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unify find_features_of_contour{,e}s

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Konstantin Baierer 2020-11-24 15:15:29 +01:00
parent 86c809b58f
commit 8796b9daf7
2 changed files with 23 additions and 36 deletions
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45
sbb_newspapers_org_image/eynollah.py
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@ -71,6 +71,7 @@ from .utils import (
find_num_col_by_vertical_lines, find_num_col_by_vertical_lines,
find_contours_mean_y_diff, find_contours_mean_y_diff,
contours_in_same_horizon, contours_in_same_horizon,
find_features_of_contours,
) )
@ -5582,34 +5583,6 @@ class eynollah:
image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j])][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j])] == 7] = 0 image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j])][image_by_region[int(spliter_y[i]) : int(spliter_y[i + 1]), peaks_neg[i][j] - int(1.0 / 20.0 * peaks_neg[i][j]) : peaks_neg[i][j] + int(1.0 / 20.0 * peaks_neg[i][j])] == 7] = 0
return image_by_region return image_by_region
def find_features_of_contoures(self, contours_main):
areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
return y_min_main, y_max_main
def find_features_of_contours(self, contours_main):
areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
return y_min_main, y_max_main, areas_main
def add_tables_heuristic_to_layout(self, image_regions_eraly_p, boxes, slope_mean_hor, spliter_y, peaks_neg_tot, image_revised): def add_tables_heuristic_to_layout(self, image_regions_eraly_p, boxes, slope_mean_hor, spliter_y, peaks_neg_tot, image_revised):
@ -5668,9 +5641,9 @@ class eynollah:
contours_line, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours_line, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
y_min_main_line, y_max_main_line = self.find_features_of_contoures(contours_line) y_min_main_line, y_max_main_line, _ = find_features_of_contours(contours_line)
# _,_,y_min_main_line ,y_max_main_line,x_min_main_line,x_max_main_line=find_new_features_of_contoures(contours_line) # _,_,y_min_main_line ,y_max_main_line,x_min_main_line,x_max_main_line=find_new_features_of_contoures(contours_line)
y_min_main_tab, y_max_main_tab = self.find_features_of_contoures(contours_tab) y_min_main_tab, y_max_main_tab, _ = find_features_of_contours(contours_tab)
cx_tab_m_text, cy_tab_m_text, x_min_tab_m_text, x_max_tab_m_text, y_min_tab_m_text, y_max_tab_m_text = find_new_features_of_contoures(contours_table_m_text) cx_tab_m_text, cy_tab_m_text, x_min_tab_m_text, x_max_tab_m_text, y_min_tab_m_text, y_max_tab_m_text = find_new_features_of_contoures(contours_table_m_text)
cx_tabl, cy_tabl, x_min_tabl, x_max_tabl, y_min_tabl, y_max_tabl, _ = find_new_features_of_contoures(contours_tab) cx_tabl, cy_tabl, x_min_tabl, x_max_tabl, y_min_tabl, y_max_tabl, _ = find_new_features_of_contoures(contours_tab)
@ -5752,8 +5725,8 @@ class eynollah:
contours_main_patch_con, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours_main_patch_con, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_main_patch_con = return_parent_contours(contours_main_patch_con, hiearchy) contours_main_patch_con = return_parent_contours(contours_main_patch_con, hiearchy)
y_patch_head_min, y_patch_head_max, _ = self.find_features_of_contours(contours_head_patch_con) y_patch_head_min, y_patch_head_max, _ = find_features_of_contours(contours_head_patch_con)
y_patch_main_min, y_patch_main_max, _ = self.find_features_of_contours(contours_main_patch_con) y_patch_main_min, y_patch_main_max, _ = find_features_of_contours(contours_main_patch_con)
for i in range(len(y_patch_head_min)): for i in range(len(y_patch_head_min)):
for j in range(len(y_patch_main_min)): for j in range(len(y_patch_main_min)):
@ -5784,8 +5757,8 @@ class eynollah:
contours_main_patch_con, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours_main_patch_con, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_main_patch_con = return_parent_contours(contours_main_patch_con, hiearchy) contours_main_patch_con = return_parent_contours(contours_main_patch_con, hiearchy)
_, _, areas_head = self.find_features_of_contours(contours_head_patch_con) _, _, areas_head = find_features_of_contours(contours_head_patch_con)
_, _, areas_main = self.find_features_of_contours(contours_main_patch_con) _, _, areas_main = find_features_of_contours(contours_main_patch_con)
if np.sum(areas_head) > np.sum(areas_main): if np.sum(areas_head) > np.sum(areas_main):
img_revised_tab[y_up:y_down, int(boxes[ind][0]) : int(boxes[ind][1]), 0][img_revised_tab[y_up:y_down, int(boxes[ind][0]) : int(boxes[ind][1]), 0] == 1] = 2 img_revised_tab[y_up:y_down, int(boxes[ind][0]) : int(boxes[ind][1]), 0][img_revised_tab[y_up:y_down, int(boxes[ind][0]) : int(boxes[ind][1]), 0] == 1] = 2
@ -5819,8 +5792,8 @@ class eynollah:
contours_main_patch_con, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours_main_patch_con, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_main_patch_con = return_parent_contours(contours_main_patch_con, hiearchy) contours_main_patch_con = return_parent_contours(contours_main_patch_con, hiearchy)
_, _, areas_head = self.find_features_of_contours(contours_head_patch_con) _, _, areas_head = find_features_of_contours(contours_head_patch_con)
_, _, areas_main = self.find_features_of_contours(contours_main_patch_con) _, _, areas_main = find_features_of_contours(contours_main_patch_con)
if np.sum(areas_head) > np.sum(areas_main): if np.sum(areas_head) > np.sum(areas_main):
img_revised_tab[y_up:y_down, int(boxes[ind][0]) : int(boxes[ind][1]), 0][img_revised_tab[y_up:y_down, int(boxes[ind][0]) : int(boxes[ind][1]), 0] == 1] = 2 img_revised_tab[y_up:y_down, int(boxes[ind][0]) : int(boxes[ind][1]), 0][img_revised_tab[y_up:y_down, int(boxes[ind][0]) : int(boxes[ind][1]), 0] == 1] = 2

14
sbb_newspapers_org_image/utils.py
View file

@ -2117,3 +2117,17 @@ def find_contours_mean_y_diff(contours_main):
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))] cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
return np.mean(np.diff(np.sort(np.array(cy_main)))) return np.mean(np.diff(np.sort(np.array(cy_main))))
def find_features_of_contours(contours_main):
areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
y_min_main = np.array([np.min(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
y_max_main = np.array([np.max(contours_main[j][:, 0, 1]) for j in range(len(contours_main))])
return y_min_main, y_max_main, areas_main