Develop Reference

How do I extract GPS metadata from DJI photographs using python? [duplicate]

I am writing a small program to get the GPS info of a iphone jpg photo.
The library I am using is the PIL in python. Now I am able to get the GPSInfo, which is something like:
{1: 'N',
2: ((1, 1), (20, 1), (5365, 100)),
3: 'E',
4: ((103, 1), (41, 1), (1052, 100)),
5: 0,
6: (43, 1),
7: ((15, 1), (32, 1), (7, 1)),
16: 'T',
17: (77473, 452),
29: '2013:10:25'}
How can I interpret this? And I notice the tag is not continuous, so is there any cheating sheet which I can refer to in order to get a better understanding of all the number tags and what they mean? Thank you!
UPDATES
Sorry, I have figured it out. In the PIL lib, there is a GPSTAGS.get() function which can help me decode the key in gps info. Thank you guys!
gpsinfo = {}
for key in exif['GPSInfo'].keys():
decode = ExifTags.GPSTAGS.get(key,key)
gpsinfo[decode] = exif['GPSInfo'][key]
print gpsinfo
and here is the result
{'GPSTimeStamp': ((15, 1), (32, 1), (7, 1)),
'GPSImgDirectionRef': 'T',
'GPSImgDirection': (77473, 452),
'GPSLongitude': ((103, 1), (41, 1), (1052, 100)),
'GPSLatitudeRef': 'N', 29: '2013:10:25',
'GPSAltitude': (43, 1),
'GPSLatitude': ((1, 1), (20, 1), (5365, 100)),
'GPSLongitudeRef': 'E',
'GPSAltitudeRef': 0}

Use exifread module.
Here is a very helpful gist
import exifread as ef
# barrowed from
# https://gist.github.com/snakeye/fdc372dbf11370fe29eb
def _convert_to_degress(value):
"""
Helper function to convert the GPS coordinates stored in the EXIF to degress in float format
:param value:
:type value: exifread.utils.Ratio
:rtype: float
"""
d = float(value.values[0].num) / float(value.values[0].den)
m = float(value.values[1].num) / float(value.values[1].den)
s = float(value.values[2].num) / float(value.values[2].den)
return d + (m / 60.0) + (s / 3600.0)
def getGPS(filepath):
'''
returns gps data if present other wise returns empty dictionary
'''
with open(filepath, 'rb') as f:
tags = ef.process_file(f)
latitude = tags.get('GPS GPSLatitude')
latitude_ref = tags.get('GPS GPSLatitudeRef')
longitude = tags.get('GPS GPSLongitude')
longitude_ref = tags.get('GPS GPSLongitudeRef')
if latitude:
lat_value = _convert_to_degress(latitude)
if latitude_ref.values != 'N':
lat_value = -lat_value
else:
return {}
if longitude:
lon_value = _convert_to_degress(longitude)
if longitude_ref.values != 'E':
lon_value = -lon_value
else:
return {}
return {'latitude': lat_value, 'longitude': lon_value}
return {}
file_path = 'file path of the file'
gps = getGPS(file_path)
print gps

Late answer, but as of 2022 you can use GPSPhoto, i.e.:
from GPSPhoto import gpsphoto
# Get the data from image file and return a dictionary
data = gpsphoto.getGPSData('IMG_20181224_201933.jpg')
print(data['Latitude'], data['Longitude'])
Output:
38.71615498471598 -9.148730635643007
Installation:
pip3 install piexif
pip3 install gpsphoto

