def get_method(dic, dir, local_dir):
try:
nonlocal_dir = "/" + dic["username"] + dir
transport = paramiko.Transport(dic["hostname"], int(dic["port"]))
transport.connect(username=dic["username"], password=dic["password"])
sftp = paramiko.SFTPClient.from_transport(transport)
sftp.get(nonlocal_dir, local_dir)
show("host [%s] download file seccess" % dic["hostname"], "info")
except Exception as e:
show("host [%s] error：%s" % (dic["hostname"], e), "error")
def host_parse(list):
while True:
command = input("Input command[q=quit]>>>:").strip()
if command == "help":
help_info = """
|------- [help] -------
1: [put ] upload files.
2: [get ] download files.
3: [df ] show disk info.
4: [ls ] view file or dirname.
5: [uname ] view the system information.
6: [ifconfig] view the network information
"""
show(help_info, "msg")
elif command == "put":
dir = input("Input upload files name>>>:").strip()
local_dir = settings.LOCAL_DIR + "/" + dir
if not os.path.exists(local_dir):
show("filename doesn't exist...", "msg")
dir0 = input("Input file path>>>:").strip()
for dic in list:
TH = threading.Thread(target=put_method,
args=(
dic,
local_dir,
dir0,
))
TH.start()
TH.join()
elif command == "get":
dir1 = input("Input download files path>>>:").strip()
res_list = dir1.split("/")
local_dir = settings.LOCAL_DIR + "/" + res_list[len(res_list) - 1]
for dic in list:
TH = threading.Thread(target=get_method,
args=(
dic,
dir1,
local_dir,
))
TH.start()
TH.join()
elif command == "q":
break
else:
for dic in list:
TH = threading.Thread(target=command_method,
args=(
dic,
command,
))
TH.start()
TH.join()
def command_method(dic, command):
try:
ssh = paramiko.SSHClient()
ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())
ssh.connect(hostname=dic["hostname"],
port=int(dic["port"]),
username=dic["username"],
password=dic["password"])
stdin, stdout, sterr = ssh.exec_command(command)
result = stdout.read()
print(result.decode())
except Exception as e:
show("host [%s] error：%s" % (dic["hostname"], e), "error")
def connect_host():
"""
connect host method,
:return:
"""
hostinfo_list = hostinfo_read()
if len(hostinfo_list) == 0:
print(
"\033[31;1m There is no host at the moment. Please create the host first \033[0m"
)
exit()
print("\033[32;1m|----- host list -----\033[0m")
for dic in hostinfo_list:
show("\t hostname: %s" % dic["hostname"], "msg")
TH = threading.Thread(target=ssh_parse, args=(dic, ))
TH.setDaemon(True)
TH.start()
TH.join()
def main_func():
"""
main function,call program function
:return:
"""
menu_dic = {"1": create_host, "2": connect_host, "3": run_host}
menu_info = """
|----- Welcome To Fabric Host management interface -----
1: create new host
2: connect host
3: command host
4: exit program
"""
while True:
show(menu_info, "info")
chioce = input("Input You Choice>>>:").strip()
if chioce == "1":
create_host()
continue
if chioce == "2":
connect_host()
continue
if chioce == "3":
run_host()
continue
if chioce == "4":
show("exit program success....", "info")
exit()
else:
show("Input Error...", "error")
def run_host():
"""
operate command host
:return:
"""
try:
active_host = []
hostinfo_list = hostinfo_read()
for dic in hostinfo_list:
if dic["status"] == 1:
active_host.append(dic)
if len(active_host) == 0:
print(
"\033[31;1m At present, there is no connection host. "
"Please connect the host first to ensure that the host can connect properly \033[0m"
)
exit()
show("connect host:", "msg")
for i, j in enumerate(active_host):
show("%s:%s" % (i + 1, j["hostname"]), "msg")
chioce = input("Input You choice host>>>:").strip()
if chioce == "all":
host_parse(active_host)
else:
list = []
list.append(active_host[int(chioce) - 1])
host_parse(list)
except Exception as e:
show("chioce run host error....", "error")
def ssh_parse(dic):
"""
use ssh connect host.if can't connect ,make it's status to 0 ,else to 1
:param dic:
:return:
"""
ssh = paramiko.SSHClient()
ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())
try:
ssh.connect(hostname=dic["hostname"],
port=int(dic["port"]),
username=dic["username"],
password=dic["password"])
except Exception as e:
show("host[%s] connect failed...\t Reason:%s" % (dic["hostname"], e),
"error")
dic["status"] = 0
hostinfo_write(dic, dic["hostname"])
else:
show("host[%s]connect success..." % (dic["hostname"]), "a")
dic["status"] = 1
hostinfo_write(dic, dic["hostname"])
ssh.close()
def create_host():
"""
create a new host method.save host name, host port, user name and user password in file form.
