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eynollah
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extract more methods, collect unused methods

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pull/8/head
Konstantin Baierer 4 years ago
parent 5915307667
commit 731b8ded6f
3 changed files with 227 additions and 201 deletions
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sbb_newspapers_org_image/eynollah.py
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@ -35,7 +35,15 @@ from matplotlib import pyplot, transforms
import matplotlib.patches as mpatches import matplotlib.patches as mpatches
import imutils import imutils
from .utils import filter_contours_area_of_image_tables from .utils import (
resize_image,
filter_contours_area_of_image_tables,
filter_contours_area_of_image_interiors,
rotatedRectWithMaxArea,
rotate_max_area_new,
rotation_image_new,
)
SLOPE_THRESHOLD = 0.13 SLOPE_THRESHOLD = 0.13
VERY_LARGE_NUMBER = 1000000000000000000000 VERY_LARGE_NUMBER = 1000000000000000000000
@ -93,79 +101,6 @@ class eynollah:
###self.model_region_dir_p = dir_models +'/model_layout_newspapers.h5'#'/model_ensemble_s.h5'#'/model_layout_newspapers.h5'#'/model_ensemble_s.h5'#'/model_main_home_5_soft_new.h5'#'/model_home_soft_5_all_data.h5' #'/model_main_office_long_soft.h5'#'/model_20_cat_main.h5' ###self.model_region_dir_p = dir_models +'/model_layout_newspapers.h5'#'/model_ensemble_s.h5'#'/model_layout_newspapers.h5'#'/model_ensemble_s.h5'#'/model_main_home_5_soft_new.h5'#'/model_home_soft_5_all_data.h5' #'/model_main_office_long_soft.h5'#'/model_20_cat_main.h5'
self.model_textline_dir = dir_models + "/model_textline_newspapers.h5" #'/model_hor_ver_home_trextline_very_good.h5'# '/model_hor_ver_1_great.h5'#'/model_curved_office_works_great.h5' self.model_textline_dir = dir_models + "/model_textline_newspapers.h5" #'/model_hor_ver_home_trextline_very_good.h5'# '/model_hor_ver_1_great.h5'#'/model_curved_office_works_great.h5'
def find_polygons_size_filter(self, contours, median_area, scaler_up=1.2, scaler_down=0.8):
found_polygons_early = list()
for c in contours:
if len(c) < 3: # A polygon cannot have less than 3 points
continue
polygon = geometry.Polygon([point[0] for point in c])
area = polygon.area
# Check that polygon has area greater than minimal area
if area >= median_area * scaler_down and area <= median_area * scaler_up:
found_polygons_early.append(np.array([point for point in polygon.exterior.coords], dtype=np.uint))
return found_polygons_early
def filter_contours_area_of_image(self, image, contours, hirarchy, max_area, min_area):
found_polygons_early = list()
jv = 0
for c in contours:
if len(c) < 3: # A polygon cannot have less than 3 points
continue
polygon = geometry.Polygon([point[0] for point in c])
area = polygon.area
if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]) and hirarchy[0][jv][3] == -1: # and hirarchy[0][jv][3]==-1 :
found_polygons_early.append(np.array([[point] for point in polygon.exterior.coords], dtype=np.uint))
jv += 1
return found_polygons_early
def filter_contours_area_of_image_interiors(self, image, contours, hirarchy, max_area, min_area):
found_polygons_early = list()
jv = 0
for c in contours:
if len(c) < 3: # A polygon cannot have less than 3 points
continue
polygon = geometry.Polygon([point[0] for point in c])
area = polygon.area
if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]) and hirarchy[0][jv][3] != -1:
# print(c[0][0][1])
found_polygons_early.append(np.array([point for point in polygon.exterior.coords], dtype=np.uint))
jv += 1
return found_polygons_early
def resize_image(self, img_in, input_height, input_width):
return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
def resize_ann(self, seg_in, input_height, input_width):
return cv2.resize(seg_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
def rotatedRectWithMaxArea(self, w, h, angle):
if w <= 0 or h <= 0:
return 0, 0
width_is_longer = w >= h
side_long, side_short = (w, h) if width_is_longer else (h, w)
# since the solutions for angle, -angle and 180-angle are all the same,
# if suffices to look at the first quadrant and the absolute values of sin,cos:
sin_a, cos_a = abs(math.sin(angle)), abs(math.cos(angle))
if side_short <= 2.0 * sin_a * cos_a * side_long or abs(sin_a - cos_a) < 1e-10:
# half constrained case: two crop corners touch the longer side,
# the other two corners are on the mid-line parallel to the longer line
x = 0.5 * side_short
wr, hr = (x / sin_a, x / cos_a) if width_is_longer else (x / cos_a, x / sin_a)
else:
# fully constrained case: crop touches all 4 sides
cos_2a = cos_a * cos_a - sin_a * sin_a
wr, hr = (w * cos_a - h * sin_a) / cos_2a, (h * cos_a - w * sin_a) / cos_2a
return wr, hr
def get_one_hot(self, seg, input_height, input_width, n_classes): def get_one_hot(self, seg, input_height, input_width, n_classes):
seg = seg[:, :, 0] seg = seg[:, :, 0]
seg_f = np.zeros((input_height, input_width, n_classes)) seg_f = np.zeros((input_height, input_width, n_classes))
@ -189,23 +124,6 @@ class eynollah:
seg_img[:, :, 2] = segl * c seg_img[:, :, 2] = segl * c
return seg_img return seg_img
def color_images_diva(self, seg, n_classes):
ann_u = range(n_classes)
if len(np.shape(seg)) == 3:
seg = seg[:, :, 0]
seg_img = np.zeros((np.shape(seg)[0], np.shape(seg)[1], 3)).astype(float)
# colors=sns.color_palette("hls", n_classes)
colors = [[1, 0, 0], [8, 0, 0], [2, 0, 0], [4, 0, 0]]
for c in ann_u:
c = int(c)
segl = seg == c
seg_img[:, :, 0][seg == c] = colors[c][0] # segl*(colors[c][0])
seg_img[:, :, 1][seg == c] = colors[c][1] # seg_img[:,:,1]=segl*(colors[c][1])
seg_img[:, :, 2][seg == c] = colors[c][2] # seg_img[:,:,2]=segl*(colors[c][2])
return seg_img
def rotate_image(self, img_patch, slope): def rotate_image(self, img_patch, slope):
(h, w) = img_patch.shape[:2] (h, w) = img_patch.shape[:2]
center = (w // 2, h // 2) center = (w // 2, h // 2)
@ -499,7 +417,7 @@ class eynollah:
img_new = np.copy(img) img_new = np.copy(img)
num_column_is_classified = False num_column_is_classified = False
else: else:
img_new = self.resize_image(img, img_h_new, img_w_new) img_new = resize_image(img, img_h_new, img_w_new)
num_column_is_classified = True num_column_is_classified = True
if img_new.shape[1] > img.shape[1]: if img_new.shape[1] > img.shape[1]:
@ -629,10 +547,10 @@ class eynollah:
img_new = np.copy(img) img_new = np.copy(img)
num_column_is_classified = False num_column_is_classified = False
else: else:
img_new = self.resize_image(img, img_h_new, img_w_new) img_new = resize_image(img, img_h_new, img_w_new)
num_column_is_classified = True num_column_is_classified = True
# img_new=self.resize_image(img,img_h_new,img_w_new) # img_new=resize_image(img,img_h_new,img_w_new)
image_res = self.predict_enhancement(img_new) image_res = self.predict_enhancement(img_new)