OP, has already posted a solution using PIL. If you wants to just get GPS info from Python, you can get it by using exifread
Install package using pip
$ pip install exifread
and get GPS data
In [10]: import exifread
In [11]: tags = exifread.process_file(open('./tests/demo-project/content/test.jpg', 'rb'))
In [12]: geo = {i:tags[i] for i in tags.keys() if i.startswith('GPS')}
In [13]: geo
Out[13]:
{'GPS GPSAltitude': (0x0006) Ratio=186188/239 # 898,
'GPS GPSAltitudeRef': (0x0005) Byte=0 # 722,
'GPS GPSDate': (0x001D) ASCII=2015:12:06 # 954,
'GPS GPSDestBearing': (0x0018) Ratio=43771/526 # 946,
'GPS GPSDestBearingRef': (0x0017) ASCII=T # 806,
'GPS GPSImgDirection': (0x0011) Ratio=43771/526 # 938,
'GPS GPSImgDirectionRef': (0x0010) ASCII=T # 782,
'GPS GPSLatitude': (0x0002) Ratio=[46, 3803/100, 0] # 850,
'GPS GPSLatitudeRef': (0x0001) ASCII=N # 674,
'GPS GPSLongitude': (0x0004) Ratio=[13, 2429/100, 0] # 874,
'GPS GPSLongitudeRef': (0x0003) ASCII=E # 698,
'GPS GPSSpeed': (0x000D) Ratio=139/50 # 930,
'GPS GPSSpeedRef': (0x000C) ASCII=K # 758,
'GPS GPSTimeStamp': (0x0007) Ratio=[10, 37, 33] # 906,
'GPS Tag 0x001F': (0x001F) Ratio=30 # 966}

write data in to a csv according to headers names, which indicate occurrences of items

I need to enter data in to csv using headers and put a value if the flag is available in the event else zero it. Required output is:
I am currently getting:
This is my current code, I would like to know how to generate my desired output:
inputs for code is counter1-4 shown below :
OrderedDict([('flags=40', 3971), ('flags=10004', 6244), ('flags=10100', 236), ('flags=90002', 2), ('flags=80', 2009), ('flags=10080', 5421), ('flags=4', 2886), ('flags=100', 227), ('flags=80002', 58), ('flags=10040', 8990), ('flags=0', 5)])
OrderedDict([('flags=40', 16), ('flags=10004', 6244), ('flags=10100', 236), ('flags=90002', 2), ('flags=10080', 5421), ('flags=4', 16), ('flags=80002', 11), ('flags=10040', 8990), ('flags=0', 4), ('Total', 20940)])
OrderedDict([('flags=4', 1332), ('flags=40', 1839), ('flags=80002', 3), ('flags=100', 197), ('flags=80', 935), ('Total', 4306)])
OrderedDict([('Total', 0)])
OrderedDict([('flags=40', 2116), ('flags=80', 1074), ('flags=4', 1538), ('flags=100', 30), ('flags=80002', 44), ('flags=0', 1), ('Total', 4803)])
dat = 1
with open(outputcsv,'wb') as outcsv:
writer = csv.writer(outcsv,delimiter=',')
appname = inputfile[:-3]
writer.writerow(appname.split(','))
for x in threads:
writer.writerows([x.split(',')])
#w.writeheader([x.split(',')])
if dat == 1:
w = csv.DictWriter(outcsv,counter1.keys())
w.writeheader()
w.writerow(counter1)
elif dat == 2:
w = csv.DictWriter(outcsv,counter2.keys())
w.writeheader()
w.writerow(counter2)
elif dat == 3:
w = csv.DictWriter(outcsv,counter3.keys())
w.writeheader()
w.writerow(counter3)
elif dat == 4:
w = csv.DictWriter(outcsv,counter4.keys())
w.writeheader()
w.writerow(counter4)
dat = dat +1
writer.writerows('\n')
code for how threads are being read:
exampleFile = open('top_tasks.csv')
exampleReader = csv.reader(exampleFile)
exampleData = list(exampleReader)
thread1 = exampleData[11][0]
thread2 = exampleData[12][0]
thread3 = exampleData[13][0]
thread4 = exampleData[14][0]
threads = [thread1,thread2,thread3,thread4]

