def article_no_list(board_name, page_no, category=''):
html2no_list = \
pipe(lambda html:
html.find_all('td', class_='eng list_vspace'),
list)
return base.article_no_list(
PPOM_LIST_STEM,
{'id':board_name, 'page':str(page_no), 'category':str(category)},
pipe(lambda html:
html.find_all('td', class_='eng list_vspace'),
cfilter(lambda tag:tag.get('nowrap') == None),
cfilter(lambda tag:tag.text.isdigit()),
cmap(lambda tag:tag.text),
list)
)
def article_id_list(url_stem, page_no):
all_tr = lambda html: html.find_all('tr', class_='table_body')
is_not_notice = lambda tag: 'notice' not in tag['class']
get_id = lambda tag: tag.find('td', class_='id').text.strip()
return base.article_no_list(
url_stem, {'page': str(page_no)},
pipe(all_tr, cfilter(is_not_notice), cmap(get_id), list))
def comment_list(comments_html):
gen = \
pipe(lambda html:
html.find_all('div', class_='comment_line'),
cmap(lambda tag:tag.find('span')),
cfilter(lambda x:x is not None),
cmap(lambda span:span.text))
return list(gen(comments_html))
def article_no_list(gall_id, page_no):
html2no_list = \
pipe(lambda html:html.find_all('td', class_='gall_num'),
cfilter(lambda tag:tag.text != '공지'),
cmap(lambda tag:tag.text),
list)
return base.article_no_list(DC_LIST_STEM, {
'id': gall_id,
'page': str(page_no)
}, html2no_list)
def article_no_list(board_name, page_no, category):
return base.article_no_list(
INSTIZ_LIST_STEM,
{'id':board_name,
'page':str(page_no), 'category':str(category)},
pipe(lambda html:html.find_all('td', class_='listno'),
cmap(lambda tag:tag.find('a')),
cfilter(lambda x:x is not None),
cmap(lambda a:a.text),
list)
)
def article_url_list(board_name, page_no):
html2urls = \
pipe(lambda html:html.find('table', class_='tbl_type01')
.find_all('tr'),
cfilter(lambda tag:tag.find('td').text.isdigit()),
cmap(lambda tag:tag.find('a')['href']),
list)
return base.article_no_list(MLB_LIST_STEM, {
'p': (int(page_no) - 1) * 30 + 1,
'b': board_name,
'm': 'list'
}, html2urls)
def new_job_records(rootpath):
'''
rootpath is root of directory structure like below
rootpath
dirname1
filepath1
filepath2
...
dirname2
filepath1
...
return (now_index, list<dirname,list<filepath>>, selected)
now_index is last worked index.
selected is empty list. img paths would be saved.
'''
dirnames = os.listdir(rootpath)
filepaths = \
pipe(cmap(lambda filename: os.path.join(rootpath,filename)),
cmap(utils.file_paths),
cmap(list))
return 0, list(zip(dirnames, filepaths(dirnames))), []
bgr_img[:, :, 1] = g_minus_b
#cv2.imshow('intersection removed',bgr_img); cv2.waitKey(0)
return bgr_img
if __name__ == '__main__':
mask_dir = sys.argv[1]
mask_paths = list(utils.file_paths(mask_dir))
check_img = cv2.imread(mask_paths[0])
if (np.array_equal(check_img[:, :, 0], check_img[:, :, 1])
and np.array_equal(check_img[:, :, 1], check_img[:, :, 2])):
img_type = cv2.IMREAD_GRAYSCALE
else:
img_type = cv2.IMREAD_COLOR
leaving_channels = sys.argv[2] if len(sys.argv) == 3 else 'rgb'
masks \
= pipe(cmap(lambda path: cv2.imread(path, img_type)),
cfilter(lambda img: img is not None),
cmap(binarization),
cmap(dilation),
cmap(remove_intersection),
cmap(lambda img: grayscale(img, leaving_channels)))
for path, mask in zip(mask_paths, masks(mask_paths)):
cv2.imwrite(path, mask)
print('Now all masks in %s are clean!' % mask_dir)
shutil.copyfile(file_path, os.path.join(eval_result_dirpath, file_name))
print("file '%s' is copyed into '%s'" % (file_name,eval_result_dirpath))
import sys,pathlib
from fp import pipe,cmap,cfilter
if __name__ == '__main__':
'''
python evaluator.py segnet.h5 imgs_dir output_dir
'''
segnet_model_path = sys.argv[1]
imgs_dir = sys.argv[2]
output_dir = sys.argv[3]
utils.safe_copytree(imgs_dir, output_dir,['*.*'])
segnet = model.unet(segnet_model_path, (None,None,1))
f = pipe(utils.file_paths,
