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avoid unnecessary 3-channel conversions: for tables, too

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Robert Sachunsky 2025-10-07 22:37:05 +02:00
parent dfdc705375
commit 0a80cd5dff
1 changed files with 65 additions and 90 deletions
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src/eynollah/eynollah.py
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@ -930,10 +930,8 @@ class Eynollah:
img_w = img.shape[1] img_w = img.shape[1]
prediction_true = np.zeros((img_h, img_w, 3)) prediction_true = np.zeros((img_h, img_w, 3))
mask_true = np.zeros((img_h, img_w)) mask_true = np.zeros((img_h, img_w))
nxf = img_w / float(width_mid) nxf = math.ceil(img_w / float(width_mid))
nyf = img_h / float(height_mid) nyf = math.ceil(img_h / float(height_mid))
nxf = int(nxf) + 1 if nxf > int(nxf) else int(nxf)
nyf = int(nyf) + 1 if nyf > int(nyf) else int(nyf)
list_i_s = [] list_i_s = []
list_j_s = [] list_j_s = []
@ -946,16 +944,8 @@ class Eynollah:
img_patch = np.zeros((n_batch_inference, img_height_model, img_width_model, 3)) img_patch = np.zeros((n_batch_inference, img_height_model, img_width_model, 3))
for i in range(nxf): for i in range(nxf):
for j in range(nyf): for j in range(nyf):
if i == 0:
index_x_d = i * width_mid index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model index_x_u = index_x_d + img_width_model
else:
index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model
if j == 0:
index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model
else:
index_y_d = j * height_mid index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model index_y_u = index_y_d + img_height_model
if index_x_u > img_w: if index_x_u > img_w:
@ -2600,23 +2590,20 @@ class Eynollah:
self, layout, table_prediction_early, pixel_table, num_col_classifier): self, layout, table_prediction_early, pixel_table, num_col_classifier):
layout_org = np.copy(layout) layout_org = np.copy(layout)
layout_org[:,:,0][layout_org[:,:,0]==pixel_table] = 0 layout_org[layout_org == pixel_table] = 0
layout = (layout[:,:,0]==pixel_table)*1 layout = (layout == pixel_table).astype(np.uint8) * 1
layout = layout.astype(np.uint8)
_, thresh = cv2.threshold(layout, 0, 255, 0) _, thresh = cv2.threshold(layout, 0, 255, 0)
contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnt_size = np.array([cv2.contourArea(contours[j]) cnt_size = np.array([cv2.contourArea(cnt) for cnt in contours])
for j in range(len(contours))])
contours_new = [] contours_new = []
for i in range(len(contours)): for i, contour in enumerate(contours):
x, y, w, h = cv2.boundingRect(contours[i]) x, y, w, h = cv2.boundingRect(contour)
iou = cnt_size[i] /float(w*h) *100 iou = cnt_size[i] /float(w*h) *100
if iou<80: if iou<80:
layout_contour = np.zeros(layout_org.shape[:2]) layout_contour = np.zeros(layout_org.shape[:2])
layout_contour = cv2.fillPoly(layout_contour, pts=[contours[i]] ,color=1) layout_contour = cv2.fillPoly(layout_contour, pts=[contour] ,color=1)
layout_contour_sum = layout_contour.sum(axis=0) layout_contour_sum = layout_contour.sum(axis=0)
layout_contour_sum_diff = np.diff(layout_contour_sum) layout_contour_sum_diff = np.diff(layout_contour_sum)