I think this code meets your requirements:
from collections import OrderedDict
import csv
# build an OrderedDict of all keys
all_keys = OrderedDict()
# first column gets name of data set
all_keys[data_set_name] = data_set_name
# collect all of the known keys, and insert the thread name
for counter, thread in zip(counters, threads):
all_keys.update(counter)
counter[data_set_name] = thread
with open(outputcsv, 'wb') as outcsv:
# using all known keys, create a csv writer
w = csv.DictWriter(outcsv, fieldnames=all_keys.keys())
# output the header and data rows
w.writeheader()
w.writerows(counters)
Data Used:
outputcsv = 'output.csv'
counters = [
OrderedDict(
[('flags=40', 3971), ('flags=10004', 6244), ('flags=10100', 236),
('flags=90002', 2), ('flags=80', 2009), ('flags=10080', 5421),
('flags=4', 2886), ('flags=100', 227), ('flags=80002', 58),
('flags=10040', 8990), ('flags=0', 5)]),
OrderedDict(
[('flags=40', 16), ('flags=10004', 6244), ('flags=10100', 236),
('flags=90002', 2), ('flags=10080', 5421), ('flags=4', 16),
('flags=80002', 11), ('flags=10040', 8990), ('flags=0', 4),
('Total', 20940)]),
OrderedDict([('flags=4', 1332), ('flags=40', 1839), ('flags=80002', 3),
('flags=100', 197), ('flags=80', 935), ('Total', 4306)]),
OrderedDict([('Total', 0)]),
OrderedDict([('flags=40', 2116), ('flags=80', 1074), ('flags=4', 1538),
('flags=100', 30), ('flags=80002', 44), ('flags=0', 1),
('Total', 4803)]),
]
# code assumes thread names are in a list, make some sample names
threads = ['thread%d' % (i+1) for i in range(len(counters))]
# first column header if the name of the data set
data_set_name = 'CandyCrush 1'

Sort tuples by time interval? Python

How can I sort these tuples by time interval, say every hour?
[('172.18.74.146', datetime.time(11, 28, 58)), ('10.227.211.244',
datetime.time(11, 54, 19)), ('10.227.215.68', datetime.time(11, 54, 34)),
('10.227.209.139', datetime.time(12, 14, 47)), ('10.227.147.98',
datetime.time(14, 47, 25))]
The result should be:
[["172.18.74.146, 10.227.211.244, 10.227.215.68", "11-12"], etc...]
I tried to use group by, but doesnt get what I want:
for dd in data[1:]:
ips = dd[1].split(",")
dates = dd[2].split(",")
i = 0
while(i < len(dates)):
ips[i] = ips[i].strip()
hour, mins, second = dates[i].strip().split(":")
dates[i] = datetime.time(int(hour), int(mins), int(second))
i+=1
order = [(k, ', '.join(str(s[0]) for s in v)) for k, v in groupby(sorted(zip(ips, dates), key=operator.itemgetter(1)), lambda x: x[1].hour)]

In [17]: a = [('172.18.74.146', datetime.time(11, 28, 58)), ('10.227.211.244',
datetime.time(11, 54, 19)), ('10.227.215.68', datetime.time(11, 54, 34)),
('10.227.209.139', datetime.time(12, 14, 47)), ('10.227.147.98',
datetime.time(14, 47, 25))]
In [18]: [(k, ', '.join(str(s[0]) for s in v)) for k, v in groupby(a, lambda x: x[1].hour)]
Out[18]:
[(11, '172.18.74.146, 10.227.211.244, 10.227.215.68'),
(12, '10.227.209.139'),
(14, '10.227.147.98')]

This should work for you:
from __future__ import print_function
import datetime
import itertools
def iter_len(iterable):
return sum(1 for __ in iterable)
def by_hour(item): # Hour key
timestamp = item[1]
return '{}-{}'.format(timestamp.hour, (timestamp.hour+1) % 24)
def by_half_hour(item): # Half-hour key
timestamp = item[1]
half_hour = timestamp.hour + (0.5 * (timestamp.minute // 30))
return '{:.1f}-{:.1f}'.format(half_hour, (half_hour+0.5) % 24)
def get_results(data, key): # Name this more appropriately
data = sorted(data, key=key)
for key, grouper in itertools.groupby(data, key):
yield (key, iter_len(grouper))
data = [
('172.18.74.146', datetime.time(11, 28, 58)),
('10.227.211.244', datetime.time(11, 54, 19)),
('10.227.215.68', datetime.time(11, 54, 34)),
('10.227.209.139', datetime.time(12, 14, 47)),
('10.227.147.98', datetime.time(14, 47, 25)),
]
print('By Hour')
print(list(get_results(data, by_hour)))
print()
print("By Half Hour")
print(list(get_results(data, by_half_hour)))
Output:
$ ./SO_32081251.py
By Hour
[('11-12', 3), ('12-13', 1), ('14-15', 1)]
By Half Hour
[('11.0-11.5', 1), ('11.5-12.0', 2), ('12.0-12.5', 1), ('14.5-15.0', 1)]