:return:
"""
hostname = input("Input hostname IP>>>:").strip()
port = input("Input host ssh port>>>:").strip()
username = input("Input host login username>>>:").strip()
password = input("Input host login password>>>:").strip()
host_dict = {
"hostname": hostname,
"port": port,
"username": username,
"password": password,
"status": 0
}
host_dir = settings.HOST_DIR + "/" + hostname
if hostinfo_write(host_dict, hostname):
show("create host success....", "info")
return True
else:
show("create host error....", "error")
return False
def pair(n):
return order(n), degree(n)
def pair_int(n):
return order_int(n), degree_int(n)
if __name__ == "__main__":
def show_n(n, gbc):
G = gbc(n)
show(G)
for i in range(2, 20):
show(gbc(i))
'''
for p in pyprimes.primes_below(50):
print(p)
show_n(p, lambda n: gbc(n, nondegenerate_nodes))
for m in range(1,6):
print(m)
show_n(p, lambda n: gbc60(n, nondegenerate_nodes, m))
print()
'''
'''
for n in range(2,40):
print(n)
show_n(n, lambda n: gbc(n, nodes))
show_n(n, lambda n: gbc(n, nondegenerate_nodes))
#show_n(n, gbc60)
except SystemExit:
sys.exit(0)
N = args.n
NR = int((N-1)/2)
# Load the image
image = scipy.ndimage.imread(args.image_path)[:,:,0:3]/255.0
# Cut the image into blocks
blocks = image_split_blocks(image, N, N)
print(blocks.shape)
kernel = img_gen(KernelGenCircle(args.aperture, NR), (N,N,1))[:,:,0]
print("Aperture kernel:")
show(kernel)
kernel = kernel[:, :, np.newaxis, np.newaxis, np.newaxis]
blocks = blocks*kernel;
def gather(blocks, shift):
print("Gathering for shift %f" % shift)
# Perform shifting for focal adjustment
for y in range(N):
for x in range(N):
if kernel[y,x] < 0.001:
continue
dy, dx = y - NR, x - NR
translate = np.array([dx * shift/(N-1), dy * shift/(N-1)])
# blocks[y,x] = img_transform_translate(blocks[y,x], -translate, blocks[x,y].shape, order=1)
blocks[y,x] = scipy.ndimage.interpolation.shift(blocks[y,x], (dy * shift/(N-1), dx * shift/(N-1), 0), order=1)
G = nx.relabel.convert_node_labels_to_integers(nx.read_edgelist(filename))
ig_G = nx_to_ig(G)
N2, ds2, D2, aspl2 = len(G), set(
G.degree().values()), ig_G.diameter(), ig_G.average_path_length()
assert (N1, ds1, D1, aspl1) == (N2, ds2, D2, aspl2)
print N2, ds2, D2, aspl2
'''
for node in G.nodes():
if G.degree()[node] == q - 1:
for node2 in G.nodes():
if G.degree()[node2] == q - 1:
if nx_to_ig(G).shortest_paths()[node][node2] == 3:
G.add_edge(node, node2)
break
show(G)
'''
filename = os.path.join('results', 'n' + str(len(G)) + 'd' +
str(max(G.degree().values())) + '_edgelist.txt')
nx.write_edgelist(G, filename)
if args.verbose:
G = nx.read_edgelist(filename)
ig_G = nx_to_ig(G)
N2, ds2, D2, aspl2 = len(G), set(
G.degree().values()), ig_G.diameter(), ig_G.average_path_length()
assert (N1, ds1, D1, aspl1) == (N2, ds2, D2, aspl2)
print N2, ds2, D2, aspl2
'''
sys.exit(0)
TAKE = 900
MATCHES = 600
SCALE = 1.0
RANSAC_TRESHOLD = 5
RANSAC_SAMPLES = 4500
show_epipolar = False
make_last_image_green = False
P_TEST_POINTS = 20
CORRESP_2D_3D = 8
K = np.array([[2759.48, 0.00000, 1520.69], [0.00000, 2764.16, 1006.81],
[0.00000, 0.00000, 1.00000]])
print("Note: Using K = ")
show(K)
K_inv = np.linalg.inv(K)
image_files = [args.image1, args.image2] + args.IMAGES
# Open image files
print("Opening image files...")
