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#!/usr/bin/env python
# Copyright (c) 2022, National Diet Library, Japan
#
# This software is released under the CC BY 4.0.
# https://creativecommons.org/licenses/by/4.0/
import sys
import os
from pathlib import Path
from .utils import auto_run
from typing import List
import xml.etree.ElementTree as ET
import mmcv
from mmdet.apis import (inference_detector, init_detector)
def generate_class_colors(class_num):
import cv2
import numpy as np
colors = 255 * np.ones((class_num, 3), dtype=np.uint8)
colors[:, 0] = np.linspace(0, 179, class_num)
colors = cv2.cvtColor(colors[None, ...], cv2.COLOR_HSV2BGR)[0]
return colors
def draw_legand(img, origin, classes, colors, ssz: int = 16):
import cv2
c_num = len(classes)
x, y = origin[0], origin[1]
for c in range(c_num):
color = colors[c]
color = (int(color[0]), int(color[1]), int(color[2]))
text = classes[c]
img = cv2.rectangle(img, (x, y), (x + ssz - 1, y + ssz - 1), color, -1)
img = cv2.putText(img, text, (x + ssz, y + ssz), cv2.FONT_HERSHEY_PLAIN,
1, (255, 0, 0), 1, cv2.LINE_AA)
y += ssz
return img
class LayoutDetector:
def __init__(self, config: str, checkpoint: str, device: str):
print(f'load from config={config}, checkpoint={checkpoint}')
self.load(config, checkpoint, device)
cfg = mmcv.Config.fromfile(config)
self.classes = cfg.classes
self.colors = generate_class_colors(len(self.classes))
def load(self, config: str, checkpoint: str, device: str):
self.model = init_detector(config, checkpoint, device)
def predict(self, img_path: str):
return inference_detector(self.model, img_path)
def show(self, img_path: str, result, score_thr: float = 0.3, border: int = 3, show_legand: bool = True):
import cv2
img = cv2.imread(img_path)
for c in range(len(result)):
color = self.colors[c]
color = (int(color[0]), int(color[1]), int(color[2]))
for pred in result[c]:
if float(pred[4]) < score_thr:
continue
x0, y0 = int(pred[0]), int(pred[1])
x1, y1 = int(pred[2]), int(pred[3])
img = cv2.rectangle(img, (x0, y0), (x1, y1), color, border)
sz = max(img.shape[0], img.shape[1])
scale = 1024.0 / sz
img = cv2.resize(img, dsize=None, fx=scale, fy=scale)
if show_legand:
ssz = 16
c_num = len(self.classes)
org_width = img.shape[1]
img = cv2.copyMakeBorder(
img, 0, 0, 0, 8 * c_num, cv2.BORDER_REPLICATE)
x = org_width
y = img.shape[0] - ssz * c_num
img = draw_legand(img, (x, y),
self.classes, self.colors, ssz=ssz)
return img
def draw_rects_with_data(self, img, result, score_thr: float = 0.3, border: int = 3, show_legand: bool = True):
import cv2
for c in range(len(result)):
color = self.colors[c]
color = (int(color[0]), int(color[1]), int(color[2]))
for pred in result[c]:
if float(pred[4]) < score_thr:
continue
x0, y0 = int(pred[0]), int(pred[1])
x1, y1 = int(pred[2]), int(pred[3])
img = cv2.rectangle(img, (x0, y0), (x1, y1), color, border)
sz = max(img.shape[0], img.shape[1])
scale = 1024.0 / sz
img = cv2.resize(img, dsize=None, fx=scale, fy=scale)
if show_legand:
ssz = 16
c_num = len(self.classes)
org_width = img.shape[1]
img = cv2.copyMakeBorder(
img, 0, 0, 0, 8 * c_num, cv2.BORDER_REPLICATE)
x = org_width
y = img.shape[0] - ssz * c_num
img = draw_legand(img, (x, y), self.classes, self.colors, ssz=ssz)
return img
def convert_to_xml_string(img_path, classes, result, score_thr: float = 0.3):
import cv2
img = cv2.imread(img_path)
img_h, img_w = img.shape[0:2]
from .ndl_parser import name_to_org_name
img_name = os.path.basename(img_path)
s = f'<PAGE IMAGENAME = "{img_name}" WIDTH = "{img_w}" HEIGHT = "{img_h}">\n'
for c in range(len(classes)):
cls = classes[c]
if cls.startswith('line_'):
for line in result[c]:
conf = float(line[4])
if conf < score_thr:
continue
x, y = int(line[0]), int(line[1])
w, h = int(line[2] - line[0]), int(line[3] - line[1])
s += f'<LINE TYPE = "{name_to_org_name(cls)}" X = "{x}" Y = "{y}" WIDTH = "{w}" HEIGHT = "{h}" CONF = "{conf:0.3f}"></LINE>\n'
