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from typing import List
import cv2
import numpy as np
from utils.textblock import TextBlock
from utils.imgproc_utils import draw_connected_labels, expand_textwindow, union_area, enlarge_window
WHITE = (255, 255, 255)
BLACK = (0, 0, 0)
LANG_ENG = 0
LANG_JPN = 1
REFINEMASK_INPAINT = 0
REFINEMASK_ANNOTATION = 1
def get_topk_color(color_list, bins, k=3, color_var=10, bin_tol=0.001):
idx = np.argsort(bins * -1)
color_list, bins = color_list[idx], bins[idx]
top_colors = [color_list[0]]
bin_tol = np.sum(bins) * bin_tol
if len(color_list) > 1:
for color, bin in zip(color_list[1:], bins[1:]):
if np.abs(np.array(top_colors) - color).min() > color_var:
top_colors.append(color)
if len(top_colors) >= k or bin < bin_tol:
break
return top_colors
def minxor_thresh(threshed, mask, dilate=False):
neg_threshed = 255 - threshed
e_size = 1
if dilate:
element = cv2.getStructuringElement(cv2.MORPH_RECT, (2 * e_size + 1, 2 * e_size + 1),(e_size, e_size))
neg_threshed = cv2.dilate(neg_threshed, element, iterations=1)
threshed = cv2.dilate(threshed, element, iterations=1)
neg_xor_sum = cv2.bitwise_xor(neg_threshed, mask).sum()
xor_sum = cv2.bitwise_xor(threshed, mask).sum()
if neg_xor_sum < xor_sum:
return neg_threshed, neg_xor_sum
else:
return threshed, xor_sum
def get_otsuthresh_masklist(img, pred_mask, per_channel=False) -> List[np.ndarray]:
channels = [img[..., 0], img[..., 1], img[..., 2]]
mask_list = []
for c in channels:
_, threshed = cv2.threshold(c, 1, 255, cv2.THRESH_OTSU+cv2.THRESH_BINARY)
threshed, xor_sum = minxor_thresh(threshed, pred_mask, dilate=False)
mask_list.append([threshed, xor_sum])
mask_list.sort(key=lambda x: x[1])
if per_channel:
return mask_list
else:
return [mask_list[0]]
def get_topk_masklist(im_grey, pred_mask):
if len(im_grey.shape) == 3 and im_grey.shape[-1] == 3:
im_grey = cv2.cvtColor(im_grey, cv2.COLOR_RGB2GRAY)
msk = np.ascontiguousarray(pred_mask)
candidate_grey_px = im_grey[np.where(cv2.erode(msk, np.ones((3,3), np.uint8), iterations=1) > 127)]
bin, his = np.histogram(candidate_grey_px, bins=255)
topk_color = get_topk_color(his, bin, color_var=10, k=3)
color_range = 30
mask_list = list()
for ii, color in enumerate(topk_color):
c_top = min(color+color_range, 255)
c_bottom = c_top - 2 * color_range
threshed = cv2.inRange(im_grey, c_bottom, c_top)
threshed, xor_sum = minxor_thresh(threshed, msk)
mask_list.append([threshed, xor_sum])
return mask_list
def merge_mask_list(mask_list, pred_mask, blk: TextBlock = None, pred_thresh=30, text_window=None, filter_with_lines=False, refine_mode=REFINEMASK_INPAINT):
mask_list.sort(key=lambda x: x[1])
linemask = None
if blk is not None and filter_with_lines:
linemask = np.zeros_like(pred_mask)
lines = blk.lines_array(dtype=np.int64)
for line in lines:
line[..., 0] -= text_window[0]
line[..., 1] -= text_window[1]
cv2.fillPoly(linemask, [line], 255)
linemask = cv2.dilate(linemask, np.ones((3, 3), np.uint8), iterations=3)
if pred_thresh > 0:
e_size = 1
element = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2 * e_size + 1, 2 * e_size + 1),(e_size, e_size))
pred_mask = cv2.erode(pred_mask, element, iterations=1)
_, pred_mask = cv2.threshold(pred_mask, 60, 255, cv2.THRESH_BINARY)
connectivity = 8
mask_merged = np.zeros_like(pred_mask)
for ii, (candidate_mask, xor_sum) in enumerate(mask_list):
num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(candidate_mask, connectivity, cv2.CV_16U)
for label_index, stat, centroid in zip(range(num_labels), stats, centroids):
if label_index != 0: # skip background label
x, y, w, h, area = stat
if w * h < 3:
continue
x1, y1, x2, y2 = x, y, x+w, y+h
label_local = labels[y1: y2, x1: x2]
label_cordinates = np.where(label_local==label_index)
tmp_merged = np.zeros_like(label_local, np.uint8)
tmp_merged[label_cordinates] = 255