@ -2648,26 +2635,26 @@ class Eynollah:
#print(iou_in,'iou_in_in1') #print(iou_in,'iou_in_in1')
if iou_in>30: if iou_in>30:
layout_org= cv2.fillPoly(layout_org, pts=[contours_sep[ji]], color=3 * (pixel_table,)) layout_org = cv2.fillPoly(layout_org, pts=[contours_sep[ji]], color=pixel_table)
else: else:
pass pass
else: else:
layout_org= cv2.fillPoly(layout_org, pts=[contours_sep[ji]], color=3 * (pixel_table,)) layout_org= cv2.fillPoly(layout_org, pts=[contours_sep[ji]], color=pixel_table)
else: else:
contours_new.append(contours[i]) contours_new.append(contour)
if num_col_classifier>=2: if num_col_classifier>=2:
only_recent_contour_image = np.zeros((layout.shape[0],layout.shape[1])) only_recent_contour_image = np.zeros(layout.shape[:2])
only_recent_contour_image= cv2.fillPoly(only_recent_contour_image,pts=[contours[i]] ,color=(1,1,1)) only_recent_contour_image = cv2.fillPoly(only_recent_contour_image, pts=[contour],color=1)
table_pixels_masked_from_early_pre = only_recent_contour_image * table_prediction_early table_pixels_masked_from_early_pre = only_recent_contour_image * table_prediction_early
iou_in = 100. * table_pixels_masked_from_early_pre.sum() / only_recent_contour_image.sum() iou_in = 100. * table_pixels_masked_from_early_pre.sum() / only_recent_contour_image.sum()
#print(iou_in,'iou_in') #print(iou_in,'iou_in')
if iou_in>30: if iou_in>30:
layout_org= cv2.fillPoly(layout_org, pts=[contours[i]], color=3 * (pixel_table,)) layout_org = cv2.fillPoly(layout_org, pts=[contour], color=pixel_table)
else: else:
pass pass
else: else:
layout_org= cv2.fillPoly(layout_org, pts=[contours[i]], color=3 * (pixel_table,)) layout_org = cv2.fillPoly(layout_org, pts=[contour], color=pixel_table)
return layout_org, contours_new return layout_org, contours_new
@ -2714,16 +2701,10 @@ class Eynollah:
pass pass
boxes = np.array(boxes, dtype=int) # to be on the safe side boxes = np.array(boxes, dtype=int) # to be on the safe side
img_comm_e = np.zeros(image_revised_1.shape) img_comm = np.zeros(image_revised_1.shape, dtype=np.uint8)
img_comm = np.repeat(img_comm_e[:, :, np.newaxis], 3, axis=2)
for indiv in np.unique(image_revised_1): for indiv in np.unique(image_revised_1):
image_col=(image_revised_1==indiv)*255 image_col = (image_revised_1 == indiv).astype(np.uint8) * 255
img_comm_in=np.repeat(image_col[:, :, np.newaxis], 3, axis=2) _, thresh = cv2.threshold(image_col, 0, 255, 0)
img_comm_in=img_comm_in.astype(np.uint8)
imgray = cv2.cvtColor(img_comm_in, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours,hirarchy=cv2.findContours(thresh.copy(), cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) contours,hirarchy=cv2.findContours(thresh.copy(), cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
if indiv==pixel_table: if indiv==pixel_table:
@ -2733,35 +2714,27 @@ class Eynollah:
main_contours = filter_contours_area_of_image_tables(thresh, contours, hirarchy, main_contours = filter_contours_area_of_image_tables(thresh, contours, hirarchy,
max_area=1, min_area=min_area) max_area=1, min_area=min_area)
img_comm = cv2.fillPoly(img_comm, pts = main_contours, color = (indiv, indiv, indiv)) img_comm = cv2.fillPoly(img_comm, pts=main_contours, color=indiv)