# cv2.imwrite(os.path.join(self.dir_out, self.f_name) + ".tif",self.image) # cv2.imwrite(os.path.join(self.dir_out, self.f_name) + ".tif",self.image)
# self.image=self.image.astype(np.uint16) # self.image=self.image.astype(np.uint16)
@ -647,7 +565,7 @@ class eynollah:
if img.shape[0]<=2530 and img.shape[0]>=img.shape[1]: if img.shape[0]<=2530 and img.shape[0]>=img.shape[1]:
img_h_new=3000 img_h_new=3000
img_w_new=int(img.shape[1]/float(img.shape[0]) * 3000) img_w_new=int(img.shape[1]/float(img.shape[0]) * 3000)
img_new=self.resize_image(img,img_h_new,img_w_new) img_new=resize_image(img,img_h_new,img_w_new)
image_res=self.predict_enhancement(img_new) image_res=self.predict_enhancement(img_new)
#cv2.imwrite(os.path.join(self.dir_out, self.f_name) + ".tif",self.image) #cv2.imwrite(os.path.join(self.dir_out, self.f_name) + ".tif",self.image)
#self.image=self.image.astype(np.uint16) #self.image=self.image.astype(np.uint16)
@ -691,7 +609,7 @@ class eynollah:
else: else:
img_h_new = int(img.shape[0] * 1.5) img_h_new = int(img.shape[0] * 1.5)
img_w_new = int(img.shape[1] * 1.5) img_w_new = int(img.shape[1] * 1.5)
img_new = self.resize_image(img, img_h_new, img_w_new) img_new = resize_image(img, img_h_new, img_w_new)
image_res = self.predict_enhancement(img_new) image_res = self.predict_enhancement(img_new)
# cv2.imwrite(os.path.join(self.dir_out, self.f_name) + ".tif",self.image) # cv2.imwrite(os.path.join(self.dir_out, self.f_name) + ".tif",self.image)
# self.image=self.image.astype(np.uint16) # self.image=self.image.astype(np.uint16)
@ -728,7 +646,7 @@ class eynollah:
self.scale_y = self.img_hight_int / float(self.image.shape[0]) self.scale_y = self.img_hight_int / float(self.image.shape[0])
self.scale_x = self.img_width_int / float(self.image.shape[1]) self.scale_x = self.img_width_int / float(self.image.shape[1])
self.image = self.resize_image(self.image, self.img_hight_int, self.img_width_int) self.image = resize_image(self.image, self.img_hight_int, self.img_width_int)
del img_res del img_res
del img_org del img_org
@ -760,7 +678,7 @@ class eynollah:
self.scale_y = self.img_hight_int / float(self.image.shape[0]) self.scale_y = self.img_hight_int / float(self.image.shape[0])
self.scale_x = self.img_width_int / float(self.image.shape[1]) self.scale_x = self.img_width_int / float(self.image.shape[1])
self.image = self.resize_image(self.image, self.img_hight_int, self.img_width_int) self.image = resize_image(self.image, self.img_hight_int, self.img_width_int)
def start_new_session_and_model(self, model_dir): def start_new_session_and_model(self, model_dir):
config = tf.ConfigProto() config = tf.ConfigProto()
@ -951,10 +869,10 @@ class eynollah:
if patches: if patches:
if img.shape[0] < img_height_model: if img.shape[0] < img_height_model:
img = self.resize_image(img, img_height_model, img.shape[1]) img = resize_image(img, img_height_model, img.shape[1])
if img.shape[1] < img_width_model: if img.shape[1] < img_width_model:
img = self.resize_image(img, img.shape[0], img_width_model) img = resize_image(img, img.shape[0], img_width_model)
# print(img_height_model,img_width_model) # print(img_height_model,img_width_model)
# margin = int(0.2 * img_width_model) # margin = int(0.2 * img_width_model)
@ -1095,13 +1013,13 @@ class eynollah:
img_h_page = img.shape[0] img_h_page = img.shape[0]
img_w_page = img.shape[1] img_w_page = img.shape[1]
img = img / float(255.0) img = img / float(255.0)
img = self.resize_image(img, img_height_model, img_width_model) 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]))
seg = np.argmax(label_p_pred, axis=3)[0] seg = np.argmax(label_p_pred, axis=3)[0]
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
prediction_true = self.resize_image(seg_color, img_h_page, img_w_page) prediction_true = resize_image(seg_color, img_h_page, img_w_page)
prediction_true = prediction_true.astype(np.uint8) prediction_true = prediction_true.astype(np.uint8)
del img del img
@ -1297,7 +1215,7 @@ class eynollah:
###img = self.otsu_copy_binary(img)#self.otsu_copy(img) ###img = self.otsu_copy_binary(img)#self.otsu_copy(img)
###img = img.astype(np.uint8) ###img = img.astype(np.uint8)
###img= self.resize_image(img, int(img_height_h*1), int(img_width_h*1) ) ###img= resize_image(img, int(img_height_h*1), int(img_width_h*1) )
if patches: if patches:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully) model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully)
@ -1307,28 +1225,28 @@ class eynollah:
if patches and cols == 1: if patches and cols == 1:
img2 = self.otsu_copy_binary(img) # self.otsu_copy(img) img2 = self.otsu_copy_binary(img) # self.otsu_copy(img)
img2 = img2.astype(np.uint8) img2 = img2.astype(np.uint8)
img2 = self.resize_image(img2, int(img_height_h * 0.7), int(img_width_h * 0.7)) img2 = resize_image(img2, int(img_height_h * 0.7), int(img_width_h * 0.7))
marginal_of_patch_percent = 0.1 marginal_of_patch_percent = 0.1
prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent) prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent)
prediction_regions2 = self.resize_image(prediction_regions2, img_height_h, img_width_h) prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h)
if patches and cols == 2: if patches and cols == 2:
img2 = self.otsu_copy_binary(img) # self.otsu_copy(img) img2 = self.otsu_copy_binary(img) # self.otsu_copy(img)
img2 = img2.astype(np.uint8) img2 = img2.astype(np.uint8)
img2 = self.resize_image(img2, int(img_height_h * 0.4), int(img_width_h * 0.4)) img2 = resize_image(img2, int(img_height_h * 0.4), int(img_width_h * 0.4))
marginal_of_patch_percent = 0.1 marginal_of_patch_percent = 0.1
prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent) prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent)
prediction_regions2 = self.resize_image(prediction_regions2, img_height_h, img_width_h) prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h)
elif patches and cols > 2: elif patches and cols > 2:
img2 = self.otsu_copy_binary(img) # self.otsu_copy(img) img2 = self.otsu_copy_binary(img) # self.otsu_copy(img)
img2 = img2.astype(np.uint8) img2 = img2.astype(np.uint8)
img2 = self.resize_image(img2, int(img_height_h * 0.3), int(img_width_h * 0.3)) img2 = resize_image(img2, int(img_height_h * 0.3), int(img_width_h * 0.3))
marginal_of_patch_percent = 0.1 marginal_of_patch_percent = 0.1
prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent) prediction_regions2 = self.do_prediction(patches, img2, model_region, marginal_of_patch_percent)
prediction_regions2 = self.resize_image(prediction_regions2, img_height_h, img_width_h) prediction_regions2 = resize_image(prediction_regions2, img_height_h, img_width_h)
