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eynollah
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fixed #38

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pull/42/head
vahid 4 years ago
parent c4b2c71e68
commit 799a7c7632
1 changed files with 72 additions and 60 deletions
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132
qurator/eynollah/eynollah.py
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@ -158,6 +158,9 @@ class Eynollah:
if uint8: if uint8:
key += '_uint8' key += '_uint8'
return self._imgs[key].copy() return self._imgs[key].copy()
def isNaN(self, num):
return num != num
def predict_enhancement(self, img): def predict_enhancement(self, img):
@ -920,16 +923,16 @@ class Eynollah:
textline_con, hierarchy = return_contours_of_image(img_int_p) textline_con, hierarchy = return_contours_of_image(img_int_p)
textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, hierarchy, max_area=1, min_area=0.0008) textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, hierarchy, max_area=1, min_area=0.0008)
y_diff_mean = find_contours_mean_y_diff(textline_con_fil) y_diff_mean = find_contours_mean_y_diff(textline_con_fil)
sigma_des = max(1, int(y_diff_mean * (4.0 / 40.0))) if self.isNaN(y_diff_mean):
slope_for_all = MAX_SLOPE
else:
sigma_des = max(1, int(y_diff_mean * (4.0 / 40.0)))
img_int_p[img_int_p > 0] = 1
slope_for_all = return_deskew_slop(img_int_p, sigma_des, plotter=self.plotter)
img_int_p[img_int_p > 0] = 1 if abs(slope_for_all) < 0.5:
slope_for_all = return_deskew_slop(img_int_p, sigma_des, plotter=self.plotter) slope_for_all = [slope_deskew][0]
if abs(slope_for_all) < 0.5:
slope_for_all = [slope_deskew][0]
# old method
# slope_for_all=self.textline_contours_to_get_slope_correctly(self.all_text_region_raw[mv],denoised,contours[mv])
# text_patch_processed=textline_contours_postprocessing(gada)
except Exception as why: except Exception as why:
self.logger.error(why) self.logger.error(why)
slope_for_all = MAX_SLOPE slope_for_all = MAX_SLOPE
@ -1031,13 +1034,16 @@ class Eynollah:
textline_con, hierarchy = return_contours_of_image(img_int_p) textline_con, hierarchy = return_contours_of_image(img_int_p)
textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, hierarchy, max_area=1, min_area=0.00008) textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, hierarchy, max_area=1, min_area=0.00008)
y_diff_mean = find_contours_mean_y_diff(textline_con_fil) y_diff_mean = find_contours_mean_y_diff(textline_con_fil)
sigma_des = int(y_diff_mean * (4.0 / 40.0)) if self.isNaN(y_diff_mean):
if sigma_des < 1: slope_for_all = MAX_SLOPE
sigma_des = 1 else:
img_int_p[img_int_p > 0] = 1 sigma_des = int(y_diff_mean * (4.0 / 40.0))
slope_for_all = return_deskew_slop(img_int_p, sigma_des, plotter=self.plotter) if sigma_des < 1:
if abs(slope_for_all) <= 0.5: sigma_des = 1
slope_for_all = [slope_deskew][0] img_int_p[img_int_p > 0] = 1
slope_for_all = return_deskew_slop(img_int_p, sigma_des, plotter=self.plotter)
if abs(slope_for_all) <= 0.5:
slope_for_all = [slope_deskew][0]
except Exception as why: except Exception as why:
self.logger.error(why) self.logger.error(why)
slope_for_all = MAX_SLOPE slope_for_all = MAX_SLOPE
@ -1890,53 +1896,60 @@ class Eynollah:
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(contours_only_text_parent_d[j]) for j in range(len(contours_only_text_parent_d))])
areas_cnt_text_d = areas_cnt_text_d / float(text_only_d.shape[0] * text_only_d.shape[1]) 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] )
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)
try:
if len(cx_bigest_d) >= 5:
cx_bigest_d_last5 = cx_bigest_d[-5:]
cy_biggest_d_last5 = cy_biggest_d[-5:]
dists_d = [math.sqrt((cx_bigest_big[0] - cx_bigest_d_last5[j]) ** 2 + (cy_biggest_big[0] - cy_biggest_d_last5[j]) ** 2) for j in range(len(cy_biggest_d_last5))]
ind_largest = len(cx_bigest_d) -5 + np.argmin(dists_d)
else:
cx_bigest_d_last5 = cx_bigest_d[-len(cx_bigest_d):]
cy_biggest_d_last5 = cy_biggest_d[-len(cx_bigest_d):]
dists_d = [math.sqrt((cx_bigest_big[0]-cx_bigest_d_last5[j])**2 + (cy_biggest_big[0]-cy_biggest_d_last5[j])**2) for j in range(len(cy_biggest_d_last5))]
ind_largest = len(cx_bigest_d) - len(cx_bigest_d) + np.argmin(dists_d)
cx_bigest_d_big[0] = cx_bigest_d[ind_largest]
cy_biggest_d_big[0] = cy_biggest_d[ind_largest]
except Exception as why:
self.logger.error(why)
contours_biggest_d = contours_only_text_parent_d[np.argmax(areas_cnt_text_d)] (h, w) = text_only.shape[:2]
index_con_parents_d=np.argsort(areas_cnt_text_d) center = (w // 2.0, h // 2.0)
contours_only_text_parent_d=list(np.array(contours_only_text_parent_d)[index_con_parents_d] ) M = cv2.getRotationMatrix2D(center, slope_deskew, 1.0)
