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import itertools
import math
from typing import Callable, List, Set, Optional, Tuple, Union
from collections import defaultdict, Counter
import os
import shutil
import cv2
from PIL import Image
import numpy as np
import einops
import networkx as nx
from shapely.geometry import Polygon
import torch
import torch.nn as nn
import torch.nn.functional as F
from manga_ocr import MangaOcr
from .xpos_relative_position import XPOS
from .common import OfflineOCR
from .model_48px import OCR
from ..textline_merge import split_text_region
from ..utils import TextBlock, Quadrilateral, quadrilateral_can_merge_region, chunks
from ..utils.generic import AvgMeter
from ..utils.bubble import is_ignore
async def merge_bboxes(bboxes: List[Quadrilateral], width: int, height: int):
# step 1: divide into multiple text region candidates
G = nx.Graph()
for i, box in enumerate(bboxes):
G.add_node(i, box=box)
for ((u, ubox), (v, vbox)) in itertools.combinations(enumerate(bboxes), 2):
# if quadrilateral_can_merge_region_coarse(ubox, vbox):
if quadrilateral_can_merge_region(ubox, vbox, aspect_ratio_tol=1.3, font_size_ratio_tol=2,
char_gap_tolerance=1, char_gap_tolerance2=3):
G.add_edge(u, v)
# step 2: postprocess - further split each region
region_indices: List[Set[int]] = []
for node_set in nx.algorithms.components.connected_components(G):
region_indices.extend(split_text_region(bboxes, node_set, width, height))
# step 3: return regions
merge_box = []
merge_idx = []
for node_set in region_indices:
# for node_set in nx.algorithms.components.connected_components(G):
nodes = list(node_set)
txtlns: List[Quadrilateral] = np.array(bboxes)[nodes]
# majority vote for direction
dirs = [box.direction for box in txtlns]
majority_dir_top_2 = Counter(dirs).most_common(2)
if len(majority_dir_top_2) == 1 :
majority_dir = majority_dir_top_2[0][0]
elif majority_dir_top_2[0][1] == majority_dir_top_2[1][1] : # if top 2 have the same counts
max_aspect_ratio = -100
for box in txtlns :
if box.aspect_ratio > max_aspect_ratio :
max_aspect_ratio = box.aspect_ratio
majority_dir = box.direction
if 1.0 / box.aspect_ratio > max_aspect_ratio :
max_aspect_ratio = 1.0 / box.aspect_ratio
majority_dir = box.direction
else :
majority_dir = majority_dir_top_2[0][0]
# sort textlines
if majority_dir == 'h':
nodes = sorted(nodes, key=lambda x: bboxes[x].centroid[1])
elif majority_dir == 'v':
nodes = sorted(nodes, key=lambda x: -bboxes[x].centroid[0])
txtlns = np.array(bboxes)[nodes]
# yield overall bbox and sorted indices
merge_box.append(txtlns)
merge_idx.append(nodes)
return_box = []
for bbox in merge_box:
if len(bbox) == 1:
return_box.append(bbox[0])
else:
prob = [q.prob for q in bbox]
prob = sum(prob)/len(prob)
base_box = bbox[0]
for box in bbox[1:]:
min_rect = np.array(Polygon([*base_box.pts, *box.pts]).minimum_rotated_rectangle.exterior.coords[:4])
base_box = Quadrilateral(min_rect, '', prob)
return_box.append(base_box)
return return_box, merge_idx
class ModelMangaOCR(OfflineOCR):
_MODEL_MAPPING = {
'model': {
