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"""
Copyright (c) 2019-present NAVER Corp.
MIT License
"""
# -*- coding: utf-8 -*-
import numpy as np
import cv2
import math
""" auxiliary functions """
# unwarp corodinates
def warpCoord(Minv, pt):
out = np.matmul(Minv, (pt[0], pt[1], 1))
return np.array([out[0]/out[2], out[1]/out[2]])
""" end of auxiliary functions """
def getDetBoxes_core(textmap, linkmap, text_threshold, link_threshold, low_text):
# prepare data
linkmap = linkmap.copy()
textmap = textmap.copy()
img_h, img_w = textmap.shape
""" labeling method """
ret, text_score = cv2.threshold(textmap, low_text, 1, 0)
ret, link_score = cv2.threshold(linkmap, link_threshold, 1, 0)
text_score_comb = np.clip(text_score + link_score, 0, 1)
#cv2.imshow('text_score_comb', text_score_comb)
nLabels, labels, stats, centroids = cv2.connectedComponentsWithStats(text_score_comb.astype(np.uint8), connectivity=4)
det = []
mapper = []
for k in range(1,nLabels):
# size filtering
size = stats[k, cv2.CC_STAT_AREA]
if size < 10: continue
# thresholding
if np.max(textmap[labels==k]) < text_threshold: continue
# make segmentation map
segmap = np.zeros(textmap.shape, dtype=np.uint8)
segmap[labels==k] = 255
segmap[np.logical_and(link_score==1, text_score==0)] = 0 # remove link area
x, y = stats[k, cv2.CC_STAT_LEFT], stats[k, cv2.CC_STAT_TOP]
w, h = stats[k, cv2.CC_STAT_WIDTH], stats[k, cv2.CC_STAT_HEIGHT]
niter = int(math.sqrt(size * min(w, h) / (w * h)) * 2)
sx, ex, sy, ey = x - niter, x + w + niter + 1, y - niter, y + h + niter + 1
# boundary check
if sx < 0 : sx = 0
if sy < 0 : sy = 0
if ex >= img_w: ex = img_w
if ey >= img_h: ey = img_h
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(2 + niter, 2 + niter))
segmap[sy:ey, sx:ex] = cv2.dilate(segmap[sy:ey, sx:ex], kernel)
# make box
np_contours = np.roll(np.array(np.where(segmap!=0)),1,axis=0).transpose().reshape(-1,2)
rectangle = cv2.minAreaRect(np_contours)
box = cv2.boxPoints(rectangle)
# align diamond-shape
w, h = np.linalg.norm(box[0] - box[1]), np.linalg.norm(box[1] - box[2])
box_ratio = max(w, h) / (min(w, h) + 1e-5)
if abs(1 - box_ratio) <= 0.1:
l, r = min(np_contours[:,0]), max(np_contours[:,0])
t, b = min(np_contours[:,1]), max(np_contours[:,1])
box = np.array([[l, t], [r, t], [r, b], [l, b]], dtype=np.float32)
# make clock-wise order
startidx = box.sum(axis=1).argmin()
box = np.roll(box, 4-startidx, 0)
box = np.array(box)
det.append(box)
mapper.append(k)
return det, labels, mapper
def getPoly_core(boxes, labels, mapper, linkmap):
# configs
num_cp = 5
max_len_ratio = 0.7
expand_ratio = 1.45
max_r = 2.0
step_r = 0.2
polys = []
for k, box in enumerate(boxes):
# size filter for small instance
w, h = int(np.linalg.norm(box[0] - box[1]) + 1), int(np.linalg.norm(box[1] - box[2]) + 1)
if w < 10 or h < 10:
polys.append(None); continue
# warp image
tar = np.float32([[0,0],[w,0],[w,h],[0,h]])
M = cv2.getPerspectiveTransform(box, tar)
word_label = cv2.warpPerspective(labels, M, (w, h), flags=cv2.INTER_NEAREST)
try:
Minv = np.linalg.inv(M)
except Exception:
polys.append(None); continue
# binarization for selected label
cur_label = mapper[k]
word_label[word_label != cur_label] = 0
word_label[word_label > 0] = 1
""" Polygon generation """
# find top/bottom contours
cp = []
max_len = -1
for i in range(w):
region = np.where(word_label[:,i] != 0)[0]
if len(region) < 2 : continue
cp.append((i, region[0], region[-1]))
length = region[-1] - region[0] + 1
if length > max_len: max_len = length
# pass if max_len is similar to h
if h * max_len_ratio < max_len:
polys.append(None); continue
# get pivot points with fixed length
tot_seg = num_cp * 2 + 1
seg_w = w / tot_seg # segment width
pp = [None] * num_cp # init pivot points