elif cls.startswith('block_'):
for block in result[c]:
conf = float(block[4])
if conf < score_thr:
continue
x, y = int(block[0]), int(block[1])
w, h = int(block[2] - block[0]), int(block[3] - block[1])
s += f'<BLOCK TYPE = "{name_to_org_name(cls)}" X = "{x}" Y = "{y}" WIDTH = "{w}" HEIGHT = "{h}" CONF = "{conf:0.3f}"></BLOCK>\n'
s += '</PAGE>\n'
return s
def convert_to_xml_string_with_data(img, img_path, classes, result, score_thr: float = 0.3):
img_h, img_w = img.shape[0:2]
from .ndl_parser import name_to_org_name
img_name = os.path.basename(img_path)
s = f'<PAGE IMAGENAME = "{img_name}" WIDTH = "{img_w}" HEIGHT = "{img_h}">\n'
for c in range(len(classes)):
cls = classes[c]
if cls.startswith('line_'):
for line in result[c]:
conf = float(line[4])
if conf < score_thr:
continue
x, y = int(line[0]), int(line[1])
w, h = int(line[2] - line[0]), int(line[3] - line[1])
s += f'<LINE TYPE = "{name_to_org_name(cls)}" X = "{x}" Y = "{y}" WIDTH = "{w}" HEIGHT = "{h}" CONF = "{conf:0.3f}"></LINE>\n'
elif cls.startswith('block_'):
for block in result[c]:
conf = float(block[4])
if conf < score_thr:
continue
x, y = int(block[0]), int(block[1])
w, h = int(block[2] - block[0]), int(block[3] - block[1])
s += f'<BLOCK TYPE = "{name_to_org_name(cls)}" X = "{x}" Y = "{y}" WIDTH = "{w}" HEIGHT = "{h}" CONF = "{conf:0.3f}"></BLOCK>\n'
s += '</PAGE>\n'
return s
def run_layout_detection(img_paths: List[str] = None, list_path: str = None, output_path: str = "layout_prediction.xml",
config: str = './models/config_file.py',
checkpoint: str = './models/trained_weights.pth',
device: str = 'cuda:0', score_thr: float = 0.3, use_show: bool = False, dump_dir: str = None):
detector = LayoutDetector(config, checkpoint, device)
if list_path is not None:
img_paths = list([s.strip() for s in open(list_path).readlines()])
if img_paths is None:
print('Please specify --img_paths or --list_path')
return -1
if dump_dir is not None:
Path(dump_dir).mkdir(exist_ok=True)
with open(output_path, 'w') as f:
def tee(s):
print(s, file=f, end="")
print(s, file=sys.stdout, end="")
tee('<?xml version="1.0" encoding="utf-8" standalone="yes"?><OCRDATASET xmlns="">\n')
for img_path in img_paths:
result = detector.predict(img_path)
xml_str = convert_to_xml_string(
img_path, detector.classes, result, score_thr=score_thr)
tee(xml_str)
if use_show:
import cv2
img = detector.show(img_path, result, score_thr=score_thr)
cv2.namedWindow('show')
cv2.imshow('show', img)
if 27 == cv2.waitKey(0):
break
if dump_dir is not None:
import cv2
img = detector.show(img_path, result, score_thr=score_thr)
cv2.imwrite(str(Path(dump_dir) / Path(img_path).name), img)
tee('</OCRDATASET>\n')
class InferencerWithCLI:
def __init__(self, conf_dict):
config = conf_dict['config_path']
checkpoint = conf_dict['checkpoint_path']
device = conf_dict['device']
self.detector = LayoutDetector(config, checkpoint, device)
def inference_wich_cli(self, img=None, img_path='',
score_thr: float = 0.3, dump: bool = False):
ET.register_namespace('', 'NDLOCRDATASET')
node = ET.fromstring(
'<?xml version="1.0" encoding="utf-8" standalone="yes"?><OCRDATASET xmlns="">\n</OCRDATASET>\n')
# prediction
if self.detector is None:
print('ERROR: Layout detector is not created.')
return None
result = self.detector.predict(img)
# xml creation
xml_str = convert_to_xml_string_with_data(
img, img_path, self.detector.classes, result, score_thr=score_thr)
# xml conversion from string
result_xml = ET.fromstring(xml_str)
node.append(result_xml)
tree = ET.ElementTree(node)
# xml conversion from string
dump_img = None
if dump is not None:
dump_img = self.detector.draw_rects_with_data(
img, result, score_thr=score_thr)
return {'xml': tree, 'dump_img': dump_img}
if __name__ == '__main__':
auto_run(run_layout_detection)
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