You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
371 lines
14 KiB
Python
371 lines
14 KiB
Python
"""
|
|
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)
|
|
|
|
def get_one_hot(seg, input_height, input_width, n_classes):
|
|
seg = seg[:, :, 0]
|
|
seg_f = np.zeros((input_height, input_width, n_classes))
|
|
for j in range(n_classes):
|
|
seg_f[:, :, j] = (seg == j).astype(int)
|
|
return seg_f
|
|
|
|
def color_images(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(np.uint8)
|
|
colors = sns.color_palette("hls", n_classes)
|
|
|
|
for c in ann_u:
|
|
c = int(c)
|
|
segl = seg == c
|
|
seg_img[:, :, 0] = segl * c
|
|
seg_img[:, :, 1] = segl * c
|
|
seg_img[:, :, 2] = segl * c
|
|
return seg_img
|
|
|
|
def cleaning_probs(probs: np.ndarray, sigma: float) -> np.ndarray:
|
|
# Smooth
|
|
if sigma > 0.0:
|
|
return cv2.GaussianBlur(probs, (int(3 * sigma) * 2 + 1, int(3 * sigma) * 2 + 1), sigma)
|
|
elif sigma == 0.0:
|
|
return cv2.fastNlMeansDenoising((probs * 255).astype(np.uint8), h=20) / 255
|
|
else: # Negative sigma, do not do anything
|
|
return probs
|
|
|
|
|
|
def early_deskewing_slope_calculation_based_on_lines(region_pre_p):
|
|
# lines are labels by 6 in this model
|
|
seperators_closeup = ((region_pre_p[:, :, :] == 6)) * 1
|
|
|
|
seperators_closeup = seperators_closeup.astype(np.uint8)
|
|
imgray = cv2.cvtColor(seperators_closeup, cv2.COLOR_BGR2GRAY)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours_lines, hierachy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
slope_lines, dist_x, x_min_main, x_max_main, cy_main, slope_lines_org, y_min_main, y_max_main, cx_main = find_features_of_lines(contours_lines)
|
|
|
|
slope_lines_org_hor = slope_lines_org[slope_lines == 0]
|
|
args = np.array(range(len(slope_lines)))
|
|
len_x = seperators_closeup.shape[1] / 4.0
|
|
|
|
args_hor = args[slope_lines == 0]
|
|
dist_x_hor = dist_x[slope_lines == 0]
|
|
x_min_main_hor = x_min_main[slope_lines == 0]
|
|
x_max_main_hor = x_max_main[slope_lines == 0]
|
|
cy_main_hor = cy_main[slope_lines == 0]
|
|
|
|
args_hor = args_hor[dist_x_hor >= len_x / 2.0]
|
|
x_max_main_hor = x_max_main_hor[dist_x_hor >= len_x / 2.0]
|
|
x_min_main_hor = x_min_main_hor[dist_x_hor >= len_x / 2.0]
|
|
cy_main_hor = cy_main_hor[dist_x_hor >= len_x / 2.0]
|
|
slope_lines_org_hor = slope_lines_org_hor[dist_x_hor >= len_x / 2.0]
|
|
|
|
slope_lines_org_hor = slope_lines_org_hor[np.abs(slope_lines_org_hor) < 1.2]
|
|
slope_mean_hor = np.mean(slope_lines_org_hor)
|
|
|
|
if np.abs(slope_mean_hor) > 1.2:
|
|
slope_mean_hor = 0
|
|
|
|
# deskewed_new=rotate_image(image_regions_eraly_p[:,:,:],slope_mean_hor)
|
|
|
|
args_ver = args[slope_lines == 1]
|
|
y_min_main_ver = y_min_main[slope_lines == 1]
|
|
y_max_main_ver = y_max_main[slope_lines == 1]
|
|
x_min_main_ver = x_min_main[slope_lines == 1]
|
|
x_max_main_ver = x_max_main[slope_lines == 1]
|
|
cx_main_ver = cx_main[slope_lines == 1]
|
|
dist_y_ver = y_max_main_ver - y_min_main_ver
|
|
len_y = seperators_closeup.shape[0] / 3.0
|
