if len(images) == 0:
return m
i = images[0]
h = i.shape[0]
w = i.shape[1]
emptyrows = np.zeros((h, w * cols, i.shape[2]), np.uint8)
if m == None:
return mosaic(images, cols, emptyrows, col)
elif col == cols:
return mosaic(images, cols, np.append(m, emptyrows, axis = 0), 0)
else:
m[-h:, col*w:col*w+w, :] = i
return mosaic(images[1:], cols, m, col + 1)
tdb = TinyDB(dimensions = WIDTH * HEIGHT * CHANNELS, parse_args = False)
p = tdb.arg_parser()
p.add_argument("-o", required = True)
p.add_argument("-k", type = int, default = 100)
p.add_argument("-c", type = int, default = 10)
p.add_argument("--seed", type = int, default = -1)
p.add_argument("idx", type = int, nargs = '*')
args = tdb.parse_args()
# number of images
n = tdb.rows()
# if no index is given select indexes at random
idx = []
if len(args.idx) > 0:
idx = args.idx
else:
#!/usr/bin/env python
import cv2
import numpy as np
from tinydb import TinyDB
tdb = TinyDB(parse_args = None)
tdb.arg_parser().add_argument("-o", required = True)
tdb.arg_parser().add_argument("-i", type = int, required = True)
args = tdb.parse_args()
x = np.fromstring(tdb.at(args.i), np.uint8).reshape((32, 32, 3), order = 'F')
r = x[:, :, 0]
g = x[:, :, 1]
b = x[:, :, 2]
x[:, :, 0], x[:, :, 2] = b.copy(), r.copy()
cv2.imwrite(args.o, x)
#!/usr/bin/env python
from tinydb import TinyDB
import numpy as np
tdb = TinyDB(parse_args = False)
tdb.arg_parser().add_argument("-o", required = True)
args = tdb.parse_args()
z = np.zeros(tdb.dim(), np.int64)
for i in tdb.chunks():
z += np.fromstring(i, np.uint8)
z = np.float64(z) / tdb.count()
open(args.o, "w").write(" ".join([repr(i) for i in z.flatten()]))
#!/usr/bin/env python
import cv2
import numpy as np
from tinydb import TinyDB
tdb = TinyDB(parse_args=None)
tdb.arg_parser().add_argument("-o", required=True)
tdb.arg_parser().add_argument("-i", type=int, required=True)
args = tdb.parse_args()
x = np.fromstring(tdb.at(args.i), np.uint8).reshape((32, 32, 3), order='F')
r = x[:, :, 0]
g = x[:, :, 1]
b = x[:, :, 2]
x[:, :, 0], x[:, :, 2] = b.copy(), r.copy()
cv2.imwrite(args.o, x)
#!/usr/bin/env python
import sys
import numpy as np
import scipy.io as sio
from tinydb import TinyDB
from parallel import process
WIDTH = 32
HEIGHT = 32
CHANNELS = 3
DIM = WIDTH * HEIGHT * CHANNELS
tdb = TinyDB(dimensions=DIM, parse_args=None)
tdb.arg_parser().add_argument("--mean", required=True)
tdb.arg_parser().add_argument("--std", required=True)
tdb.arg_parser().add_argument("--rows", type=int, default=20000)
tdb.arg_parser().add_argument("-o", required=True)
args = tdb.parse_args()
# read the mean for each dimension
mean = np.array(
[float(i) for i in open(args.mean).readline().strip().split(" ")],
np.float64)
assert (len(mean) == DIM)
# read the standard deviation for each dimension
std = np.array(
[float(i) for i in open(args.std).readline().strip().split(" ")],
np.float64)
#!/usr/bin/env python
from tinydb import TinyDB
from parallel import process
import numpy as np
import scipy.io as sio
db = TinyDB(parse_args = False)
# add additional parameters
db.arg_parser().add_argument("-k", type = int, required = True)
db.arg_parser().add_argument("--rows", type = int, default = 20000)
db.arg_parser().add_argument("-o", required = True)
db.arg_parser().add_argument("--umatrix", required = True)
args = db.parse_args()
# load umatrix
def compute(data):
with open(args.o, "w") as f:
for r in process(db.groups(args.rows), compute):
f.write(r)
x = rand(5000, 3);
y = (x(:,1) > 0.2) .* (x(:,1) < 0.8) .* (x(:,2) > 0.2) .* (x(:,2) < 0.8) .* (x(:,3) > 0.2) .* (x(:,3) < 0.8);
z = x(y == 0, :)
plot3(z(:,1), z(:,2),z(:,3), 'x', 'color', 'r');
#!/usr/bin/env python
from tinydb import TinyDB
from parallel import process
import imageprocessing as ip
import cv2, sys
import numpy as np
db = TinyDB(parse_args = False)
db.arg_parser().add_argument('--image', required = True)
db.arg_parser().add_argument('--filter', default = None)
db.arg_parser().add_argument('--filterout', default = None)
args = db.parse_args()
d = 32
qi = ip.flatten_rgb_image(ip.read_rgb_image(args.image))
# ---------- filter -----------
def do_filter(arr, filt):
if filt == None:
return arr
elif filt == 'raw,sobel':
i = ip.unflatten_rgb_image(arr, d, d)
i = ip.sobel_scipy(i)
i = ip.gray_as_rgb(i)
return ip.flatten_rgb_image(i)
raise Exception('unknown filter')
#!/usr/bin/env python
import sys
import numpy as np
import scipy.io as sio
from tinydb import TinyDB
from parallel import process
WIDTH = 32
HEIGHT = 32
CHANNELS = 3
DIM = WIDTH * HEIGHT * CHANNELS
tdb = TinyDB(dimensions = DIM, parse_args = None)
tdb.arg_parser().add_argument("--mean", required = True)
tdb.arg_parser().add_argument("--std", required = True)
tdb.arg_parser().add_argument("--rows", type = int, default = 20000)
tdb.arg_parser().add_argument("-o", required = True)
args = tdb.parse_args()
# read the mean for each dimension
mean = np.array([float(i) for i in open(args.mean).readline().strip().split(" ")], np.float64)
assert(len(mean) == DIM)
# read the standard deviation for each dimension
std = np.array([float(i) for i in open(args.std).readline().strip().split(" ")], np.float64)
assert(len(std) == DIM)
def compute(m):
#!/usr/bin/env python
from tinydb import TinyDB
from parallel import process
import imageprocessing as ip
import cv2, sys
import numpy as np
db = TinyDB(parse_args=False)
db.arg_parser().add_argument('--image', required=True)
db.arg_parser().add_argument('--filter', default=None)
db.arg_parser().add_argument('--filterout', default=None)
args = db.parse_args()
d = 32
qi = ip.flatten_rgb_image(ip.read_rgb_image(args.image))
# ---------- filter -----------
def do_filter(arr, filt):
if filt == None:
return arr
elif filt == 'raw,sobel':
i = ip.unflatten_rgb_image(arr, d, d)
i = ip.sobel_scipy(i)
i = ip.gray_as_rgb(i)
return ip.flatten_rgb_image(i)
raise Exception('unknown filter')
