class ZipArchive(BaseArchive):
def __init__(self, file):
self._archive = ZipFile(file)
def list(self, *args, **kwargs):
self._archive.printdir(*args, **kwargs)
def filenames(self):
return self._archive.namelist()
def namelist(self):
return self._archive.namelist()
def close(self):
return self._archive.close()
def is_encrypted(self):
for file_ in self._archive.infolist():
if file_.flag_bits & 0x1 != 0:
return True
else:
return False
def extractall(self, file):
return self._archive.extractall(file)
def printContents(zip_file_path):
#validate the source zip file
zf = ZipFile(zip_file_path, 'r')
#unzip the contents of the file
print "\"%s\" contents:" % (zip_file_path)
zf.printdir()
return 0
def DownloadAndExtractZipFile():
zip_loc = "https://www.bseindia.com/markets/MarketInfo/BhavCopy.aspx"
# connect to a URL
website = urllib.request.urlopen(zip_loc)
# read html code
html = website.read()
# use re.findall to get all the links
# b'"((http|ftp)s?://.*?)"'.decode(encoding)
links = re.findall(
"http://www.bseindia.com/download/BhavCopy/Equity/.*_CSV.ZIP",
html.decode('utf-8'))
if (len(links) > 0):
content = requests.get(links[0])
# unzip the content
f = ZipFile(BytesIO(content.content))
f.extractall() # f.extract("EQ080120.CSV")
f.printdir()
# print(f.namelist()[0])
filepath = os.getcwd() + '\\' + f.namelist()[0]
# filepath = os.path.join(os.getcwd(), f.namelist()[0])
return filepath
else:
return ""
def extractZipFile(fileName):
zip = ZipFile(fileName,'r')
print("\nAll files :-")
zip.printdir()
zip.extractall()
foldName = os.path.splitext(fileName)[0]
print(f"\nSuccessfully Save.\nFolder name :- {foldName}")
def main(args):
visual_recognition = VisualRecognition(
version='2016-05-20',
api_key='7b851fccf7f17a35fc7569a5dad6e1eb4f650f70')
with open('ingredients.txt') as f:
lines = f.read().splitlines()
for line in lines:
directory = "C:/Dev/GitHub/flavortown/imagetesting/zips" + line
query = line
max_images = 25
save_directory = directory
image_type = "Action"
query = query.split()
query = '+'.join(query)
query = query + "+walmart+OR+amazon"
url = "https://www.google.com/search?q=" + query + "&source=lnms&tbm=isch"
header = {
'User-Agent':
"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/43.0.2357.134 Safari/537.36"
}
soup = get_soup(url, header)
ActualImages = [
] # contains the link for Large original images, type of image
for a in soup.find_all("div", {"class": "rg_meta"}):
link, Type = json.loads(a.text)["ou"], json.loads(a.text)["ity"]
ActualImages.append((link, Type))
fileNames = []
for i, (img, Type) in enumerate(ActualImages[0:max_images]):
try:
req = urllib2.Request(img, headers={'User-Agent': header})
raw_img = urllib2.urlopen(req).read()
fileName = ""
if len(Type) == 0:
fileName = "img" + "_" + str(i) + ".jpg"
f = open(fileName, 'wb')
else:
fileName = "img" + "_" + str(i) + "." + Type
f = open(fileName, 'wb')
f.write(raw_img)
f.close()
fileNames.append(fileName)
except Exception as e:
print("could not load : " + img)
myzip = ZipFile(line + '.zip', 'w', zipfile.ZIP_DEFLATED)
for fileName in fileNames:
myzip.write(fileName)
myzip.printdir()
myzip.close()
for fileName in fileNames:
os.remove(fileName)
def test_zip(self):
url = "https://www.python.org/ftp/python/3.5.0/python-3.5.0-embed-amd64.zip"
f = SeekableHTTPFile(url, debug=True)
zf = ZipFile(f)
zf.printdir()
filelist = set(zf.namelist())
self.assertIn("python.exe", filelist)
pyenv = zf.read("pyvenv.cfg")
self.assertEqual(pyenv.rstrip(), b"applocal = true")
def unzip_fF(fFname):
from zipfile import ZipFile
'''
fFname (str): a file path to a target zip folder to be unzipped
'''
zip = ZipFile(fFname, 'r')
zip.printdir() # To print all the contents of the zip file
zip.extractall()
zip.close()
def test_zip(self):
url = "https://www.python.org/ftp/python/3.5.0/python-3.5.0-embed-amd64.zip"
f = SeekableHTTPFile(url, debug=True)
zf = ZipFile(f)
zf.printdir()
filelist = set(zf.namelist())
self.assertIn("python.exe", filelist)
pyenv = zf.read("pyvenv.cfg")
self.assertEqual(pyenv.rstrip(), b"applocal = true")
def Chatbot():
os.makedirs('Chatbot')
print('Making Model...')
