Need to use mss witchout saving and open images in order to "optimize" this task here is mine code and sorry my bad english.
from PIL import Image
import pytesseract
import mss
import mss.tools
with mss.mss() as sct:
monitor = {'top': 171, 'left': 1090, 'width': 40, 'height': 17}
output = 'capture.png'.format(**monitor)
sct_img = sct.grab(monitor)
mss.tools.to_png(sct_img.rgb, sct_img.size, output=output)
text = pytesseract.image_to_string(Image.open('capture.png'))
print(text)
Do you mind using Numpy?
import mss
import numpy
import pytesseract
monitor = {'top': 171, 'left': 1090, 'width': 40, 'height': 17}
with mss.mss() as sct:
im = numpy.array(sct.grab(monitor), dtype=numpy.uint8)
im = numpy.flip(im[:, :, :3], 2) # BGRA -> RGB conversion
text = pytesseract.image_to_string(im)
print(text)
A simple one shot time comparison gives me:
MSS.tools + PIL: 0.00988 s
MSS + Numpy : 0.00222 s
Related
I am trying to get an ipywidget button to change colour when clicked. I figured a way to do it as below
import ipywidgets as ipw
from ipywidgets import *
from IPython.display import display, HTML, clear_output
from IPython.display import display, HTML
pdf_btn = ipw.Button(description = 'Run PDF',button_style = 'danger',
layout=Layout(width='150px', height='30px'))
new_btn = ipw.Button(description = 'PDF done',button_style = 'success',
layout=Layout(width='150px', height='30px'))
HBox = ipw.HBox([pdf_btn])
HBox1 = ipw.HBox([new_btn])
def clear(b):
clear_output()
display(HBox1)
pdf_btn.on_click(clear)
display(HBox)
When inputting this code in Jupyter Notebook and rendering through Voila nothing happens.
Any ideas why ? And any suggestions ?
You can just update the button style maybe? Like so:
import ipywidgets as ipw
from ipywidgets import *
from IPython.display import display, HTML, clear_output
from IPython.display import display, HTML
pdf_btn = ipw.Button(description = 'Run PDF',button_style = 'danger',
layout=Layout(width='150px', height='30px'))
HBox = ipw.HBox([pdf_btn])
def clear(b):
pdf_btn.button_style="success"
pdf_btn.description="PDF done"
pdf_btn.on_click(clear)
display(HBox)
Note that your code fails in JupyterLab.
Voila is much more similar to JupyterLab's rendering machinery than the classic interface. For now, it is best to use JupyterLab in conjunction with developing for Voila. (Note that this will change soon as the underlying machinery for what is now the traditional Jupyter notebook interface will soon run on machinery more in line with JupyterLab, see Build Jupyter Notebook v7 off of JupyterLab components. A lot of the old code / approaches will then consistently not work and either interface will be as suitable for developing with Voila in mind.)
When your code fails in JupyterLab it logs that to the console, similar to as shown here. I suspect you could follow Jason's advice, what I reference in my reply, which is here, if you actually want the other button there. (I guess I should say if you actually want the other HBox there since for some reason you have each button in a separate HBox in your code.) In other words, you'd need to add in how to handle the output from HBox1 correctly as part of the main output. Yours is going to the JupyterLab Log console as it is now.