areas_cnt_text_d=list(np.array(areas_cnt_text_d)[index_con_parents_d] ) M_22 = np.array(M)[:2, :2]
p_big = np.dot(M_22, [cx_bigest_big, cy_biggest_big])
cx_bigest_d_big, cy_biggest_d_big, _, _, _, _, _ = find_new_features_of_contours([contours_biggest_d]) x_diff = p_big[0] - cx_bigest_d_big
cx_bigest_d, cy_biggest_d, _, _, _, _, _ = find_new_features_of_contours(contours_only_text_parent_d) y_diff = p_big[1] - cy_biggest_d_big
try:
if len(cx_bigest_d) >= 5: contours_only_text_parent_d_ordered = []
cx_bigest_d_last5 = cx_bigest_d[-5:] for i in range(len(contours_only_text_parent)):
cy_biggest_d_last5 = cy_biggest_d[-5:] p = np.dot(M_22, [cx_bigest[i], cy_biggest[i]])
dists_d = [math.sqrt((cx_bigest_big[0] - cx_bigest_d_last5[j]) ** 2 + (cy_biggest_big[0] - cy_biggest_d_last5[j]) ** 2) for j in range(len(cy_biggest_d_last5))] p[0] = p[0] - x_diff[0]
ind_largest = len(cx_bigest_d) -5 + np.argmin(dists_d) p[1] = p[1] - y_diff[0]
else: dists = [math.sqrt((p[0] - cx_bigest_d[j]) ** 2 + (p[1] - cy_biggest_d[j]) ** 2) for j in range(len(cx_bigest_d))]
cx_bigest_d_last5 = cx_bigest_d[-len(cx_bigest_d):] contours_only_text_parent_d_ordered.append(contours_only_text_parent_d[np.argmin(dists)])
cy_biggest_d_last5 = cy_biggest_d[-len(cx_bigest_d):] # img2=np.zeros((text_only.shape[0],text_only.shape[1],3))
dists_d = [math.sqrt((cx_bigest_big[0]-cx_bigest_d_last5[j])**2 + (cy_biggest_big[0]-cy_biggest_d_last5[j])**2) for j in range(len(cy_biggest_d_last5))] # img2=cv2.fillPoly(img2,pts=[contours_only_text_parent_d[np.argmin(dists)]] ,color=(1,1,1))
ind_largest = len(cx_bigest_d) - len(cx_bigest_d) + np.argmin(dists_d) # plt.imshow(img2[:,:,0])
# plt.show()
cx_bigest_d_big[0] = cx_bigest_d[ind_largest] else:
cy_biggest_d_big[0] = cy_biggest_d[ind_largest] contours_only_text_parent_d_ordered = []
except Exception as why: contours_only_text_parent_d = []
self.logger.error(why) contours_only_text_parent = []
(h, w) = text_only.shape[:2]
center = (w // 2.0, h // 2.0)
M = cv2.getRotationMatrix2D(center, slope_deskew, 1.0)
M_22 = np.array(M)[:2, :2]
p_big = np.dot(M_22, [cx_bigest_big, cy_biggest_big])
x_diff = p_big[0] - cx_bigest_d_big
y_diff = p_big[1] - cy_biggest_d_big
contours_only_text_parent_d_ordered = []
for i in range(len(contours_only_text_parent)):
p = np.dot(M_22, [cx_bigest[i], cy_biggest[i]])
p[0] = p[0] - x_diff[0]
p[1] = p[1] - y_diff[0]
dists = [math.sqrt((p[0] - cx_bigest_d[j]) ** 2 + (p[1] - cy_biggest_d[j]) ** 2) for j in range(len(cx_bigest_d))]
contours_only_text_parent_d_ordered.append(contours_only_text_parent_d[np.argmin(dists)])
# img2=np.zeros((text_only.shape[0],text_only.shape[1],3))
# img2=cv2.fillPoly(img2,pts=[contours_only_text_parent_d[np.argmin(dists)]] ,color=(1,1,1))
# plt.imshow(img2[:,:,0])
# plt.show()
else: else:
contours_only_text_parent_d_ordered = [] contours_only_text_parent_d_ordered = []
contours_only_text_parent_d = [] contours_only_text_parent_d = []
contours_only_text_parent = []
else: else:
contours_only_text, hir_on_text = return_contours_of_image(text_only) contours_only_text, hir_on_text = return_contours_of_image(text_only)
@ -1964,11 +1977,10 @@ class Eynollah:
txt_con_org = get_textregion_contours_in_org_image(contours_only_text_parent, self.image, slope_first) 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_text, _ = get_text_region_boxes_by_given_contours(contours_only_text_parent)
boxes_marginals, _ = get_text_region_boxes_by_given_contours(polygons_of_marginals) boxes_marginals, _ = get_text_region_boxes_by_given_contours(polygons_of_marginals)
if not self.curved_line: if not self.curved_line:
slopes, all_found_texline_polygons, boxes_text, txt_con_org, contours_only_text_parent, all_box_coord, index_by_text_par_con = self.get_slopes_and_deskew_new(txt_con_org, contours_only_text_parent, textline_mask_tot_ea, image_page_rotated, boxes_text, slope_deskew) slopes, all_found_texline_polygons, boxes_text, txt_con_org, contours_only_text_parent, all_box_coord, index_by_text_par_con = self.get_slopes_and_deskew_new(txt_con_org, contours_only_text_parent, textline_mask_tot_ea, image_page_rotated, boxes_text, slope_deskew)
slopes_marginals, all_found_texline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, _ = self.get_slopes_and_deskew_new(polygons_of_marginals, polygons_of_marginals, textline_mask_tot_ea, image_page_rotated, boxes_marginals, slope_deskew) slopes_marginals, all_found_texline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, _ = self.get_slopes_and_deskew_new(polygons_of_marginals, polygons_of_marginals, textline_mask_tot_ea, image_page_rotated, boxes_marginals, slope_deskew)
else: else:
scale_param = 1 scale_param = 1

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