def test_loadFile_wrong_file(self):
f1 = FileIO("tests/test1.csv", "r")
loaded1 = f1.loadFile()
f1.close()
f2 = FileIO("tests/test2.csv", "r")
loaded2 = f2.loadFile()
f2.close()
self.assertEqual((loaded1 == loaded2), False)
def test_readlines(self):
filename = "kasta.env"
assert not path.exists(filename)
contents = ["one\n", "two three\n"]
FileIO().writelines(filename, contents)
assert FileIO().readlines(filename) == contents
remove(filename)
assert not path.exists(filename)
def test_writelines(self):
filename = "kasta.env"
assert not path.exists(filename)
lines = ["first\n", "Second\n"]
FileIO().writelines(filename, lines)
assert path.exists(filename)
assert FileIO().readlines(filename) == lines
remove(filename)
assert not path.exists(filename)
def test_close_file(self):
if (os.path.exists("foo")):
os.remove("foo")
f = FileIO("foo", "w")
with self.assertRaises(Exception):
f.close()
def main():
"""
Get configuration, get driver, and build handler and start it.
"""
args = get_args()
port = args.port[0]
baud = args.baud[0]
use_ftdi = args.ftdi
# Driver with context
with serial_link.get_driver(use_ftdi, port, baud) as driver:
# Handler with context
with Handler(Framer(driver.read, driver.write)) as link:
link.add_callback(serial_link.log_printer, SBP_MSG_LOG)
link.add_callback(serial_link.printer, SBP_MSG_PRINT_DEP)
data = open(args.file, 'rb').read()
def progress_cb(size):
sys.stdout.write("\rProgress: %d%% \r" %
(100 * size / len(data)))
sys.stdout.flush()
print('Transferring image file...')
FileIO(link).write("upgrade.image_set.bin",
data,
progress_cb=progress_cb)
print('Committing file to flash...')
code = shell_command(link, "upgrade_tool upgrade.image_set.bin",
300)
if code != 0:
print('Failed to perform upgrade (code = %d)' % code)
return
print('Resetting Piksi...')
link(MsgReset(flags=0))
def fileOpen(self) -> None:
if not self._menuFile.isEnabled():
return
result: Tuple[str, str] = QFileDialog.getOpenFileName(
self, "Select file", "", "JSON Files (*.json);;All Files (*.*)")
if len(result) != 2 or len(result[0]) == 0:
return
fn = result[0]
print("FILE READ \"{}\"".format(fn))
self.polygonDataHelper.clearAll()
hdl = FileIO(self.polygonFactory)
_, errs = hdl.readFile(fn)
errs += self.polygonDataHelper.updateAllPolygonCache()
self.polygonDataHelper.generateMapping()
self._polygonList.polygonsChange()
self._rasterSurface.repaint()
if len(errs) > 0:
ErrorListDrawer(
"There were some errors while importing the JSON file:", errs,
self).show()
def OnFdTextChanged(root, pgbar, pglabel, btn, entry, ft_label):
fd_name = entry.get()
file_io = FileIO()
ft_existed = False
if file_io.check_obj("save"):
pic_dataset_paths = file_io.load_obj("save")
for path in pic_dataset_paths:
if path == fd_name:
ft_existed = True
break
if ft_existed:
pgbar["value"] = 100
pglabel.configure(text="100.00%")
btn["text"] = "重新提取特征"
if getattr(sys, 'frozen', False):
application_path = os.path.dirname(sys.executable)
elif __file__:
application_path = os.path.dirname(__file__)
ft_name = "feature_" + hashlib.md5(fd_name.encode()).hexdigest() + ".h5"
ft_label["text"] = os.path.abspath(os.path.join(application_path, ft_name))
else:
pgbar["value"] = 0
pglabel.configure(text="0.00%")
btn["text"] = "提取特征"
ft_label["text"] = "当前文件夹没有特征被提取"
root.update()
def main(self, argv):
try:
opts, arg = getopt(argv, "hti:u:",
["help", "test", "input=", "user="******" {}'.format(opt))
self.usage()
sys.exit(2)
elif opt in ('-i', '--input'):
logger.info('Detected switch" {}'.format(opt))
self.input_file = arg
elif opt in ('-u', '--user'):
logger.info('Detected switch" {}'.format(opt))
self.made_by = arg
elif opt in ('-t', '--test'):
logger.info('Detected switch" {}'.format(opt))
self.CONFIG = 'Test'
self.show_params()
file_io = FileIO()
file_io.read(self.input_file, self.made_by, self.CONFIG)
def test___init___file_exists(self):
if (os.path.exists("foo")):
os.remove("foo")
f = open("foo", "w")
f.close()
with self.assertRaises(Exception):
FileIO("foo", "w")
def ShowOriginalImage(event):
img_no = int(str(event.widget).split(".")[-1])
file_io = FileIO()
imlist = file_io.load_obj("imlist")
ipath = imlist[img_no]
img = mpimg.imread(ipath)
imgplot = plt.imshow(img)
plt.show()
def __init__(self, filename=None):
self.__sheet = Sheet()
self.__cursor = Cursor()
self.__io = FileIO(filename)
self.__programIsRunning = True
self.__lastCommand = ""
self.load()
def __init__(self):
#load configuration settings
_config = FileIO().loadJSONFile("config.json")
self.twData = _config["twitter_auth_data"]
self.mongoData = _config["mongo_config"]
#parse command line options
self.parser = ArgumentParser(
description=
"Grabs data from the Twitter API and stores it in a MongoDB Database."