|
|
|
return slope_mean_hor, cx_main_ver, dist_y_ver
|
|
|
|
def boosting_text_only_regions_by_header(textregion_pre_np, img_only_text):
|
|
result = ((img_only_text[:, :] == 1) | (textregion_pre_np[:, :, 0] == 2)) * 1
|
|
return result
|
|
|
|
def return_rotated_contours(slope, img_patch):
|
|
dst = rotate_image(img_patch, slope)
|
|
dst = dst.astype(np.uint8)
|
|
dst = dst[:, :, 0]
|
|
dst[dst != 0] = 1
|
|
|
|
imgray = cv2.cvtColor(dst, cv2.COLOR_BGR2GRAY)
|
|
_, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
|
|
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
|
|
contours, _ = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
|
return contours
|
|
|
|
def get_textlines_for_each_textregions(self, textline_mask_tot, boxes):
|
|
textline_mask_tot = cv2.erode(textline_mask_tot, self.kernel, iterations=1)
|
|
self.area_of_cropped = []
|
|
self.all_text_region_raw = []
|
|
for jk in range(len(boxes)):
|
|
crop_img, crop_coor = crop_image_inside_box(boxes[jk], np.repeat(textline_mask_tot[:, :, np.newaxis], 3, axis=2))
|
|
crop_img = crop_img.astype(np.uint8)
|
|
self.all_text_region_raw.append(crop_img[:, :, 0])
|
|
self.area_of_cropped.append(crop_img.shape[0] * crop_img.shape[1])
|
|
|
|
def deskew_region_prediction(regions_prediction, slope):
|
|
image_regions_deskewd = np.zeros(regions_prediction[:, :].shape)
|
|
for ind in np.unique(regions_prediction[:, :]):
|
|
interest_reg = (regions_prediction[:, :] == ind) * 1
|
|
interest_reg = interest_reg.astype(np.uint8)
|
|
deskewed_new = rotate_image(interest_reg, slope)
|
|
deskewed_new = deskewed_new[:, :]
|
|
deskewed_new[deskewed_new != 0] = ind
|
|
|
|
image_regions_deskewd = image_regions_deskewd + deskewed_new
|
|
return image_regions_deskewd
|
|
|
|
def deskew_erarly(textline_mask):
|
|
textline_mask_org = np.copy(textline_mask)
|
|
# print(textline_mask.shape,np.unique(textline_mask),'hizzzzz')
|
|
# slope_new=0#deskew_images(img_patch)
|
|
|
|
textline_mask = np.repeat(textline_mask[:, :, np.newaxis], 3, axis=2) * 255
|
|
|
|
textline_mask = textline_mask.astype(np.uint8)
|
|
kernel = np.ones((5, 5), np.uint8)
|
|
|
|
imgray = cv2.cvtColor(textline_mask, cv2.COLOR_BGR2GRAY)
|
|
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
contours, hirarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
# print(hirarchy)
|
|
|
|
commenst_contours = filter_contours_area_of_image(thresh, contours, hirarchy, max_area=0.01, min_area=0.003)
|
|
main_contours = filter_contours_area_of_image(thresh, contours, hirarchy, max_area=1, min_area=0.003)
|
|
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_in = cv2.fillPoly(img_comm, pts=main_contours, color=(255, 255, 255))
|
|
###img_comm_in=cv2.fillPoly(img_comm, pts =interior_contours, color=(0,0,0))
|
|
|
|
img_comm_in = np.repeat(img_comm_in[:, :, np.newaxis], 3, axis=2)
|
|
img_comm_in = img_comm_in.astype(np.uint8)
|
|
|
|
imgray = cv2.cvtColor(img_comm_in, cv2.COLOR_BGR2GRAY)
|
|
##imgray = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
|
|
|
|