images = []
for image_file in image_files:
image = scipy.ndimage.imread(image_file)[:, :, 0:3]
image = zoom_3ch(image, SCALE)
images += [image]
if make_last_image_green:
images[-1][:, :] = np.array([0, 255, 0])
print(Hs)
"""
Hci = np.linalg.inv(Hs[center])
Hs = [Hci . dot(H) for H in Hs]
"""
His = [np.linalg.inv(H) for H in Hs]
imgBBs = [
np.einsum('ba,na->nb', Hi, pointlist_to_homog(image_bounds_pointlist(img)))
for Hi, img in zip(His, imgs)
]
BB = pointlist_from_homog(np.vstack(imgBBs))
show(BB)
xmin, ymin = BB.min(axis=0)
xmax, ymax = BB.max(axis=0)
xsize = int(xmax - xmin)
ysize = int(ymax - ymin)
offset = np.array([xmin, ymin])
result = np.zeros((ysize, xsize, 3))
mask_gen_func = img_gen_mask_smooth
print("Sampling images")
warps = [
img_transform_H(img, H, result.shape, offset=offset)
for H, img in zip(Hs, imgs)
def show_n(n):
R = Zmod(n)
G = gbc(R)
show(G)
cost = compute_cost(x_vals, y_vals, Theta, hypothesis_func)
print("itr=%d, cost=%f, Theta0=%f, Theta1=%f" % (i, cost, Theta[0], Theta[1]))
return Theta
if __name__ == "__main__":
# 01. データを読み込む
#---------------------------------------------
# 今回利用するデータを読み込みます
data, x_vals, y_vals = cmn.load_data()
# 上10件ほど、見てみましょう
print('-----------------\n#今回利用するデータ（上10件）')
pprint(data[:10])
# データをグラフに表示します
cmn.show(data)
# 02. （最適化前）予測とコストを計算する
#---------------------------------------------
# 初期値のシータは10にしておきます（別の値でも良いです）
# 「Theta0 = 10, Theta1 = 10」の意味です。
Theta = [10, 10]
# このシータを使って、上位10件のデータの予測を作ってみましょう
hypo = hypothesis(x_vals, Theta)
# 上位3件の予測結果を表示します（最適化前）
print('-----------------\n#回帰直線（最適化前）（上3件）')
pprint(hypo[:3])
# 以下の値が表示されればOKです
print("should be:", [1310.0, 1310.0, 1530.0])
print(offset)
img1_rect = img_transform_H(img1, H1, size, offset=offset)
img2_rect = img_transform_H(img2, H2, size, offset=(offset + np.array([0, 0])))