So one option is to add HBox1 in as a displayed item, yet hide it initially:
import ipywidgets as ipw
from ipywidgets import *
from IPython.display import display, HTML, clear_output
from IPython.display import display, HTML
pdf_btn = ipw.Button(description = 'Run PDF',button_style = 'danger',
layout=Layout(width='150px', height='30px'))
new_btn = ipw.Button(description = 'PDF done',button_style = 'success',
layout=Layout(width='150px', height='30px'))
HBox = ipw.HBox([pdf_btn])
HBox1 = ipw.HBox([new_btn])
HBox1.layout.visibility = 'hidden' #based on https://github.com/jupyter-widgets/ipywidgets/issues/674#issuecomment-234321603
def clear(b):
HBox1.layout.visibility = 'visible'
HBox.layout.display = 'none' #based on https://stackoverflow.com/a/54134344/8508004
pdf_btn.on_click(clear)
display(HBox)
display(HBox1)
You could also make an overarching vertical box for the items and then toggle which one it contains, similar to here. This is the 'cleanest' option I'll list here, other than just having one button & changing the button itself:
import ipywidgets as ipw
from ipywidgets import *
from IPython.display import display, HTML, clear_output
from IPython.display import display, HTML
pdf_btn = ipw.Button(description = 'Run PDF',button_style = 'danger',
layout=Layout(width='150px', height='30px'))
new_btn = ipw.Button(description = 'PDF done',button_style = 'success',
layout=Layout(width='150px', height='30px'))
HBox = ipw.HBox([pdf_btn])
HBox1 = ipw.HBox([new_btn])
vb = VBox(children = [HBox])
def clear(b):
vb.children = [HBox1]
pdf_btn.on_click(clear)
display(vb)
('cleanest' in regards to it not creating extra space in the output area.)
Or combine the vertical box approach with the approach of toggling off via the layout settings:
import ipywidgets as ipw
from ipywidgets import *
from IPython.display import display, HTML, clear_output
from IPython.display import display, HTML
pdf_btn = ipw.Button(description = 'Run PDF',button_style = 'danger',
layout=Layout(width='150px', height='30px'))
new_btn = ipw.Button(description = 'PDF done',button_style = 'success',
layout=Layout(width='150px', height='30px'))
HBox = ipw.HBox([pdf_btn])
HBox1 = ipw.HBox([new_btn])
HBox1.layout.visibility = 'hidden' #based on https://github.com/jupyter-widgets/ipywidgets/issues/674#issuecomment-234321603
vb = VBox(children = [HBox, HBox1])
def clear(b):
HBox1.layout.visibility = 'visible'
HBox.layout.display = 'none' #based on https://stackoverflow.com/a/54134344/8508004
pdf_btn.on_click(clear)
display(vb)
And if you want to fully leverage Jupyter's display system further, combine it with an overarching out so you can use print(), too:
import ipywidgets as ipw
from ipywidgets import *
from IPython.display import display, HTML, clear_output
from IPython.display import display, HTML
out = widgets.Output()
pdf_btn = ipw.Button(description = 'Run PDF',button_style = 'danger',
layout=Layout(width='150px', height='30px'))
new_btn = ipw.Button(description = 'PDF done',button_style = 'success',
layout=Layout(width='150px', height='30px'))
HBox = ipw.HBox([pdf_btn])
HBox1 = ipw.HBox([new_btn])
vb = VBox(children = [HBox])
def clear(b):
vb.children = [HBox1]
with out:
print('Enjoy!')
pdf_btn.on_click(clear)
with out:
display(vb)
out
Here's what I tried (from one suggestion in this group):
from pandas_datareader import data as dreader
symbols = ['GOOG', 'AAPL', 'MMM', 'ACN', 'A', 'ADP']
pnls = {i:dreader.DataReader(i,'yahoo','1985-01-01','2016-09-01') for i in symbols}
this is the error
RemoteDataError: Unable to read URL: https://finance.yahoo.com/quote/GOOG/history?period1=473380200&period2=1472768999&interval=1d&frequency=1d&filter=history
Here's another code that does not seem to work:
import time
import datetime
import pandas as pd
tickers = ['TSLA', 'TWTR', 'MSFT', 'GOOG', 'AAPL']
interval = '1d'
period1 = int(time.mktime(datetime.datetime(2021, 1, 1, 23, 59).timetuple()))
period2 = int(time.mktime(datetime.datetime(2022, 5, 31, 23, 59).timetuple()))
xlwriter = pd.ExcelWriter('historicalprices.xlsx', engine='openpyxl')
for ticker in tickers:
query_string = 'https://query1.finance.yahoo.com/v7/finance/download/{ticker}?period1={period1}&period2={period2}&interval={interval}&events=history&includeAdjustedClose=true'
df = pd.read_csv(query_string)
df.to_excel(xlwriter, sheet_name=ticker, index = False)
xlwriter.save()
this is the error:
HTTPError: HTTP Error 500: Internal Server Error
This code works for individual stocks:
yahoo_financials = YahooFinancials('AAPL')
data = yahoo_financials.get_historical_price_data(start_date='2021-12-01', end_date='2022-05-31',
time_interval='daily')
aapl_df = pd.DataFrame(data['AAPL']['prices'])
aapl_df = aapl_df.drop('date', axis=1).set_index('formatted_date')
aapl_df.to_csv('/Users/rangapriyas/Desktop/Prices/AAPL.csv')
I am new to python can anyone help me with a for loop on this above code pls?