)
self.parser.add_argument('-n',
'--name',
type=str,
nargs=1,
required=True,
help='Unique name of the current search.')
self.parser.add_argument(
'-t',
'--terms',
type=str,
nargs='+',
required=False,
help=
'Provide a list of search terms used to collect data from the API.'
)
self.parser.add_argument(
'-f',
'--file',
type=str,
nargs=1,
required=False,
help=
'Provide a path to a JSON file of terms to collect data from the API.'
)
self.parser.add_argument(
'-c',
'--count',
type=int,
required=False,
help="Collect the specified number of tweets.")
self.parser.add_argument(
'-l',
'--listen',
action='store_true',
required=False,
help="Just open the stream and listen. Don't connect to database.")
self.args = self.parser.parse_args()
def __init__(self, image_path, output_filename):
self._point_coords = []
self._point_count = 0
self._line_slope = 0
self._lock_gui = False
self._output_filename = output_filename
self._file = FileIO("./data/")
self._original_img = cv2.imread(image_path, cv2.IMREAD_COLOR)
self._new_img = self._original_img.copy()
self.setup_image_gui(self._new_img)
def AnalyseInputImage(queryDir, maxNo, model_name, img_canvas, root):
h5f = h5py.File(model_name,'r')
feats = h5f['dataset_1'][:]
imgNames_utf = h5f['dataset_2'][:]
imgNames = []
for i in imgNames_utf:
imgNames.append(i.decode('utf-8'))
h5f.close()
model = VGGNet()
queryVec = model.extract_feat(queryDir)
scores = np.dot(queryVec, feats.T)
rank_ID = np.argsort(scores)[::-1]
rank_score = scores[rank_ID]
maxres = int(maxNo)
imlist = [imgNames[index] for i,index in enumerate(rank_ID[0:maxres])]
file_io = FileIO()
file_io.save_obj(imlist,"imlist")
img_canvas.delete('all')
vsbar = Scrollbar(frame_canvas, orient=VERTICAL, command=img_canvas.yview)
vsbar.grid(row=0, column=1, sticky=NS)
vsbar.config(command=img_canvas.yview)
img_canvas.configure(yscrollcommand=vsbar.set)
frame_images = Frame(img_canvas, bg="grey")
img_canvas.create_window((0,0), window=frame_images, anchor='nw')
img_no = 0
max_in_row = 0
height_total = 0
for i in imlist:
basewidth = 300
img = Image.open(i)
wpercent = (basewidth/float(img.size[0]))
hsize = int((float(img.size[1])*float(wpercent)))
max_in_row = max(max_in_row, hsize)
img = img.resize((basewidth,hsize), Image.ANTIALIAS)
render = ImageTk.PhotoImage(img)
img_show = Label(frame_images, image=render, name=str(img_no))
img_show.bind("<Button-1>", ShowOriginalImage)
img_show.image = render
img_show.grid(row=img_no//3, column=img_no%3)
img_no += 1
if img_no%3==0:
height_total += max_in_row
max_in_row = 0
frame_canvas.config(height=height_total)
root.update()
img_canvas.config(scrollregion=img_canvas.bbox("all"))
def fileSaveAs(self) -> None:
if not self._menuFile.isEnabled():
return
result: Tuple[str, str] = QFileDialog.getSaveFileName(
self, "Select file", "", "JSON Files (*.json);;All Files (*.*)")
if len(result) != 2 or len(result[0]) == 0:
return
fn = result[0]
print("FILE WRITE \"{}\"".format(fn))
hdl = FileIO(self.polygonFactory)
_, err = hdl.writeFile(fn)
if err is not None:
ErrorListDrawer(
"There were some errors while exporting the JSON file:", [err],
self).show()
def startStreaming(self):
if self.db and self.auth:
terms = []
if self.args.file: #load a list of terms from a file
data = FileIO().loadFile(self.args.file[0])
terms = [term for term in data.strip().split('\n')]
elif self.args.terms: #read in a list of terms from the commandline
terms = self.args.terms
self.db.tweets.insert(
{"meta_data": {
"terms": terms,
"name": self.args.name[0]
}})
#connect to twitter stream and collect some tweets!