if patches and cols == 2: if patches and cols == 2:
img = self.otsu_copy_binary(img) # self.otsu_copy(img) img = self.otsu_copy_binary(img) # self.otsu_copy(img)
@ -1336,35 +1254,35 @@ class eynollah:
img = img.astype(np.uint8) img = img.astype(np.uint8)
if img_width_h >= 2000: if img_width_h >= 2000:
img = self.resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9)) img = resize_image(img, int(img_height_h * 0.9), int(img_width_h * 0.9))
else: else:
pass # img= self.resize_image(img, int(img_height_h*1), int(img_width_h*1) ) pass # img= resize_image(img, int(img_height_h*1), int(img_width_h*1) )
img = img.astype(np.uint8) img = img.astype(np.uint8)
if patches and cols == 1: if patches and cols == 1:
img = self.otsu_copy_binary(img) # self.otsu_copy(img) img = self.otsu_copy_binary(img) # self.otsu_copy(img)
img = img.astype(np.uint8) img = img.astype(np.uint8)
img = self.resize_image(img, int(img_height_h * 0.5), int(img_width_h * 0.5)) img = resize_image(img, int(img_height_h * 0.5), int(img_width_h * 0.5))
img = img.astype(np.uint8) img = img.astype(np.uint8)
if patches and cols == 3: if patches and cols == 3:
img = self.otsu_copy_binary(img) # self.otsu_copy(img) img = self.otsu_copy_binary(img) # self.otsu_copy(img)
img = img.astype(np.uint8) img = img.astype(np.uint8)
# img= self.resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) ) # img= resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) )
if patches and cols == 4: if patches and cols == 4:
img = self.otsu_copy_binary(img) # self.otsu_copy(img) img = self.otsu_copy_binary(img) # self.otsu_copy(img)
img = img.astype(np.uint8) img = img.astype(np.uint8)
# img= self.resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) ) # img= resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) )
if patches and cols >= 5: if patches and cols >= 5:
img = self.otsu_copy_binary(img) # self.otsu_copy(img) img = self.otsu_copy_binary(img) # self.otsu_copy(img)
img = img.astype(np.uint8) img = img.astype(np.uint8)
# img= self.resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) ) # img= resize_image(img, int(img_height_h*0.9), int(img_width_h*0.9) )
if not patches: if not patches:
img = self.otsu_copy_binary(img) # self.otsu_copy(img) img = self.otsu_copy_binary(img) # self.otsu_copy(img)
@ -1373,7 +1291,7 @@ class eynollah:
marginal_of_patch_percent = 0.1 marginal_of_patch_percent = 0.1
prediction_regions = self.do_prediction(patches, img, model_region, marginal_of_patch_percent) prediction_regions = self.do_prediction(patches, img, model_region, marginal_of_patch_percent)
prediction_regions = self.resize_image(prediction_regions, img_height_h, img_width_h) prediction_regions = resize_image(prediction_regions, img_height_h, img_width_h)
session_region.close() session_region.close()
del model_region del model_region
@ -1392,21 +1310,21 @@ class eynollah:
img_h = img_org.shape[0] img_h = img_org.shape[0]
img_w = img_org.shape[1] img_w = img_org.shape[1]
img = self.resize_image(img_org, int(img_org.shape[0] * 1), int(img_org.shape[1] * 1)) img = resize_image(img_org, int(img_org.shape[0] * 1), int(img_org.shape[1] * 1))
prediction_regions1 = self.do_prediction(patches, img, model_region) prediction_regions1 = self.do_prediction(patches, img, model_region)
prediction_regions1 = self.resize_image(prediction_regions1, img_h, img_w) prediction_regions1 = resize_image(prediction_regions1, img_h, img_w)
# prediction_regions1 = cv2.dilate(prediction_regions1, self.kernel, iterations=4) # prediction_regions1 = cv2.dilate(prediction_regions1, self.kernel, iterations=4)
# prediction_regions1 = cv2.erode(prediction_regions1, self.kernel, iterations=7) # prediction_regions1 = cv2.erode(prediction_regions1, self.kernel, iterations=7)
# prediction_regions1 = cv2.dilate(prediction_regions1, self.kernel, iterations=2) # prediction_regions1 = cv2.dilate(prediction_regions1, self.kernel, iterations=2)
img = self.resize_image(img_org, int(img_org.shape[0] * 1), int(img_org.shape[1] * 1)) img = resize_image(img_org, int(img_org.shape[0] * 1), int(img_org.shape[1] * 1))
prediction_regions2 = self.do_prediction(patches, img, model_region) prediction_regions2 = self.do_prediction(patches, img, model_region)
prediction_regions2 = self.resize_image(prediction_regions2, img_h, img_w) prediction_regions2 = resize_image(prediction_regions2, img_h, img_w)
# prediction_regions2 = cv2.dilate(prediction_regions2, self.kernel, iterations=2) # prediction_regions2 = cv2.dilate(prediction_regions2, self.kernel, iterations=2)
prediction_regions2 = cv2.erode(prediction_regions2, self.kernel, iterations=2) prediction_regions2 = cv2.erode(prediction_regions2, self.kernel, iterations=2)
@ -1428,7 +1346,7 @@ class eynollah:
img_h = img.shape[0] img_h = img.shape[0]
img_w = img.shape[1] img_w = img.shape[1]
img = self.resize_image(img, int(img.shape[0] * 1), int(img.shape[1] * 1)) img = resize_image(img, int(img.shape[0] * 1), int(img.shape[1] * 1))
prediction_regions = self.do_prediction(patches, img, model_bin) prediction_regions = self.do_prediction(patches, img, model_bin)
@ -1589,7 +1507,7 @@ class eynollah:
try: try:
textline_con, hierachy = self.return_contours_of_image(img_int_p) textline_con, hierachy = self.return_contours_of_image(img_int_p)
textline_con_fil = self.filter_contours_area_of_image(img_int_p, textline_con, hierachy, max_area=1, min_area=0.0008) textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, hierachy, max_area=1, min_area=0.0008)
y_diff_mean = self.find_contours_mean_y_diff(textline_con_fil) y_diff_mean = self.find_contours_mean_y_diff(textline_con_fil)
sigma_des = int(y_diff_mean * (4.0 / 40.0)) sigma_des = int(y_diff_mean * (4.0 / 40.0))
@ -1671,7 +1589,7 @@ class eynollah:
pixel_img = 1 pixel_img = 1
mask_biggest2 = self.resize_image(mask_biggest2, int(mask_biggest2.shape[0] * scale_par), int(mask_biggest2.shape[1] * scale_par)) mask_biggest2 = resize_image(mask_biggest2, int(mask_biggest2.shape[0] * scale_par), int(mask_biggest2.shape[1] * scale_par))
cnt_textlines_in_image_ind = self.return_contours_of_interested_textline(mask_biggest2, pixel_img) cnt_textlines_in_image_ind = self.return_contours_of_interested_textline(mask_biggest2, pixel_img)
@ -1745,7 +1663,7 @@ class eynollah:
try: try:
textline_con, hierachy = self.return_contours_of_image(img_int_p) textline_con, hierachy = self.return_contours_of_image(img_int_p)
textline_con_fil = self.filter_contours_area_of_image(img_int_p, textline_con, hierachy, max_area=1, min_area=0.00008) textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, hierachy, max_area=1, min_area=0.00008)
y_diff_mean = self.find_contours_mean_y_diff(textline_con_fil) y_diff_mean = self.find_contours_mean_y_diff(textline_con_fil)
@ -1823,7 +1741,7 @@ class eynollah:
