def bse_ml():
# step 1: nii.gz -> bmp
subject_path = sys.argv[2]
engine = matlab.engine.start_matlab()
path = os.path.abspath(__file__)
path_to_functions = path[:-11]
engine.addpath(path_to_functions, nargout=0)
engine.addpath(path_to_functions + '/NIfTI', nargout=0)
n3 = engine.input_fn(subject_path)
n3 = int(n3)
engine.desktop(nargout=0)
# step 2: create h5py
command1 = 'python ' + path_to_functions + '/create_h5.py '
os.system(command1)
# step 3: predict
command2 = 'python ' + path_to_functions + '/main.py ' + subject_path + ' ' + str(n3)
os.system(command2)
# step 4: save results
if len(sys.argv) > 4:
output_name = sys.argv[4]
engine.save_pred(output_name, nargout=0)
engine.desktop(nargout=0)
else:
engine.save_pred('meaningless', nargout=0)
engine.desktop(nargout=0)
# step 5: delete intermediate files
shutil.rmtree('./pred_results/')
shutil.rmtree('./pred_results_png/')
shutil.rmtree('./test_data/')
def add_dir_of_file_to_path(args):
(file_dir, file_name, file_ext) = file_attributes(args[u'file_path'])
engine = connect_to_engine()
engine.addpath(file_dir, nargout=0)
is_file_in_path(args)
def mWrapper():
"""
Returns a MATLAB engine instance, with mcode in PATH
"""
engine = matlab.engine.start_matlab()
mcode_path = work_dir = os.path.dirname(os.path.realpath(__file__))
engine.addpath(mcode_path)
return engine
def find_engine():
global engine
if engine is not None:
return engine
engines = matlab.engine.find_matlab()
print(engines)
if engines:
print("Found MATLAB engine! connecting ...")
engine = matlab.engine.connect_matlab(engines[0])
print("Found MATLAB engine! connected")
else:
print("No MATLAB engine found, launching new one ...")
engine = matlab.engine.start_matlab()
print("No MATLAB engine found, launched new one")
engine.addpath('amsbss/ILRMA')
engine.addpath('amsbss/AuxIVA')
engine.addpath('amsbss/AuxIVA/STFT')
return engine
def main(request):
name_num = []
base_folder = os.path.dirname(os.path.realpath(__file__)) # get base dir
engine.addpath(base_folder)
folder_path = os.path.join(base_folder, "static\\djoncharts\\jpgs") # insert the path to your directory
file_list =os.listdir(folder_path)
for f in file_list:
num = len(os.listdir(os.path.join(folder_path, f)))
name_num.append((f, num))
if request.method == 'POST':
typ = int(request.POST.get('type'))
# this part is for
if typ == 0:
page_id = int(request.POST.get('page_id'))
res = {}
res['result'] = name_num[15*(page_id - 1) : 15 * page_id]
return JsonResponse(res)
# this part is for simplify the centerline
if typ == 1:
x1 = int(request.POST.get('x1'))
x2 = int(request.POST.get('x2'))
res = {}
engine.selected(x1, x2)
simple = loadmat(os.path.join(base_folder, 'simple'))['mypath']
print(simple.shape)
json_data = [['X', 'Y', 'Z', 'ind']]
total = simple.shape[0]
for index in range(total):
json_data.append([int(simple[index][0]), int(simple[index][1]), int(simple[index][2]), index])
with open(os.path.join(base_folder, 'static\\djoncharts', 'data_centerline_ind_simple.json'), 'w') as f:
json.dump(json_data, f)
res['result'] = 'success'
return JsonResponse(res)
# this part is for get centerline from the mask
if typ == 2:
path2mask = os.path.join(base_folder, "static\\djoncharts\\masks", request.POST.get('id'), 'mask.mat')
print(path2mask)
res = {}
engine.get_centerline(path2mask, nargout=0)
x = loadmat(os.path.join(base_folder, 'section_x'))['x']