Thanks in advance!
This code works for anyone who may benefit:
import yfinance as yf
import pandas as pd
tickers_list = ['AAPL', 'WMT', 'IBM', 'MU', 'BA', 'AXP']
# Fetch the data
import yfinance as yf
data = yf.download(tickers_list,'2015-1-1', '2022-05-31')['Adj Close']
# Print first 5 rows of the data
print(data.head())
Im trying to merge two layers and an already created shape file(1) into a new shape file(2).
I have to copy this newly created shapefile(2) into shapefile(1) so that this new file is used for merging next time.
This I am running in a loop so that at the end I get a shapefile with all the features merged. But my shapefile(1) is not getting updated so my merging is also not correct.
Please find the code below and help me
`
import qgis
from qgis.core import QgsVectorLayer, QgsProject,QgsGeometry
from PyQt5.QtCore import QVariant
import pandas as pd
import processing
import del_temp2 as d
layer2= QgsVectorLayer('LineString', 'line' , "memory")
QgsVectorFileWriter.writeAsVectorFormat(layer2, 'D:/Users/Lahari/ground_trace/orbit_orig.shp', "UTF-8", layer2.crs(), "ESRI Shapefile")
for i in orb:
list1= list.loc[(list['Orbit'] == orb[i])]
list1.to_csv ('D:/Users/Lahari/ground_trace/int/Test3.csv',
index = None,
header=True)
uri='file:///D:/Users/Lahari/ground_trace/int/Test3.csv?delimiter=,&yField=long&xField=Lat'
layer1 = QgsVectorLayer(uri, 'orb', 'delimitedtext')
crs = layer1.crs()
crs.createFromId(4326)
layer1.setCrs(crs)
QgsVectorFileWriter.writeAsVectorFormat(layer1, 'D:/Users/Lahari/ground_trace/orbit_temp1.shp', "UTF-8", layer1.crs(), "ESRI Shapefile", layerOptions=['SHPT=POINT'])
processing.run("qgis:pointstopath",{'INPUT':'D:/Users/Lahari/ground_trace/orbit_temp1.shp','ORDER_FIELD':'Lat','OUTPUT':'D:/Users/Lahari/ground_trace/orbit_temp2.shp'})
processing.run("qgis:arrayoffsetlines",{'INPUT':'D:/Users/Lahari/ground_trace/orbit_temp2.shp','OFFSET':-1,'OUTPUT':'D:/Users/Lahari/ground_trace/orbit_trans1.shp'})
processing.run("qgis:arrayoffsetlines",{'INPUT':'D:/Users/Lahari/ground_trace/orbit_temp2.shp','OFFSET': 1,'OUTPUT':'D:/Users/Lahari/ground_trace/orbit_trans2.shp'})
processing.run("qgis:mergevectorlayers",{'LAYERS':['D:/Users/Lahari/ground_trace/orbit_trans1.shp','D:/Users/Lahari/ground_trace/orbit_trans2.shp','D:/Users/Lahari/ground_trace/orbit_orig.shp#shapefile1'],'CRS':layer1.crs,'OUTPUT':'D:/Users/Lahari/ground_trace/orbit_actual.shp'#shapefile2})
layer3 = QgsVectorLayer("D:/Users/Lahari/ground_trace/orbit_actual.shp", "Test", "ogr")
QgsProject.instance().addMapLayer(layer3)
QgsVectorFileWriter.writeAsVectorFormat(layer3, 'D:/Users/Lahari/ground_trace/orbit_orig.shp', "UTF-8", layer3.crs(), "ESRI Shapefile")
QgsProject.instance().removeMapLayer(layer3.id())
`
I have a simple beam pipline that takes some text and gets embeddings using universal sentence encoder with tf transform. Very similar to the demo made using tf 1.
