I want download mp3 file from UTL and save it to a folder.
This is my code:
local sound = HTTPS.request('https://hozory.com/translate/?target='..matches[1]..'&text='..text)
local voice = json:decode(sound)
if voice.result.Voice_link ~= "false" then
local f = assert(io.open('test.mp3', 'w'))
f:write(voice.result.Voice_link)
f:close()
But this creates an empty mp3 file.
Related
I am trying to copy .zip files from a shared network folder to a unix environment using pysmb. The process will copy the .zip file names, but not the contents of the files
smbFolder = "networkdrive"
conn = SMBConnection('username', 'password', smbFolder,'')
conn.connect(smbFolder)
Share='shareFolder'
ShareFolder='TargetFolder'
ShareFilename = ShareFolder
Contents = conn.listPath(Share, ShareFolder)
for Content in Contents:
try:
conn.retrieveFile(Content, open(savePath + '/' + Content.filename, 'wb'))
except: None
conn.close()
Expecting this to copy zip files to the savePath folder along with the contents of the zip file, but zip files are copied as empty folders
I'm writing a program to take large PDF's and convert each page to a .jpg, then add the .jpg's of each pdf file to their own directory (which the program needs to create).
I have completed the conversion part of the program, but I am stuck on creating a directory and adding the files to the directory.
Here's my code so far.
import glob, sys, fitz, os, shutil
zoom_x = 2.0
zoom_y = 2.0
mat = fitz.Matrix(zoom_x, zoom_y) # to get better resolution
all_files = glob.glob('/Users/homefolder/Downloads/*.pdf') # image path
print(all_files)
for filename in all_files:
doc = fitz.open(filename)
head, tail = os.path.split(doc.name)
save_file_name = tail.split('.')[0]
for page in doc: # iterate through the pages
# print(page)
pix = page.get_pixmap(matrix=mat)
# render the image
filepath_save = '/Users/homefolder/Downloads/files' + save_file_name + str(page.number) + '.jpg'
pix.save(filepath_save) # save image
sample = glob.glob('/Users/homefolder/Downloads/*.jpg')
How would I write the code to create a directory for each pdf file and add those .jpg's to the directory?
You can create directory and save to it your processed files, I also refactored your code a bit:
import glob, fitz, os
zoom_x = 2.0
zoom_y = 2.0
mat = fitz.Matrix(zoom_x, zoom_y)
pdf_files = glob.glob('/Users/homefolder/Downloads/*.pdf')
save_to = '/Users/homefolder/Downloads/pdf_as_img/'
for path in pdf_files:
doc = fitz.open(path)
base_name, _ = os.path.splitext(os.path.basename(doc.name))
directory_to_save = os.path.join(save_to, base_name)
if not os.path.exists(directory_to_save):
os.makedirs(directory_to_save)
for page in doc:
pix = page.get_pixmap(matrix=mat)
filepath_save = os.path.join(directory_to_save, str(page.number) + '.jpg')
pix.save(filepath_save)
This script creates a directory for every pdf file and saves pages as jpg to it.
i am trying to upload files to S3 before that i am trying to Gzip files, if you see the code below, the files uploaded to the S3 have no change in the size, so i am trying to figure out if i have missed something.
import gzip
import shutil
from io import BytesIO
def upload_gzipped(bucket, key, fp, compressed_fp=None, content_type='text/plain'):
"""Compress and upload the contents from fp to S3.
If compressed_fp is None, the compression is performed in memory.
"""
if not compressed_fp:
compressed_fp = BytesIO()
with gzip.GzipFile(fileobj=compressed_fp, mode='wb') as gz:
shutil.copyfileobj(fp, gz)
compressed_fp.seek(0)
bucket.upload_fileobj(
compressed_fp,
key,
{'ContentType': content_type, 'ContentEncoding': 'gzip'})
Courtesy Link for the source
And this is how i am using this fucntion, so basically reading files as stream from SFTP and then trying to Gzip them and then write them to S3.
with pysftp.Connection(host_name, username=user, password=password, cnopts=cnopts, port=int(port)) as sftp:
list_of_files = sftp.listdir('{}{}'.format(base_path, file_path))
is_file_found = False
for file_name in list_of_files:
if entity_name in str(file_name.lower()):
is_file_found = True
flo = BytesIO()
# Step 1: Read File Using SFTP as input Stream
sftp.getfo('{}{}/{}'.format(base_path, file_path, file_name), flo)
s3_destination_key = '{}/{}'.format(s3_path, file_name)
# Step 2: Write files to desitination S3
logger.info('Moving file to S3 {} '.format(s3_destination_key))
# Creating a bucket resource to use bucket object for file upload
input_bucket_object = S3.Bucket(environment_config['S3_INBOX_BUCKET'])
flo.seek(0)
upload_gzipped(input_bucket_object, s3_destination_key, flo)
It seems like the upload_gzipped function uses shutil.copyfileobj incorrectly.
