def main():
matlab = matlab_wrapper.MatlabSession()
print("matlab.version:", matlab.version)
print("raw version string from MATLAB workspace:", matlab.workspace.version())
def sample_dual_dpp(L, q, k=None):
'''
Wrapper function for the sample_dual_dpp Matlab code written by Alex Kulesza
Given a kernel matrix L, returns a sample from a k-DPP.
L is the kernel matrix
q is the number of used eigenvalues
k is the number of elements in the sample from the DPP
'''
# Matlab link
global mtb
if mtb == None:
import matlab_wrapper
mtb = matlab_wrapper.MatlabSession()
# Extract the feature matrix from the kernel
evals, evecs = largest_eigsh(L, q, which='LM')
B = np.dot(evecs, np.diag(evals))
# load values in Matlab and get sample
mtb.put('B', B)
if k != None:
k = np.array([[k]]) # matlab only undernstand matrices
mtb.put('k', k)
mtb.eval("dpp_sample = sample_dual_dpp(B,decompose_kernel(B'*B),k)")
else:
mtb.eval("dpp_sample = sample_dual_dpp(B,decompose_kernel(B'*B))")
dpp_sample = mtb.get('dpp_sample')
return dpp_sample.astype(int)
def initialize(cmd, cm):
if cmd == 'ss':
model_dirname = '20160828_SS_train0.8'
elif cmd == 'ed':
model_dirname = '20160808_EAR0.4.2_train0.8'
elif cmd == 'BING':
model_dirname = '20160807_BING800_train0.8'
else:
raise IOError('Wrong cmd name, choose from ss, ed, BING')
# configuration for the caffe net
prototxt = os.path.join(cfg.ROOT_DIR, 'models', NETS['caffenet'][0],
'test.prototxt')
caffemodel = os.path.join(cfg.ROOT_DIR, 'output', model_dirname, 'soton_ear',
NETS['caffenet'][cm])
caffe.set_mode_gpu()
caffe.set_device(0)
net = caffe.Net(prototxt, caffemodel, caffe.TEST)
print '\nLoaded network {:s}'.format(caffemodel)
# initialize the MATLAB server
print '\nMATLAB Connected'
import matlab_wrapper
matlab = matlab_wrapper.MatlabSession()
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
return net, matlab
def avg_score(test_dir,
gt_dir,
metric_name='ssim',
smooth=False,
sigma=75,
verbose=False):
"""
Read images from two folders and calculate the average score.
"""
metric_name = metric_name.lower()
all_score = []
if metric_name == 'fsim':
matlab = matlab_wrapper.MatlabSession()
for name in sorted(sorted(os.listdir(gt_dir))):
test_img = Image.open(os.path.join(test_dir, name)).convert('L')
gt_img = Image.open(os.path.join(gt_dir, name)).convert('L')
if smooth:
test_img = cv.bilateralFilter(np.array(test_img), 7, sigma, sigma)
if metric_name == 'ssim':
tmp_score = SSIM(gt_img, test_img)
elif metric_name == 'fsim':
tmp_score = FSIM(matlab, gt_img, test_img)
if verbose:
print('Image: {}, Metric: {}, Smooth: {}, Score: {}'.format(
name, metric_name, smooth, tmp_score))
all_score.append(tmp_score)
return np.mean(np.array(all_score))
def main():
if not os.path.exists("./logfiles"):
os.makedirs("logfiles")
if os.path.exists("./logfiles/test_matlab_engine.log"):
os.remove("./logfiles/test_matlab_engine.log")
logging.basicConfig(filename="./logfiles/test_matlab_engine.log",
level=logging.INFO)
print("\nNumber of threads: 4")
print("Maximum number of evaluations: 500")
print("Sampling method: CandidateDYCORS")
print("Experimental design: Latin Hypercube")
print("Surrogate: Cubic RBF")
nthreads = 4
maxeval = 500
data = Ackley(dim=10)
print(data.info)
# Use the serial controller (uses only one thread)
controller = ThreadController()
