def read_temp_bib(self):
"""Read content of temporary bibliography.
"""
path = self.wf.cachefile('temp_bibliography.html')
bib = utils.read_path(path)
text = self.export_formatted(bib)
bib = self._bib_sort(text, '\n\n')
utils.set_clipboard(bib)
return self.zotquery.output_format
def scan_codepath(self):
"""Scan Markdown document for reference
"""
if self.flag == 'temp_bib':
return self.read_temp_bib()
else:
md_text = utils.read_path(self.flag)
key_dicts = self.reference_scan(md_text)
self.generate_bibliography(key_dicts, md_text)
def read_temp_bib(self):
"""Read content of temporary bibliography.
"""
path = self.wf.cachefile('temp_bibliography.html')
bib = utils.read_path(path)
text = self.export_formatted(bib)
bib = self._bib_sort(text, '\n\n')
utils.set_clipboard(bib)
return self.zotquery.output_format
def scan_codepath(self):
"""Scan Markdown document for reference
"""
if self.flag == 'temp_bib':
return self.read_temp_bib()
else:
md_text = utils.read_path(self.flag)
key_dicts = self.reference_scan(md_text)
self.generate_bibliography(key_dicts, md_text)
def read_temp_bib(wf):
"""Read content of temporary bibliography.
"""
path = wf.cachefile('temp_bibliography.html')
bib = utils.read_path(path)
text = export.export_formatted(bib)
bib = export._bib_sort(text, '\n\n')
utils.set_clipboard(bib)
return zq.backend.output_format
def scan(flag, arg, wf):
"""Scan Markdown document for reference
"""
if flag == 'temp_bib':
return read_temp_bib(wf)
else:
md_text = utils.read_path(flag)
key_dicts = reference_scan(md_text)
generate_bibliography(key_dicts, md_text)
def read_temp_bib(wf):
"""Read content of temporary bibliography.
"""
path = wf.cachefile('temp_bibliography.html')
bib = utils.read_path(path)
text = export.export_formatted(bib)
bib = export._bib_sort(text, '\n\n')
utils.set_clipboard(bib)
return zq.backend.output_format
def scan(flag, arg, wf):
"""Scan Markdown document for reference
"""
if flag == 'temp_bib':
return read_temp_bib(wf)
else:
md_text = utils.read_path(flag)
key_dicts = reference_scan(md_text)
generate_bibliography(key_dicts, md_text)
def main(prefix, path_dic, method):
for tar in path_dic:
paths = read_path(path_dic[tar])
datas = []
miss_cnt = 0
for path in paths:
feature = face.feat(path, method)
if feature is None:
miss_cnt += 1
continue
else:
datas.append(feature)
datas = np.array(datas)
np.save('./datas/new_' + prefix + '_' + tar + '_' + method, datas)
print('there is(are) %d picture(s) cannot be detected' % miss_cnt)
def _get_group_name(self):
"""Get name of group from stored result.
:returns: name of group from it's ID
:rtype: :class:`unicode`
"""
# get group type (`collection` vs `tag`)
flag = self.flag.split('-')[1]
# Read saved group info
path = self.wf.cachefile('{}_query_result.txt'.format(flag))
group_id = utils.read_path(path)
# Split group type from group ID
kind, uid = group_id.split('_')
if kind == 'c':
group = self._get_collection_name(uid)
elif kind == 't':
group = self._get_tag_name(uid)
return group
def _get_group_name(self):
"""Get name of group from stored result.
:returns: name of group from it's ID
:rtype: :class:`unicode`
"""
# get group type (`collection` vs `tag`)
flag = self.flag.split('-')[1]
# Read saved group info
path = self.wf.cachefile('{}_query_result.txt'.format(flag))
group_id = utils.read_path(path)
# Split group type from group ID
kind, uid = group_id.split('_')
if kind == 'c':
group = self._get_collection_name(uid)
elif kind == 't':
group = self._get_tag_name(uid)
return group
def get_pref(self, pref):
"""Retrieve the value for ``pref`` in Zotero's preferences.
