def _test_video_folder():
from textwrap import fill, indent
batch_size = 5
video_data_set = VideoFolder('small_data_set/')
nb_of_classes = len(video_data_set.classes)
print('There are', nb_of_classes, 'classes')
print(indent(fill(' '.join(video_data_set.classes), 77), ' '))
print('There are {} frames'.format(len(video_data_set)))
print('Videos in the data set:', *video_data_set.videos, sep='\n')
import inflect
ordinal = inflect.engine().ordinal
def print_list(my_list):
for a, b in enumerate(my_list):
print(a, ':', end=' [')
print(*b, sep=',\n ', end=']\n')
# get first 3 batches
n = ceil(len(video_data_set) / batch_size)
print('Batch size:', batch_size)
print('Frames per row:', n)
for big_j in range(0, n, 90):
batch = list()
for j in range(big_j, big_j + 90):
if j >= n: break # there are no more frames
batch.append(
tuple(video_data_set[i * n + j][0] for i in range(batch_size)))
batch[-1] = concatenate(batch[-1], 0)
batch = concatenate(batch, 1)
_show_numpy(batch, 1e-1)
print(ordinal(big_j // 90 + 1), '90 batches of shape', batch.shape)
print_list(video_data_set.opened_videos)
print('Freeing resources')
video_data_set.free()
print_list(video_data_set.opened_videos)
# get frames 50 -> 52
batch = list()
for i in range(50, 53):
batch.append(video_data_set[i][0])
_show_numpy(concatenate(batch, 1))
print_list(video_data_set.opened_videos)
def _from_string(str, gdict, ldict):
rows = str.split(';')
rowtup = []
for row in rows:
trow = row.split(',')
newrow = []
for x in trow:
newrow.extend(x.split())
trow = newrow
coltup = []
for col in trow:
col = col.strip()
try:
thismat = ldict[col]
except KeyError:
try:
thismat = gdict[col]
except KeyError as e:
raise NameError(f"name {col!r} is not defined") from None
coltup.append(thismat)
rowtup.append(concatenate(coltup, axis=-1))
return concatenate(rowtup, axis=0)
def assemble(self, results, axis=1):
d = {r.idx: r.arr for r in results}
l = [d[k] for k in sorted(d.keys())]
self.assembled = concatenate(l, axis=axis)
return self.assembled
import numpy as np
from numpy.core.multiarray import concatenate
a = np.genfromtxt('info_day.csv', delimiter=',', dtype=None,
encoding='ascii').astype(object)
b = np.genfromtxt('info_night.csv',
delimiter=',',
dtype=None,
encoding='ascii').astype(object)
a[0][1] = 'Temperature(Day)'
b[0][1] = 'Temperature(Night)'
a[0][2] = 'Humidity(Day)'
b[0][2] = 'Humidity(Night)'
a[0][3] = 'Light(Day)'
b[0][3] = 'Light(Night)'
a[0][4] = 'CO2(Day)'
b[0][4] = 'CO2(Night)'
c = concatenate((a, b), axis=1)
my_permutation = [0, 2, 4, 6, 8, 1, 3, 5, 7, 9]
i = np.argsort(my_permutation)
d = c[:, i]
e = np.delete(d, np.s_[1:2], axis=1)
np.savetxt("info_combine.csv", e, fmt='%s', delimiter=",")
def bmat(obj, ldict=None, gdict=None):
"""
Build a matrix object from a string, nested sequence, or array.
Parameters
----------
obj : str or array_like
Input data. If a string, variables in the current scope may be
referenced by name.
ldict : dict, optional
A dictionary that replaces local operands in current frame.
Ignored if `obj` is not a string or `gdict` is None.
gdict : dict, optional
A dictionary that replaces global operands in current frame.
Ignored if `obj` is not a string.
Returns
-------
out : matrix
Returns a matrix object, which is a specialized 2-D array.
See Also
--------
block :
A generalization of this function for N-d arrays, that returns normal
ndarrays.
Examples
--------
>>> A = np.mat('1 1; 1 1')
>>> B = np.mat('2 2; 2 2')
>>> C = np.mat('3 4; 5 6')
>>> D = np.mat('7 8; 9 0')
All the following expressions construct the same block matrix:
>>> np.bmat([[A, B], [C, D]])
matrix([[1, 1, 2, 2],
[1, 1, 2, 2],
[3, 4, 7, 8],
[5, 6, 9, 0]])
>>> np.bmat(np.r_[np.c_[A, B], np.c_[C, D]])
matrix([[1, 1, 2, 2],
[1, 1, 2, 2],
[3, 4, 7, 8],
[5, 6, 9, 0]])
>>> np.bmat('A,B; C,D')
matrix([[1, 1, 2, 2],
[1, 1, 2, 2],
[3, 4, 7, 8],
[5, 6, 9, 0]])
"""
if isinstance(obj, str):
if gdict is None:
# get previous frame
frame = sys._getframe().f_back
glob_dict = frame.f_globals
loc_dict = frame.f_locals
else:
glob_dict = gdict
loc_dict = ldict
return matrix(_from_string(obj, glob_dict, loc_dict))
if isinstance(obj, (tuple, list)):
# [[A,B],[C,D]]
arr_rows = []
for row in obj:
if isinstance(row, N.ndarray): # not 2-d
return matrix(concatenate(obj, axis=-1))
else:
arr_rows.append(concatenate(row, axis=-1))
return matrix(concatenate(arr_rows, axis=0))
if isinstance(obj, N.ndarray):
return matrix(obj)