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# -*- coding:utf-8 -*-
from PIL import Image
from ssd_tools.ssd_utils import BBoxUtility
from ssd_tools.ssd import SSD300
import cv2
import argparse
import os
from keras.applications.imagenet_utils import preprocess_input
from keras.preprocessing import image
import numpy as np
import gc
import glob
import json
from keras import backend as K
K.clear_session()
os.environ["OPENCV_IO_ENABLE_JASPER"] = "true"
np.set_printoptions(suppress=True)
# パラメータ
batch_size = 10
NUM_CLASSES = 2
input_shape = (300, 300, 3)
model = SSD300(input_shape, num_classes=NUM_CLASSES)
bbox_util = BBoxUtility(NUM_CLASSES)
dpiinfo = {}
def cv2pil(image):
''' OpenCV型 -> PIL型 '''
new_image = image.copy()
if new_image.ndim == 2: # モノクロ
pass
elif new_image.shape[2] == 3: # カラー
new_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
elif new_image.shape[2] == 4: # 透過
new_image = cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA)
new_image = Image.fromarray(new_image)
return new_image
def resize_pil(pil_img, short):
w, h = pil_img.size
if w < h:
h = int(h*short/w+0.5)
w = short
else:
w = int(w*short/h+0.5)
h = short
return (pil_img.resize((w, h)))
def divide_facing_page(input, input_path=None, output="NO_DUMP",
left='_01', right='_02', single='_00', ext='.jpg',
quality=100, # output jpeg quality
short=None,
debug=False,
log='trim_pos.tsv',
conf_th=0.2,
with_cli=False):
if not with_cli:
model.load_weights(os.path.join('ssd_tools', 'weights.hdf5'), by_name=True)
if log:
if not os.path.exists(log):
with open(log, mode='a') as f:
line = 'image_name\ttrimming_x\n'
f.write(line)
imglist = []
filenames = []
if with_cli:
if type(input) is np.ndarray:
imglist = [input]
elif type(input) is not list:
raise ValueError(
'input for divide_facing_page_with_cli must be np.array or list.')
if type(input_path) is str:
filenames = [input_path]
elif type(input_path) is not list:
raise ValueError(
'input_path for divide_facing_page_with_cli must be str or list.')
else:
filenames = input_path
else: # without_cli
if os.path.isdir(input):
imgpathlist = list(glob.glob(os.path.join(input, "*")))
else:
imgpathlist = [input]
for imgpath in imgpathlist:
imglist.append(cv2.imread(imgpath, cv2.IMREAD_COLOR))
filenames.append(os.path.basename(imgpath))
cnt = 0
while cnt < len(imglist):
inputs = []
images = []
for cv_img in imglist[cnt:min(cnt+batch_size, len(imglist))]:
img = image.img_to_array(cv2pil(cv_img).resize((300, 300)))
images.append(cv_img) # original size images
inputs.append(img.copy()) # resized to (300, 300)
inputs = preprocess_input(np.array(inputs))
preds = model.predict(inputs, batch_size=1, verbose=1)
results = bbox_util.detection_out(preds)
# results[i][b, p] ... i: image index; b: bbox index; p: [label, confidence, xmin, ymin, xmax, ymax]
cnt += batch_size
for i, cvimg in enumerate(images):
if len(results[i]) == 0:
top_conf = 0.0
else:
top_conf = results[i][0, 1]
top_xmin = results[i][0, 2]
top_xmax = results[i][0, 4]
print('img {} top conf: {}'.format(i, top_conf))
div_x = 0
basename, ext_ori = os.path.splitext(
os.path.basename(filenames[i]))
if ext == "SAME":
ext = ext_ori
if top_conf <= conf_th:
# save log
if log:
with open(log, mode='a') as f:
line = '{}\t{}\n'.format(basename+single+ext, 0)
f.write(line)
if with_cli:
return [cvimg]
elif output != "NO_DUMP":
im = cv2pil(cvimg)
if short:
im = resize_pil(im, short)
im.save(os.path.join(output, basename+single+ext),
dpi=(dpiinfo["width_dpi"], dpiinfo["height_dpi"]), quality=100)
else:
xmin = int(round(top_xmin * cvimg.shape[1]))
xmax = int(round(top_xmax * cvimg.shape[1]))
div_x = (xmin+xmax)//2
# save log
if log:
with open(log, mode='a') as f:
line = '{}\t{}\n'.format(basename+left+ext, div_x-1)
f.write(line)
line = '{}\t{}\n'.format(basename+right+ext, div_x)
f.write(line)
# save split images
if with_cli:
return [cvimg[:, :div_x, :], cvimg[:, div_x:, :]]
else:
if output != "NO_DUMP":
im1 = cv2pil(cvimg[:, :div_x, :])
im2 = cv2pil(cvimg[:, div_x:, :])
if short:
im1 = resize_pil(im1, short)
im2 = resize_pil(im2, short)
im1.save(os.path.join(output, basename+left+ext),
dpi=(dpiinfo["width_dpi"], dpiinfo["height_dpi"]),