contours, hirarchy = cv2.findContours(thresh.copy(), cv2.cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours, hirarchy = cv2.findContours(thresh.copy(), cv2.cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
main_contours = self.filter_contours_area_of_image(thresh, contours, hirarchy, max_area=1, min_area=0.00001) main_contours = filter_contours_area_of_image(thresh, contours, hirarchy, max_area=1, min_area=0.00001)
self.boxes = [] self.boxes = []
for jj in range(len(main_contours)): for jj in range(len(main_contours)):
@ -1867,25 +1785,25 @@ class eynollah:
img_h = img_org.shape[0] img_h = img_org.shape[0]
img_w = img_org.shape[1] img_w = img_org.shape[1]
img = self.resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w)) img = resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w))
prediction_textline = self.do_prediction(patches, img, model_textline) prediction_textline = self.do_prediction(patches, img, model_textline)
prediction_textline = self.resize_image(prediction_textline, img_h, img_w) prediction_textline = resize_image(prediction_textline, img_h, img_w)
patches = False patches = False
prediction_textline_longshot = self.do_prediction(patches, img, model_textline) prediction_textline_longshot = self.do_prediction(patches, img, model_textline)
prediction_textline_longshot_true_size = self.resize_image(prediction_textline_longshot, img_h, img_w) prediction_textline_longshot_true_size = resize_image(prediction_textline_longshot, img_h, img_w)
# scaler_w=1.5 # scaler_w=1.5
# scaler_h=1.5 # scaler_h=1.5
# patches=True # patches=True
# img= self.resize_image(img_org, int(img_org.shape[0]*scaler_h), int(img_org.shape[1]*scaler_w)) # img= resize_image(img_org, int(img_org.shape[0]*scaler_h), int(img_org.shape[1]*scaler_w))
# prediction_textline_streched=self.do_prediction(patches,img,model_textline) # prediction_textline_streched=self.do_prediction(patches,img,model_textline)
# prediction_textline_streched= self.resize_image(prediction_textline_streched, img_h, img_w) # prediction_textline_streched= resize_image(prediction_textline_streched, img_h, img_w)
##plt.imshow(prediction_textline_streched[:,:,0]) ##plt.imshow(prediction_textline_streched[:,:,0])
##plt.show() ##plt.show()
@ -1998,7 +1916,7 @@ class eynollah:
peaks_new_tot = peaks_e[:] peaks_new_tot = peaks_e[:]
textline_con, hierachy = self.return_contours_of_image(img_patch) textline_con, hierachy = self.return_contours_of_image(img_patch)
textline_con_fil = self.filter_contours_area_of_image(img_patch, textline_con, hierachy, max_area=1, min_area=0.0008) textline_con_fil = filter_contours_area_of_image(img_patch, textline_con, hierachy, max_area=1, min_area=0.0008)
y_diff_mean = np.mean(np.diff(peaks_new_tot)) # self.find_contours_mean_y_diff(textline_con_fil) y_diff_mean = np.mean(np.diff(peaks_new_tot)) # self.find_contours_mean_y_diff(textline_con_fil)
sigma_gaus = int(y_diff_mean * (7.0 / 40.0)) sigma_gaus = int(y_diff_mean * (7.0 / 40.0))
@ -2724,7 +2642,7 @@ class eynollah:
peaks_new_tot = peaks_e[:] peaks_new_tot = peaks_e[:]
textline_con, hierachy = self.return_contours_of_image(img_patch) textline_con, hierachy = self.return_contours_of_image(img_patch)
textline_con_fil = self.filter_contours_area_of_image(img_patch, textline_con, hierachy, max_area=1, min_area=0.0008) textline_con_fil = filter_contours_area_of_image(img_patch, textline_con, hierachy, max_area=1, min_area=0.0008)
y_diff_mean = np.mean(np.diff(peaks_new_tot)) # self.find_contours_mean_y_diff(textline_con_fil) y_diff_mean = np.mean(np.diff(peaks_new_tot)) # self.find_contours_mean_y_diff(textline_con_fil)
sigma_gaus = int(y_diff_mean * (7.0 / 40.0)) sigma_gaus = int(y_diff_mean * (7.0 / 40.0))
@ -3244,7 +3162,7 @@ class eynollah:
peaks_new_tot = peaks_e[:] peaks_new_tot = peaks_e[:]
textline_con, hierachy = self.return_contours_of_image(img_patch) textline_con, hierachy = self.return_contours_of_image(img_patch)
textline_con_fil = self.filter_contours_area_of_image(img_patch, textline_con, hierachy, max_area=1, min_area=0.0008) textline_con_fil = filter_contours_area_of_image(img_patch, textline_con, hierachy, max_area=1, min_area=0.0008)
y_diff_mean = np.mean(np.diff(peaks_new_tot)) # self.find_contours_mean_y_diff(textline_con_fil) y_diff_mean = np.mean(np.diff(peaks_new_tot)) # self.find_contours_mean_y_diff(textline_con_fil)
sigma_gaus = int(y_diff_mean * (7.0 / 40.0)) sigma_gaus = int(y_diff_mean * (7.0 / 40.0))
@ -3918,7 +3836,7 @@ class eynollah:
plt.savefig(os.path.join(self.dir_of_all, self.f_name + "_density_of_textline.png")) plt.savefig(os.path.join(self.dir_of_all, self.f_name + "_density_of_textline.png"))
# print(np.max(img_patch_org.sum(axis=0)) ,np.max(img_patch_org.sum(axis=1)),'axislar') # print(np.max(img_patch_org.sum(axis=0)) ,np.max(img_patch_org.sum(axis=1)),'axislar')
# img_patch_org=self.resize_image(img_patch_org,int(img_patch_org.shape[0]*2.5),int(img_patch_org.shape[1]/2.5)) # img_patch_org=resize_image(img_patch_org,int(img_patch_org.shape[0]*2.5),int(img_patch_org.shape[1]/2.5))
# print(np.max(img_patch_org.sum(axis=0)) ,np.max(img_patch_org.sum(axis=1)),'axislar2') # print(np.max(img_patch_org.sum(axis=0)) ,np.max(img_patch_org.sum(axis=1)),'axislar2')
@ -4335,7 +4253,7 @@ class eynollah:
def return_deskew_slope_new(self, img_patch, sigma_des): def return_deskew_slope_new(self, img_patch, sigma_des):
max_x_y = max(img_patch.shape[0], img_patch.shape[1]) max_x_y = max(img_patch.shape[0], img_patch.shape[1])
##img_patch=self.resize_image(img_patch,max_x_y,max_x_y) ##img_patch=resize_image(img_patch,max_x_y,max_x_y)
img_patch_copy = np.zeros((img_patch.shape[0], img_patch.shape[1])) img_patch_copy = np.zeros((img_patch.shape[0], img_patch.shape[1]))
img_patch_copy[:, :] = img_patch[:, :] # img_patch_org[:,:,0] img_patch_copy[:, :] = img_patch[:, :] # img_patch_org[:,:,0]
@ -4459,7 +4377,7 @@ class eynollah:
try: try:
textline_con, hierachy = self.return_contours_of_image(crop_img) textline_con, hierachy = self.return_contours_of_image(crop_img)
textline_con_fil = self.filter_contours_area_of_image(crop_img, textline_con, hierachy, max_area=1, min_area=0.0008) textline_con_fil = filter_contours_area_of_image(crop_img, textline_con, hierachy, max_area=1, min_area=0.0008)
y_diff_mean = self.find_contours_mean_y_diff(textline_con_fil) y_diff_mean = self.find_contours_mean_y_diff(textline_con_fil)
sigma_des = int(y_diff_mean * (4.0 / 40.0)) sigma_des = int(y_diff_mean * (4.0 / 40.0))
@ -5743,9 +5661,9 @@ class eynollah:
# print(hirarchy) # print(hirarchy)
commenst_contours = self.filter_contours_area_of_image(thresh, contours, hirarchy, max_area=0.01, min_area=0.003) commenst_contours = filter_contours_area_of_image(thresh, contours, hirarchy, max_area=0.01, min_area=0.003)
