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from typing import Tuple, List
import numpy as np
import cv2
import math
from tqdm import tqdm
from shapely.geometry import Polygon
# from sklearn.mixture import BayesianGaussianMixture
# from functools import reduce
# from collections import defaultdict
# from scipy.optimize import linear_sum_assignment
from ..utils import Quadrilateral, image_resize
COLOR_RANGE_SIGMA = 1.5 # how many stddev away is considered the same color
def save_rgb(fn, img):
if len(img.shape) == 3 and img.shape[2] == 3:
cv2.imwrite(fn, cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
else:
cv2.imwrite(fn, img)
def area_overlap(x1, y1, w1, h1, x2, y2, w2, h2): # returns None if rectangles don't intersect
x_overlap = max(0, min(x1 + w1, x2 + w2) - max(x1, x2))
y_overlap = max(0, min(y1 + h1, y2 + h2) - max(y1, y2))
return x_overlap * y_overlap
def dist(x1, y1, x2, y2):
return math.sqrt((x1 - x2) * (x1 - x2) + (y1 - y2) * (y1 - y2))
def rect_distance(x1, y1, x1b, y1b, x2, y2, x2b, y2b):
left = x2b < x1
right = x1b < x2
bottom = y2b < y1
top = y1b < y2
if top and left:
return dist(x1, y1b, x2b, y2)
elif left and bottom:
return dist(x1, y1, x2b, y2b)
elif bottom and right:
return dist(x1b, y1, x2, y2b)
elif right and top:
return dist(x1b, y1b, x2, y2)
elif left:
return x1 - x2b
elif right:
return x2 - x1b
elif bottom:
return y1 - y2b
elif top:
return y2 - y1b
else: # rectangles intersect
return 0
def extend_rect(x, y, w, h, max_x, max_y, extend_size):
x1 = max(x - extend_size, 0)
y1 = max(y - extend_size, 0)
w1 = min(w + extend_size * 2, max_x - x1 - 1)
h1 = min(h + extend_size * 2, max_y - y1 - 1)
return x1, y1, w1, h1
def complete_mask_fill(text_lines: List[Tuple[int, int, int, int]]):
for (x, y, w, h) in text_lines:
final_mask = cv2.rectangle(final_mask, (x, y), (x + w, y + h), (255), -1)
return final_mask
from pydensecrf.utils import compute_unary, unary_from_softmax
import pydensecrf.densecrf as dcrf
def refine_mask(rgbimg, rawmask):
if len(rawmask.shape) == 2:
rawmask = rawmask[:, :, None]
mask_softmax = np.concatenate([cv2.bitwise_not(rawmask)[:, :, None], rawmask], axis=2)
mask_softmax = mask_softmax.astype(np.float32) / 255.0
n_classes = 2
feat_first = mask_softmax.transpose((2, 0, 1)).reshape((n_classes,-1))
unary = unary_from_softmax(feat_first)
unary = np.ascontiguousarray(unary)
d = dcrf.DenseCRF2D(rgbimg.shape[1], rgbimg.shape[0], n_classes)
d.setUnaryEnergy(unary)
d.addPairwiseGaussian(sxy=1, compat=3, kernel=dcrf.DIAG_KERNEL,
normalization=dcrf.NO_NORMALIZATION)
d.addPairwiseBilateral(sxy=23, srgb=7, rgbim=rgbimg,
compat=20,
kernel=dcrf.DIAG_KERNEL,
normalization=dcrf.NO_NORMALIZATION)
Q = d.inference(5)
res = np.argmax(Q, axis=0).reshape((rgbimg.shape[0], rgbimg.shape[1]))
crf_mask = np.array(res * 255, dtype=np.uint8)
return crf_mask
def complete_mask(img: np.ndarray, mask: np.ndarray, textlines: List[Quadrilateral], keep_threshold = 1e-2, dilation_offset = 0,kernel_size=3):
bboxes = [txtln.aabb.xywh for txtln in textlines]
polys = [Polygon(txtln.pts) for txtln in textlines]
for (x, y, w, h) in bboxes:
cv2.rectangle(mask, (x, y), (x + w, y + h), (0), 1)
num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(mask)
M = len(textlines)
textline_ccs = [np.zeros_like(mask) for _ in range(M)]
iinfo = np.iinfo(labels.dtype)
textline_rects = np.full(shape = (M, 4), fill_value = [iinfo.max, iinfo.max, iinfo.min, iinfo.min], dtype = labels.dtype)
ratio_mat = np.zeros(shape = (num_labels, M), dtype = np.float32)
dist_mat = np.zeros(shape = (num_labels, M), dtype = np.float32)
valid = False
for label in range(1, num_labels):
# skip area too small
if stats[label, cv2.CC_STAT_AREA] <= 9:
continue
x1 = stats[label, cv2.CC_STAT_LEFT]
y1 = stats[label, cv2.CC_STAT_TOP]
w1 = stats[label, cv2.CC_STAT_WIDTH]
h1 = stats[label, cv2.CC_STAT_HEIGHT]
area1 = stats[label, cv2.CC_STAT_AREA]
cc_pts = np.array([[x1, y1], [x1 + w1, y1], [x1 + w1, y1 + h1], [x1, y1 + h1]])
cc_poly = Polygon(cc_pts)
for tl_idx in range(M):
area2 = polys[tl_idx].area
overlapping_area = polys[tl_idx].intersection(cc_poly).area
ratio_mat[label, tl_idx] = overlapping_area / min(area1, area2)
dist_mat[label, tl_idx] = polys[tl_idx].distance(cc_poly.centroid)
# print(textlines[tl_idx].pts, cc_pts, '->', overlapping_area, min(area1, area2), '=', overlapping_area / min(area1, area2), '|', polys[tl_idx].distance(cc_poly))
avg = np.argmax(ratio_mat[label])
# print(avg, 'overlap:', ratio_mat[label, avg], '<=', keep_threshold)
area2 = polys[avg].area
if area1 >= area2:
continue
if ratio_mat[label, avg] <= keep_threshold:
avg = np.argmin(dist_mat[label])
area2 = polys[avg].area
unit = max(min([textlines[avg].font_size, w1, h1]), 10)
# print("unit", unit, textlines[avg].font_size, w1, h1)
# if area1 < 0.4 * w1 * h1:
# # ccs is probably angled
# unit /= 2
# if avg == 0:
# print('no intersect', area1, '>=', area2, dist_mat[label, avg], '>=', 0.5 * unit)
if dist_mat[label, avg] >= 0.5 * unit:
# print(dist_mat[label])
# print('CONTINUE')
continue
textline_ccs[avg][y1:y1+h1, x1:x1+w1][labels[y1:y1+h1, x1:x1+w1] == label] = 255
# if avg == 0:
# print(avg)
# cv2.imshow('ccs', image_resize(textline_ccs[avg], height = 800))
# cv2.waitKey(0)
textline_rects[avg, 0] = min(textline_rects[avg, 0], x1)
textline_rects[avg, 1] = min(textline_rects[avg, 1], y1)
textline_rects[avg, 2] = max(textline_rects[avg, 2], x1 + w1)
textline_rects[avg, 3] = max(textline_rects[avg, 3], y1 + h1)
valid = True
if not valid:
return None
# tblr to xywh
textline_rects[:, 2] -= textline_rects[:, 0]
textline_rects[:, 3] -= textline_rects[:, 1]
final_mask = np.zeros_like(mask)
img = cv2.bilateralFilter(img, 17, 80, 80)
for i, cc in enumerate(tqdm(textline_ccs, '[mask]')):
x1, y1, w1, h1 = textline_rects[i]
text_size = min(w1, h1, textlines[i].font_size)
x1, y1, w1, h1 = extend_rect(x1, y1, w1, h1, img.shape[1], img.shape[0], int(text_size * 0.1))
# TODO: Need to think of better way to determine dilate_size.
dilate_size = max((int((text_size + dilation_offset) * 0.3) // 2) * 2 + 1, 3)
# print(textlines[i].font_size, min(w1, h1), dilate_size)
kern = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (dilate_size, dilate_size))
cc_region = np.ascontiguousarray(cc[y1: y1 + h1, x1: x1 + w1])
if cc_region.size == 0:
continue
# cv2.imshow('cc before', image_resize(cc_region, height = 800))
img_region = np.ascontiguousarray(img[y1: y1 + h1, x1: x1 + w1])
# cv2.imshow('img', image_resize(img_region, height = 800))
cc_region = refine_mask(img_region, cc_region)
# cv2.imshow('cc after', image_resize(cc_region, height = 800))
# cv2.waitKey(0)
cc[y1: y1 + h1, x1: x1 + w1] = cc_region
# cc = cv2.dilate(cc, kern)
x2, y2, w2, h2 = extend_rect(x1, y1, w1, h1, img.shape[1], img.shape[0], -(-dilate_size // 2))
cc[y2:y2+h2, x2:x2+w2] = cv2.dilate(cc[y2:y2+h2, x2:x2+w2], kern)
final_mask[y2:y2+h2, x2:x2+w2] = cv2.bitwise_or(final_mask[y2:y2+h2, x2:x2+w2], cc[y2:y2+h2, x2:x2+w2])
kern = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kernel_size, kernel_size))
# for (x, y, w, h) in text_lines:
# final_mask = cv2.rectangle(final_mask, (x, y), (x + w, y + h), (255), -1)
return cv2.dilate(final_mask, kern)
def unsharp(image):
gaussian_3 = cv2.GaussianBlur(image, (3, 3), 2.0)
return cv2.addWeighted(image, 1.5, gaussian_3, -0.5, 0, image)
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