I want to download only person class and binary segmentation from COCO dataset. How can I do it?
use pycocotools .
import library
from pycocotools.coco import COCO
load json file of coco annotation
coco = COCO('/home/office/cocoDataset/annotations/instances_train2017.json')
get category IDs of coco dataset
category_ids = coco.getCatIds(catNms=['person'])
get annotations of a single image
annotations = coco.getAnnIds(imgIds=image_id, catIds=category_ids, iscrowd=False)
here each person has different annotation, and i'th person's annotation is annotation[i] hence merge all the annotations and save it
if annotations:
mask = coco.annToMask(annotations[0])
for i in range(len(annotations)):
mask |= coco.annToMask(annotations[i])
mask = mask * 255
im = Image.fromarray(mask)
im.save('~/mask_name.png')
Related
I'm fairly new to Python programming and am attempting to extract data from a JSON array. Below code results in an error for
js[jstring][jkeys]['5. volume'])
Any help would be much appreciated.
import urllib.request, urllib.parse, urllib.error
import json
def DailyData(symb):
url = https://www.alphavantage.co/queryfunction=TIME_SERIES_DAILY&symbol=MSFT&apikey=demo
stockdata = urllib.request.urlopen(url)
data = stockdata.read().decode()
try:
js = json.loads(data)
except:
js = None
jstring = 'Time Series (Daily)'
for entry in js:
i = js[jstring].keys()
for jkeys in i:
return (jkeys,
js[jstring][jkeys]['1. open'],
js[jstring][jkeys]['2. high'],
js[jstring][jkeys]['3. low'],
js[jstring][jkeys]['4. close'],
js[jstring][jkeys]['5. volume'])
print('volume',DailyData(symbol)[5])
Looks like the reason for the error is because the returned data from the URL is a bit more hierarchical than you may realize. To see that, print out js (I recommend using a jupyter notebook):
import urllib.request, urllib.parse, urllib.error
import ssl
import json
import sqlite3
url = "https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=MSFT&apikey=demo"
stockdata = urllib.request.urlopen(url)
data = stockdata.read().decode()
js = json.loads(data)
js
You can see that js (now a python dict) has a "Meta Data" key before the actual time series begins. You need to start operating on the dict at that key.
Having said that, to get the data into a table like structure (for plotting, time series analysis, etc), you can use pandas package to read the dict key directly into a dataframe. The pandas DataFrame constructor accepts a dict as input. In this case, the data was transposed, so the T at the end rotates it (try with and without the T and you will see it.
import pandas as pd
df=pd.DataFrame(js['Time Series (Daily)']).T
df
Added edit... You could get the data into a dataframe with a single line of code:
import requests
import pandas as pd
url = "https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=MSFT&apikey=demo"
data = pd.DataFrame(requests.get(url).json()['Time Series (Daily)']).T
DataFrame: The contructor from Pandas to make data into a table like structure
requests.get(): method from the requests library to fetch data..
.json(): directly converts from JSON to a dict
['Time Series (Daily)']: pulls out the key from the dict that is the time series
.T: transposes the rows and columns.
Good luck!
Following code worked for me
import urllib.request, urllib.parse, urllib.error
import json
def DailyData(symb):
# Your code was missing the ? after query
url = "https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol={}&apikey=demo".format(symb)
stockdata = urllib.request.urlopen(url)
data = stockdata.read().decode()
js = json.loads(data)
jstring = 'Time Series (Daily)'
for entry in js:
i = js[jstring].keys()
for jkeys in i:
return (jkeys,
js[jstring][jkeys]['1. open'],
js[jstring][jkeys]['2. high'],
js[jstring][jkeys]['3. low'],
js[jstring][jkeys]['4. close'],
js[jstring][jkeys]['5. volume'])
# query multiple times, just to print one item?
print('open',DailyData('MSFT')[1])
print('high',DailyData('MSFT')[2])
print('low',DailyData('MSFT')[3])
print('close',DailyData('MSFT')[4])
print('volume',DailyData('MSFT')[5])
Output:
open 99.8850
high 101.4300
low 99.6700
close 101.1600
volume 19234627
Without seeing the error, it's hard to know what exact problem you were having.
For example consider this dataset:
(1)
https://archive.ics.uci.edu/ml/machine-learning-databases/annealing/anneal.data
Or
(2)
http://data.worldbank.org/topic
How does one call such external datasets into scikit-learn to do anything with it?
The only kind of dataset calling that I have seen in scikit-learn is through a command like:
from sklearn.datasets import load_digits
digits = load_digits()
You need to learn a little pandas, which is a data frame implementation in python. Then you can do
import pandas
my_data_frame = pandas.read_csv("/path/to/my/data")
To create model matrices from your data frame, I recommend the patsy library, which implements a model specification language, similar to R formulas
import patsy
model_frame = patsy.dmatrix("my_response ~ my_model_fomula", my_data_frame)
then the model frame can be passed in as an X into the various sklearn models.