y = loadmat(os.path.join(base_folder, 'section_y'))['y']
z = loadmat(os.path.join(base_folder, 'section_z'))['z']
ind = loadmat(os.path.join(base_folder, 'section_ind'))['ind']
print(x.shape)
json_data = [['X', 'Y', 'Z', 'ind']]
total = x.shape[0]
for index in range(total):
json_data.append([int(x[index][0]), int(y[index][0]), int(z[index][0]), int(ind[index][0])])
with open(os.path.join(base_folder, 'static\\djoncharts', 'data_centerline_ind.json'), 'w') as f:
json.dump(json_data, f)
res['result'] = 'success'
return JsonResponse(res)
if typ == 3:
path2mask = os.path.join(base_folder, "static\\djoncharts\\masks", request.POST.get('id'), 'mask.mat')
path2centerline = os.path.join(base_folder, 'simple.mat')
path2dicom = os.path.join(base_folder, "static\\djoncharts\\raw", request.POST.get('id'), 'raw_volume.mat')
print(path2dicom)
engine.get_section(path2dicom, path2centerline, path2mask, nargout=0)
data_mean = loadmat(os.path.join(base_folder, 'mean_sec'))['mean_sec']
data_std = loadmat(os.path.join(base_folder, 'std_sec'))['std_sec']
data_long = loadmat(os.path.join(base_folder, 'long_sec'))['long_sec']
data_short = loadmat(os.path.join(base_folder, 'short_sec'))['short_sec']
data_section = loadmat(os.path.join(base_folder, 'section'))['section']
print(data_mean.shape)
total = data_mean.shape[1]
assert data_mean.shape == data_std.shape
axis_data = []
value_data = []
for index in range(total):
axis_data.append([data_short[0][index], data_long[0][index]])
value_data.append([data_mean[0][index], data_std[0][index]])
img_data = data_section[:, :, index]
max_data = np.max(img_data)
min_data = np.min(img_data)
img_data = 1.0 * (img_data - min_data)/(max_data - min_data + 1) * 256
img = Image.fromarray(img_data.astype(np.uint8))
img.convert('RGB').save(os.path.join(base_folder, 'static\\djoncharts\\sections', str(index).zfill(5) + '.jpg'))
with open(os.path.join(base_folder, 'static\\djoncharts', 'section_value.json'), 'w') as f:
json.dump(value_data, f)
with open(os.path.join(base_folder, 'static\\djoncharts', 'section_axis.json'), 'w') as f:
json.dump(axis_data, f)
res = {}
res['result'] = 'success'
return JsonResponse(res)
return render(request, 'djoncharts/main.html', {'dicom_folders': name_num})
import sys import matlab import matlab.engine import os import shutil # step 1: nii.gz -> bmp subjects_folder_path = sys.argv[1] engine = matlab.engine.start_matlab() path = os.path.abspath(__file__) path_to_functions = path[:-7] engine.addpath(path_to_functions, nargout=0) engine.addpath(path_to_functions + '/NIfTI', nargout=0) n3 = engine.input_fn(subjects_folder_path) n3 = int(n3) engine.desktop(nargout=0) # step 2: create h5py command1 = 'python ' + path_to_functions + '/create_h5.py ' + subjects_folder_path os.system(command1) # step 3: predict command2 = 'python ' + path_to_functions + '/main.py ' + subjects_folder_path + ' ' + str(n3) os.system(command2) # step 4: save results engine.save_pred(subjects_folder_path, nargout=0) engine.desktop(nargout=0) # step 5: delete intermediate files
w = s * (x2 - x1) + 1
h = s * (y2 - y1) + 1
x11 = cx - w // 2
y11 = cy - h // 2
return x11, y11, w, h
refine = params['refine']
if refine:
import matlab
import matlab.engine
engine = matlab.engine.start_matlab()
engine.addpath(ROOT_DIR)
else:
engine = None
handle = vot.VOT("polygon")
gt_bbox = handle.region()
gt_bbox = np.array(gt_bbox).reshape((1, 4, 2))