This is almost what you want. Use the hour to group by:
for k,g in itertools.groupby(order, lambda x: x[1].hour):
print k,list(g)
Results in:
11 [('172.18.74.146', datetime.time(11, 28, 58)), ('10.227.211.244', datetime.time(11, 54, 19)), ('10.227.215.68', datetime.time(11, 54, 34))]
12 [('10.227.209.139', datetime.time(12, 14, 47))]
14 [('10.227.147.98', datetime.time(14, 47, 25))]

Writing numbers into file python

I was trying to extract all the elements of the my data points (x,y) tuples, and put them into list of x values and y list, and transfer them to two columns in excel spreadsheet. It seems writing numbers into file is quite difficult. Can anyone shed a light on this problem?
Current state:
xlist=[list[i][0] for i in range(len(list))]
ylist=[list[i][1] for i in range(len(list))]
fob=open('c:/test/a.txt','w')
fob.write(xlist[i] for i in range(len(xlist))
i want to write down a column of numbers in notepad so that I can highlight and copy into spread sheet directly .
Below are my data.
list = [(0.496, 12.49), (0.531, 12.40), (0.578, 12.18), (0.615,
11.96), (0.657, 11.75), (0.731, 11.28), (0.785, 10.85), (0.812,
10.61), (0.883, 9.92), (0.930, 9.40), (0.979, 8.77), (1.026,
8.10), (1.081, 7.23), (1.134, 6.33), (1.189, 5.39), (1.220,
4.85), (1.273, 3.92), (1.332, 2.91), (1.364, 2.55), (1.418,
2.16), (1.467, 1.65), (1.523, 1.17), (1.569, 0.82), (1.626,
0.47), (1.678, 0.21), (1.723, 0.01), (1.776, 0.19), (1.814,
0.28), (1.869, 0.36), (1.933, 0.36), (1.972, 0.31), (2.021,
0.18), (2.081, 0.13), (2.129, 0.46), (2.169, 0.79), (2.219,
1.24), (2.280, 1.84), (2.306, 2.11), (2.358, 2.67), (2.414,
3.37), (2.471, 4.05), (2.505, 4.51), (2.562, 5.22), (2.613,
5.84), (2.652, 6.31), (2.712, 7.01), (2.758, 7.52), (2.802,
7.99), (2.869, 8.63), (2.930, 9.16), (2.971, 9.57), (3.043,
10.35), (3.078, 10.69), (3.119, 11.00), (3.174, 11.26), (3.217,
11.40), (3.261, 11.53), (3.307, 11.55), (3.371, 11.51), (3.432,
11.40), (3.479, 11.26), (3.507, 11.20), (3.557, 11.00), (3.623,
10.55), (3.663, 10.28), (3.729, 9.79), (3.768, 9.57), (3.825,
9.24), (3.880, 8.85), (3.944, 8.41), (3.969, 8.04), (4.014,
7.55), (4.086, 6.67), (4.105, 6.37), (4.166, 5.50), (4.212,
4.88), (4.266, 4.20), (4.311, 3.69), (4.364, 3.06), (4.401,
2.65), (4.453, 2.09), (4.497, 1.68), (4.556, 1.18), (4.602,
0.85), (4.644, 0.57), (4.695, 0.29), (4.754, 0.04), (4.799,
0.11), (4.847, 0.17), (4.918, 0.11), (4.959, 0.04), (4.992,
0.19), (5.063, 0.64), (5.098, 0.90), (5.157, 1.40), (5.201,
1.79), (5.245, 2.20), (5.291, 2.65), (5.326, 3.00), (5.387,
3.65), (5.420, 4.02), (5.469, 4.62), (5.538, 5.44), (5.579,
5.96), (5.629, 6.57), (5.674, 7.14), (5.724, 7.73), (5.798,
8.60), (5.823, 8.88), (5.888, 9.62), (5.919, 9.94), (5.963,
10.41), (6.009, 10.85), (6.050, 11.22), (6.115, 11.71), (6.153,
11.99), (6.222, 12.39), (6.263, 12.61), (6.302, 12.77), (6.377,
12.99), (6.414, 13.03), (6.454, 13.02), (6.522, 12.89), (6.558,
12.74), (6.626, 12.41), (6.677, 12.05), (6.729, 11.64), (6.791,
11.00), (6.832, 10.58), (6.887, 9.92), (6.949, 9.13), (6.996,
8.48), (7.028, 8.09), (7.094, 7.13), (7.123, 6.70), (7.161,
6.16), (7.213, 5.35), (7.250, 4.81), (7.332, 3.61), (7.382,
2.93), (7.420, 2.45), (7.474, 1.88), (7.514, 1.40), (7.576,
0.71), (7.600, 0.50), (7.662, 0.12), (7.725, 0.16), (7.768,
0.26), (7.810, 0.30), (7.858, 0.26), (7.904, 0.18), (7.980,
0.10), (8.021, 0.29), (8.078, 0.65), (8.133, 1.06), (8.165,
1.33), (8.218, 1.83), (8.267, 2.31), (8.321, 2.87), (8.355,
3.27), (8.413, 3.91), (8.473, 4.61), (8.519, 5.22), (8.553,
5.65), (8.643, 6.74), (8.678, 7.23), (8.734, 7.94), (8.760,
8.27), (8.803, 8.81), (8.851, 9.35), (8.905, 9.94), (8.961,
10.45), (9.009, 10.92), (9.053, 11.34), (9.106, 11.75), (9.166,
12.14), (9.228, 12.48), (9.292, 12.71), (9.340, 12.86), (9.384,
13.01), (9.412, 13.05), (9.452, 13.03), (9.472, 13.00)]
Cheers