time.sleep(2.5)
ic = ZipFile(os.getcwd() + '//MODEL//CHATBOT.zip', 'r')
ic.printdir()
time.sleep(2.5)
print('Finishing Up!')
ic.extractall()
print('Done!')
def ObjectDetection(): # Extracting The Object Detection VirtualEnviorment=
os.makedirs('Object Detection')
print('Making Model...')
time.sleep(2.5)
ic = ZipFile(os.getcwd() + '//MODEL//OD.zip', 'r')
ic.printdir()
time.sleep(2.5)
print('Finishing Up!')
ic.extractall()
print('Done!')
def StockPrediction():
os.makedirs('Stock Predictor')
print('Making Model...')
time.sleep(2.5)
ic = ZipFile(os.getcwd() + '//MODEL//STOCK.zip', 'r')
ic.printdir()
time.sleep(2.5)
print('Finishing Up!')
ic.extractall()
print('Done!')
def SpeechRecognition():
os.makedirs('Speech Recognition')
print('Making Model...')
time.sleep(2.5)
ic = ZipFile(os.getcwd() + '//MODEL//SR.zip', 'r')
ic.printdir()
time.sleep(2.5)
print('Finishing Up!')
ic.extractall()
print('Done!')
def ImageClassifier(): # Extracting The Image Classifier VirtualEnviorment
os.makedirs('Image Classifier')
print('Making Model...')
time.sleep(2.5)
ic = ZipFile(os.getcwd() + '//MODEL//IMAGE_CLASSIFIER.zip', 'r')
ic.printdir()
time.sleep(2.5)
print('Finishing Up!')
ic.extractall()
print('Done!')
def unzip_ff(fname):
"""
fname (str): File path to a target zip folder to be unzipped
"""
# Library
from zipfile import ZipFile
z = ZipFile(fname, 'r')
z.printdir() # To print all the contents of the zip file
z.extractall()
z.close()
def main(zfname, keys, generate=True):
zf = ZipFile(zfname)
#print(zf.infolist()[-3:])
zf.printdir()
# print(zf.namelist())
if generate:
dict_generator(keys)
# zf.read(zf.namelist()[-1])
# RuntimeError: File LabAccessCodes-TopSecret.txt is encrypted, password required for extraction
filename = zf.namelist()[-1]
try:
zf.read(filename)
except RuntimeError as e:
print(e)
print("attempting to crack now..")
attempt_cracker(zf, filename)
""" Many ==> One Deflate Algo is used to compress Why .zip ? - reduced size - encapsulate data in single file """ # printing contents of python file from zipfile import ZipFile # Note Capital # builting file file_name="MyZip.zip" f=ZipFile(file_name,'r') f.printdir() f.close()
def classify(self, imgFile):
rgb = io.imread(imgFile)
aspect_ratio = len(rgb) / len(rgb[1])
rgb = transform.resize(rgb, [int(1000 * aspect_ratio), 1000])
img = color.rgb2lab(rgb)
thresholded = np.logical_and(
*[img[..., i] > t for i, t in enumerate([40, 0, 0])])
'''
fig, ax = plt.subplots(ncols=2)
ax[0].imshow(rgb); ax[0].axis('off')
ax[1].imshow(thresholded); ax[1].axis('off')
plt.show()
'''
X = np.argwhere(thresholded)[::5]
X = np.fliplr(X)
db = DBSCAN(eps=50, min_samples=200).fit(X)
core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
labels = db.labels_
# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
print('Estimated number of clusters: %d' % n_clusters_)
unique_labels = set(labels)
'''
# #############################################################################
# Plot result
import matplotlib.pyplot as plt
# Black removed and is used for noise instead.
colors = [plt.cm.Spectral(each)
for each in np.linspace(0, 1, len(unique_labels))]
for k, col in zip(unique_labels, colors):
if k == -1:
# Black used for noise.
col = [0, 0, 0, 1]
class_member_mask = (labels == k)
xy = X[class_member_mask & core_samples_mask]
plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
markeredgecolor='k', markersize=14)
xy = X[class_member_mask & ~core_samples_mask]
plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
markeredgecolor='k', markersize=6)
plt.title('Estimated number of clusters: %d' % n_clusters_)
plt.show()
x = edge_roberts.sum(axis=0)
x = x - np.min(x[np.nonzero(x)])
averageVal = x.mean()
x = x - 5
x[x < (averageVal / 6)] = 0
y = range(len(img[1]))
plt.plot(y, x)
X = np.array(list(zip(x,np.zeros(len(x)))), dtype=np.int)
bandwidth = estimate_bandwidth(X, quantile=0.25)
ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)
ms.fit(X)
labels = ms.labels_
cluster_centers = ms.cluster_centers_
labels_unique = np.unique(labels)
n_clusters_ = len(labels_unique)
'''
cropped_images = []
unique_labels.remove(-1)
col = 0
for k in unique_labels:
#my_members = labels == k
#members = X[my_members, 0]
left = min(X[labels == k][:, 0])
right = max(X[labels == k][:, 0])
padding = 20
if left > padding:
left = left - padding
if right < len(img[1]) - padding:
right = right + padding
cropped_images.append(rgb[0:len(img), left:right])
# save each cropped image by its index number
myzip = ZipFile('cutimgs.zip', 'w', zipfile.ZIP_DEFLATED)
for c, cropped_image in enumerate(cropped_images):
io.imsave(str(c) + ".png", cropped_image)
myzip.write(str(c) + ".png")
myzip.printdir()
myzip.close()
for c, cropped_image in enumerate(cropped_images):
os.remove(str(c) + ".png")
with open('cutimgs.zip', 'rb') as img:
param = {'classifier_ids': "foodtest_843163904"}
params = json.dumps(param)
response = self.vr.classify(images_file=img, parameters=params)
classes = []
for image in response['images']:
if (image['classifiers'][0]['classes'][0]['class']
) not in classes:
classes.append(
(image['classifiers'][0]['classes'][0]['class']))
os.remove('cutimgs.zip')
return classes
from zipfile import ZipFile
import os
import sys
if len(sys.argv) < 1:
print "No filename specified here. Exiting..."
sys.exit()
filepath = sys.argv[1]
if not os.path.exists(filepath):
print "filepath {0} doesnot exist".format(filepath)
sys.exit()
zipfile_object = ZipFile(filepath)
zipfile_object.printdir()
zipfile_object.close()
def full(fileName):
vr = VisualRecognition(version='2016-05-20', api_key='7b851fccf7f17a35fc7569a5dad6e1eb4f650f70')
rgb = scipy.misc.imread(fileName, mode='RGB')
aspect_ratio = len(rgb) / len(rgb[1])
rgb = transform.resize(rgb, [int(1000*aspect_ratio), 1000])
img = color.rgb2lab(rgb)
thresholded = np.logical_and(*[img[..., i] > t for i, t in enumerate([40, 0, 0])])
if (np.sum(thresholded) > (thresholded.size / 2)):
thresholded = np.invert(thresholded)
X = np.argwhere(thresholded)[::5]
X = np.fliplr(X)
db = DBSCAN(eps=25, min_samples=200).fit(X)
core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
labels = db.labels_
# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
print('Estimated number of clusters: %d' % n_clusters_)
unique_labels = set(labels)
cropped_images = []
unique_labels.remove(-1)
col=0
for k in unique_labels:
#my_members = labels == k
#members = X[my_members, 0]
left = min(X[labels==k][:,0])
right = max(X[labels==k][:,0])
padding = 20
if left > padding:
left = left - padding
if right < len(img[1]) - padding:
right = right + padding