import tensorflow as tf
import apache_beam as beam
import tensorflow_transform.beam as tft_beam
import tensorflow_transform.coders as tft_coders
from apache_beam.options.pipeline_options import PipelineOptions
import tempfile
model = None
def embed_text(text):
import tensorflow_hub as hub
global model
if model is None:
model = hub.load(
'https://tfhub.dev/google/universal-sentence-encoder/4')
embedding = model(text)
return embedding
def get_metadata():
from tensorflow_transform.tf_metadata import dataset_schema
from tensorflow_transform.tf_metadata import dataset_metadata
metadata = dataset_metadata.DatasetMetadata(dataset_schema.Schema({
'id': dataset_schema.ColumnSchema(
tf.string, [], dataset_schema.FixedColumnRepresentation()),
'text': dataset_schema.ColumnSchema(
tf.string, [], dataset_schema.FixedColumnRepresentation())
}))
return metadata
def preprocess_fn(input_features):
text_integerized = embed_text(input_features['text'])
output_features = {
'id': input_features['id'],
'embedding': text_integerized
}
return output_features
def run(pipeline_options, known_args):
argv = None # if None, uses sys.argv
pipeline_options = PipelineOptions(argv)
pipeline = beam.Pipeline(options=pipeline_options)
with tft_beam.Context(temp_dir=tempfile.mkdtemp()):
articles = (
pipeline
| beam.Create([
{'id':'01','text':'To be, or not to be: that is the question: '},
{'id':'02','text':"Whether 'tis nobler in the mind to suffer "},
{'id':'03','text':'The slings and arrows of outrageous fortune, '},
{'id':'04','text':'Or to take arms against a sea of troubles, '},
]))
articles_dataset = (articles, get_metadata())
transformed_dataset, transform_fn = (
articles_dataset
| 'Extract embeddings' >> tft_beam.AnalyzeAndTransformDataset(preprocess_fn)
)
transformed_data, transformed_metadata = transformed_dataset
_ = (
transformed_data | 'Write embeddings to TFRecords' >> beam.io.tfrecordio.WriteToTFRecord(
file_path_prefix='{0}'.format(known_args.output_dir),
file_name_suffix='.tfrecords',
coder=tft_coders.example_proto_coder.ExampleProtoCoder(
transformed_metadata.schema),
num_shards=1
)
)
result = pipeline.run()
result.wait_until_finished()
python 3.6.8, tf==2.0, tf_transform==0.15, apache-beam[gcp]==0.16 (I tried various compatible combos from https://github.com/tensorflow/transform)
I am getting an error when tf_transform calls the graph analyser:
...