Export it into a CSV file. Your use case is very simple and you should be able to do it using standard Python.
with open('output.csv', 'w') as f:
for x, y in l:
f.write("%s, %s\n" % (x, y))
Note: list is a reserved word in python and you should not be using it.

Use openpyxl to write .xslx files from Python:
import openpyxl
my_list = [(0.496, 12.49), (0.531, 12.40), (0.578, 12.18), (0.615,
11.96), (0.657, 11.75), (0.731, 11.28), (0.785, 10.85), (0.812,
10.61), (0.883, 9.92), (0.930, 9.40), (0.979, 8.77), (1.026,
8.10), (1.081, 7.23), (1.134, 6.33), (1.189, 5.39), (1.220,
4.85), (1.273, 3.92), (1.332, 2.91), (1.364, 2.55), (1.418,
2.16), (1.467, 1.65), (1.523, 1.17), (1.569, 0.82), (1.626,
0.47), (1.678, 0.21), (1.723, 0.01), (1.776, 0.19), (1.814,
0.28), (1.869, 0.36), (1.933, 0.36), (1.972, 0.31), (2.021,
0.18), (2.081, 0.13), (2.129, 0.46), (2.169, 0.79), (2.219,
1.24), (2.280, 1.84), (2.306, 2.11), (2.358, 2.67), (2.414,
3.37), (2.471, 4.05), (2.505, 4.51), (2.562, 5.22), (2.613,
5.84), (2.652, 6.31), (2.712, 7.01), (2.758, 7.52), (2.802,
7.99), (2.869, 8.63), (2.930, 9.16), (2.971, 9.57), (3.043,
10.35), (3.078, 10.69), (3.119, 11.00), (3.174, 11.26), (3.217,
11.40), (3.261, 11.53), (3.307, 11.55), (3.371, 11.51), (3.432,
11.40), (3.479, 11.26), (3.507, 11.20), (3.557, 11.00), (3.623,
10.55), (3.663, 10.28), (3.729, 9.79), (3.768, 9.57), (3.825,
9.24), (3.880, 8.85), (3.944, 8.41), (3.969, 8.04), (4.014,
7.55), (4.086, 6.67), (4.105, 6.37), (4.166, 5.50), (4.212,
4.88), (4.266, 4.20), (4.311, 3.69), (4.364, 3.06), (4.401,
2.65), (4.453, 2.09), (4.497, 1.68), (4.556, 1.18), (4.602,
0.85), (4.644, 0.57), (4.695, 0.29), (4.754, 0.04), (4.799,
0.11), (4.847, 0.17), (4.918, 0.11), (4.959, 0.04), (4.992,
0.19), (5.063, 0.64), (5.098, 0.90), (5.157, 1.40), (5.201,
1.79), (5.245, 2.20), (5.291, 2.65), (5.326, 3.00), (5.387,
3.65), (5.420, 4.02), (5.469, 4.62), (5.538, 5.44), (5.579,
5.96), (5.629, 6.57), (5.674, 7.14), (5.724, 7.73), (5.798,
8.60), (5.823, 8.88), (5.888, 9.62), (5.919, 9.94), (5.963,
10.41), (6.009, 10.85), (6.050, 11.22), (6.115, 11.71), (6.153,
11.99), (6.222, 12.39), (6.263, 12.61), (6.302, 12.77), (6.377,
12.99), (6.414, 13.03), (6.454, 13.02), (6.522, 12.89), (6.558,
12.74), (6.626, 12.41), (6.677, 12.05), (6.729, 11.64), (6.791,
11.00), (6.832, 10.58), (6.887, 9.92), (6.949, 9.13), (6.996,
8.48), (7.028, 8.09), (7.094, 7.13), (7.123, 6.70), (7.161,