cropped_images.append(rgb[0:len(img), left:right])
# save each cropped image by its index number
myzip = ZipFile('test.zip', 'w',zipfile.ZIP_DEFLATED)
for c, cropped_image in enumerate(cropped_images):
io.imsave(str(c) + ".png", cropped_image)
myzip.write(str(c) + ".png")
myzip.printdir()
myzip.close()
for c, cropped_image in enumerate(cropped_images):
os.remove(str(c) + ".png")
classes = []
with open('test.zip', 'rb') as img:
param = {'classifier_ids':"foodtest_1606116153"}
params = json.dumps(param)
response = vr.classify(images_file=img, parameters=params)
for image in response['images']:
max_score = 0
max_class = ""
for classifier in image['classifiers']:
for classif in classifier['classes']:
if (classif['score'] > max_score):
max_class = classif['class']
if max_class:
max_class = max_class.replace('_', ' ')
if (max_class) not in classes:
classes.append(max_class)
os.remove('test.zip')
return(classes)
from zipfile import ZipFile
zip = ZipFile("d:/demo1.war", 'r')
zip.printdir()
def flash_release(path=None, st_serial=None):
from panda import Panda, PandaDFU, ESPROM, CesantaFlasher
from zipfile import ZipFile
def status(x):
print("\033[1;32;40m" + x + "\033[00m")
if st_serial == None:
# look for Panda
panda_list = Panda.list()
if len(panda_list) == 0:
raise Exception(
"panda not found, make sure it's connected and your user can access it"
)
elif len(panda_list) > 1:
raise Exception("Please only connect one panda")
st_serial = panda_list[0]
print("Using panda with serial %s" % st_serial)
if path == None:
print(
"Fetching latest firmware from github.com/commaai/panda-artifacts")
r = requests.get(
"https://raw.githubusercontent.com/commaai/panda-artifacts/master/latest.json"
)
url = json.loads(r.text)['url']
r = requests.get(url)
print("Fetching firmware from %s" % url)
path = io.StringIO(r.content)
zf = ZipFile(path)
zf.printdir()
version = zf.read("version")
status("0. Preparing to flash " + version)
code_bootstub = zf.read("bootstub.panda.bin")
code_panda = zf.read("panda.bin")
code_boot_15 = zf.read("boot_v1.5.bin")
code_boot_15 = code_boot_15[0:2] + "\x00\x30" + code_boot_15[4:]
code_user1 = zf.read("user1.bin")
code_user2 = zf.read("user2.bin")
# enter DFU mode
status("1. Entering DFU mode")
panda = Panda(st_serial)
panda.enter_bootloader()
time.sleep(1)
# program bootstub
status("2. Programming bootstub")
dfu = PandaDFU(PandaDFU.st_serial_to_dfu_serial(st_serial))
dfu.program_bootstub(code_bootstub)
time.sleep(1)
# flash main code
status("3. Flashing main code")
panda = Panda(st_serial)
panda.flash(code=code_panda)
panda.close()
# flashing ESP
if panda.is_white():
status("4. Flashing ESP (slow!)")
align = lambda x, sz=0x1000: x + "\xFF" * ((sz - len(x)) % sz)
esp = ESPROM(st_serial)
esp.connect()
flasher = CesantaFlasher(esp, 230400)
flasher.flash_write(0x0, align(code_boot_15), True)
flasher.flash_write(0x1000, align(code_user1), True)
flasher.flash_write(0x81000, align(code_user2), True)
flasher.flash_write(0x3FE000, "\xFF" * 0x1000)
flasher.boot_fw()
del flasher
del esp
time.sleep(1)
else:
status("4. No ESP in non-white panda")
# check for connection
status("5. Verifying version")
panda = Panda(st_serial)
my_version = panda.get_version()
print("dongle id: %s" % panda.get_serial()[0])
print(my_version, "should be", version)
assert (str(version) == str(my_version))
# done!
status("6. Success!")