File "/Users/justingrace/.pyenv/versions/hlx36/lib/python3.6/site-packages/tensorflow_transform/beam/impl.py", line 462, in process
lambda: self._make_graph_state(saved_model_dir))
File "/Users/justingrace/.pyenv/versions/hlx36/lib/python3.6/site-packages/tfx_bsl/beam/shared.py", line 221, in acquire
return _shared_map.acquire(self._key, constructor_fn)
File "/Users/justingrace/.pyenv/versions/hlx36/lib/python3.6/site-packages/tfx_bsl/beam/shared.py", line 184, in acquire
result = control_block.acquire(constructor_fn)
File "/Users/justingrace/.pyenv/versions/hlx36/lib/python3.6/site-packages/tfx_bsl/beam/shared.py", line 87, in acquire
result = constructor_fn()
File "/Users/justingrace/.pyenv/versions/hlx36/lib/python3.6/site-packages/tensorflow_transform/beam/impl.py", line 462, in <lambda>
lambda: self._make_graph_state(saved_model_dir))
File "/Users/justingrace/.pyenv/versions/hlx36/lib/python3.6/site-packages/tensorflow_transform/beam/impl.py", line 438, in _make_graph_state
self._exclude_outputs, self._tf_config)
File "/Users/justingrace/.pyenv/versions/hlx36/lib/python3.6/site-packages/tensorflow_transform/beam/impl.py", line 357, in __init__
tensor_inputs = graph_tools.get_dependent_inputs(graph, inputs, fetches)
File "/Users/justingrace/.pyenv/versions/hlx36/lib/python3.6/site-packages/tensorflow_transform/graph_tools.py", line 686, in get_dependent_inputs
sink_tensors_ready)
File "/Users/justingrace/.pyenv/versions/hlx36/lib/python3.6/site-packages/tensorflow_transform/graph_tools.py", line 499, in __init__
table_init_op, graph_analyzer_for_table_init, translate_path_fn)
File "/Users/justingrace/.pyenv/versions/hlx36/lib/python3.6/site-packages/tensorflow_transform/graph_tools.py", line 560, in _get_table_init_op_source_info
if table_init_op.type not in _TABLE_INIT_OP_TYPES:
AttributeError: 'Tensor' object has no attribute 'type' [while running 'Extract embeddings/TransformDataset/Transform']
Exception ignored in: <bound method CapturableResourceDeleter.__del__ of <tensorflow.python.training.tracking.tracking.CapturableResourceDeleter object at 0x14152fbe0>>
Traceback (most recent call last):
File "/Users/justingrace/.pyenv/versions/hlx36/lib/python3.6/site-packages/tensorflow_core/python/training/tracking/tracking.py", line 190, in __del__
File "/Users/justingrace/.pyenv/versions/hlx36/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py", line 3872, in as_default
File "/Users/justingrace/.pyenv/versions/3.6.8/lib/python3.6/contextlib.py", line 159, in helper
TypeError: 'NoneType' object is not callable
It appears like the graph analyser is expecting a list of operations with a type attribute but it is receiving a tensor. I can't grasp why this error is occuring other than a bug in the graph analyzer or a compatibility issue with tfx_bsl (there seem to be issues with pyarrow 0.14 so I have downgraded to 0.13)
Output of pip freeze:
absl-py==0.8.1
annoy==1.12.0
apache-beam==2.16.0
appnope==0.1.0
astor==0.8.1
astunparse==1.6.3
attrs==19.1.0
avro-python3==1.9.1
backcall==0.1.0
bleach==3.1.0
cachetools==3.1.1
certifi==2019.11.28
chardet==3.0.4
crcmod==1.7
cymem==1.31.2
cytoolz==0.9.0.1
decorator==4.4.1
defusedxml==0.6.0
dill==0.3.0
docopt==0.6.2
en-core-web-lg==2.0.0
en-coref-lg==3.0.0
en-ner-trained==2.0.0
entrypoints==0.3