main_contours = self.filter_contours_area_of_image(thresh, contours, hirarchy, max_area=1, min_area=0.003) main_contours = filter_contours_area_of_image(thresh, contours, hirarchy, max_area=1, min_area=0.003)
interior_contours = self.filter_contours_area_of_image_interiors(thresh, contours, hirarchy, max_area=1, min_area=0) interior_contours = filter_contours_area_of_image_interiors(thresh, contours, hirarchy, max_area=1, min_area=0)
img_comm = np.zeros(thresh.shape) img_comm = np.zeros(thresh.shape)
img_comm_in = cv2.fillPoly(img_comm, pts=main_contours, color=(255, 255, 255)) img_comm_in = cv2.fillPoly(img_comm, pts=main_contours, color=(255, 255, 255))
@ -8777,7 +8695,7 @@ class eynollah:
ratio_y = 1 ratio_y = 1
median_blur = False median_blur = False
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x)) img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
if binary: if binary:
img = self.otsu_copy_binary(img) # self.otsu_copy(img) img = self.otsu_copy_binary(img) # self.otsu_copy(img)
@ -8790,7 +8708,7 @@ class eynollah:
img = img.astype(np.uint16) img = img.astype(np.uint16)
prediction_regions_org = self.do_prediction(patches, img, model_region) prediction_regions_org = self.do_prediction(patches, img, model_region)
prediction_regions_org = self.resize_image(prediction_regions_org, img_height_h, img_width_h) prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h)
# plt.imshow(prediction_regions_org[:,:,0]) # plt.imshow(prediction_regions_org[:,:,0])
# plt.show() # plt.show()
@ -8805,7 +8723,7 @@ class eynollah:
ratio_y = 1 ratio_y = 1
median_blur = False median_blur = False
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x)) img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
if binary: if binary:
img = self.otsu_copy_binary(img) # self.otsu_copy(img) img = self.otsu_copy_binary(img) # self.otsu_copy(img)
@ -8819,7 +8737,7 @@ class eynollah:
img = img.astype(np.uint16) img = img.astype(np.uint16)
prediction_regions_orgt = self.do_prediction(patches, img, model_region) prediction_regions_orgt = self.do_prediction(patches, img, model_region)
prediction_regions_orgt = self.resize_image(prediction_regions_orgt, img_height_h, img_width_h) prediction_regions_orgt = resize_image(prediction_regions_orgt, img_height_h, img_width_h)
# plt.imshow(prediction_regions_orgt[:,:,0]) # plt.imshow(prediction_regions_orgt[:,:,0])
# plt.show() # plt.show()
@ -8850,7 +8768,7 @@ class eynollah:
ratio_y = 1 ratio_y = 1
median_blur = False median_blur = False
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x)) img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
one_third_upper_ny = int(img.shape[0] / 3.0) one_third_upper_ny = int(img.shape[0] / 3.0)
@ -8868,9 +8786,9 @@ class eynollah:
img = img.astype(np.uint16) img = img.astype(np.uint16)
prediction_regions_longshot_one_third = self.do_prediction(patches, img, model_region) prediction_regions_longshot_one_third = self.do_prediction(patches, img, model_region)
prediction_regions_longshot_one_third = self.resize_image(prediction_regions_longshot_one_third, one_third_upper_ny, img_width_h) prediction_regions_longshot_one_third = resize_image(prediction_regions_longshot_one_third, one_third_upper_ny, img_width_h)
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x)) img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
img = img[one_third_upper_ny : int(2 * one_third_upper_ny), :, :] img = img[one_third_upper_ny : int(2 * one_third_upper_ny), :, :]
if binary: if binary:
@ -8885,9 +8803,9 @@ class eynollah:
img = img.astype(np.uint16) img = img.astype(np.uint16)
prediction_regions_longshot_one_third_middle = self.do_prediction(patches, img, model_region) prediction_regions_longshot_one_third_middle = self.do_prediction(patches, img, model_region)
prediction_regions_longshot_one_third_middle = self.resize_image(prediction_regions_longshot_one_third_middle, one_third_upper_ny, img_width_h) prediction_regions_longshot_one_third_middle = resize_image(prediction_regions_longshot_one_third_middle, one_third_upper_ny, img_width_h)
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x)) img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
img = img[int(2 * one_third_upper_ny) :, :, :] img = img[int(2 * one_third_upper_ny) :, :, :]
if binary: if binary:
@ -8902,7 +8820,7 @@ class eynollah:
img = img.astype(np.uint16) img = img.astype(np.uint16)
prediction_regions_longshot_one_third_down = self.do_prediction(patches, img, model_region) prediction_regions_longshot_one_third_down = self.do_prediction(patches, img, model_region)
prediction_regions_longshot_one_third_down = self.resize_image(prediction_regions_longshot_one_third_down, img_height_h - int(2 * one_third_upper_ny), img_width_h) prediction_regions_longshot_one_third_down = resize_image(prediction_regions_longshot_one_third_down, img_height_h - int(2 * one_third_upper_ny), img_width_h)
# plt.imshow(prediction_regions_org[:,:,0]) # plt.imshow(prediction_regions_org[:,:,0])
# plt.show() # plt.show()
@ -8927,8 +8845,8 @@ class eynollah:
ratio_y = 1 ratio_y = 1
median_blur = False median_blur = False
# img= self.resize_image(img_org, int(img_org.shape[0]*0.8), int(img_org.shape[1]*1.6)) # img= resize_image(img_org, int(img_org.shape[0]*0.8), int(img_org.shape[1]*1.6))
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x)) img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
if binary: if binary:
img = self.otsu_copy_binary(img) # self.otsu_copy(img) img = self.otsu_copy_binary(img) # self.otsu_copy(img)
@ -8941,7 +8859,7 @@ class eynollah:
img = img.astype(np.uint16) img = img.astype(np.uint16)
prediction_regions = self.do_prediction(patches, img, model_region) prediction_regions = self.do_prediction(patches, img, model_region)
text_region1 = self.resize_image(prediction_regions, img_height_h, img_width_h) text_region1 = resize_image(prediction_regions, img_height_h, img_width_h)
# plt.imshow(text_region1[:,:,0]) # plt.imshow(text_region1[:,:,0])
# plt.show() # plt.show()
@ -8950,7 +8868,7 @@ class eynollah:
binary = False binary = False
median_blur = False median_blur = False
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x)) img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
if binary: if binary:
img = self.otsu_copy_binary(img) # self.otsu_copy(img) img = self.otsu_copy_binary(img) # self.otsu_copy(img)
@ -8963,7 +8881,7 @@ class eynollah:
img = img.astype(np.uint16) img = img.astype(np.uint16)
prediction_regions = self.do_prediction(patches, img, model_region) prediction_regions = self.do_prediction(patches, img, model_region)
text_region2 = self.resize_image(prediction_regions, img_height_h, img_width_h) text_region2 = resize_image(prediction_regions, img_height_h, img_width_h)