''' ZipFile-创建zip压缩文件，并向其中添加指定的文件 zipfile.ZipFile(file, mode='r', compression=ZIP_STORED, allowZip64=True, compresslevel=None, *, strict_timestamps=True) 形参mode值： 'r' 来读取一个存在的文件 'w' 向压缩包中添加文件，删除压缩包内已有的文件，只有新文件 'a' 向压缩包中添加文件，不会删除压缩包内已有的文件，新老文件都存在 'x' 来仅新建并写入新的文件 ''' from zipfile import ZipFile #创建一个空的压缩文件,并将指定文件添加到压缩包 file_name = 'new.zip' myzip = ZipFile(file=file_name, mode='w') file_name = 'test.txt' myzip.write(filename=file_name) #将zip文档内的信息打印到控制台上。 myzip.printdir()
def flash_release(path=None, st_serial=None):
from panda import Panda, PandaDFU, ESPROM, CesantaFlasher
from zipfile import ZipFile
def status(x):
print("\033[1;32;40m"+x+"\033[00m")
if st_serial == None:
# look for Panda
panda_list = Panda.list()
if len(panda_list) == 0:
raise Exception("panda not found, make sure it's connected and your user can access it")
elif len(panda_list) > 1:
raise Exception("Please only connect one panda")
st_serial = panda_list[0]
print("Using panda with serial %s" % st_serial)
if path == None:
print("Fetching latest firmware from github.com/commaai/panda-artifacts")
r = requests.get("https://raw.githubusercontent.com/commaai/panda-artifacts/master/latest.json")
url = json.loads(r.text)['url']
r = requests.get(url)
print("Fetching firmware from %s" % url)
path = StringIO.StringIO(r.content)
zf = ZipFile(path)
zf.printdir()
version = zf.read("version")
status("0. Preparing to flash "+version)
code_bootstub = zf.read("bootstub.panda.bin")
code_panda = zf.read("panda.bin")
code_boot_15 = zf.read("boot_v1.5.bin")
code_boot_15 = code_boot_15[0:2] + "\x00\x30" + code_boot_15[4:]
code_user1 = zf.read("user1.bin")
code_user2 = zf.read("user2.bin")
# enter DFU mode
status("1. Entering DFU mode")
panda = Panda(st_serial)
panda.enter_bootloader()
time.sleep(1)
# program bootstub
status("2. Programming bootstub")
dfu = PandaDFU(PandaDFU.st_serial_to_dfu_serial(st_serial))
dfu.program_bootstub(code_bootstub)
time.sleep(1)
# flash main code
status("3. Flashing main code")
panda = Panda(st_serial)
panda.flash(code=code_panda)
panda.close()
# flashing ESP
status("4. Flashing ESP (slow!)")
align = lambda x, sz=0x1000: x+"\xFF"*((sz-len(x)) % sz)
esp = ESPROM(st_serial)
esp.connect()
flasher = CesantaFlasher(esp, 230400)
flasher.flash_write(0x0, align(code_boot_15), True)
flasher.flash_write(0x1000, align(code_user1), True)
flasher.flash_write(0x81000, align(code_user2), True)
flasher.flash_write(0x3FE000, "\xFF"*0x1000)
flasher.boot_fw()
del flasher
del esp
time.sleep(1)
# check for connection
status("5. Verifying version")
panda = Panda(st_serial)
my_version = panda.get_version()
print("dongle id: %s" % panda.get_serial()[0])
print(my_version, "should be", version)
assert(str(version) == str(my_version))
# done!
status("6. Success!")
def flash_release(path=None, st_serial=None):
from panda import Panda, PandaDFU
from zipfile import ZipFile
def status(x):
print("\033[1;32;40m" + x + "\033[00m")
if st_serial is not None:
# look for Panda
panda_list = Panda.list()
if len(panda_list) == 0:
raise Exception(
"panda not found, make sure it's connected and your user can access it"
)
elif len(panda_list) > 1:
raise Exception("Please only connect one panda")
st_serial = panda_list[0]
print("Using panda with serial %s" % st_serial)
if path is None:
print(
"Fetching latest firmware from github.com/commaai/panda-artifacts")
r = requests.get(
"https://raw.githubusercontent.com/commaai/panda-artifacts/master/latest.json"
)
url = json.loads(r.text)['url']
r = requests.get(url)
print("Fetching firmware from %s" % url)
path = io.BytesIO(r.content)
zf = ZipFile(path)
zf.printdir()
version = zf.read("version").decode()
status("0. Preparing to flash " + str(version))
code_bootstub = zf.read("bootstub.panda.bin")
code_panda = zf.read("panda.bin")
# enter DFU mode
status("1. Entering DFU mode")
panda = Panda(st_serial)
panda.reset(enter_bootstub=True)
panda.reset(enter_bootloader=True)
time.sleep(1)
# program bootstub
status("2. Programming bootstub")
dfu = PandaDFU(PandaDFU.st_serial_to_dfu_serial(st_serial))
dfu.program_bootstub(code_bootstub)
time.sleep(1)
# flash main code
status("3. Flashing main code")
panda = Panda(st_serial)
panda.flash(code=code_panda)
panda.close()
# check for connection
status("4. Verifying version")
panda = Panda(st_serial)
my_version = panda.get_version()
print("dongle id: %s" % panda.get_serial()[0])
print(my_version, "should be", version)
assert (str(version) == str(my_version))
# done!
status("6. Success!")
def analyse_zip_archive(zip_archive: zipfile.ZipFile):
is_encrypted = __archive_is_encrypted(zip_archive=zip_archive)
print('encrypted:'.ljust(20), is_encrypted)
print()
zip_archive.printdir()