controller.strategy = \
SyncStrategyNoConstraints(
worker_id=0, data=data,
maxeval=maxeval, nsamples=nthreads,
exp_design=LatinHypercube(dim=data.dim, npts=2*(data.dim+1)),
response_surface=RBFInterpolant(kernel=CubicKernel, tail=LinearTail,
maxp=maxeval),
sampling_method=CandidateDYCORS(data=data, numcand=100*data.dim))
print("\nNOTE: You may need to specify the matlab_root keyword in "
"order \n to start a MATLAB session using the matlab_wrapper "
"module\n")
# We need to tell MATLAB where the script is
mfile_location = os.getcwd()
# Launch the threads
for _ in range(nthreads):
worker = MatlabWorker(controller)
try:
worker.matlab = matlab_wrapper.MatlabSession(options='-nojvm')
except Exception as err:
print("\nERROR: Failed to initialize a MATLAB session.\n")
exit()
worker.matlab.workspace.addpath(mfile_location)
controller.launch_worker(worker)
# Run the optimization strategy
result = controller.run()
# Print the final result
print('Best value found: {0}'.format(result.value))
print('Best solution found: {0}\n'.format(
np.array_str(result.params[0],
max_line_width=np.inf,
precision=5,
suppress_small=True)))
def sample_dpp(L, k=None):
'''
Wrapper function for the sample_dpp Matlab code written by Alex Kulesza
Given a kernel matrix L, returns a sample from a k-DPP.
The code is hacked in a way that if a set A is provied, samples from a conditional
dpp given A are produced
L: kernel matrix
k: size of the sample from the DPP
set: index of the conditional elements. Integer numpy array containing the locations
(starting in zero) relative to the rows of L.
'''
# Matlab link
global mtb
if mtb == None:
import matlab_wrapper
mtb = matlab_wrapper.MatlabSession()
# load values in Matlab and get sample
mtb.put('L', L)
if k != None:
k = np.array([[k]]) # matlab only undenstand matrices
mtb.put('k', k)
mtb.eval("dpp_sample = sample_dpp(decompose_kernel(L),k)")
else:
mtb.eval("dpp_sample = sample_dpp(decompose_kernel(L))")
#dpp_sample = mtb.getvalue('dpp_sample')
dpp_sample = mtb.get('dpp_sample')
return dpp_sample.astype(int) - 1 # index start in zero in python
def main():
matlab = matlab_wrapper.MatlabSession()
x = np.random.randn(100)
matlab.workspace.plot(x)
raw_input("Press enter to finish...")
def imResample(img, hs, ws):
matlab = matlab_wrapper.MatlabSession()
matlab.put('img', img)
matlab.put('hs', hs)
matlab.put('ws', ws)
matlab.eval('script')
im_data = matlab.get('im_data')
return im_data
def main():
matlab = matlab_wrapper.MatlabSession()
matlab.put('x', 2.)
matlab.eval('my_script')
y = matlab.get('y')
print("And the winner is:", y)
def main():
matlab = matlab_wrapper.MatlabSession()
matlab.put('x', 2.)
matlab.eval('demo')
y = matlab.get('y')
print("And the winner is:", y)
for i in y:
print(int(i))
def main():
matlab = matlab_wrapper.MatlabSession()
matlab.put('x', 2.)
matlab.eval('my_script')
# matlab.eval('scenarios/cartPole/cartPole_learn') #doesnt understand directory navigation!!!
y = matlab.get('y')
a = matlab.get('a')
print("And the winner is:", y)
print(a)
def main():
matlab = matlab_wrapper.MatlabSession()
kaiser = matlab.workspace.kaiser(81., 8.)
b = matlab.workspace.fir1(80., 0.5, kaiser)
matlab.workspace.freqz(b, 1., nout=0)
raw_input("Press enter to finish...")