cp_section = [[0, 0]] * tot_seg
seg_height = [0] * num_cp
seg_num = 0
num_sec = 0
prev_h = -1
for i in range(0,len(cp)):
(x, sy, ey) = cp[i]
if (seg_num + 1) * seg_w <= x and seg_num <= tot_seg:
# average previous segment
if num_sec == 0: break
cp_section[seg_num] = [cp_section[seg_num][0] / num_sec, cp_section[seg_num][1] / num_sec]
num_sec = 0
# reset variables
seg_num += 1
prev_h = -1
# accumulate center points
cy = (sy + ey) * 0.5
cur_h = ey - sy + 1
cp_section[seg_num] = [cp_section[seg_num][0] + x, cp_section[seg_num][1] + cy]
num_sec += 1
if seg_num % 2 == 0: continue # No polygon area
if prev_h < cur_h:
pp[int((seg_num - 1)/2)] = (x, cy)
seg_height[int((seg_num - 1)/2)] = cur_h
prev_h = cur_h
# processing last segment
if num_sec != 0:
cp_section[-1] = [cp_section[-1][0] / num_sec, cp_section[-1][1] / num_sec]
# pass if num of pivots is not sufficient or segment widh is smaller than character height
if None in pp or seg_w < np.max(seg_height) * 0.25:
polys.append(None); continue
# calc median maximum of pivot points
half_char_h = np.median(seg_height) * expand_ratio / 2
# calc gradient and apply to make horizontal pivots
new_pp = []
for i, (x, cy) in enumerate(pp):
dx = cp_section[i * 2 + 2][0] - cp_section[i * 2][0]
dy = cp_section[i * 2 + 2][1] - cp_section[i * 2][1]
if dx == 0: # gradient if zero
new_pp.append([x, cy - half_char_h, x, cy + half_char_h])
continue
rad = - math.atan2(dy, dx)
c, s = half_char_h * math.cos(rad), half_char_h * math.sin(rad)
new_pp.append([x - s, cy - c, x + s, cy + c])
# get edge points to cover character heatmaps
isSppFound, isEppFound = False, False
grad_s = (pp[1][1] - pp[0][1]) / (pp[1][0] - pp[0][0]) + (pp[2][1] - pp[1][1]) / (pp[2][0] - pp[1][0])
grad_e = (pp[-2][1] - pp[-1][1]) / (pp[-2][0] - pp[-1][0]) + (pp[-3][1] - pp[-2][1]) / (pp[-3][0] - pp[-2][0])
for r in np.arange(0.5, max_r, step_r):
dx = 2 * half_char_h * r
if not isSppFound:
line_img = np.zeros(word_label.shape, dtype=np.uint8)
dy = grad_s * dx
p = np.array(new_pp[0]) - np.array([dx, dy, dx, dy])
cv2.line(line_img, (int(p[0]), int(p[1])), (int(p[2]), int(p[3])), 1, thickness=1)
if np.sum(np.logical_and(word_label, line_img)) == 0 or r + 2 * step_r >= max_r:
spp = p
isSppFound = True
if not isEppFound:
line_img = np.zeros(word_label.shape, dtype=np.uint8)
dy = grad_e * dx
p = np.array(new_pp[-1]) + np.array([dx, dy, dx, dy])
cv2.line(line_img, (int(p[0]), int(p[1])), (int(p[2]), int(p[3])), 1, thickness=1)
if np.sum(np.logical_and(word_label, line_img)) == 0 or r + 2 * step_r >= max_r:
epp = p
isEppFound = True
if isSppFound and isEppFound:
break
# pass if boundary of polygon is not found
if not (isSppFound and isEppFound):
polys.append(None); continue
# make final polygon
poly = []
poly.append(warpCoord(Minv, (spp[0], spp[1])))
for p in new_pp:
poly.append(warpCoord(Minv, (p[0], p[1])))
poly.append(warpCoord(Minv, (epp[0], epp[1])))
poly.append(warpCoord(Minv, (epp[2], epp[3])))
for p in reversed(new_pp):
poly.append(warpCoord(Minv, (p[2], p[3])))
poly.append(warpCoord(Minv, (spp[2], spp[3])))
# add to final result
polys.append(np.array(poly))
return polys
def getDetBoxes(textmap, linkmap, text_threshold, link_threshold, low_text, poly=False):
boxes, labels, mapper = getDetBoxes_core(textmap, linkmap, text_threshold, link_threshold, low_text)
if poly:
polys = getPoly_core(boxes, labels, mapper, linkmap)
else:
polys = [None] * len(boxes)
return boxes, polys
def adjustResultCoordinates(polys, ratio_w, ratio_h, ratio_net = 2):
if len(polys) > 0:
polys = np.array(polys)
for k in range(len(polys)):
if polys[k] is not None:
polys[k] *= (ratio_w * ratio_net, ratio_h * ratio_net)
return polys
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