init_bbox = get_axis_aligned_bbox(gt_bbox)
tracker = create_tracker(params)
image_file = handle.frame()
if not image_file:
sys.exit(0)
im = cv2.cvtColor(cv2.imread(image_file), cv2.COLOR_BGR2RGB) # HxWxC
tracker.initialize(im, init_bbox)
tracker.engine = engine
#!/usr/bin/env python
import os,sys,io
import time,subprocess
import matlab.engine
engine=matlab.engine.start_matlab("-Desktop")
engine.addpath(r'/home/user/Documents/matlab',nargout=0)
print('Engine started...');
mac =[
'6c:72:20',
'90:03:b7',
'60:60:1f',
'd4:d9:19',
'f4:dd:9e',
'd4:32:60',
'd4:41:69',
'd8:96:85']
print("Initiating Tracking Protocol")
os.system("sudo modprobe -r iwldvm iwlwifi mac80211")
print("Initiating Driver")
os.system("sudo modprobe iwlwifi connector_log=0x1")
time.sleep(0.5)
print("Activating WiFi Antennas")
os.system("sudo ifconfig wlan0 up")
time.sleep(0.2)
print("Connecting to Specified WiFi Object")
result = subprocess.check_output('sudo iw dev wlan0 connect -w accesspoint',shell=True)
time.sleep(0.2)
density = 'mog'
n_mades = 5 # number of MADES
hyperparameters = dict()
hyperparameters['n_train'] = n_train
hyperparameters['n_rounds'] = n_rounds
hyperparameters['val_frac'] = val_frac
hyperparameters['n_hiddens'] = n_hiddens
hyperparameters['density'] = density
hyperparameters['n_mades'] = n_mades
# start matlab enigne and set path
engine = matlab.engine.start_matlab()
try:
ps = engine.genpath('/scratch/sliu88/APT')
engine.addpath(ps)
except:
pass
# save inference method settings (hyperparameters)
savemat(os.path.join(savefolder, 'hyperparameters.mat'),
{'hyperparameters': hyperparameters})
priorC_list = [1, 2, 3]
gamma_list = [1, 2, 3]
lapse_list = [1, 2, 3]
samples_list = [1, 2, 3]
for s in samples_list:
for l in lapse_list:
for g in gamma_list:
for C in priorC_list:
# load data
+ str(pos[0]) + ' ' + str(pos[1]))
while True:
get_screenshot()
screenshot = cv2.imread('./autojump.png')
score, restart_pos = check.check_result(screenshot)
if score >= 0:
with open("./result.txt", 'a') as f:
f.write("score: ", score)
f.write("\n")
restart(restart_pos)
press_time = 0
if lang == "python":
press_time = jumper.jumper()
elif lang == "cpp":
if platform == "win32":
press_time = subprocess.getoutput('cd jump_cpp && jumper.exe')
if platform == "darwin":
press_time = subprocess.getoutput('cd jump_cpp && ./jumper')
else:
engine.addpath("./jump_matlab")
press_time = engine.jumper()
print('Press time: ' + str(press_time))
press_screen(press_time)
time.sleep(2)
# Check test platform
platform = sys.platform
if platform != "win32" and platform != "darwin":
print("Unsupported platform. Exiting...")
exit()
# Check source files
engine = None
if lang == "matlab":
if not os.path.isfile(work_dir + '/jumper.m'):
print('Source code does not exist! Exiting...')
exit()
import matlab.engine
engine = matlab.engine.start_matlab()
engine.addpath(work_dir)
elif lang == "python":
if not os.path.isfile(work_dir + '/jumper.py'):
print('Source code does not exist! Exiting...')
exit()
for f in os.listdir(work_dir):
shutil.copy(work_dir + '/' + f, './' + student_id)
jumper = importlib.import_module(student_id + '.jumper')
else:
if platform == "win32":
if not os.path.isfile(work_dir + '/jumper.exe'):
print('Executable file does not exist! Exiting...')
exit()
if platform == "darwin":
if not os.path.isfile(work_dir + '/jumper'):
print('Executable file does not exist! Exiting...')