def main():
p = psutil.Process()
i = 0
while True:
print(i, p.memory_percent(), p.memory_info())
matlab = matlab_wrapper.MatlabSession()
i += 1
def face(self, file):
matlab = matlab_wrapper.MatlabSession()
matlab.put('filename', file)
matlab.put('IMAGE_DIR', IMAGE_DIR)
matlab.eval('face')
count = matlab.get('count')
has_crop = matlab.get('has_crop')
return int(count), int(has_crop) == 1
def initialize_mtb(seed):
print("starting matlab..."),
sys.stdout.flush()
import matlab_wrapper
matlb = matlab_wrapper.MatlabSession()
matlb.put('current_dir', os.path.dirname(os.path.realpath(__file__)))
matlb.eval("addpath(current_dir)")
if not seed is None:
matlb.put('seed', seed)
matlb.eval("rng(seed)")
print("done!")
sys.stdout.flush()
return matlb
def __init__(self):
print("Loading forest model...")
tic()
self.forest = utilites.loadVariableFromFile(
"static/Corel5K/forest/forest_128.pkl")
print("Done.")
toc()
self.train_vectors = loadmat(
utilites.getAbsPath('static/Corel5K/train_vectors_original.mat'))
self.train_vectors = self.train_vectors['train_vectors']
self.train_file_path = utilites.loadVariableFromFile(
"static/Corel5K/train_file_path.pkl")
# load contents of concepts
self.concepts = utilites.loadVariableFromFile(
"static/Corel5K/cluster_contents.pkl")
self.tag_scores = utilites.loadVariableFromFile(
"static/Corel5K/all_tags_scores.pkl")
self.train_vectors_classic = loadmat(
utilites.getAbsPath(
'static/Corel5K/baseline_features/corel5k_train_feats_classic.mat'
))
self.train_vectors_classic = self.train_vectors_classic[
'corel5k_train_feats_classic']
self.test_vectors_classic = loadmat(
utilites.getAbsPath(
'static/Corel5K/baseline_features/corel5k_test_feats_classic.mat'
))
self.test_vectors_classic = self.test_vectors_classic[
'corel5k_test_feats_classic']
self.test_file_name = utilites.loadVariableFromFile(
'static/Corel5K/corel5k_test_file_name.pkl')
self.feat_dict_classic = dict(
zip(self.test_file_name, self.test_vectors_classic))
# start a matlab session for feature extraction
self.matlab = matlab_wrapper.MatlabSession(
matlab_root="/Applications/MATLAB_R2015b.app") # start matlab
self.matlab.eval('run MatConvNet/matlab/vl_setupnn') # basic config
self.matlab.eval('run vlfeat/toolbox/vl_setup') ## basic config
self.matlab.eval("feature('DefaultCharacterSet', 'UTF8')")
print("Loading cnn model...")
tic()
self.matlab.eval(
"net = load('/Users/TONYSUN/Desktop/SIR_Corel5K_demo/static/cnnmodel/imagenet-matconvnet-vgg-verydeep-16.mat')"
)
toc()
print("Matlab session started.")
print("Ready for work ^_^.")