quality=quality)
im2.save(os.path.join(output, basename+right+ext),
dpi=(dpiinfo["width_dpi"], dpiinfo["height_dpi"]),
quality=quality)
# (debug) add bounding box and gutter line to the image
if debug:
for k in range(len(results[i])):
xmin = int(round(results[i][k, 2] * cvimg.shape[1]))
ymin = int(round(results[i][k, 3] * cvimg.shape[0]))
xmax = int(round(results[i][k, 4] * cvimg.shape[1]))
ymax = int(round(results[i][k, 5] * cvimg.shape[0]))
print(results[i][k, :])
bgr = (0, 0, 255)
t = 2
if k == 0:
if top_conf > 0.2:
t = 5
cv2.line(cvimg, ((xmin+xmax)//2, 0), ((xmin+xmax)//2, cvimg.shape[0]),
color=(255, 0, 0), thickness=t)
cv2.rectangle(cvimg, (xmin, ymin),
(xmax, ymax), bgr, thickness=t)
im = cv2pil(cvimg)
os.makedirs(output+'_rect', exist_ok=True)
im.save(os.path.join(output+'_rect', basename+ext),
dpi=(dpiinfo["width_dpi"], dpiinfo["height_dpi"]),
quality=quality)
del inputs, images
gc.collect()
def divide_facing_page_with_cli(input, input_path,
left='_01', right='_02', single='_00', ext='.jpg',
quality=100, # output jpeg quality
short=None,
conf_th=0.2,
log='trim_pos.tsv'):
return divide_facing_page(input=input,
input_path=input_path,
output="NO_DUMP",
left=left, right=right, single=single, ext=ext,
quality=quality, # output jpeg quality
short=short,
debug=False,
log=log,
conf_th=conf_th,
with_cli=True)
def load_weightfile(model_path):
model.load_weights(model_path, by_name=True)
def parse_args():
usage = 'python3 {} [-i INPUT] [-o OUTPUT] [-l LEFT] [-r RIGHT] [-s SINGLE] \
[-e EXT] [-q QUALITY]'.format(__file__)
argparser = argparse.ArgumentParser(
usage=usage,
description='Divide facing images at the gutter',
formatter_class=argparse.RawTextHelpFormatter)
argparser.add_argument(
'-i',
'--input',
default='inference_input',
help='input image file or directory path\n'
'(default: inference_input)',
type=str)
argparser.add_argument(
'-o',
'--out',
default='inference_output',
help='directory path (default: inference_output)\n'
'if OUT is "NO_DUMP", no images is output',
type=str)
argparser.add_argument(
'-l',
'--left',
default='_01',
help='file name footer of left side page image to be output\n'
'e.g) input image: input.jpg, LEFT: _01(default)\n'
' output image: input_01.jpg',
type=str)
argparser.add_argument(
'-r',
'--right',
default='_02',
help='file name footer of right side page image to be output\n'
'e.g) input image: input.jpg, RIGHT: _02(default)\n'
' output image: input_02.jpg',
type=str)
argparser.add_argument(
'-s',
'--single',
default='_00',
help='file name footer of the image with no detected gutters to be output\n'
'e.g) input image: input.jpg, SINGLE: _00(default)\n'
' output image: input_00.jpg',
type=str)
argparser.add_argument(
'-e',
'--ext',
default='.jpg',
help='output image extension. default: .jpg \n'
'if EXT is \"SAME\", the same extension as the input image will be used.',
type=str)
argparser.add_argument(
'-q', '--quality',
default=100,
dest='quality',
help='output jpeg image quality.\n'
'1 is worst quality and smallest file size,\n'
'and 100 is best quality and largest file size.\n'
'[1, 100], default: 100',
type=int)
argparser.add_argument(
'--short',
default=None,
dest='short',
help='the length of the short side of the output image.',
type=int)
argparser.add_argument(
'--debug',
action='store_true')
argparser.add_argument(
'-lg', '--log',
default=None,
help='path of the tsv file that records the split x position'
'output format:'
'file name <tab> trimming_x',
type=str)
return argparser.parse_args()
if __name__ == '__main__':
args = parse_args()
with open(os.path.join('ssd_tools', 'dpiconfig.json'))as f:
dpiinfo = json.load(f)
if args.out != "NO_DUMP":
os.makedirs(args.out, exist_ok=True)
else:
print('Not dump split images')
if args.debug:
print('Run in debug mode: dump images added bounding box and gutter lines')
if args.log is not None:
print('Export estimated gutter position to {}'.format(args.log))
divide_facing_page(input=args.input,
output=args.out,
left=args.left,
right=args.right,
single=args.single,
ext=args.ext,
quality=args.quality,
short=args.short,
debug=args.debug,
log=args.log)
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