# plt.imshow(text_region2[:,:,0]) # plt.imshow(text_region2[:,:,0])
# plt.show() # plt.show()
@ -9090,8 +9008,8 @@ class eynollah:
ratio_y = 1 ratio_y = 1
median_blur = False median_blur = False
# img= self.resize_image(img_org, int(img_org.shape[0]*0.8), int(img_org.shape[1]*1.6)) # img= resize_image(img_org, int(img_org.shape[0]*0.8), int(img_org.shape[1]*1.6))
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x)) img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
if binary: if binary:
img = self.otsu_copy_binary(img) # self.otsu_copy(img) img = self.otsu_copy_binary(img) # self.otsu_copy(img)
@ -9104,14 +9022,14 @@ class eynollah:
img = img.astype(np.uint16) img = img.astype(np.uint16)
prediction_regions = self.do_prediction(patches, img, model_region) prediction_regions = self.do_prediction(patches, img, model_region)
text_region1 = self.resize_image(prediction_regions, img_height_h, img_width_h) text_region1 = resize_image(prediction_regions, img_height_h, img_width_h)
ratio_x = 1 ratio_x = 1
ratio_y = 1.1 ratio_y = 1.1
binary = False binary = False
median_blur = False median_blur = False
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x)) img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
if binary: if binary:
img = self.otsu_copy_binary(img) # self.otsu_copy(img) img = self.otsu_copy_binary(img) # self.otsu_copy(img)
@ -9124,7 +9042,7 @@ class eynollah:
img = img.astype(np.uint16) img = img.astype(np.uint16)
prediction_regions = self.do_prediction(patches, img, model_region) prediction_regions = self.do_prediction(patches, img, model_region)
text_region2 = self.resize_image(prediction_regions, img_height_h, img_width_h) text_region2 = resize_image(prediction_regions, img_height_h, img_width_h)
session_region.close() session_region.close()
del model_region del model_region
@ -9166,19 +9084,6 @@ class eynollah:
##plt.show() ##plt.show()
return text_region2_1st_channel return text_region2_1st_channel
def rotation_image_new(self, img, thetha):
rotated = imutils.rotate(img, thetha)
return self.rotate_max_area_new(img, rotated, thetha)
def rotate_max_area_new(self, image, rotated, angle):
wr, hr = self.rotatedRectWithMaxArea(image.shape[1], image.shape[0], math.radians(angle))
h, w, _ = rotated.shape
y1 = h // 2 - int(hr / 2)
y2 = y1 + int(hr)
x1 = w // 2 - int(wr / 2)
x2 = x1 + int(wr)
return rotated[y1:y2, x1:x2]
def rotation_not_90_func(self, img, textline, text_regions_p_1, thetha): def rotation_not_90_func(self, img, textline, text_regions_p_1, thetha):
rotated = imutils.rotate(img, thetha) rotated = imutils.rotate(img, thetha)
rotated_textline = imutils.rotate(textline, thetha) rotated_textline = imutils.rotate(textline, thetha)
@ -9186,7 +9091,7 @@ class eynollah:
return self.rotate_max_area(img, rotated, rotated_textline, rotated_layout, thetha) return self.rotate_max_area(img, rotated, rotated_textline, rotated_layout, thetha)
def rotate_max_area(self, image, rotated, rotated_textline, rotated_layout, angle): def rotate_max_area(self, image, rotated, rotated_textline, rotated_layout, angle):
wr, hr = self.rotatedRectWithMaxArea(image.shape[1], image.shape[0], math.radians(angle)) wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0], math.radians(angle))
h, w, _ = rotated.shape h, w, _ = rotated.shape
y1 = h // 2 - int(hr / 2) y1 = h // 2 - int(hr / 2)
y2 = y1 + int(hr) y2 = y1 + int(hr)
@ -9202,7 +9107,7 @@ class eynollah:
return self.rotate_max_area_full_layout(img, rotated, rotated_textline, rotated_layout, rotated_layout_full, thetha) return self.rotate_max_area_full_layout(img, rotated, rotated_textline, rotated_layout, rotated_layout_full, thetha)
def rotate_max_area_full_layout(self, image, rotated, rotated_textline, rotated_layout, rotated_layout_full, angle): def rotate_max_area_full_layout(self, image, rotated, rotated_textline, rotated_layout, rotated_layout_full, angle):
wr, hr = self.rotatedRectWithMaxArea(image.shape[1], image.shape[0], math.radians(angle)) wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0], math.radians(angle))
h, w, _ = rotated.shape h, w, _ = rotated.shape
y1 = h // 2 - int(hr / 2) y1 = h // 2 - int(hr / 2)
y2 = y1 + int(hr) y2 = y1 + int(hr)
@ -9224,11 +9129,11 @@ class eynollah:
ratio_x = 1 ratio_x = 1
ratio_y = 1 ratio_y = 1
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x)) img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
prediction_regions_long = self.do_prediction(patches, img, model_region) prediction_regions_long = self.do_prediction(patches, img, model_region)
prediction_regions_long = self.resize_image(prediction_regions_long, img_height_h, img_width_h) prediction_regions_long = resize_image(prediction_regions_long, img_height_h, img_width_h)
gaussian_filter = False gaussian_filter = False
patches = True patches = True
@ -9238,7 +9143,7 @@ class eynollah:
ratio_y = 1.2 ratio_y = 1.2
median_blur = False median_blur = False
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x)) img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
if binary: if binary:
img = self.otsu_copy_binary(img) # self.otsu_copy(img) img = self.otsu_copy_binary(img) # self.otsu_copy(img)
@ -9251,7 +9156,7 @@ class eynollah:
img = img.astype(np.uint16) img = img.astype(np.uint16)
prediction_regions_org_y = self.do_prediction(patches, img, model_region) prediction_regions_org_y = self.do_prediction(patches, img, model_region)
prediction_regions_org_y = self.resize_image(prediction_regions_org_y, img_height_h, img_width_h) prediction_regions_org_y = resize_image(prediction_regions_org_y, img_height_h, img_width_h)
# plt.imshow(prediction_regions_org[:,:,0]) # plt.imshow(prediction_regions_org[:,:,0])
# plt.show() # plt.show()
@ -9264,7 +9169,7 @@ class eynollah:
ratio_y = 1 ratio_y = 1
median_blur = False median_blur = False
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x)) img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
if binary: if binary:
img = self.otsu_copy_binary(img) # self.otsu_copy(img) img = self.otsu_copy_binary(img) # self.otsu_copy(img)
@ -9277,7 +9182,7 @@ class eynollah:
img = img.astype(np.uint16) img = img.astype(np.uint16)
prediction_regions_org = self.do_prediction(patches, img, model_region) prediction_regions_org = self.do_prediction(patches, img, model_region)
prediction_regions_org = self.resize_image(prediction_regions_org, img_height_h, img_width_h) prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h)
# plt.imshow(prediction_regions_org[:,:,0]) # plt.imshow(prediction_regions_org[:,:,0])
# plt.show() # plt.show()
@ -9312,7 +9217,7 @@ class eynollah:
median_blur = False median_blur = False
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x)) img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