##mask = cv2.inRange(imgray, lower_blue, upper_blue)
|
|
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
# print(np.unique(mask))
|
|
##ret, thresh = cv2.threshold(imgray, 0, 255, 0)
|
|
|
|
##plt.imshow(thresh)
|
|
##plt.show()
|
|
|
|
contours, hirarchy = cv2.findContours(thresh.copy(), cv2.cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
|
|
|
|
areas = [cv2.contourArea(contours[jj]) for jj in range(len(contours))]
|
|
|
|
median_area = np.mean(areas)
|
|
contours_slope = contours # self.find_polugons_size_filter(contours,median_area=median_area,scaler_up=100,scaler_down=0.5)
|
|
|
|
if len(contours_slope) > 0:
|
|
for jv in range(len(contours_slope)):
|
|
new_poly = list(contours_slope[jv])
|
|
if jv == 0:
|
|
merged_all = new_poly
|
|
else:
|
|
merged_all = merged_all + new_poly
|
|
|
|
merge = np.array(merged_all)
|
|
|
|
img_in = np.zeros(textline_mask.shape)
|
|
img_p_in = cv2.fillPoly(img_in, pts=[merge], color=(255, 255, 255))
|
|
|
|
##plt.imshow(img_p_in)
|
|
##plt.show()
|
|
|
|
rect = cv2.minAreaRect(merge)
|
|
|
|
box = cv2.boxPoints(rect)
|
|
|
|
box = np.int0(box)
|
|
|
|
indexes = [0, 1, 2, 3]
|
|
x_list = box[:, 0]
|
|
y_list = box[:, 1]
|
|
|
|
index_y_sort = np.argsort(y_list)
|
|
|
|
index_upper_left = index_y_sort[np.argmin(x_list[index_y_sort[0:2]])]
|
|
index_upper_right = index_y_sort[np.argmax(x_list[index_y_sort[0:2]])]
|
|
|
|
index_lower_left = index_y_sort[np.argmin(x_list[index_y_sort[2:]]) + 2]
|
|
index_lower_right = index_y_sort[np.argmax(x_list[index_y_sort[2:]]) + 2]
|
|
|
|
alpha1 = float(box[index_upper_right][1] - box[index_upper_left][1]) / (float(box[index_upper_right][0] - box[index_upper_left][0]))
|
|
alpha2 = float(box[index_lower_right][1] - box[index_lower_left][1]) / (float(box[index_lower_right][0] - box[index_lower_left][0]))
|
|
|
|
slope_true = (alpha1 + alpha2) / 2.0
|
|
|
|
# slope=0#slope_true/np.pi*180
|
|
|
|
# if abs(slope)>=1:
|
|
# slope=0
|
|
|
|
# dst=rotate_image(textline_mask,slope_true)
|
|
# dst=dst[:,:,0]
|
|
# dst[dst!=0]=1
|
|
image_regions_deskewd = np.zeros(textline_mask_org[:, :].shape)
|
|
for ind in np.unique(textline_mask_org[:, :]):
|
|
interest_reg = (textline_mask_org[:, :] == ind) * 1
|
|
interest_reg = interest_reg.astype(np.uint8)
|
|
deskewed_new = rotate_image(interest_reg, slope_true)
|
|
deskewed_new = deskewed_new[:, :]
|
|
deskewed_new[deskewed_new != 0] = ind
|
|
|
|
image_regions_deskewd = image_regions_deskewd + deskewed_new
|
|
return image_regions_deskewd, slope_true
|
|
|
|
def get_all_image_patches_coordination(self, image_page):
|
|
self.all_box_coord = []
|
|
for jk in range(len(self.boxes)):
|
|
_, crop_coor = crop_image_inside_box(self.boxes[jk], image_page)
|
|
self.all_box_coord.append(crop_coor)
|
|
|
|
def find_num_col_olddd(self, regions_without_seperators, sigma_, multiplier=3.8):
|
|
regions_without_seperators_0 = regions_without_seperators[:, :].sum(axis=1)
|
|
|
|
meda_n_updown = regions_without_seperators_0[len(regions_without_seperators_0) :: -1]
|
|
|
|