cmap(lambda path: (cv2.imread(path,0), path)),
cfilter(lambda img_path: img_path[0] is not None),
cmap(lambda img_path: (utils.bgr_float32(img_path[0]), img_path[1]) ),
cmap(lambda im_p: (im_p[0].reshape((1,)+im_p[0].shape), im_p[1]) ),
cmap(lambda im_p: (inference(segnet,im_p[0]), im_p[1])))
old_parent_dir = pathlib.Path(imgs_dir).parts[-1]
for segmap_list, img_path in f(imgs_dir):
new_path = utils.make_dstpath(img_path, old_parent_dir, output_dir)
segmap = segmap_list[0]
segmap = (segmap.reshape(segmap.shape[:2]) * 255).astype(np.uint8)
#cv2.imshow('segmap',segmap); cv2.waitKey(0)
cv2.imwrite(new_path, segmap)
def view_comments(comment_list):
gen = \
pipe(enumerate,
cmap(lambda s:'[%d] %s \n' % s))
print(*gen(comment_list))
create new argv[2] directory(tree structure preserved),
separate images in argv[1], and move rgb imgs to new directory.
ex)
python separator.py ./data/examples/ ./data/rgb
^~~~~~~~~~~~~~~~ ^~~~~~~~~~
origin img dir new directory for rgb imgs.
''')
is_grayscale = (lambda img: np.all(img[:, :, 0] == img[:, :, 1]) and np.all(
img[:, :, 1] == img[:, :, 2]))
if __name__ == '__main__':
mixed_imgs_path = sys.argv[1]
rgb_imgs_path = sys.argv[2]
utils.safe_copytree(mixed_imgs_path, rgb_imgs_path,
('*.jpg', '*.jpeg', '*.png'))
f = pipe(utils.file_paths, cmap(lambda path: (cv2.imread(path), path)),
cfilter(lambda img_path: img_path[0] is not None),
cfilter(lambda img_path: not is_grayscale(img_path[0])))
old_parent_dir = pathlib.Path(mixed_imgs_path).parts[-1]
timer = utils.ElapsedTimer('moving in')
for img, img_path in f(mixed_imgs_path):
new_path = utils.make_dstpath(img_path, old_parent_dir, rgb_imgs_path)
#print(img_path, old_parent_dir, new_path)
shutil.move(img_path, new_path)
timer.elapsed_time()
def main(src_imgs_path, dataset_name, num_crop, crop_size, chk_size):
if num_crop != 0:
rand_sqr_crop = img2rand_sqr_crops(crop_size)
gen \
= pipe(utils.file_paths,
cmap(lambda path: cv2.imread(path)),
cfilter(lambda img: img is not None),
cfilter(lambda img: is_cuttable(img, crop_size)),
cmap(utils.slice1channel),
cflatMap(crepeat(num_crop)),
cmap(lambda img: rand_sqr_crop(img)),
cmap(lambda img: (img / 255).astype(np.float32)),
lambda imgs: split_every(chk_size, imgs))
else:
print('!')
num_crop = 100 # big enough value..
gen \
= pipe(utils.file_paths,
cmap(lambda path: cv2.imread(path)),
cfilter(lambda img: img is not None),
cfilter(lambda img: is_cuttable(img, crop_size)),
cmap(utils.slice1channel),
cflatMap(lambda img: img2sqr_crops(img, crop_size)),
cmap(lambda img: (img / 255).astype(np.float32)),
lambda imgs: split_every(chk_size, imgs))
print(src_imgs_path)
expected_num_imgs = len(list(utils.file_paths(src_imgs_path))) * num_crop
print('-------------- SUMARY --------------')
print(' dataset name = ', dataset_name)
print(' size of crop = ', crop_size)
print(' num crops per img = ', num_crop)
print(' expected num imgs = ', expected_num_imgs)
print(' chunk size = ', chk_size)
f = h5py.File(dataset_name, 'w')
timer = utils.ElapsedTimer()
#-------------------------------------------------------------
f.create_dataset('images', (expected_num_imgs, crop_size, crop_size, 1),
maxshape=(None, crop_size, crop_size, 1),
chunks=(chk_size, crop_size, crop_size, 1))
mean = 0
num_img_elems = (crop_size**2)
for chk_no, chunk in tqdm(enumerate(gen(src_imgs_path)),
total=expected_num_imgs // chk_size):
beg_idx = chk_no * chk_size
f['images'][beg_idx:beg_idx + len(chunk)] = chunk
mean = iter_mean(mean, beg_idx * num_img_elems, np.sum(chunk),
len(chunk) * num_img_elems)
f.create_dataset('mean_pixel_value', data=mean)
last_chunk_size = len(chunk)
actual_num_img = chk_no * chk_size + last_chunk_size
if actual_num_img != expected_num_imgs:
print(expected_num_imgs, ' != ', actual_num_img)
print('dataset resized!')
