self.plotter.save_plot_of_textlines(textline_mask_tot_ea, image_page)
return textline_mask_tot_ea
@ -2026,7 +1943,7 @@ class Eynollah:
if self.plotter:
self.plotter.save_deskewed_image(slope_deskew)
self.logger.info("slope_deskew: %s", slope_deskew)
self.logger.info("slope_deskew: %.2f°", slope_deskew)
return slope_deskew, slope_first
def run_marginals(self, image_page, textline_mask_tot_ea, mask_images, mask_lines, num_col_classifier, slope_deskew, text_regions_p_1, table_prediction):
@ -2075,7 +1992,6 @@ class Eynollah:
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines)
K.clear_session()
self.logger.info("num_col_classifier: %s", num_col_classifier)
@ -2141,7 +2057,6 @@ class Eynollah:
contours_tables = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
K.clear_session()
self.logger.debug('exit run_boxes_no_full_layout')
return 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
@ -2172,8 +2087,6 @@ class Eynollah:
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
num_col_d, peaks_neg_fin_d, matrix_of_lines_ch_d, splitter_y_new_d, seperators_closeup_n_d = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2),num_col_classifier, self.tables, pixel_lines)
K.clear_session()
gc.collect()
if num_col_classifier>=3:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
@ -2240,21 +2153,18 @@ class Eynollah:
text_regions_p[:, :][text_regions_p[:, :] == 3] = 6
text_regions_p[:, :][text_regions_p[:, :] == 4] = 8
K.clear_session()
image_page = image_page.astype(np.uint8)
regions_fully, regions_fully_only_drop = self.extract_text_regions(image_page, True, cols=num_col_classifier)
text_regions_p[:,:][regions_fully[:,:,0]==6]=6
regions_fully_only_drop = put_drop_out_from_only_drop_model(regions_fully_only_drop, text_regions_p)
regions_fully[:, :, 0][regions_fully_only_drop[:, :, 0] == 4] = 4
K.clear_session()
# plt.imshow(regions_fully[:,:,0])
# plt.show()
regions_fully = putt_bb_of_drop_capitals_of_model_in_patches_in_layout(regions_fully)
# plt.imshow(regions_fully[:,:,0])
# plt.show()
K.clear_session()
regions_fully_np, _ = self.extract_text_regions(image_page, False, cols=num_col_classifier)
# plt.imshow(regions_fully_np[:,:,0])
# plt.show()
@ -2265,7 +2175,6 @@ class Eynollah:
# plt.imshow(regions_fully_np[:,:,0])
# plt.show()
K.clear_session()
# plt.imshow(regions_fully[:,:,0])
# plt.show()
regions_fully = boosting_headers_by_longshot_region_segmentation(regions_fully, regions_fully_np, img_only_regions)
@ -2291,7 +2200,6 @@ class Eynollah:
if not self.tables:
regions_without_separators = (text_regions_p[:, :] == 1) * 1
K.clear_session()
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')
@ -2353,15 +2261,15 @@ class Eynollah:
contours_only_text_parent = return_parent_contours(contours_only_text, hir_on_text)
if len(contours_only_text_parent) > 0:
areas_cnt_text = np.array([cv2.contourArea(contours_only_text_parent[j]) for j in range(len(contours_only_text_parent))])
areas_cnt_text = np.array([cv2.contourArea(c) for c in contours_only_text_parent])
areas_cnt_text = areas_cnt_text / float(text_only.shape[0] * text_only.shape[1])
#self.logger.info('areas_cnt_text %s', areas_cnt_text)
contours_biggest = contours_only_text_parent[np.argmax(areas_cnt_text)]
contours_only_text_parent = [contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz] > min_con_area]
areas_cnt_text_parent = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > min_con_area]
contours_only_text_parent = [c for jz, c in enumerate(contours_only_text_parent) if areas_cnt_text[jz] > min_con_area]
areas_cnt_text_parent = [area for area in areas_cnt_text if area > min_con_area]
index_con_parents = np.argsort(areas_cnt_text_parent)
contours_only_text_parent = list(np.array(contours_only_text_parent)[index_con_parents])
contours_only_text_parent = list(np.array(contours_only_text_parent, dtype=object)[index_con_parents])
areas_cnt_text_parent = list(np.array(areas_cnt_text_parent)[index_con_parents])
cx_bigest_big, cy_biggest_big, _, _, _, _, _ = find_new_features_of_contours([contours_biggest])
@ -2370,14 +2278,14 @@ class Eynollah:
contours_only_text_d, hir_on_text_d = return_contours_of_image(text_only_d)
contours_only_text_parent_d = return_parent_contours(contours_only_text_d, hir_on_text_d)
areas_cnt_text_d = np.array([cv2.contourArea(contours_only_text_parent_d[j]) for j in range(len(contours_only_text_parent_d))])
areas_cnt_text_d = np.array([cv2.contourArea(c) for c in contours_only_text_parent_d])
areas_cnt_text_d = areas_cnt_text_d / float(text_only_d.shape[0] * text_only_d.shape[1])
if len(areas_cnt_text_d)>0:
contours_biggest_d = contours_only_text_parent_d[np.argmax(areas_cnt_text_d)]
index_con_parents_d=np.argsort(areas_cnt_text_d)
contours_only_text_parent_d=list(np.array(contours_only_text_parent_d)[index_con_parents_d] )
areas_cnt_text_d=list(np.array(areas_cnt_text_d)[index_con_parents_d] )
index_con_parents_d = np.argsort(areas_cnt_text_d)
contours_only_text_parent_d = list(np.array(contours_only_text_parent_d, dtype=object)[index_con_parents_d])
areas_cnt_text_d = list(np.array(areas_cnt_text_d)[index_con_parents_d])