:param pref: name of desired Zotero preference
:type pref: ``unicode`` or ``str``
:returns: Zotero preference value
:rtype: ``unicode``
"""
dirs = self.find_name('prefs.js')
for path in dirs:
if 'Zotero' or 'Firefox' in path:
# Read text from file at `path`
prefs = utils.read_path(path)
pref_re = r'{}",\s"(.*?)"'.format(pref)
data_dir = re.search(pref_re, prefs)
try:
return data_dir.group(1)
except AttributeError:
pass
return None
def search_within_group(scope, query):
group_type = scope.split('-')[-1]
# Read saved group info
path = config.WF.cachefile('{}_query_result.txt'.format(group_type))
group_id = utils.read_path(path)
group_name = get_group_name(group_id)
sqlite_query = make_in_group_sqlite_query(scope, query, group_name)
config.log.info('Item sqlite query : {}'.format(sqlite_query))
# Run sqlite query and get back item keys
item_keys = run_item_sqlite_query(sqlite_query)
# Get JSON data of user's Zotero library
data = utils.read_json(zq.backend.json_data)
results_dict = []
for key in item_keys:
item = data.get(key, None)
if item:
# Prepare dictionary for Alfred
formatter = ResultsFormatter(item)
alfred_dict = formatter.prepare_item_feedback()
results_dict.append(alfred_dict)
return results_dict
def get_pref(self, pref):
"""Retrieve the value for ``pref`` in Zotero's preferences.
:param pref: name of desired Zotero preference
:type pref: ``unicode`` or ``str``
:returns: Zotero preference value
:rtype: ``unicode``
"""
dirs = self.find_name('prefs.js')
for path in dirs:
if 'Zotero' or 'Firefox' in path:
# Read text from file at `path`
prefs = utils.read_path(path)
pref_re = r'{}",\s"(.*?)"'.format(pref)
data_dir = re.search(pref_re, prefs)
try:
return data_dir.group(1)
except AttributeError:
pass
return None
if __name__ == '__main__':
method = 'uniform'
data, label = load_data('training', method)
clf = svm.SVC(C=1.0, gamma=0.1)
clf.fit(data, label)
#data, label = load_data('testing', method)
#score = clf.score(data, label)
#print(score)
#exit(0)
# formal testing(full data, including those whose face cannot be deteceted)
posi_paths = read_path(posi_test)
nega_paths = read_path(nega_test)
paths = posi_paths + nega_paths
labels = [1] * len(posi_paths) + [-1] * len(nega_paths)
total = len(labels)
correct = 0
ind = 0
for path, label in zip(paths, labels):
feature = feat(path, method)
if feature is None:
# print('None')
pred = -1
else:
pred = clf.predict(np.expand_dims(feature, axis=0))
import cv2
import numpy as np
from lib import face
from lib.get_LBP_from_Image import LBP
from matplotlib import pyplot as plt
from lib.utils import read_path
import os
import numpy as np
posi_test = 'client_test_raw.txt'
nega_test = 'imposter_test_raw.txt'
paths = read_path(nega_test)
for path in paths[:10]:
image = cv2.imread(path)
gray = face.detect(image)
w, h = gray.shape
#croped = gray[int(w/2-32):int(w/2+32),int(h/2-32):int(h/2+32)]
croped = gray[int(w * 0.15):int(w * 0.95), int(h * 0.15):int(h * 0.85)]
croped = cv2.resize(croped, (64, 64))
cv2.imshow('', croped)
cv2.waitKey(0)
'''
#faceCascade = cv2.CascadeClassifier("./haarcascade_frontalface_alt.xml")
#pimage = cv2.imread('./raw/ImposterRaw/0001/0001_00_00_01_0.jpg')
nima ge = cv2.imread('/raw/ClientRaw/0006/0006_00_00_01_169.jpg')
#crop = face.detect(pimage)
cv2.imshow('',nimage)