def main():
matlab = matlab_wrapper.MatlabSession()
p = psutil.Process()
i = 0
while True:
print(i, p.memory_percent(), p.memory_info())
a = np.random.randn(1e7)
matlab.put('a', a)
matlab.get('a')
i += 1
def test_nnresample():
""" Compare matlab and nnresample resample : FAILING """
from nnresample import resample
from pystoi.stoi import FS
import matlab_wrapper
matlab = matlab_wrapper.MatlabSession()
matlab.put('FS', float(FS))
RTOL = 1e-4
for fs in [8000, 11025, 16000, 22050, 32000, 44100, 48000]:
x = np.random.randn(2*fs,)
x_r = resample(x, FS, fs)
matlab.put('x', x)
matlab.put('fs', float(fs))
matlab.eval('x_r = resample(x, FS, fs)')
assert_allclose(x_r, matlab.get('x_r'), atol=ATOL, rtol=RTOL)
def initialize_matlab():
matlab = matlab_wrapper.MatlabSession()
# edge_detector OP_method
matlab.eval("cd('/home/harrysocool/Github/fast-rcnn/OP_methods/edges')")
matlab.eval(
"addpath(genpath('/home/harrysocool/Github/fast-rcnn/OP_methods/edges'))"
)
matlab.eval("toolboxCompile")
# # selective_search OP_method
# matlab.eval("cd('/home/harrysocool/Github/fast-rcnn/OP_methods/selective_search_ijcv_with_python')")
# matlab.eval("addpath(genpath('/home/harrysocool/Github/fast-rcnn/OP_methods/selective_search_ijcv_with_python'))")
return matlab
def __init__ ( self,options = '-nosplash', scoperoot = '~/Simulations/Matlab/SCOPE/SCOPE_v1.xx/code/'):
import matlab_wrapper
# matlab = matlab_wrapper.MatlabSession(options=options,matlab_root='/usr/local/matlab_r2014a/')
import socket
hostname = socket.gethostname()
if hostname=='hephaestus.geog.ucl.ac.uk':
matlab_root='/usr/local/matlab_r2014a/'
else:
matlab_root='/usr/local/matlab_r2016a/'
matlab = matlab_wrapper.MatlabSession(options=options,matlab_root=matlab_root)
# options=options, buffer_size=buffer_size
######################################################
############# Initialize SCOPE model #################
######################################################
# change matlab working directory to SCOPE directory
matlab.put('scoperoot',scoperoot)
matlab.eval('cd(scoperoot)')
# Import default values to run scope
matlab.eval('SCOPE')
# Translate the Parameter input-structures to arrays in matlab workspace
matlab.eval('SCOPE_translate_fwd')
# Retrieve values Input Parameters from initial set
Names = ['Ta', 'p', 'ea', 'u', 'Rin', 'Rli', 'Cab', 'Cca', 'Cdm', 'Cw', 'Cs', 'N' , 'emissivity_leaf', 'emissivity_soil', 'Vcmo', 'lai', 'hc', 'lw', 'M1', 'M2', 'M3', 'M4', 'M5', 'M6', 'tts','tto','psi']
Input_real = OrderedDict()
for varname in Names:
Input_real[varname] = matlab.get(varname)
# input_parameters = ['Ta', 'p', 'ea', 'u', 'Rin', 'Rli', 'Cab', 'Cca','Cdm', 'Cw', 'Cs', 'N', 'emissivity_leaf', 'emissivity_soil', 'Vcmo', 'lai', 'hc', 'lw']
# default_values = [22.05,993.8/1.0e3, 67.8, 2.3, 440./1.0e3, 381.69/1.0e3, 0.89, 0.99, 0.38, 0.78, 0.60, 1.6, 0.98, 0.95, 30., 0.95, 49.55,0.054] #transformed
self.matlab = matlab
self.Input_real = Input_real
self.Input_transformed = self.forward_transform(copy.deepcopy(Input_real))
self.wl = matlab.get('wlS')
self.names = Names
def matlab_fmincon_optimization(ifs, distortion, bounds, verbose=True):
matlab = matlab_wrapper.MatlabSession()
matlab.put('solver_path', axro_opt_matlab_func)
matlab.eval('addpath(solver_path)')
matlab.put('ifs', ifs)
matlab.put('distortion', fliperroo(distortion))
matlab.put('lb', fliperroo(np.asarray(bounds)[:, 0]))
matlab.put('ub', fliperroo(np.asarray(bounds)[:, 1]))
matlab.eval('[x,fval,exitflag] = matlab_run_fmincon(ifs,distortion,lb,ub)')
optv = matlab.get('x')
if verbose is True:
print 'Optimization achieved, Merit Function Value: ' + str(
matlab.get('fval'))
return optv
def main():
matlab = matlab_wrapper.MatlabSession(
options='-nojvm',
buffer_size=1000,
)
matlab.eval("c = {1, 2, 3; 'text', eye(2,3), {11; 22; 33}}")
print(matlab.get('c'))
matlab.put(
'a',
np.array([[1, 'asdf'], [3, np.array([1, 2, 3], dtype='O')]],
dtype='O'))
matlab.eval('a')
print(matlab.output_buffer)
def start_engines(self):
'''Starts engines required by non-Python modules in the pipeline.