first_nonzero = next((i for i, x in enumerate(regions_without_seperators_0) if x), 0)
|
|
last_nonzero = next((i for i, x in enumerate(meda_n_updown) if x), 0)
|
|
|
|
last_nonzero = len(regions_without_seperators_0) - last_nonzero
|
|
|
|
y = regions_without_seperators_0 # [first_nonzero:last_nonzero]
|
|
|
|
y_help = np.zeros(len(y) + 20)
|
|
|
|
y_help[10 : len(y) + 10] = y
|
|
|
|
x = np.array(range(len(y)))
|
|
|
|
zneg_rev = -y_help + np.max(y_help)
|
|
|
|
zneg = np.zeros(len(zneg_rev) + 20)
|
|
|
|
zneg[10 : len(zneg_rev) + 10] = zneg_rev
|
|
|
|
z = gaussian_filter1d(y, sigma_)
|
|
zneg = gaussian_filter1d(zneg, sigma_)
|
|
|
|
peaks_neg, _ = find_peaks(zneg, height=0)
|
|
peaks, _ = find_peaks(z, height=0)
|
|
|
|
peaks_neg = peaks_neg - 10 - 10
|
|
|
|
last_nonzero = last_nonzero - 0 # 100
|
|
first_nonzero = first_nonzero + 0 # +100
|
|
|
|
peaks_neg = peaks_neg[(peaks_neg > first_nonzero) & (peaks_neg < last_nonzero)]
|
|
|
|
peaks = peaks[(peaks > 0.06 * regions_without_seperators.shape[1]) & (peaks < 0.94 * regions_without_seperators.shape[1])]
|
|
|
|
interest_pos = z[peaks]
|
|
|
|
interest_pos = interest_pos[interest_pos > 10]
|
|
|
|
interest_neg = z[peaks_neg]
|
|
|
|
if interest_neg[0] < 0.1:
|
|
interest_neg = interest_neg[1:]
|
|
if interest_neg[len(interest_neg) - 1] < 0.1:
|
|
interest_neg = interest_neg[: len(interest_neg) - 1]
|
|
|
|
min_peaks_pos = np.min(interest_pos)
|
|
min_peaks_neg = 0 # np.min(interest_neg)
|
|
|
|
dis_talaei = (min_peaks_pos - min_peaks_neg) / multiplier
|
|
grenze = min_peaks_pos - dis_talaei # np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
|
|
|
|
interest_neg_fin = interest_neg # [(interest_neg<grenze)]
|
|
peaks_neg_fin = peaks_neg # [(interest_neg<grenze)]
|
|
interest_neg_fin = interest_neg # [(interest_neg<grenze)]
|
|
|
|
num_col = (len(interest_neg_fin)) + 1
|
|
|
|
p_l = 0
|
|
p_u = len(y) - 1
|
|
p_m = int(len(y) / 2.0)
|
|
p_g_l = int(len(y) / 3.0)
|
|
p_g_u = len(y) - int(len(y) / 3.0)
|
|
|
|
diff_peaks = np.abs(np.diff(peaks_neg_fin))
|
|
diff_peaks_annormal = diff_peaks[diff_peaks < 30]
|
|
|
|
return interest_neg_fin
|
|
|
|
def return_regions_without_seperators_new(self, regions_pre, regions_only_text):
|
|
kernel = np.ones((5, 5), np.uint8)
|
|
|
|
regions_without_seperators = ((regions_pre[:, :] != 6) & (regions_pre[:, :] != 0) & (regions_pre[:, :] != 1) & (regions_pre[:, :] != 2)) * 1
|
|
|
|
# plt.imshow(regions_without_seperators)
|
|
# plt.show()
|
|
|
|
regions_without_seperators_n = ((regions_without_seperators[:, :] == 1) | (regions_only_text[:, :] == 1)) * 1
|
|
|
|
# regions_without_seperators=( (image_regions_eraly_p[:,:,:]!=6) & (image_regions_eraly_p[:,:,:]!=0) & (image_regions_eraly_p[:,:,:]!=5) & (image_regions_eraly_p[:,:,:]!=8) & (image_regions_eraly_p[:,:,:]!=7))*1
|
|
|
|
regions_without_seperators_n = regions_without_seperators_n.astype(np.uint8)
|
|
|
|
regions_without_seperators_n = cv2.erode(regions_without_seperators_n, kernel, iterations=6)
|
|
|
|
return regions_without_seperators_n
|