if binary: if binary:
img = self.otsu_copy_binary(img) # self.otsu_copy(img) img = self.otsu_copy_binary(img) # self.otsu_copy(img)
@ -9325,7 +9230,7 @@ class eynollah:
img = img.astype(np.uint16) img = img.astype(np.uint16)
prediction_regions_org_y = self.do_prediction(patches, img, model_region) prediction_regions_org_y = self.do_prediction(patches, img, model_region)
prediction_regions_org_y = self.resize_image(prediction_regions_org_y, img_height_h, img_width_h) prediction_regions_org_y = resize_image(prediction_regions_org_y, img_height_h, img_width_h)
# plt.imshow(prediction_regions_org_y[:,:,0]) # plt.imshow(prediction_regions_org_y[:,:,0])
# plt.show() # plt.show()
@ -9342,7 +9247,7 @@ class eynollah:
ratio_y = 1 ratio_y = 1
median_blur = False median_blur = False
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x)) img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
if binary: if binary:
img = self.otsu_copy_binary(img) # self.otsu_copy(img) img = self.otsu_copy_binary(img) # self.otsu_copy(img)
@ -9355,7 +9260,7 @@ class eynollah:
img = img.astype(np.uint16) img = img.astype(np.uint16)
prediction_regions_org = self.do_prediction(patches, img, model_region) prediction_regions_org = self.do_prediction(patches, img, model_region)
prediction_regions_org = self.resize_image(prediction_regions_org, img_height_h, img_width_h) prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h)
##plt.imshow(prediction_regions_org[:,:,0]) ##plt.imshow(prediction_regions_org[:,:,0])
##plt.show() ##plt.show()
@ -9379,7 +9284,7 @@ class eynollah:
ratio_y = 1 ratio_y = 1
median_blur = False median_blur = False
img = self.resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x)) img = resize_image(img_org, int(img_org.shape[0] * ratio_y), int(img_org.shape[1] * ratio_x))
if binary: if binary:
img = self.otsu_copy_binary(img) # self.otsu_copy(img) img = self.otsu_copy_binary(img) # self.otsu_copy(img)
@ -9392,7 +9297,7 @@ class eynollah:
img = img.astype(np.uint16) img = img.astype(np.uint16)
prediction_regions_org2 = self.do_prediction(patches, img, model_region) prediction_regions_org2 = self.do_prediction(patches, img, model_region)
prediction_regions_org2 = self.resize_image(prediction_regions_org2, img_height_h, img_width_h) prediction_regions_org2 = resize_image(prediction_regions_org2, img_height_h, img_width_h)
# plt.imshow(prediction_regions_org2[:,:,0]) # plt.imshow(prediction_regions_org2[:,:,0])
# plt.show() # plt.show()
@ -9444,7 +9349,7 @@ class eynollah:
# ratio_y=1 # ratio_y=1
# median_blur=False # median_blur=False
# img= self.resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x)) # img= resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
# if binary: # if binary:
# img = self.otsu_copy_binary(img)#self.otsu_copy(img) # img = self.otsu_copy_binary(img)#self.otsu_copy(img)
@ -9457,7 +9362,7 @@ class eynollah:
# img = img.astype(np.uint16) # img = img.astype(np.uint16)
# prediction_regions_org2=self.do_prediction(patches,img,model_region) # prediction_regions_org2=self.do_prediction(patches,img,model_region)
# prediction_regions_org2=self.resize_image(prediction_regions_org2, img_height_h, img_width_h ) # prediction_regions_org2=resize_image(prediction_regions_org2, img_height_h, img_width_h )
##plt.imshow(prediction_regions_org2[:,:,0]) ##plt.imshow(prediction_regions_org2[:,:,0])
##plt.show() ##plt.show()
@ -9550,7 +9455,7 @@ class eynollah:
# plt.show() # plt.show()
# print(img.shape,'img') # print(img.shape,'img')
img_copy = self.rotation_image_new(img_copy, -slope_first) img_copy = rotation_image_new(img_copy, -slope_first)
##print(img_copy.shape,'img_copy') ##print(img_copy.shape,'img_copy')
# plt.imshow(img_copy) # plt.imshow(img_copy)
# plt.show() # plt.show()
@ -9583,7 +9488,7 @@ class eynollah:
box = [x, y, w, h] box = [x, y, w, h]
croped_page, page_coord = self.crop_image_inside_box(box, image_page) croped_page, page_coord = self.crop_image_inside_box(box, image_page)
croped_page = self.resize_image(croped_page, int(croped_page.shape[0] / self.scale_y), int(croped_page.shape[1] / self.scale_x)) croped_page = resize_image(croped_page, int(croped_page.shape[0] / self.scale_y), int(croped_page.shape[1] / self.scale_x))
path = os.path.join(dir_of_cropped_imgs, self.f_name + "_" + str(index) + ".jpg") path = os.path.join(dir_of_cropped_imgs, self.f_name + "_" + str(index) + ".jpg")
cv2.imwrite(path, croped_page) cv2.imwrite(path, croped_page)
@ -9601,13 +9506,13 @@ class eynollah:
if text_with_lines.shape[0] <= 1500: if text_with_lines.shape[0] <= 1500:
pass pass
elif text_with_lines.shape[0] > 1500 and text_with_lines.shape[0] <= 1800: elif text_with_lines.shape[0] > 1500 and text_with_lines.shape[0] <= 1800:
text_with_lines = self.resize_image(text_with_lines, int(text_with_lines.shape[0] * 1.5), text_with_lines.shape[1]) text_with_lines = resize_image(text_with_lines, int(text_with_lines.shape[0] * 1.5), text_with_lines.shape[1])
text_with_lines = cv2.erode(text_with_lines, self.kernel, iterations=5) text_with_lines = cv2.erode(text_with_lines, self.kernel, iterations=5)
text_with_lines = self.resize_image(text_with_lines, text_with_lines_eroded.shape[0], text_with_lines_eroded.shape[1]) text_with_lines = resize_image(text_with_lines, text_with_lines_eroded.shape[0], text_with_lines_eroded.shape[1])
else: else:
text_with_lines = self.resize_image(text_with_lines, int(text_with_lines.shape[0] * 1.8), text_with_lines.shape[1]) text_with_lines = resize_image(text_with_lines, int(text_with_lines.shape[0] * 1.8), text_with_lines.shape[1])
text_with_lines = cv2.erode(text_with_lines, self.kernel, iterations=7) text_with_lines = cv2.erode(text_with_lines, self.kernel, iterations=7)
text_with_lines = self.resize_image(text_with_lines, text_with_lines_eroded.shape[0], text_with_lines_eroded.shape[1]) text_with_lines = resize_image(text_with_lines, text_with_lines_eroded.shape[0], text_with_lines_eroded.shape[1])
text_with_lines_y = text_with_lines.sum(axis=0) text_with_lines_y = text_with_lines.sum(axis=0)
text_with_lines_y_eroded = text_with_lines_eroded.sum(axis=0) text_with_lines_y_eroded = text_with_lines_eroded.sum(axis=0)
@ -11168,8 +11073,8 @@ class eynollah:
if np.abs(slope_deskew) >= SLOPE_THRESHOLD: if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
image_page_rotated_n, textline_mask_tot_d, text_regions_p_1_n = self.rotation_not_90_func(image_page, textline_mask_tot, text_regions_p, slope_deskew) image_page_rotated_n, textline_mask_tot_d, text_regions_p_1_n = self.rotation_not_90_func(image_page, textline_mask_tot, text_regions_p, slope_deskew)
text_regions_p_1_n = self.resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1]) text_regions_p_1_n = resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1])