fastavro==0.21.24
fasteners==0.15
flashtext==2.7
future==0.18.2
fuzzywuzzy==0.16.0
gast==0.2.2
google-api-core==1.16.0
google-apitools==0.5.28
google-auth==1.11.0
google-auth-oauthlib==0.4.1
google-cloud-bigquery==1.17.1
google-cloud-bigtable==1.0.0
google-cloud-core==1.3.0
google-cloud-datastore==1.7.4
google-cloud-pubsub==1.0.2
google-pasta==0.1.8
google-resumable-media==0.4.1
googleapis-common-protos==1.51.0
grpc-google-iam-v1==0.12.3
grpcio==1.24.0
h5py==2.10.0
hdfs==2.5.8
httplib2==0.12.0
idna==2.8
importlib-metadata==1.5.0
ipykernel==5.1.4
ipython==7.12.0
ipython-genutils==0.2.0
ipywidgets==7.5.1
jedi==0.16.0
Jinja2==2.11.1
jsonpickle==1.2
jsonschema==3.2.0
jupyter==1.0.0
jupyter-client==5.3.4
jupyter-console==6.1.0
jupyter-core==4.6.2
Keras-Applications==1.0.8
Keras-Preprocessing==1.1.0
lxml==4.2.1
Markdown==3.2.1
MarkupSafe==1.1.1
mistune==0.8.4
mock==2.0.0
monotonic==1.5
more-itertools==8.2.0
msgpack==0.6.2
msgpack-numpy==0.4.4
murmurhash==0.28.0
nbconvert==5.6.1
nbformat==5.0.4
networkx==2.1
nltk==3.4.5
notebook==6.0.3
numpy==1.18.1
oauth2client==3.0.0
oauthlib==3.1.0
opt-einsum==3.1.0
packaging==20.1
pandas==0.23.0
pandocfilters==1.4.2
parso==0.6.1
pathlib2==2.3.5
pbr==5.4.4
pexpect==4.8.0
pickleshare==0.7.5
plac==0.9.6
pluggy==0.13.1
preshed==1.0.1
prometheus-client==0.7.1
prompt-toolkit==3.0.3
proto-google-cloud-datastore-v1==0.90.4
protobuf==3.11.3
psutil==5.6.7
ptyprocess==0.6.0
py==1.8.1
pyahocorasick==1.4.0
pyarrow==0.13.0
pyasn1==0.4.8
pyasn1-modules==0.2.8
pydot==1.4.1
Pygments==2.5.2
PyHamcrest==1.9.0
pymongo==3.10.1
pyparsing==2.4.6
pyrsistent==0.15.7
pytest==5.3.5
python-dateutil==2.8.0
python-Levenshtein==0.12.0
pytz==2019.3
PyYAML==3.13
pyzmq==18.1.1
qtconsole==4.6.0
regex==2017.4.5
repoze.lru==0.7
requests==2.22.0
requests-oauthlib==1.3.0
rsa==4.0
scikit-learn==0.19.1
scipy==1.4.1
Send2Trash==1.5.0
six==1.14.0
spacy==2.0.12
tb-nightly==2.2.0a20200217
tensorboard==2.0.2
tensorflow==2.0.0
tensorflow-estimator==2.0.1
tensorflow-hub==0.6.0
tensorflow-metadata==0.15.2
tensorflow-serving-api==2.1.0
tensorflow-transform==0.15.0
termcolor==1.1.0
terminado==0.8.3
testpath==0.4.4
textblob==0.15.1
tf-estimator-nightly==2.1.0.dev2020012309
tf-nightly==2.2.0.dev20200217
tfx-bsl==0.15.0
thinc==6.10.3
toolz==0.10.0
tornado==6.0.3
tqdm==4.23.3
traitlets==4.3.3
typing==3.7.4.1
typing-extensions==3.7.4.1
ujson==1.35
Unidecode==1.0.22
urllib3==1.25.8
wcwidth==0.1.8
webencodings==0.5.1
Werkzeug==1.0.0
Whoosh==2.7.4
widgetsnbextension==3.5.1
wrapt==1.11.2
zipp==2.2.0
This could be an underlying issue according to this github post. Try using an updated version of tensorflow (2.1.0), or maybe even an updated version of your keras packages.
import the necessary packages
from matplotlib import pyplot as plt
import numpy as np
import argparse
import cv2
# construct the argument parser and parse the arguments
**ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required = True, help = "Path to the image")
args = vars(ap.parse_args())**
# load the image and show it
image = cv2.imread(args["image"])
cv2.imshow("image", image)**
My error is:
usage: -c [-h] -i IMAGE
-c: error: argument **-i/--image is required**
If this is my current wd: C:\Users\Jeremy\Documents\IPython Notebooks
what is wrong? what should I be inputing for -i/--image?