cx_bigest_d_big, cy_biggest_d_big, _, _, _, _, _ = find_new_features_of_contours([contours_biggest_d])
cx_bigest_d, cy_biggest_d, _, _, _, _, _ = find_new_features_of_contours(contours_only_text_parent_d)
@ -2432,15 +2340,15 @@ class Eynollah:
contours_only_text_parent = return_parent_contours(contours_only_text, hir_on_text)
if len(contours_only_text_parent) > 0:
areas_cnt_text = np.array([cv2.contourArea(contours_only_text_parent[j]) for j in range(len(contours_only_text_parent))])
areas_cnt_text = np.array([cv2.contourArea(c) for c in contours_only_text_parent])
areas_cnt_text = areas_cnt_text / float(text_only.shape[0] * text_only.shape[1])
contours_biggest = contours_only_text_parent[np.argmax(areas_cnt_text)]
contours_only_text_parent = [contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz] > min_con_area]
areas_cnt_text_parent = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > min_con_area]
contours_only_text_parent = [c for jz, c in enumerate(contours_only_text_parent) if areas_cnt_text[jz] > min_con_area]
areas_cnt_text_parent = [area for area in areas_cnt_text if area > min_con_area]
index_con_parents = np.argsort(areas_cnt_text_parent)
contours_only_text_parent = list(np.array(contours_only_text_parent)[index_con_parents])
contours_only_text_parent = list(np.array(contours_only_text_parent, dtype=object)[index_con_parents])
areas_cnt_text_parent = list(np.array(areas_cnt_text_parent)[index_con_parents])
cx_bigest_big, cy_biggest_big, _, _, _, _, _ = find_new_features_of_contours([contours_biggest])
@ -2450,6 +2358,14 @@ class Eynollah:
# self.logger.debug('len(contours_only_text_parent) %s', len(contours_only_text_parent_d))
else:
pass
self.logger.info("Found %d text regions", len(contours_only_text_parent))
self.logger.info("Found %d margin regions", len(polygons_of_marginals))
self.logger.info("Found %d image regions", len(polygons_of_images))
self.logger.info("Found %d separator lines", len(polygons_lines_xml))
if self.tables:
self.logger.info("Found %d tables", len(contours_tables))
txt_con_org = get_textregion_contours_in_org_image(contours_only_text_parent, self.image, slope_first)
boxes_text, _ = get_text_region_boxes_by_given_contours(contours_only_text_parent)
boxes_marginals, _ = get_text_region_boxes_by_given_contours(polygons_of_marginals)
@ -2464,10 +2380,10 @@ class Eynollah:
all_found_texline_polygons = small_textlines_to_parent_adherence2(all_found_texline_polygons, textline_mask_tot_ea, num_col_classifier)
all_found_texline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, _, slopes_marginals = self.get_slopes_and_deskew_new_curved(polygons_of_marginals, polygons_of_marginals, cv2.erode(textline_mask_tot_ea, kernel=KERNEL, iterations=1), image_page_rotated, boxes_marginals, text_only, num_col_classifier, scale_param, slope_deskew)
all_found_texline_polygons_marginals = small_textlines_to_parent_adherence2(all_found_texline_polygons_marginals, textline_mask_tot_ea, num_col_classifier)
K.clear_session()
if self.full_layout:
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con])
contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered, dtype=object)[index_by_text_par_con])
text_regions_p, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, slopes, slopes_h, contours_only_text_parent_d_ordered, contours_only_text_parent_h_d_ordered = check_any_text_region_in_model_one_is_main_or_header(text_regions_p, regions_fully, contours_only_text_parent, all_box_coord, all_found_texline_polygons, slopes, contours_only_text_parent_d_ordered)
else:
contours_only_text_parent_d_ordered = None
@ -2477,8 +2393,6 @@ class Eynollah:
self.plotter.save_plot_of_layout(text_regions_p, image_page)
self.plotter.save_plot_of_layout_all(text_regions_p, image_page)
K.clear_session()
pixel_img = 4
polygons_of_drop_capitals = return_contours_of_interested_region_by_min_size(text_regions_p, pixel_img)
all_found_texline_polygons = adhere_drop_capital_region_into_corresponding_textline(text_regions_p, polygons_of_drop_capitals, contours_only_text_parent, contours_only_text_parent_h, all_box_coord, all_box_coord_h, all_found_texline_polygons, all_found_texline_polygons_h, kernel=KERNEL, curved_line=self.curved_line)
@ -2560,7 +2474,7 @@ class Eynollah:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot)
else:
contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered)[index_by_text_par_con])
contours_only_text_parent_d_ordered = list(np.array(contours_only_text_parent_d_ordered, dtype=object)[index_by_text_par_con])
order_text_new, id_of_texts_tot = self.do_order_of_regions(contours_only_text_parent_d_ordered, contours_only_text_parent_h, boxes_d, textline_mask_tot_d)
pcgts = self.writer.build_pagexml_no_full_layout(txt_con_org, page_coord, order_text_new, id_of_texts_tot, all_found_texline_polygons, all_box_coord, polygons_of_images, polygons_of_marginals, all_found_texline_polygons_marginals, all_box_coord_marginals, slopes, slopes_marginals, cont_page, polygons_lines_xml, contours_tables)
self.logger.info("Job done in %.1fs", time.time() - t0)