Simply run the following command and replace the name 'EXTERNALDATASETNAME' with the name of your dataset
import sklearn.datasets
data = sklearn.datasets.fetch_EXTERNALDATASETNAME()
It would have been very gratifying to figure this out by myself but I haven't been able to.
I want to grab a random value from a text file that contains data in the form of a dictionary eg:
{'One': '1111111', 'Two': '2222222', 'Three': '3333333'}
I've tried a few variations, but code is currently:
from random import *
table = open('file.txt')
random_value = random.choice(table.values())
When I try and print 'random_value' (to see if it is working), I get the error:
AttributeError: 'file' object has no attribute 'values'
table is a file object, and thus you want to turn it into a dictionary. Here I use the ast module:
from random import choice # No need to import everything if you're going to use just one function
import ast
table = open('file.txt').read()
mydict = ast.literal_eval(table)
random_value = choice(mydict.values())
I'm using objectify-appengine in my app. In the DB I store latitude & longitude of places.
at some point I'd like to find the closest place (from the DB) to a specific point.
As far as i understood i can't perform regular SQL-like queries.
So my question is how can it be done in the best way?
You should take a look at GeoModel, which enables Geospatial Queries with Google App Engine.
Update:
Let's assume that you have in your Objectify annotated model class, a GeoPt property called coordinates.
You need to have in your project two libraries:
GeoLocation.java
Java GeoModel
In the code that you want to perform a geo query, you have the following:
import com.beoui.geocell.GeocellManager;
import com.beoui.geocell.model.BoundingBox;
import you.package.path.GeoLocation;
// other imports
// in your method
GeoLocation specificPointLocation = GeoLocation.fromDegrees(specificPoint.latitude, specificPoint.longitude);
GeoLocation[] bc = specificPointLocation.boundingCoordinates(radius);
// Transform this to a bounding box
BoundingBox bb = new BoundingBox((float) bc[0].getLatitudeInDegrees(),
(float) bc[1].getLongitudeInDegrees(),
(float) bc[1].getLatitudeInDegrees(),
(float) bc[0].getLongitudeInDegrees());
// Calculate the geocells list to be used in the queries (optimize
// list of cells that complete the given bounding box)
List<String> cells = GeocellManager.bestBboxSearchCells(bb, null);
// calculate geocells of your model class instance
List <String> modelCells = GeocellManager.generateGeoCell(myInstance.getCoordinate);
// matching
for (String c : cells) {
if (modelCells.contains(c)) {
// success, do sth with it
break;
}
}
I'm unable to workout how you can get objects from the Google App Engine Datastore using get_by_id. Here is the model
from google.appengine.ext import db
class Address(db.Model):
description = db.StringProperty(multiline=True)
latitude = db.FloatProperty()
longitdue = db.FloatProperty()
date = db.DateTimeProperty(auto_now_add=True)
I can create them, put them, and retrieve them with gql.
address = Address()
address.description = self.request.get('name')
address.latitude = float(self.request.get('latitude'))
address.longitude = float(self.request.get('longitude'))
address.put()
A saved address has values for
>> address.key()
aglndWVzdGJvb2tyDQsSB0FkZHJlc3MYDQw
>> address.key().id()
14
I can find them using the key
from google.appengine.ext import db
address = db.get('aglndWVzdGJvb2tyDQsSB0FkZHJlc3MYDQw')
But can't find them by id
>> from google.appengine.ext import db
>> address = db.Model.get_by_id(14)
The address is None, when I try
>> Address.get_by_id(14)
AttributeError: type object 'Address' has no attribute 'get_by_id'
How can I find by id?
EDIT: It turns out I'm an idiot and was trying find an Address Model in a function called Address. Thanks for your answers, I've marked Brandon as the correct answer as he got in first and demonstrated it should all work.
I just tried it on shell.appspot.com and it seems to work fine:
Google Apphosting/1.0
Python 2.5.2 (r252:60911, Feb 25 2009, 11:04:42)
[GCC 4.1.0]
>>> class Address(db.Model):
description = db.StringProperty(multiline=True)
latitude = db.FloatProperty()
longitdue = db.FloatProperty()
date = db.DateTimeProperty(auto_now_add=True)
>>> addy = Address()
>>> addyput = addy.put()
>>> addyput.id()
136522L
>>> Address.get_by_id(136522)
<__main__.Address object at 0xa6b33ae3bf436250>
An app's key is a list of (kind, id_or_name) tuples - for root entities, always only one element long. Thus, an ID alone doesn't identify an entity - the type of entity is also required. When you call db.Model.get_by_id(x), you're asking for the entity with key (Model, x). What you want is to call Address.get_by_id(x), which fetches the entity with key (Address, x).
You should use long type in get_by_id("here").
Int type must have a error message.