Looking at https://docs.python.org/3/library/shutil.html#shutil.copyfileobj shows that you put the source first, and destination second.
Also, you're just writing your object to a gzipped object without ever actually compressing it.
You need to compress fp into a Gzip object, then upload that specific object to S3.
I'd recommend not using that gist from github as it seems wrong.
While trying to download sentinel image for a specific location, the tif file is generated by default in drive but its not readable by openCV or PIL.Image().Below is the code for the same. If I use the file format as tfrecord. There are no Images downloaded in the drive.
starting_time = '2018-12-15'
delta = 15
L = -96.98
B = 28.78
R = -97.02
T = 28.74
cordinates = [L,B,R,T]
my_scale = 30
fname = 'sinton_texas_30'
llx = cordinates[0]
lly = cordinates[1]
urx = cordinates[2]
ury = cordinates[3]
geometry = [[llx,lly], [llx,ury], [urx,ury], [urx,lly]]
tstart = datetime.datetime.strptime(starting_time, '%Y-%m-%d') tend =
tstart+datetime.timedelta(days=delta)
collSent = ee.ImageCollection('COPERNICUS/S2').filterDate(str(tstart).split('')[0], str(tend).split(' ')[0]).filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20)).map(mask2clouds)
medianSent = ee.Image(collSent.reduce(ee.Reducer.median())) cropLand = ee.ImageCollection('USDA/NASS/CDL').filterDate('2017-01-01','2017-12-31').first()
task_config = {
'scale': my_scale,
'region': geometry,
'fileFormat':'TFRecord'
}
f1 = medianSent.select(['B1_median','B2_median','B3_median'])
taskSent = ee.batch.Export.image(f1,fname+"_Sent",task_config)
taskSent.start()
I expect the output to be readable in python so I can covert into numpy. In case of file format 'tfrecord', I expect the file to be downloaded in my drive.
I think you should think about the following things:
File format
If you want to open your file with PIL or OpenCV, and not with TensorFlow, you would rather use GeoTIFF. Try with this format and see if things are improved.
Saving to drive
Normally saving to your Drive is the default behavior. However, you can try to force writing to your drive:
ee.batch.Export.image.toDrive(image=f1, ...)
You can further try to setup a folder, where the images should be sent to:
ee.batch.Export.image.toDrive(image=f1, folder='foo', ...)
In addition, the Export data help page and this tutorial are good starting points for further research.
I have set up 3 Google Cloud Storge buckets and 3 functions (one for each bucket) that will trigger when a PDF file is uploaded to a bucket. Functions convert PDF to png image and do further processing.
When I am trying to create a 4th bucket and similar function, strangely it is not working. Even if I copy one of the existing 3 functions, it is still not working and I am getting this error:
Traceback (most recent call last): File "/env/local/lib/python3.7/site-packages/google/cloud/functions_v1beta2/worker.py", line 333, in run_background_function _function_handler.invoke_user_function(event_object) File "/env/local/lib/python3.7/site-packages/google/cloud/functions_v1beta2/worker.py", line 199, in invoke_user_function return call_user_function(request_or_event) File "/env/local/lib/python3.7/site-packages/google/cloud/functions_v1beta2/worker.py", line 196, in call_user_function event_context.Context(**request_or_event.context)) File "/user_code/main.py", line 27, in pdf_to_img with Image(filename=tmp_pdf, resolution=300) as image: File "/env/local/lib/python3.7/site-packages/wand/image.py", line 2874, in __init__ self.read(filename=filename, resolution=resolution) File "/env/local/lib/python3.7/site-packages/wand/image.py", line 2952, in read self.raise_exception() File "/env/local/lib/python3.7/site-packages/wand/resource.py", line 222, in raise_exception raise e wand.exceptions.PolicyError: not authorized/tmp/tmphm3hiezy' # error/constitute.c/ReadImage/412`
It is baffling me why same functions are working on existing buckets but not on new one.
UPDATE:
Even this is not working (getting "cache resources exhausted" error):
In requirements.txt:
google-cloud-storage
wand
In main.py:
import tempfile
from google.cloud import storage
from wand.image import Image
storage_client = storage.Client()
def pdf_to_img(data, context):
file_data = data
pdf = file_data['name']
if pdf.startswith('v-'):
return
bucket_name = file_data['bucket']
blob = storage_client.bucket(bucket_name).get_blob(pdf)
_, tmp_pdf = tempfile.mkstemp()
_, tmp_png = tempfile.mkstemp()
tmp_png = tmp_png+".png"
blob.download_to_filename(tmp_pdf)
with Image(filename=tmp_pdf) as image:
image.save(filename=tmp_png)
print("Image created")
new_file_name = "v-"+pdf.split('.')[0]+".png"
blob.bucket.blob(new_file_name).upload_from_filename(tmp_png)
Above code is supposed to just create a copy of image file which is uploaded to bucket.