f['images'].resize((actual_num_img, crop_size, crop_size, 1))
# [mean test code]
#li = list(flatten(gen(src_imgs_path)))
#real_mean = np.mean(li)
#print('real MEAN:', real_mean)
#print(len(li))
#print('saved MEAN:', f['mean_pixel_value'][()])
#-------------------------------------------------------------
f.close()
print('------------------------------------')
print('dataset generated successfully.')
msg = timer.elapsed_time()
'''
import mailing
mailing.send_mail_to_kur(
'Dataset generated successfully.',msg
)
'''
# [load test code]
f = h5py.File(dataset_name, 'r')
#-------------------------------------------------------------
print('f', f['images'].shape)
print('loaded MEAN:', f['mean_pixel_value'][()])
#for i in range(f['images'].shape[0] ):
#cv2.imshow('img',f['images'][i]);cv2.waitKey(0)
cv2.imshow('img', f['images'][-1])
cv2.waitKey(0)
#-------------------------------------------------------------
f.close()
'''
'''
'''
print(src_imgs_path)
expected_num_imgs = len(list(utils.file_paths(src_imgs_path))) * num_crop
print('-------------- SUMARY --------------')
print(' dataset name = ', dataset_name)
print(' size of crop = ', crop_size)
print(' num crops per img = ', num_crop)
print(' expected num imgs = ', expected_num_imgs)
print(' chunk size = ', chk_size)
img2_128x128crop = img2sqr_crop(crop_size)
gen = pipe(utils.file_paths,
cmap(lambda path: cv2.imread(path)),
cfilter(lambda img: img is not None),
cfilter(lambda img: is_cuttable(img, crop_size)),
cmap(slice1channel),
cflatMap(crepeat(num_crop)),
cmap(lambda img: img2_128x128crop(img)),
cmap(lambda img: (img / 255).astype(np.float32)),
lambda imgs: split_every(chk_size, imgs))
f = h5py.File(dataset_name,'w')
timer = utils.ElapsedTimer()
#-------------------------------------------------------------
f.create_dataset('images',
(expected_num_imgs,crop_size,crop_size,1),
maxshape = (None,crop_size,crop_size,1),
chunks = (chk_size,crop_size,crop_size,1))
mean = 0
num_img_elems = (crop_size**2)
def path2path_img(path):
return (path, cv2.imread(path))
crop_size = int(sys.argv[1])
imgs_dir = sys.argv[2]
pieces_dir = sys.argv[3]
utils.safe_copytree(imgs_dir, pieces_dir, ['*.jpg', '*.jpeg', '*.png'])
timer = utils.ElapsedTimer('Total Cutting')
#-------------------------------------------------------------
pieces \
= pipe(utils.file_paths,
cmap(path2path_img),
cfilter(lambda path_img:path_img[1] is not None),
cmap(lambda pair: path_img2path_pieces(pair,crop_size,imgs_dir,pieces_dir)),
flatten)(imgs_dir)
for path, img in pieces:
#print(path)
#print(path)
#cv2.imwrite(path, img)
cv2.imwrite(path, img)
pass
#-------------------------------------------------------------
timer.elapsed_time()
import unittest
import os, sys, cv2
import utils
from fp import pipe, cmap, cfilter, flatten
img_dir = sys.argv[1]
f = \
pipe(utils.file_paths,
cmap(lambda path: cv2.imread(path,0)),
cfilter(lambda img: img is not None),
cmap(lambda img: img.shape),
flatten,
sorted,
list)
sizes = f(img_dir)
print(sizes)
print('smallest img size =', sizes[0])