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eynollah/sbb_newspapers_org_image/unused.py

275 lines
10 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