Because the vulnerability has been fixed in Ghostscript but not updated in ImageMagick, the workaround for converting PDFs to images in Google Cloud Functions is to use this ghostscript wrapper and directly request the PDF conversion to png from Ghostscript (bypassing ImageMagick).
requirements.txt
google-cloud-storage
ghostscript==0.6
main.py
import locale
import tempfile
import ghostscript
from google.cloud import storage
storage_client = storage.Client()
def pdf_to_img(data, context):
file_data = data
pdf = file_data['name']
if pdf.startswith('v-'):
return
bucket_name = file_data['bucket']
blob = storage_client.bucket(bucket_name).get_blob(pdf)
_, tmp_pdf = tempfile.mkstemp()
_, tmp_png = tempfile.mkstemp()
tmp_png = tmp_png+".png"
blob.download_to_filename(tmp_pdf)
# create a temp folder based on temp_local_filename
# use ghostscript to export the pdf into pages as pngs in the temp dir
args = [
"pdf2png", # actual value doesn't matter
"-dSAFER",
"-sDEVICE=pngalpha",
"-o", tmp_png,
"-r300", tmp_pdf
]
# the above arguments have to be bytes, encode them
encoding = locale.getpreferredencoding()
args = [a.encode(encoding) for a in args]
#run the request through ghostscript
ghostscript.Ghostscript(*args)
print("Image created")
new_file_name = "v-"+pdf.split('.')[0]+".png"
blob.bucket.blob(new_file_name).upload_from_filename(tmp_png)
Anyway, this gets you around the issue and keeps all the processing in GCF for you. Hope it helps. Your code works for single page PDFs though. My use-case was for multipage pdf conversion, ghostscript code & solution in this question.
This actually seems to be a show stopper for ImageMagick related functionalities using PDF format. Similar code deployed by us on Google App engine via custom docker is failing with the same error on missing authorizations.
I am not sure how to edit the policy.xml file on GAE or GCF but a line there has to be changed to:
<policy domain="coder" rights="read|write" pattern="PDF" />
#Dustin: Do you have a bug link where we can see the progress ?
Update:
I fixed it on my Google app engine container by adding a line in docker image. This directly changes the policy.xml file content after imagemagick gets installed.
RUN sed -i 's/rights="none"/rights="read|write"/g' /etc/ImageMagick-6/policy.xml
This is an upstream bug in Ubuntu, we are working on a workaround for App Engine and Cloud Functions.
While we wait for the issue to be resolved in Ubuntu, I followed #DustinIngram's suggestion and created a virtual machine in Compute Engine with an ImageMagick installation. The downside is that I now have a second API that my API in App Engine has to call, just to generate the images. Having said that, it's working fine for me. This is my setup:
Main API:
When a pdf file is uploaded to Cloud Storage, I call the following:
response = requests.post('http://xx.xxx.xxx.xxx:5000/makeimages', data=data)
Where data is a JSON string with the format {"file_name": file_name}
On the API that is running on the VM, the POST request gets processed as follows:
#app.route('/makeimages', methods=['POST'])
def pdf_to_jpg():
file_name = request.form['file_name']
blob = storage_client.bucket(bucket_name).get_blob(file_name)
_, temp_local_filename = tempfile.mkstemp()
temp_local_filename_jpeg = temp_local_filename + '.jpg'
# Download file from bucket.
blob.download_to_filename(temp_local_filename)
print('Image ' + file_name + ' was downloaded to ' + temp_local_filename)
with Image(filename=temp_local_filename, resolution=300) as img:
pg_num = 0
image_files = {}
image_files['pages'] = []
for img_page in img.sequence:
img_page_2 = Image(image=img_page)
img_page_2.format = 'jpeg'
img_page_2.compression_quality = 70
img_page_2.save(filename=temp_local_filename_jpeg)
new_file_name = file_name.replace('.pdf', 'p') + str(pg_num) + '.jpg'
new_blob = blob.bucket.blob(new_file_name)
new_blob.upload_from_filename(temp_local_filename_jpeg)
print('Page ' + str(pg_num) + ' was saved as ' + new_file_name)
image_files['pages'].append({'page': pg_num, 'file_name': new_file_name})
pg_num += 1
try:
os.remove(temp_local_filename)
except (ValueError, PermissionError):
print('Could not delete the temp file!')
return jsonify(image_files)
This will download the pdf from Cloud Storage, create an image for each page, and save them back to cloud storage. The API will then return a JSON file with the list of image files created.
So, not the most elegant solution, but at least I don't need to convert the files manually.