19
qurator/eynollah/ocrd-tool.json
Unescape Escape View File

@ -1,5 +1,5 @@
{
"version": "0.1.0",
"version": "0.2.0",
"git_url": "https://github.com/qurator-spk/eynollah",
"tools": {
"ocrd-eynollah-segment": {
@ -29,6 +29,11 @@
"default": true,
"description": "Try to detect all element subtypes, including drop-caps and headings"
},
"tables": {
"type": "boolean",
"default": false,
"description": "Try to detect table regions"
},
"curved_line": {
"type": "boolean",
"default": false,
@ -44,7 +49,17 @@
"default": false,
"description": "ignore the special role of headings during reading order detection"
}
}
},
"resources": [
{
"description": "models for eynollah (TensorFlow format)",
"url": "https://qurator-data.de/eynollah/2021-04-25/SavedModel.tar.gz",
"name": "default",
"size": 1483106598,
"type": "archive",
"path_in_archive": "default"
}
]
}
}
}

1
qurator/eynollah/processor.py
Unescape Escape View File

@ -50,6 +50,7 @@ class EynollahProcessor(Processor):
'full_layout': self.parameter['full_layout'],
'allow_scaling': self.parameter['allow_scaling'],
'headers_off': self.parameter['headers_off'],
'tables': self.parameter['tables'],
'override_dpi': self.parameter['dpi'],
'logger': LOG,
'pcgts': pcgts,

2
qurator/eynollah/utils/contour.py
Unescape Escape View File

@ -19,7 +19,7 @@ def contours_in_same_horizon(cy_main_hor):
list_h.append(i)
if len(list_h) > 1:
all_args.append(list(set(list_h)))
return np.unique(all_args)
return np.unique(np.array(all_args, dtype=object))
def find_contours_mean_y_diff(contours_main):
M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]

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