img_comm = img_comm.astype(np.uint8)
if not self.isNaN(slope_mean_hor): if not self.isNaN(slope_mean_hor):
image_revised_last = np.zeros((image_regions_eraly_p.shape[0], image_regions_eraly_p.shape[1],3)) image_revised_last = np.zeros(image_regions_eraly_p.shape[:2])
for i in range(len(boxes)): for i in range(len(boxes)):
box_ys = slice(*boxes[i][2:4]) box_ys = slice(*boxes[i][2:4])
box_xs = slice(*boxes[i][0:2]) box_xs = slice(*boxes[i][0:2])
image_box = img_comm[box_ys, box_xs] image_box = img_comm[box_ys, box_xs]
try: try:
image_box_tabels_1=(image_box[:,:,0]==pixel_table)*1 image_box_tabels_1 = (image_box == pixel_table) * 1
contours_tab,_=return_contours_of_image(image_box_tabels_1) contours_tab,_=return_contours_of_image(image_box_tabels_1)
contours_tab=filter_contours_area_of_image_tables(image_box_tabels_1,contours_tab,_,1,0.003) contours_tab=filter_contours_area_of_image_tables(image_box_tabels_1,contours_tab,_,1,0.003)
image_box_tabels_1=(image_box[:,:,0]==pixel_line)*1 image_box_tabels_1 = (image_box == pixel_line).astype(np.uint8) * 1
image_box_tabels_and_m_text = ( (image_box == pixel_table) |
(image_box == 1) ).astype(np.uint8) * 1
image_box_tabels_and_m_text=( (image_box[:,:,0]==pixel_table) | (image_box[:,:,0]==1) )*1 image_box_tabels_1 = cv2.dilate(image_box_tabels_1, KERNEL, iterations=5)
image_box_tabels_and_m_text=image_box_tabels_and_m_text.astype(np.uint8)
image_box_tabels_1=image_box_tabels_1.astype(np.uint8) contours_table_m_text, _ = return_contours_of_image(image_box_tabels_and_m_text)
image_box_tabels_1 = cv2.dilate(image_box_tabels_1,KERNEL,iterations = 5) _, thresh = cv2.threshold(image_box_tabels_1, 0, 255, 0)
contours_line, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_table_m_text,_=return_contours_of_image(image_box_tabels_and_m_text)
image_box_tabels=np.repeat(image_box_tabels_1[:, :, np.newaxis], 3, axis=2)
image_box_tabels=image_box_tabels.astype(np.uint8)
imgray = cv2.cvtColor(image_box_tabels, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_line,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
y_min_main_line ,y_max_main_line=find_features_of_contours(contours_line) y_min_main_line ,y_max_main_line=find_features_of_contours(contours_line)
y_min_main_tab ,y_max_main_tab=find_features_of_contours(contours_tab) y_min_main_tab ,y_max_main_tab=find_features_of_contours(contours_tab)
@ -2793,18 +2766,20 @@ class Eynollah:
y_max_main_tab[i_t] < y_min_main_line[i_l] and y_max_main_tab[i_t] < y_min_main_line[i_l] and
y_min_main_tab[i_t] < y_min_main_line[i_l]): y_min_main_tab[i_t] < y_min_main_line[i_l]):
pass pass
elif np.abs(y_max_main_line[i_l]-y_min_main_line[i_l])<100: elif abs(y_max_main_line[i_l] - y_min_main_line[i_l]) < 100:
pass pass
else: else:
y_up_tab.append(np.min([y_min_main_line[i_l], y_min_main_tab[i_t] ]) ) y_up_tab.append(min([y_min_main_line[i_l],
y_down_tab.append( np.max([ y_max_main_line[i_l],y_max_main_tab[i_t] ]) ) y_min_main_tab[i_t]]))
y_down_tab.append(max([y_max_main_line[i_l],
y_max_main_tab[i_t]]))
if len(y_up_tab)==0: if len(y_up_tab)==0:
y_up_tabs.append(y_min_main_tab[i_t]) y_up_tabs.append(y_min_main_tab[i_t])
y_down_tabs.append(y_max_main_tab[i_t]) y_down_tabs.append(y_max_main_tab[i_t])
else: else:
y_up_tabs.append(np.min(y_up_tab)) y_up_tabs.append(min(y_up_tab))
y_down_tabs.append(np.max(y_down_tab)) y_down_tabs.append(max(y_down_tab))
else: else:
y_down_tabs=[] y_down_tabs=[]
y_up_tabs=[] y_up_tabs=[]
@ -2814,7 +2789,7 @@ class Eynollah:
y_up_tabs=[] y_up_tabs=[]
for ii in range(len(y_up_tabs)): for ii in range(len(y_up_tabs)):
image_box[y_up_tabs[ii]:y_down_tabs[ii],:,0]=pixel_table image_box[y_up_tabs[ii]:y_down_tabs[ii]] = pixel_table
image_revised_last[box_ys, box_xs] = image_box image_revised_last[box_ys, box_xs] = image_box
else: else:
@ -2825,14 +2800,14 @@ class Eynollah:
image_revised_last[box_ys, box_xs] = image_box image_revised_last[box_ys, box_xs] = image_box
if num_col_classifier==1: if num_col_classifier==1:
img_tables_col_1 = (image_revised_last[:,:,0] == pixel_table).astype(np.uint8) img_tables_col_1 = (image_revised_last == pixel_table).astype(np.uint8)
contours_table_col1, _ = return_contours_of_image(img_tables_col_1) contours_table_col1, _ = return_contours_of_image(img_tables_col_1)
_,_ ,_ , _, y_min_tab_col1 ,y_max_tab_col1, _= find_new_features_of_contours(contours_table_col1) _,_ ,_ , _, y_min_tab_col1 ,y_max_tab_col1, _= find_new_features_of_contours(contours_table_col1)
if len(y_min_tab_col1)>0: if len(y_min_tab_col1)>0:
for ijv in range(len(y_min_tab_col1)): for ijv in range(len(y_min_tab_col1)):
image_revised_last[int(y_min_tab_col1[ijv]):int(y_max_tab_col1[ijv]),:,:]=pixel_table image_revised_last[int(y_min_tab_col1[ijv]):int(y_max_tab_col1[ijv])] = pixel_table
return image_revised_last return image_revised_last
def get_tables_from_model(self, img, num_col_classifier): def get_tables_from_model(self, img, num_col_classifier):
@ -3200,7 +3175,7 @@ class Eynollah:
pass pass
else: else:
text_regions_p_tables = np.copy(text_regions_p) text_regions_p_tables = np.copy(text_regions_p)
text_regions_p_tables[:,:][(table_prediction[:,:] == 1)] = 10 text_regions_p_tables[(table_prediction == 1)] = 10
pixel_line = 3 pixel_line = 3
img_revised_tab2 = self.add_tables_heuristic_to_layout( img_revised_tab2 = self.add_tables_heuristic_to_layout(
text_regions_p_tables, boxes, 0, splitter_y_new, peaks_neg_tot_tables, text_regions_p_tables, text_regions_p_tables, boxes, 0, splitter_y_new, peaks_neg_tot_tables, text_regions_p_tables,
@ -3221,8 +3196,8 @@ class Eynollah:
pass pass
else: else:
text_regions_p_tables = np.copy(text_regions_p_1_n) text_regions_p_tables = np.copy(text_regions_p_1_n)
text_regions_p_tables =np.round(text_regions_p_tables) text_regions_p_tables = np.round(text_regions_p_tables)
text_regions_p_tables[:,:][(text_regions_p_tables[:,:] != 3) & (table_prediction_n[:,:] == 1)] = 10 text_regions_p_tables[(text_regions_p_tables != 3) & (table_prediction_n == 1)] = 10
pixel_line = 3 pixel_line = 3
img_revised_tab2 = self.add_tables_heuristic_to_layout( img_revised_tab2 = self.add_tables_heuristic_to_layout(
@ -3242,21 +3217,21 @@ class Eynollah:
if self.tables: if self.tables:
if self.light_version: if self.light_version:
text_regions_p[:,:][table_prediction[:,:]==1] = 10 text_regions_p[table_prediction == 1] = 10
img_revised_tab=text_regions_p[:,:] img_revised_tab = text_regions_p[:,:]
else: else:
if np.abs(slope_deskew) < SLOPE_THRESHOLD: if np.abs(slope_deskew) < SLOPE_THRESHOLD:
img_revised_tab = np.copy(img_revised_tab2[:,:,0]) img_revised_tab = np.copy(img_revised_tab2)
img_revised_tab[:,:][(text_regions_p[:,:] == 1) & (img_revised_tab[:,:] != 10)] = 1 img_revised_tab[(text_regions_p == 1) & (img_revised_tab != 10)] = 1
else: else:
img_revised_tab = np.copy(text_regions_p[:,:]) img_revised_tab = np.copy(text_regions_p)
img_revised_tab[:,:][img_revised_tab[:,:] == 10] = 0 img_revised_tab[img_revised_tab == 10] = 0
img_revised_tab[:,:][img_revised_tab2_d_rotated[:,:,0] == 10] = 10 img_revised_tab[img_revised_tab2_d_rotated == 10] = 10
text_regions_p[:,:][text_regions_p[:,:]==10] = 0 text_regions_p[text_regions_p == 10] = 0
text_regions_p[:,:][img_revised_tab[:,:]==10] = 10 text_regions_p[img_revised_tab == 10] = 10
else: else:
img_revised_tab=text_regions_p[:,:] img_revised_tab = text_regions_p[:,:]
#img_revised_tab = text_regions_p[:, :] #img_revised_tab = text_regions_p[:, :]
if self.light_version: if self.light_version:
polygons_of_images = return_contours_of_interested_region(text_regions_p, 2) polygons_of_images = return_contours_of_interested_region(text_regions_p, 2)
@ -3386,7 +3361,7 @@ class Eynollah:
num_col_classifier, erosion_hurts, self.tables, self.right2left) num_col_classifier, erosion_hurts, self.tables, self.right2left)
text_regions_p_tables = np.copy(text_regions_p_1_n) text_regions_p_tables = np.copy(text_regions_p_1_n)
text_regions_p_tables = np.round(text_regions_p_tables) text_regions_p_tables = np.round(text_regions_p_tables)
text_regions_p_tables[:,:][(text_regions_p_tables[:,:]!=3) & (table_prediction_n[:,:]==1)] = 10 text_regions_p_tables[(text_regions_p_tables != 3) & (table_prediction_n == 1)] = 10
pixel_line = 3 pixel_line = 3
img_revised_tab2 = self.add_tables_heuristic_to_layout( img_revised_tab2 = self.add_tables_heuristic_to_layout(
@ -3405,17 +3380,17 @@ class Eynollah:
text_regions_p.shape[1]) text_regions_p.shape[1])
if np.abs(slope_deskew) < 0.13: if np.abs(slope_deskew) < 0.13:
img_revised_tab = np.copy(img_revised_tab2[:,:,0]) img_revised_tab = np.copy(img_revised_tab2)
else: else:
img_revised_tab = np.copy(text_regions_p[:,:]) img_revised_tab = np.copy(text_regions_p)
img_revised_tab[:,:][img_revised_tab[:,:] == 10] = 0 img_revised_tab[img_revised_tab == 10] = 0
img_revised_tab[:,:][img_revised_tab2_d_rotated[:,:,0] == 10] = 10 img_revised_tab[img_revised_tab2_d_rotated == 10] = 10
##img_revised_tab=img_revised_tab2[:,:,0] ##img_revised_tab = img_revised_tab2[:,:]
#img_revised_tab=text_regions_p[:,:] #img_revised_tab = text_regions_p[:,:]
text_regions_p[:,:][text_regions_p[:,:]==10] = 0 text_regions_p[text_regions_p == 10] = 0
text_regions_p[:,:][img_revised_tab[:,:]==10] = 10 text_regions_p[img_revised_tab == 10] = 10
#img_revised_tab[img_revised_tab2[:,:,0]==10] =10 #img_revised_tab[img_revised_tab2 == 10] = 10
pixel_img = 4 pixel_img = 4
min_area_mar = 0.00001 min_area_mar = 0.00001