6.16), (7.213, 5.35), (7.250, 4.81), (7.332, 3.61), (7.382,
2.93), (7.420, 2.45), (7.474, 1.88), (7.514, 1.40), (7.576,
0.71), (7.600, 0.50), (7.662, 0.12), (7.725, 0.16), (7.768,
0.26), (7.810, 0.30), (7.858, 0.26), (7.904, 0.18), (7.980,
0.10), (8.021, 0.29), (8.078, 0.65), (8.133, 1.06), (8.165,
1.33), (8.218, 1.83), (8.267, 2.31), (8.321, 2.87), (8.355,
3.27), (8.413, 3.91), (8.473, 4.61), (8.519, 5.22), (8.553,
5.65), (8.643, 6.74), (8.678, 7.23), (8.734, 7.94), (8.760,
8.27), (8.803, 8.81), (8.851, 9.35), (8.905, 9.94), (8.961,
10.45), (9.009, 10.92), (9.053, 11.34), (9.106, 11.75), (9.166,
12.14), (9.228, 12.48), (9.292, 12.71), (9.340, 12.86), (9.384,
13.01), (9.412, 13.05), (9.452, 13.03), (9.472, 13.00)]
book = openpyxl.Workbook()
sheet = book.active
for i, value in enumerate(my_list):
sheet.cell(row=i+1, column=1).value = value[0]
sheet.cell(row=i+1, column=2).value = value[1]
book.save('test.xlsx')

When you have data like numbers or objects in memory, it's generally not correct to dump that data directly into disk, you'll want to serialize it.
The easiest way to serialize it is with print which automatically calls the "serialization" method __str__. The problem with this serialization method is that's not always easy to deserialize.
When you have a data structure, like the matrix you describe, you'll want a serialization method that will preserve the structure and allow to reconstruct it in memory. In this case you can use CSV (through the csv module), JSON (through the json module) or many others.
Use CSV.

replace element from a tuple of tuples with empty

This my schema for the tuple:
(name, age, weight)
UserList = (('steve', 17, 178), ('Mike', 19, 178),('Pull', 24, 200),('Adam', 15, 154))
I want to check is the age is less than 18 I would like to replace the the tuple for that user with ( , , )
so the final result will looks like
(('', , ), ('Mike', 19, 178),('Pull', 24, 200),('', , ))
I tried
UserList = list(UserList)
for i,e in enumerate(UserList):
if e[1] < 18:
temp=list(UserList[i])
for f, tmp in enumerate(temp):
del temp[:]
But it didn't work, any thoughts or suggestions will be highly appreciated.
Thanks!

In [13]: UserList = tuple((n, a, w) if a >= 18 else ('', None, None) for (n, a, w) in UserList)
In [14]: UserList
Out[14]: (('', None, None), ('Mike', 19, 178), ('Pull', 24, 200), ('', None, None))