tmp_merged = cv2.bitwise_or(mask_merged[y1: y2, x1: x2], tmp_merged)
xor_merged = cv2.bitwise_xor(tmp_merged, pred_mask[y1: y2, x1: x2]).sum()
xor_origin = cv2.bitwise_xor(mask_merged[y1: y2, x1: x2], pred_mask[y1: y2, x1: x2]).sum()
if xor_merged < xor_origin:
mask_merged[y1: y2, x1: x2] = tmp_merged
if refine_mode == REFINEMASK_INPAINT:
mask_merged = cv2.dilate(mask_merged, np.ones((5, 5), np.uint8), iterations=1)
# fill holes
num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(255-mask_merged, connectivity, cv2.CV_16U)
sorted_area = np.sort(stats[:, -1])
if len(sorted_area) > 1:
area_thresh = sorted_area[-2]
else:
area_thresh = sorted_area[-1]
for label_index, stat, centroid in zip(range(num_labels), stats, centroids):
x, y, w, h, area = stat
if area < area_thresh:
x1, y1, x2, y2 = x, y, x+w, y+h
label_local = labels[y1: y2, x1: x2]
label_cordinates = np.where(label_local==label_index)
tmp_merged = np.zeros_like(label_local, np.uint8)
tmp_merged[label_cordinates] = 255
tmp_merged = cv2.bitwise_or(mask_merged[y1: y2, x1: x2], tmp_merged)
xor_merged = cv2.bitwise_xor(tmp_merged, pred_mask[y1: y2, x1: x2]).sum()
xor_origin = cv2.bitwise_xor(mask_merged[y1: y2, x1: x2], pred_mask[y1: y2, x1: x2]).sum()
if xor_merged < xor_origin:
mask_merged[y1: y2, x1: x2] = tmp_merged
return mask_merged
def refine_undetected_mask(img: np.ndarray, mask_pred: np.ndarray, mask_refined: np.ndarray, blk_list: List[TextBlock], refine_mode=REFINEMASK_INPAINT):
mask_pred[np.where(mask_refined > 30)] = 0
_, pred_mask_t = cv2.threshold(mask_pred, 30, 255, cv2.THRESH_BINARY)
num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(pred_mask_t, 4, cv2.CV_16U)
valid_labels = np.where(stats[:, -1] > 50)[0]
seg_blk_list = []
if len(valid_labels) > 0:
for lab_index in valid_labels[1:]:
x, y, w, h, area = stats[lab_index]
bx1, by1 = x, y
bx2, by2 = x+w, y+h
bbox = [bx1, by1, bx2, by2]
bbox_score = -1
for blk in blk_list:
bbox_s = union_area(blk.xyxy, bbox)
if bbox_s > bbox_score:
bbox_score = bbox_s
if bbox_score / w / h < 0.5:
seg_blk_list.append(TextBlock(bbox))
if len(seg_blk_list) > 0:
mask_refined = cv2.bitwise_or(mask_refined, refine_mask(img, mask_pred, seg_blk_list, refine_mode=refine_mode))
return mask_refined
def refine_mask(img: np.ndarray, pred_mask: np.ndarray, blk_list: List[TextBlock], refine_mode: int = REFINEMASK_INPAINT) -> np.ndarray:
mask_refined = np.zeros_like(pred_mask)
for blk in blk_list:
# bx1, by1, bx2, by2 = expand_textwindow(img.shape, blk.xyxy, expand_r=16)
bx1, by1, bx2, by2 = enlarge_window(blk.xyxy, img.shape[1], img.shape[0])
im = np.ascontiguousarray(img[by1: by2, bx1: bx2])
msk = np.ascontiguousarray(pred_mask[by1: by2, bx1: bx2])
mask_list = get_topk_masklist(im, msk)
mask_list += get_otsuthresh_masklist(im, msk, per_channel=False)
mask_merged = merge_mask_list(mask_list, msk, blk=blk, text_window=[bx1, by1, bx2, by2], refine_mode=refine_mode)
mask_refined[by1: by2, bx1: bx2] = cv2.bitwise_or(mask_refined[by1: by2, bx1: bx2], mask_merged)
return mask_refined
# def extract_textballoon(img, pred_textmsk=None, global_mask=None):
# if len(img.shape) > 2 and img.shape[2] == 3:
# img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# im_h, im_w = img.shape[0], img.shape[1]
# hyp_textmsk = np.zeros((im_h, im_w), np.uint8)
# thresh_val, threshed = cv2.threshold(img, 1, 255, cv2.THRESH_OTSU+cv2.THRESH_BINARY)
# xormap_sum = cv2.bitwise_xor(threshed, pred_textmsk).sum()
# neg_threshed = 255 - threshed
# neg_xormap_sum = cv2.bitwise_xor(neg_threshed, pred_textmsk).sum()
# neg_thresh = neg_xormap_sum < xormap_sum
# if neg_thresh:
# threshed = neg_threshed
# thresh_info = {'thresh_val': thresh_val,'neg_thresh': neg_thresh}
# connectivity = 8
# num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(threshed, connectivity, cv2.CV_16U)
# label_unchanged = np.copy(labels)