This should be done only once, since engines may have long startup
times, which would otherwise slow down the execution of the pipeline.
Note
----
For Matlab, you need to set the MATLABPATH environment variable
in order to add module dependencies to the Matlab path.
Warning
-------
Matlab will be started with the ``"-nojvm"`` option.
'''
# TODO: JVM for java code
languages = [m.language for m in self.pipeline]
if 'Matlab' in languages:
logger.info('start Matlab engine')
try:
import matlab_wrapper as matlab
except ImportError:
raise ImportError('Matlab engine cannot be started, because '
'"matlab-wrapper" package is not installed.')
# NOTE: It is absolutely necessary to specify these startup options
# for use parallel processing on the cluster. Otherwise some jobs
# hang up and get killed due to timeout.
startup_ops = '-nosplash -singleCompThread -nojvm -nosoftwareopengl'
logger.debug('Matlab startup options: %s', startup_ops)
self._engines['Matlab'] = matlab.MatlabSession(options=startup_ops)
# We have to make sure that code which may be called by a module,
# are actually on the MATLAB path.
# To this end, the MATLABPATH environment variable can be used.
# However, this only adds the folder specified
# by the environment variable, but not its subfolders. To enable
# this, we add each directory specified in the environment variable
# to the path.
matlab_path = os.environ['MATLABPATH']
matlab_path = matlab_path.split(':')
for p in matlab_path:
if not p:
continue
logger.debug('add "%s" to MATLABPATH', p)
self._engines['Matlab'].eval(
'addpath(genpath(\'{0}\'));'.format(p))
def main():
matlab = matlab_wrapper.MatlabSession(
options='-nojvm',
buffer_size=1000,
)
matlab.eval("""
s = struct()
s(1,1).x = 1
s(1,1).y = 'a'
s(2,2).x = 2
s(2,2).y = [1,2,3]
""")
print(matlab.get('s'))
matlab.eval("""
s = struct()
s(1).x = 1
s(1).y = [1 2]
s(2).x = 2
s(2).y = [3 5]
""")
s = matlab.get('s')
print(s, s.dtype)
print("=" * 70)
a = np.array([(1, 'a'), (2, 'b')], dtype=[('x', '<i8'), ('y', 'S1')])
matlab.put('a', a)
matlab.eval('a\n a(1)\n a(2)')
print(matlab.output_buffer)
def test_stoi_matlab_resample():
""" Test STOI with any sampling frequency, where Matlab is doing
all the resampling. Successful test."""
from pystoi.stoi import FS
import matlab_wrapper
matlab = matlab_wrapper.MatlabSession()
matlab.workspace.cd('matlab/')
matlab.put('FS', float(FS))
for fs in [8000, 11025, 16000, 22050, 32000, 44100, 48000]:
matlab.put('fs', float(fs))
x = np.random.randn(2*fs,)
y = np.random.randn(2*fs, )
matlab.put('x', x)
matlab.put('y', y)
matlab.eval('x_r = resample(x, FS, fs)')
matlab.eval('y_r = resample(y, FS, fs)')
x_r = matlab.get('x_r')
y_r = matlab.get('y_r')
stoi_out = stoi(x_r, y_r, FS)
stoi_out_m = matlab.eval('stoi_out_m = stoi(x_r, y_r, FS)')
assert_allclose(stoi_out, matlab.get('stoi_out_m'), atol=ATOL, rtol=RTOL)
def main():
matlab = matlab_wrapper.MatlabSession(
options='-nojvm',
buffer_size=1000,
)
print(matlab.version)
print()
print('=' * 70)
print()
matlab.eval("a = pi * 2")
print(matlab.output_buffer)
a = matlab.get('a')
print(a)
print()
print('=' * 70)
print()
matlab.eval("s = 'asdf'")
s = matlab.get('s')
print(s)
print()
print('=' * 70)
print()
matlab.eval("l = logical([1 1 0])")
l = matlab.get('l')
print(l)
print(matlab.output_buffer)
print()
print('=' * 70)
print()
matlab.put('m', 'asdf')
m = matlab.get('m')
print(m)
print()
print('=' * 70)
print()
# matlab.put('m', np.array('asdf'))
# m = matlab.get('m')
# print(m)
# r = np.arange(9) + np.ones(9) * 1j
# while True:
# matlab.put('r', r)
# out = matlab.get('r')
# print(out)
a = matlab.workspace.sin(np.arange(10.))