# Ask openCV about disparities.
dispar_min = 50
dispar_max = 160
window = 11
left = np.uint8(256 * img1_rect)
right = np.uint8(256 * img2_rect)
sbm = cv2.StereoSGBM_create(dispar_min, dispar_max, window, 2000, 8000)
disparity = sbm.compute(left, right) / 16.0
show(disparity)
dmin, dmax = disparity.min(), disparity.max()
print((dmin, dmax))
drange = dmax - dmin
dnorm = (disparity - dmin) / drange
# Draw a mesh on rectified images
cx, cy = np.meshgrid(np.arange(0, left.shape[1], 40),
np.arange(0, left.shape[0], 40))
coords_sparse = np.fliplr(
np.stack((cx, cy), axis=2).reshape((-1, 2), order='F'))
res_sparse = scipy.ndimage.map_coordinates(disparity, coords_sparse.T, cval=-1)
right_dots = right.copy()
left_dots = left.copy()
def show_n(n, gbc):
G = gbc(n)
show(G)
# TODO この関数を実装して下さい
#
return 0
if __name__ == "__main__":
# 01. データを読み込む
#---------------------------------------------
# 今回利用するデータを読み込みます
data, x_vals, y_vals = cmn.load_data()
# 上10件ほど、見てみましょう
print('-----------------\n#今回利用するデータ（上10件）')
pprint(data[:10])
# データをグラフに表示します
cmn.show(data)
# 02. （最適化前）予測とコストを計算する
#---------------------------------------------
# 初期値のシータは10にしておきます（別の値でも良いです）
Theta = 10
# このシータを使って、上位10件のデータの予測を作ってみましょう
hypo = hypothesis(x_vals, Theta)
# 上位3件の予測結果を表示します（最適化前）
print('-----------------\n#原点を通る回帰直線（最適化前）（上3件）')
pprint(hypo[:3])
# 以下の値が表示されればOKです
print("should be:", [1300.0, 1300.0, 1520.0])
# データと回帰直線をグラフに表示します
cmn.show(data, x_vals, y_vals, Theta, hypothesis_func=hypothesis)
# 初期コストを計算します
from common import show
import networkx as nx
def hoffman_singleton_graph():
'''Return the Hoffman-Singleton Graph.'''
G = nx.Graph()
for i in range(5):
for j in range(5):
G.add_edge(('pentagon', i, j), ('pentagon', i, (j - 1) % 5))
G.add_edge(('pentagon', i, j), ('pentagon', i, (j + 1) % 5))
G.add_edge(('pentagram', i, j), ('pentagram', i, (j - 2) % 5))
G.add_edge(('pentagram', i, j), ('pentagram', i, (j + 2) % 5))
for k in range(5):
G.add_edge(('pentagon', i, j),
('pentagram', k, (i * k + j) % 5))
G = nx.convert_node_labels_to_integers(G)
G.name = 'Hoffman-Singleton Graph'
return G
if __name__ == '__main__':
G = hoffman_singleton_graph()
show(G)
Qangle16 = angle16[sy,sx]
Qhist, _ = np.histogram(Qangle16, 9, (-180 - 45, 180 + 45), weights=Qmagn16, density=False)
Qhist[0] += Qhist[-1]
Qhist = Qhist[:8]
d += list(Qhist)
assert(len(d) == 128)
d = np.asarray(d).reshape(16,8)
d = d/d.max();
d[d > 0.2] = 0.2
d = d/d.max();
descrips += [(x,y,scale, angle, d)]
if step_by_step_debug:
print("Dominant angle: %d" % angle)
show(hist)
show(bins)
cv2.imshow('patch', image_to_3ch(scipy.ndimage.zoom(patch, 10.0, order=0)))
cv2.imshow('kernel', image_to_3ch(scipy.ndimage.zoom(kernel, 10.0, order=0)))
cv2.imshow('grads', scipy.ndimage.zoom(cgrad_display(patch_cgrad), (10,10,1), order=0))
patch_cgrad_mult = patch_cgrad * kernel
cv2.imshow('grads_mult', scipy.ndimage.zoom(cgrad_display(patch_cgrad_mult), (10,10,1), order=0))
cv2.imshow('rotated_patch', image_to_3ch(scipy.ndimage.zoom(rotated_patch, 3.0, order=0)))
cv2.imshow('patch16', image_to_3ch(scipy.ndimage.zoom(patch16, 12.0, order=0)))
cv2.imshow('grad16', scipy.ndimage.zoom(cgrad_display(cgrad16), (10,10,1), order=0))
cv2.imshow('kernel16', image_to_3ch(scipy.ndimage.zoom(kernel16, (10,10), order=0)))
show(d)
while cv2.waitKey(20) & 0xff != 27:
pass