# if global_mask is not None:
# labels[np.where(global_mask==0)] = 0
# text_labels = []
# if pred_textmsk is not None:
# text_score_thresh = 0.5
# textbbox_map = np.zeros_like(pred_textmsk)
# for label_index, stat, centroid in zip(range(num_labels), stats, centroids):
# if label_index != 0: # skip background label
# x, y, w, h, area = stat
# area *= 255
# x1, y1, x2, y2 = x, y, x+w, y+h
# label_local = labels[y1: y2, x1: x2]
# label_cordinates = np.where(label_local==label_index)
# tmp_merged = np.zeros((h, w), np.uint8)
# tmp_merged[label_cordinates] = 255
# andmap = cv2.bitwise_and(tmp_merged, pred_textmsk[y1: y2, x1: x2])
# text_score = andmap.sum() / area
# if text_score > text_score_thresh:
# text_labels.append(label_index)
# hyp_textmsk[y1: y2, x1: x2][label_cordinates] = 255
# labels = label_unchanged
# bubble_msk = np.zeros((img.shape[0], img.shape[1]), np.uint8)
# bubble_msk[np.where(labels==0)] = 255
# # if lang == LANG_JPN:
# bubble_msk = cv2.erode(bubble_msk, (3, 3), iterations=1)
# line_thickness = 2
# cv2.rectangle(bubble_msk, (0, 0), (im_w, im_h), BLACK, line_thickness, cv2.LINE_8)
# contours, hiers = cv2.findContours(bubble_msk, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
# brect_area_thresh = im_h * im_w * 0.4
# min_brect_area = np.inf
# ballon_index = -1
# maxium_pixsum = -1
# for ii, contour in enumerate(contours):
# brect = cv2.boundingRect(contours[ii])
# brect_area = brect[2] * brect[3]
# if brect_area > brect_area_thresh and brect_area < min_brect_area:
# tmp_ballonmsk = np.zeros_like(bubble_msk)
# tmp_ballonmsk = cv2.drawContours(tmp_ballonmsk, contours, ii, WHITE, cv2.FILLED)
# andmap_sum = cv2.bitwise_and(tmp_ballonmsk, hyp_textmsk).sum()
# if andmap_sum > maxium_pixsum:
# maxium_pixsum = andmap_sum
# min_brect_area = brect_area
# ballon_index = ii
# if ballon_index != -1:
# bubble_msk = np.zeros_like(bubble_msk)
# bubble_msk = cv2.drawContours(bubble_msk, contours, ballon_index, WHITE, cv2.FILLED)
# hyp_textmsk = cv2.bitwise_and(hyp_textmsk, bubble_msk)
# return hyp_textmsk, bubble_msk, thresh_info, (num_labels, label_unchanged, stats, centroids, text_labels)
# def extract_textballoon_channelwise(img, pred_textmsk, test_grey=True, global_mask=None):
# c_list = [img[:, :, i] for i in range(3)]
# if test_grey:
# c_list.append(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY))
# best_xorpix_sum = np.inf
# best_cindex = best_hyptextmsk = best_bubblemsk = best_thresh_info = best_component_stats = None
# for c_index, channel in enumerate(c_list):
# hyp_textmsk, bubble_msk, thresh_info, component_stats = extract_textballoon(channel, pred_textmsk, global_mask=global_mask)
# pixor_sum = cv2.bitwise_xor(hyp_textmsk, pred_textmsk).sum()
# if pixor_sum < best_xorpix_sum:
# best_xorpix_sum = pixor_sum
# best_cindex = c_index
# best_hyptextmsk, best_bubblemsk, best_thresh_info, best_component_stats = hyp_textmsk, bubble_msk, thresh_info, component_stats
# return best_hyptextmsk, best_bubblemsk, best_component_stats
# def refine_textmask(img, pred_mask, channel_wise=True, find_leaveouts=True, global_mask=None):
# hyp_textmsk, bubble_msk, component_stats = extract_textballoon_channelwise(img, pred_mask, global_mask=global_mask)
# num_labels, labels, stats, centroids, text_labels = component_stats
# stats = np.array(stats)
# text_stats = stats[text_labels]
# if find_leaveouts and len(text_stats) > 0:
# median_h = np.median(text_stats[:, 3])
# for label, label_h in zip(range(num_labels), stats[:, 3]):
# if label == 0 or label in text_labels:
# continue
# if label_h > 0.5 * median_h and label_h < 1.5 * median_h:
# hyp_textmsk[np.where(labels==label)] = 255
# hyp_textmsk = cv2.bitwise_and(hyp_textmsk, bubble_msk)
# if global_mask is not None:
# hyp_textmsk = cv2.bitwise_and(hyp_textmsk, global_mask)
# return hyp_textmsk, bubble_msk |