print(a)
print()
print('=' * 70)
print()
y, i = matlab.workspace.sort([2, 1, 3], nout=2)
print(y, i)
print()
print('=' * 70)
print()
print(matlab.workspace.pi())
print()
print('=' * 70)
print()
matlab.workspace.a = [123, 12, 1]
matlab.eval("b = a*2")
print(matlab.workspace.b)
#help(matlab.workspace.sin)
print()
print('=' * 70)
print()
matlab.eval("whos")
print(matlab.output_buffer)
print()
print('=' * 70)
print()
print(matlab)
print()
print('=' * 70)
print()
matlab.workspace.a = ['asdf', 'strings']
matlab.eval('a')
print(matlab.output_buffer)
def _session_hook(self):
import matlab_wrapper
startup_ops = '-nodesktop -nodisplay' if self._num_threads != 1 else \
'-nodesktop -nodisplay -singleCompThread'
return matlab_wrapper.MatlabSession(matlab_root=self._engine_location,
options=startup_ops)
print('Processing recorded IR finished!')
################################################################################################
# SIMULATED IRS
if compute_simulated:
print('Processing simulated IR...')
## Load matlab interface
try:
import matlab_wrapper
except ImportError:
print('matlab_wrapper not available')
## Start Matlab Session
matlab = matlab_wrapper.MatlabSession(matlab_root=matlab_root)
## Add script into the matlab path
matlab.eval('addpath ' + rir_root)
## Pass parameters to matlab
matlab.eval('clear')
matlab.put('c', float(sound_speed))
matlab.put('procFs', float(recorded_sample_rate))
matlab.put('sphLocation', listener_position) # Listener position
matlab.put('s',emitter_position) # Source position
matlab.put('L', wall1-wall0) # Room dimensions
matlab.put('beta',float(recorded_t60))
matlab.put('sphType',sphType)
matlab.put('sphRadius',float(sphRadius))
matlab.put('mic',capsule_positions)
def matlab():
matlab = matlab_wrapper.MatlabSession(
options='-nojvm',
buffer_size=1024
)
return matlab
matlab.eval('clf(1)')
matlab.eval('draw_rollout_cp')
# 1. Initialization: cartpole environment in gym
env = gym.make('CartPole-v0')
ob = env.reset()
# print("cartpole-v0 is on!")
# for _ in range(10):
# env.render()
# # x, reward, done, info = env.step(action)
# ob, reward, done, _ = env.step(env.action_space.sample()) #random action
# 1. Initialization: pilco in matlab
N = 2.0 #number controller optimizations
matlab = matlab_wrapper.MatlabSession()
matlab.eval('clear all')
matlab.eval('close all')
matlab.workspace.N = N # Override Nr controller optimizations
# matlab.put('N', N) # Same as line above
# matN = matlab.get('N')
# matN = matlab.workspace.N
# print("Overriding Nr controller optimizations from here:", matN)
# os.system("settings_cp.py") # :( CREATES A SUBSHELL and loses all memory. BETTER WITH FUNCTIONS!
# ===================================
# settings_cp # As follows:
scp.init(matlab)
scp.def_state_indices(matlab)
scp.def_scenario(matlab)
scp.def_plant_st(matlab)