if terms:
print "\n\nEnter [x] to quit the stream..."
print "Connecting to stream..."
listener = MongoStreamListener(self.db,
listen=self.args.listen,
limit=self.args.count,
name=self.args.name[0])
streamer = tweepy.Stream(auth=self.auth,
listener=listener,
timeout=60)
print "Connected. Filtering tweets...\n"
streamer.filter(None, terms, async=True)
while True:
opt = getpass('')
if opt == 'x':
break
print "Quitting..."
streamer.disconnect()
else:
self.parser.print_usage()
def test_loadFile_good_file(self):
f = FileIO("tests/test1.csv", "r")
loaded = f.loadFile()
f.close()
reference = [
Cluster(
'0',
[Point([1, 1, 1]),
Point([2, 2, 2]),
Point([3.0, 3.0, 3.0])]),
Cluster('1', [
Point([1, 1, 1]),
Point([1, 1, 1]),
Point([1, 1, 1]),
Point([1, 1, 1]),
Point([1, 1, 1]),
Point([1, 1, 1]),
]),
Cluster('2', [Point([1, 1, 1])])
]
self.assertEqual(loaded, reference)
def GetFoldernameFromSelector(root, e):
root.update()
filename = askdirectory()
if filename != '':
file_io = FileIO()
if file_io.check_obj("save"):
pic_dataset_paths = file_io.load_obj("save")
ft_existed = 0
for path in pic_dataset_paths:
if path == filename:
break
ft_existed += 1
if ft_existed >= len(pic_dataset_paths):
pic_dataset_paths.append(filename)
else:
tmp = pic_dataset_paths[ft_existed]
pic_dataset_paths[ft_existed] = pic_dataset_paths[-1]
pic_dataset_paths[-1] = tmp
else:
pic_dataset_paths = [filename]
e["values"] = pic_dataset_paths
e.current(len(pic_dataset_paths) - 1)
root.update()
def test___init___file_does_not_exist(self):
with self.assertRaises(Exception):
FileIO("foo", "r")
# Show feature path
Label(root, text="提取特征存储文件名称：").grid(row=1, column=0, sticky=W)
feature_text = "暂时没有特征被提取"
label_feature_name = Label(root, text=feature_text)
label_feature_name.grid(row=1, column=1, sticky=W)
# Setup progress bar for feature extraction
pgbar = ttk.Progressbar(root, orient=HORIZONTAL, length=550, mode='determinate')
pgbar.grid(row=2, column=1, sticky=W)
l_pg = Label(root, text="0.00%")
l_pg.grid(row=2, column=1, sticky=E)
# Create database and extract features
Label(root, text="图片库路径：").grid(row=0, column=0, sticky=W)
file_io = FileIO()
dbFolder_text = [""]
if file_io.check_obj('save'):
dbFolder_text = file_io.load_obj('save')
e_dbFolder = ttk.Combobox(root, values=dbFolder_text, width=80)
e_dbFolder.grid(row=0, column=1, sticky=W)
Button(root, text="浏览文件夹", command=lambda: GetFoldernameFromSelector(root, e_dbFolder)).grid(row=0, column=1, sticky=E)
ft_extract_btn = Button(root, text="提取特征", command=lambda: GenerateFeatureDatabase(e_dbFolder, label_feature_name, pgbar, l_pg, root))
ft_extract_btn.grid(row=1, column=1, sticky=E)
e_var = StringVar()
e_var.trace("w", lambda a, b, c: OnFdTextChanged(root, pgbar, l_pg, ft_extract_btn, e_dbFolder, label_feature_name))
e_dbFolder["textvar"] = e_var
# Setup the maximum number of similar images
Label(root, text="最大图片显示数量：").grid(row=4, column=0, sticky=W)
e_maxRsltNo = Entry(root, width=20)
import random
import sys
import pygame
import numpy
from snake import Snake
from foodspawner import FoodSpawner
from fileio import FileIO
baseFramerate = 500
size = 500
fIO = FileIO()
def gameover(quitGame=False, score = 0, framerate = 0):
pygame.quit()
fIO.printScore(score,framerate)
if quitGame == True:
sys.exit(0)
init(baseFramerate)
def userInput(snake):
for event in pygame.event.get():
if event.type == pygame.QUIT:
gameover(True)
elif event.type == pygame.KEYDOWN:
if event.key == pygame.K_RIGHT:
snake.changeDirTo("R")
if event.key == pygame.K_LEFT:
snake.changeDirTo("L")
def GenerateFeatureDatabase(fd_entry, ft_label, pgbar, pglabel, root):
ft_name = "feature"
fd_name = fd_entry.get()
root.update()
if os.path.exists(fd_name) and os.path.isdir(fd_name):
pass
else:
messagebox.showerror(title="文件夹读取错误", message="无法读取文件或文件夹不存在")
return