'url': 'https://github.com/zyddnys/manga-image-translator/releases/download/beta-0.3/ocr_ar_48px.ckpt',
'hash': '29daa46d080818bb4ab239a518a88338cbccff8f901bef8c9db191a7cb97671d',
},
'dict': {
'url': 'https://github.com/zyddnys/manga-image-translator/releases/download/beta-0.3/alphabet-all-v7.txt',
'hash': 'f5722368146aa0fbcc9f4726866e4efc3203318ebb66c811d8cbbe915576538a',
},
}
def __init__(self, *args, **kwargs):
os.makedirs(self.model_dir, exist_ok=True)
if os.path.exists('ocr_ar_48px.ckpt'):
shutil.move('ocr_ar_48px.ckpt', self._get_file_path('ocr_ar_48px.ckpt'))
if os.path.exists('alphabet-all-v7.txt'):
shutil.move('alphabet-all-v7.txt', self._get_file_path('alphabet-all-v7.txt'))
super().__init__(*args, **kwargs)
async def _load(self, device: str):
with open(self._get_file_path('alphabet-all-v7.txt'), 'r', encoding = 'utf-8') as fp:
dictionary = [s[:-1] for s in fp.readlines()]
self.model = OCR(dictionary, 768)
self.mocr = MangaOcr()
sd = torch.load(self._get_file_path('ocr_ar_48px.ckpt'))
self.model.load_state_dict(sd)
self.model.eval()
self.device = device
if (device == 'cuda' or device == 'mps'):
self.use_gpu = True
else:
self.use_gpu = False
if self.use_gpu:
self.model = self.model.to(device)
async def _unload(self):
del self.model
del self.mocr
async def _infer(self, image: np.ndarray, textlines: List[Quadrilateral], args: dict, verbose: bool = False, ignore_bubble: int = 0) -> List[TextBlock]:
text_height = 48
max_chunk_size = 16
quadrilaterals = list(self._generate_text_direction(textlines))
region_imgs = [q.get_transformed_region(image, d, text_height) for q, d in quadrilaterals]
perm = range(len(region_imgs))
is_quadrilaterals = False
if len(quadrilaterals) > 0 and isinstance(quadrilaterals[0][0], Quadrilateral):
perm = sorted(range(len(region_imgs)), key = lambda x: region_imgs[x].shape[1])
is_quadrilaterals = True
texts = {}
if args['use_mocr_merge']:
merged_textlines, merged_idx = await merge_bboxes(textlines, image.shape[1], image.shape[0])
merged_quadrilaterals = list(self._generate_text_direction(merged_textlines))
else:
merged_idx = [[i] for i in range(len(region_imgs))]
merged_quadrilaterals = quadrilaterals
merged_region_imgs = []
for q, d in merged_quadrilaterals:
if d == 'h':
merged_text_height = q.aabb.w
merged_d = 'h'
elif d == 'v':
merged_text_height = q.aabb.h
merged_d = 'h'
merged_region_imgs.append(q.get_transformed_region(image, merged_d, merged_text_height))
for idx in range(len(merged_region_imgs)):
texts[idx] = self.mocr(Image.fromarray(merged_region_imgs[idx]))
ix = 0
out_regions = {}
for indices in chunks(perm, max_chunk_size):
N = len(indices)
widths = [region_imgs[i].shape[1] for i in indices]
max_width = 4 * (max(widths) + 7) // 4
region = np.zeros((N, text_height, max_width, 3), dtype = np.uint8)
idx_keys = []
for i, idx in enumerate(indices):
idx_keys.append(idx)
W = region_imgs[idx].shape[1]
tmp = region_imgs[idx]
region[i, :, : W, :]=tmp
if verbose:
os.makedirs('result/ocrs/', exist_ok=True)
if quadrilaterals[idx][1] == 'v':
cv2.imwrite(f'result/ocrs/{ix}.png', cv2.rotate(cv2.cvtColor(region[i, :, :, :], cv2.COLOR_RGB2BGR), cv2.ROTATE_90_CLOCKWISE))