textline_mask_tot_d = self.resize_image(textline_mask_tot_d, text_regions_p.shape[0], text_regions_p.shape[1]) textline_mask_tot_d = resize_image(textline_mask_tot_d, text_regions_p.shape[0], text_regions_p.shape[1])
regions_without_seperators_d = (text_regions_p_1_n[:, :] == 1) * 1 regions_without_seperators_d = (text_regions_p_1_n[:, :] == 1) * 1
@ -11299,9 +11204,9 @@ class eynollah:
if np.abs(slope_deskew) >= SLOPE_THRESHOLD: if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
image_page_rotated_n, textline_mask_tot_d, text_regions_p_1_n, regions_fully_n = self.rotation_not_90_func_full_layout(image_page, textline_mask_tot, text_regions_p, regions_fully, slope_deskew) image_page_rotated_n, textline_mask_tot_d, text_regions_p_1_n, regions_fully_n = self.rotation_not_90_func_full_layout(image_page, textline_mask_tot, text_regions_p, regions_fully, slope_deskew)
text_regions_p_1_n = self.resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1]) text_regions_p_1_n = resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1])
textline_mask_tot_d = self.resize_image(textline_mask_tot_d, text_regions_p.shape[0], text_regions_p.shape[1]) textline_mask_tot_d = resize_image(textline_mask_tot_d, text_regions_p.shape[0], text_regions_p.shape[1])
regions_fully_n = self.resize_image(regions_fully_n, text_regions_p.shape[0], text_regions_p.shape[1]) regions_fully_n = resize_image(regions_fully_n, text_regions_p.shape[0], text_regions_p.shape[1])
regions_without_seperators_d = (text_regions_p_1_n[:, :] == 1) * 1 regions_without_seperators_d = (text_regions_p_1_n[:, :] == 1) * 1
@ -11324,7 +11229,7 @@ class eynollah:
# print(img_revised_tab.shape,text_regions_p_1_n.shape) # print(img_revised_tab.shape,text_regions_p_1_n.shape)
# text_regions_p_1_n=self.resize_image(text_regions_p_1_n,img_revised_tab.shape[0],img_revised_tab.shape[1]) # text_regions_p_1_n=resize_image(text_regions_p_1_n,img_revised_tab.shape[0],img_revised_tab.shape[1])
# print(np.unique(text_regions_p_1_n),'uni') # print(np.unique(text_regions_p_1_n),'uni')
text_only = ((img_revised_tab[:, :] == 1)) * 1 text_only = ((img_revised_tab[:, :] == 1)) * 1

51
sbb_newspapers_org_image/unused.py
Unescape Escape View File

@ -0,0 +1,51 @@
"""
Unused methods from eynollah
"""
import numpy as np
from shapely import geometry
import cv2
def color_images_diva(seg, n_classes):
"""
XXX unused
"""
ann_u = range(n_classes)
if len(np.shape(seg)) == 3:
seg = seg[:, :, 0]
seg_img = np.zeros((np.shape(seg)[0], np.shape(seg)[1], 3)).astype(float)
# colors=sns.color_palette("hls", n_classes)
colors = [[1, 0, 0], [8, 0, 0], [2, 0, 0], [4, 0, 0]]
for c in ann_u:
c = int(c)
segl = seg == c
seg_img[:, :, 0][seg == c] = colors[c][0] # segl*(colors[c][0])
seg_img[:, :, 1][seg == c] = colors[c][1] # seg_img[:,:,1]=segl*(colors[c][1])
seg_img[:, :, 2][seg == c] = colors[c][2] # seg_img[:,:,2]=segl*(colors[c][2])
return seg_img
def find_polygons_size_filter(contours, median_area, scaler_up=1.2, scaler_down=0.8):
"""
XXX unused
"""
found_polygons_early = list()
for c in contours:
if len(c) < 3: # A polygon cannot have less than 3 points
continue
polygon = geometry.Polygon([point[0] for point in c])
area = polygon.area
# Check that polygon has area greater than minimal area
if area >= median_area * scaler_down and area <= median_area * scaler_up:
found_polygons_early.append(np.array([point for point in polygon.exterior.coords], dtype=np.uint))
return found_polygons_early
def resize_ann(seg_in, input_height, input_width):
"""
XXX unused
"""
return cv2.resize(seg_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)

72
sbb_newspapers_org_image/utils.py
Unescape Escape View File

@ -1,7 +1,40 @@
import numpy as np import numpy as np
from shapely import geometry from shapely import geometry
import cv2
def filter_contours_area_of_image_tables(self, image, contours, hirarchy, max_area, min_area): def filter_contours_area_of_image(image, contours, hirarchy, max_area, min_area):
found_polygons_early = list()
jv = 0
for c in contours:
if len(c) < 3: # A polygon cannot have less than 3 points
continue
polygon = geometry.Polygon([point[0] for point in c])
area = polygon.area
if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]) and hirarchy[0][jv][3] == -1: # and hirarchy[0][jv][3]==-1 :
found_polygons_early.append(np.array([[point] for point in polygon.exterior.coords], dtype=np.uint))
jv += 1
return found_polygons_early
def filter_contours_area_of_image_interiors(image, contours, hirarchy, max_area, min_area):
found_polygons_early = list()
jv = 0
for c in contours:
if len(c) < 3: # A polygon cannot have less than 3 points
continue
polygon = geometry.Polygon([point[0] for point in c])
area = polygon.area
if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]) and hirarchy[0][jv][3] != -1:
# print(c[0][0][1])
found_polygons_early.append(np.array([point for point in polygon.exterior.coords], dtype=np.uint))
jv += 1
return found_polygons_early
def filter_contours_area_of_image_tables(image, contours, hirarchy, max_area, min_area):
found_polygons_early = list() found_polygons_early = list()
jv = 0 jv = 0
@ -21,4 +54,41 @@ def filter_contours_area_of_image_tables(self, image, contours, hirarchy, max_ar
jv += 1 jv += 1
return found_polygons_early return found_polygons_early
def resize_image(img_in, input_height, input_width):
return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
def rotatedRectWithMaxArea(w, h, angle):
if w <= 0 or h <= 0:
return 0, 0
width_is_longer = w >= h
side_long, side_short = (w, h) if width_is_longer else (h, w)
# since the solutions for angle, -angle and 180-angle are all the same,
# if suffices to look at the first quadrant and the absolute values of sin,cos:
sin_a, cos_a = abs(math.sin(angle)), abs(math.cos(angle))
if side_short <= 2.0 * sin_a * cos_a * side_long or abs(sin_a - cos_a) < 1e-10:
# half constrained case: two crop corners touch the longer side,
# the other two corners are on the mid-line parallel to the longer line
x = 0.5 * side_short
wr, hr = (x / sin_a, x / cos_a) if width_is_longer else (x / cos_a, x / sin_a)
else:
# fully constrained case: crop touches all 4 sides
cos_2a = cos_a * cos_a - sin_a * sin_a
wr, hr = (w * cos_a - h * sin_a) / cos_2a, (h * cos_a - w * sin_a) / cos_2a
return wr, hr
def rotate_max_area_new(image, rotated, angle):
wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0], math.radians(angle))
h, w, _ = rotated.shape
y1 = h // 2 - int(hr / 2)
y2 = y1 + int(hr)
x1 = w // 2 - int(wr / 2)
x2 = x1 + int(wr)
return rotated[y1:y2, x1:x2]
def rotation_image_new(img, thetha):
rotated = imutils.rotate(img, thetha)
return rotate_max_area_new(img, rotated, thetha)

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