file_handler = FileHandler()
# img_list = file_handler.get_imlist(fd_name)
img_list = [os.path.join(dp, f) for dp, dn, filenames in os.walk(fd_name) for f in filenames if os.path.splitext(f)[1] == '.jpg' or os.path.splitext(f)[1] == '.jpeg']
# print(img_list)
print("--------------------------------------------------")
print(" feature extraction starts")
print("--------------------------------------------------")
feats = []
names = []
model = VGGNet()
ite_no = 0
ite_to = len(img_list)
error_occured = False
if getattr(sys, 'frozen', False):
application_path = os.path.dirname(sys.executable)
elif __file__:
application_path = os.path.dirname(__file__)
for i, img_path in enumerate(img_list):
try:
ite_no += 1
norm_feat = model.extract_feat(img_path)
img_name = img_path
feats.append(norm_feat)
names.append(img_name.encode('utf-8'))
print("extracting feature from image No. %d , %d images in total" %((i+1), len(img_list)))
pgbar['value'] = (100 * ite_no) / ite_to
print(pgbar['value'])
label_content="{0:0.2f}%".format(pgbar['value'])
pglabel.configure(text=label_content)
root.update()
except Exception as e:
error_occured = True
logger_fname = os.path.join(application_path, 'error.log')
now = datetime.now()
dt_string = now.strftime("%d/%m/%Y %H:%M:%S")
with open(logger_fname, "a+", encoding='utf-8') as f:
f.write('[Error] ' + dt_string + "\n")
f.write('[Trace Back] ' + str(e) + "\n")
f.write('[Detail] ' + img_path + "\n")
feats = np.array(feats)
print("--------------------------------------------------")
print(" writing feature extraction results ...")
print("--------------------------------------------------")
if error_occured:
messagebox.showerror(title="提取特征时发生错误", message="提取特征时发生错误，程序跳过了错误文件完成特征提取，请将error.log文件，以及后台截图发给开发者。")
output = ft_name + "_" + hashlib.md5(fd_name.encode()).hexdigest() + ".h5"
output = os.path.join(application_path, output)
h5f = h5py.File(output, 'w')
h5f.create_dataset('dataset_1', data = feats)
h5f.create_dataset('dataset_2', data = np.string_(names))
h5f.close()
ft_label.configure(text=os.path.abspath(output))
# Load array from file and update the file path array
file_io = FileIO()
if file_io.check_obj("save"):
pic_dataset_paths = file_io.load_obj("save")
ft_existed = 0
for path in pic_dataset_paths:
if path == fd_name:
break
ft_existed += 1
if ft_existed >= len(pic_dataset_paths):
pic_dataset_paths.append(fd_name)
else:
tmp = pic_dataset_paths[ft_existed]
pic_dataset_paths[ft_existed] = pic_dataset_paths[-1]
pic_dataset_paths[-1] = tmp
print(pic_dataset_paths)
else:
pic_dataset_paths = [fd_name]
file_io.save_obj(pic_dataset_paths,"save")
fd_entry["values"] = pic_dataset_paths
fd_entry.current(len(pic_dataset_paths) - 1)
def __init__(self, fileIO=FileIO()):
self.fileio: FileIO = fileIO
def reset_factory_defaults(self):
# Delete settings file
fio = FileIO(self.link)
fio.remove('config')
# Reset the Piksi
self.link.send_message(ids.RESET, '')
from data import Data
from fileio import FileIO
gData = Data({
'metricPreprocessor': 'euclidean',
'groupGenerator': 'basic',
'solutionGenerator': 'basic',
'localImprover': 'basic'
})
fileIO = FileIO(
gData, {
'problem': 'input.txt',
'data': 'data',
'groups': 'groups',
'solutions': 'solutions'
}, gData.version())
from fileio import FileIO
from nltk import word_tokenize
from preprocess import Clean
io = FileIO()
clean = Clean()
# get reviews failed to judge reasons
def make_label(stars):
if int(float(stars)) > 3:
return 2 # positive
elif int(float(stars)) < 3:
return 0 # negative
else:
return 1 # neutral
def write_tuple_data(data, path_write):
corpus = []
for label, revi in data:
corpus.append(str(label) + "--" + revi)
io.write_file_text('\n'.join(corpus), path_write)
def filterTestDataByDict(path_file, path_write_raw, path_write_clean,
path_dictionary):
"""
:return: filter data by dictionary
"""