else:
cv2.imwrite(f'result/ocrs/{ix}.png', cv2.cvtColor(region[i, :, :, :], cv2.COLOR_RGB2BGR))
ix += 1
image_tensor = (torch.from_numpy(region).float() - 127.5) / 127.5
image_tensor = einops.rearrange(image_tensor, 'N H W C -> N C H W')
if self.use_gpu:
image_tensor = image_tensor.to(self.device)
with torch.no_grad():
ret = self.model.infer_beam_batch(image_tensor, widths, beams_k = 5, max_seq_length = 255)
for i, (pred_chars_index, prob, fg_pred, bg_pred, fg_ind_pred, bg_ind_pred) in enumerate(ret):
if prob < 0.2:
continue
has_fg = (fg_ind_pred[:, 1] > fg_ind_pred[:, 0])
has_bg = (bg_ind_pred[:, 1] > bg_ind_pred[:, 0])
fr = AvgMeter()
fg = AvgMeter()
fb = AvgMeter()
br = AvgMeter()
bg = AvgMeter()
bb = AvgMeter()
for chid, c_fg, c_bg, h_fg, h_bg in zip(pred_chars_index, fg_pred, bg_pred, has_fg, has_bg) :
ch = self.model.dictionary[chid]
if ch == '<S>':
continue
if ch == '</S>':
break
if h_fg.item() :
fr(int(c_fg[0] * 255))
fg(int(c_fg[1] * 255))
fb(int(c_fg[2] * 255))
if h_bg.item() :
br(int(c_bg[0] * 255))
bg(int(c_bg[1] * 255))
bb(int(c_bg[2] * 255))
else :
br(int(c_fg[0] * 255))
bg(int(c_fg[1] * 255))
bb(int(c_fg[2] * 255))
fr = min(max(int(fr()), 0), 255)
fg = min(max(int(fg()), 0), 255)
fb = min(max(int(fb()), 0), 255)
br = min(max(int(br()), 0), 255)
bg = min(max(int(bg()), 0), 255)
bb = min(max(int(bb()), 0), 255)
cur_region = quadrilaterals[indices[i]][0]
if isinstance(cur_region, Quadrilateral):
cur_region.prob = prob
cur_region.fg_r = fr
cur_region.fg_g = fg
cur_region.fg_b = fb
cur_region.bg_r = br
cur_region.bg_g = bg
cur_region.bg_b = bb
else:
cur_region.update_font_colors(np.array([fr, fg, fb]), np.array([br, bg, bb]))
out_regions[idx_keys[i]] = cur_region
output_regions = []
for i, nodes in enumerate(merged_idx):
total_logprobs = 0
total_area = 0
fg_r = []
fg_g = []
fg_b = []
bg_r = []
bg_g = []
bg_b = []
for idx in nodes:
if idx not in out_regions:
continue
total_logprobs += np.log(out_regions[idx].prob) * out_regions[idx].area
total_area += out_regions[idx].area
fg_r.append(out_regions[idx].fg_r)
fg_g.append(out_regions[idx].fg_g)
fg_b.append(out_regions[idx].fg_b)
bg_r.append(out_regions[idx].bg_r)
bg_g.append(out_regions[idx].bg_g)
bg_b.append(out_regions[idx].bg_b)
total_logprobs /= total_area
prob = np.exp(total_logprobs)
fr = round(np.mean(fg_r))
fg = round(np.mean(fg_g))
fb = round(np.mean(fg_b))
br = round(np.mean(bg_r))
bg = round(np.mean(bg_g))
bb = round(np.mean(bg_b))
txt = texts[i]
self.logger.info(f'prob: {prob} {txt} fg: ({fr}, {fg}, {fb}) bg: ({br}, {bg}, {bb})')
cur_region = merged_quadrilaterals[i][0]
if isinstance(cur_region, Quadrilateral):
cur_region.text = txt
cur_region.prob = prob
cur_region.fg_r = fr
cur_region.fg_g = fg
cur_region.fg_b = fb
cur_region.bg_r = br
cur_region.bg_g = bg
cur_region.bg_b = bb
else:
cur_region.text.append(txt)
cur_region.update_font_colors(np.array([fr, fg, fb]), np.array([br, bg, bb]))
output_regions.append(cur_region)
if is_quadrilaterals:
return output_regions
return textlines
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