#%%
from utils import extract_data_from_matlabFile
import numpy as np
import matlab.engine
#%% read pickle
with open(
os.path.join(os.path.join(PROJROOT, "001 data"), 'sampleData_0528.p'),
'rb') as fp:
data = pickle.load(fp)
print(data.keys())
#%%
engine = matlab.engine.start_matlab()
baseFolderPath = "C:\\Users\\Evan\MyFile\\Python\\MultiFactorBackTestModule\\002 src\\03 FactorFunctions\\matlab"
engine.workspace
allMatLabPath = engine.genpath(baseFolderPath)
engine.addpath(allMatLabPath)
engine.alpha053(data['close'])
engine.double(data['close'])
close = data['close']
img = np.random.rand(87, 195, 126)
imgMatlab = matlab.double(img)
close.shape
engine.load()
def render_diagram(app, node, docname):
global engine
uri = node['uri']
# do not regenerate
if os.path.exists(uri):
pass
ensuredir(os.path.dirname(uri))
try:
# reuse last engine to save loading time
if engine == None:
engine = matlab.engine.start_matlab()
else:
# clean up used engines
engine.restoredefaultpath(nargout=0)
engine.close('all', nargout=0)
engine.bdclose('all', nargout=0)
engine.clear('classes', nargout=0)
# start engine from document directory
engine.cd(os.path.dirname(app.env.doc2path(docname)))
# then, support changing directory (relative to document)
dir = node.get('dir')
if dir:
engine.cd(dir)
# finally, add the MATLAB paths relative to the changed directory
pathlist = node.get('addpath')
if pathlist:
for path in pathlist:
engine.addpath(path)
# preload script
preload = node.get('preload')
if preload:
engine.eval(preload + ';', nargout=0)
# load system
system = node.get('system')
if system:
engine.load_system(system)
# if subsystem specified, print from this layer
subsystem = node.get('subsystem')
if subsystem:
system = "/".join([system, subsystem])
# print from Simulink handle to .png
engine.eval(
"print( get_param( '{}', 'Handle' ), '-dpng', '{}' )".format(
system, uri),
nargout=0)
except matlab.engine.MatlabExecutionError as err:
raise SimulinkDiagramError('Unable to render Simulink diagram due ' +
'to MATLAB execution error')
from waitress import serve
from flask import request
from flask import Flask
import time
import json
if __name__ == '__main__':
app = Flask(__name__)
# initialize MATLAB
engine = matlab.engine.start_matlab()
engine.cd('./matlab')
engine.addpath('./utils')
@app.route('/api/vrptw', methods=['POST'])
def callVrptw():
req_json = request.json
if req_json is None or req_json == 'null':
return 'Error parsing request! Should be in JSON format!'
incidence_mat = req_json['incidenceMat']
cost_mat = req_json['costMat']
edge_time_vec = req_json['edgeTimeV']
start_vec = req_json['startV']
end_vec = req_json['endV']
distr_vec = req_json['nodeDistributionV']
vehicle_capacity_vec = req_json['vehicleCapacityV']
def render_diagram(app, node, docname):
global engine
uri = node['uri']
# do not regenerate
if os.path.exists( uri ):
pass
ensuredir( os.path.dirname( uri ) )
try:
# reuse last engine to save loading time
if engine == None:
engine = matlab.engine.start_matlab()
else:
# clean up used engines
engine.restoredefaultpath(nargout=0)
engine.close('all', nargout=0)
engine.bdclose('all', nargout=0)
engine.clear('classes', nargout=0)
# start engine from document directory
engine.cd( os.path.dirname( app.env.doc2path( docname ) ) )
# then, support changing directory (relative to document)
dir = node.get('dir')
if dir:
engine.cd( dir )
# finally, add the MATLAB paths relative to the changed directory
pathlist = node.get('addpath')
if pathlist:
for path in pathlist:
engine.addpath( path )
# preload script
preload = node.get('preload')
if preload:
engine.eval( preload + ';', nargout=0)
# load system
system = node.get('system')
if system:
engine.load_system( system );
# if subsystem specified, print from this layer
subsystem = node.get('subsystem')
if subsystem:
system = "/".join( [ system, subsystem ] )
# print from Simulink handle to .png
engine.eval(
"print( get_param( '{}', 'Handle' ), '-dpng', '{}' )".
format( system, uri ),
nargout=0
)
except matlab.engine.MatlabExecutionError as err:
raise SimulinkDiagramError('Unable to render Simulink diagram due ' +
'to MATLAB execution error'
)
def main():
print(f"Initializing MATLAB engine.")
engine = matlab.engine.start_matlab()
engine.addpath(str(siftflow_path))
engine.addpath(str(siftflow_path / 'mexDenseSIFT'))
engine.addpath(str(siftflow_path / 'mexDiscreteFlow'))
warnings.simplefilter("error", UserWarning)
filters = []
if args.category:
filters.append(ExemplarShapePair.shape.has(category=args.category))
with session_scope() as sess:
pairs, count = controllers.fetch_pairs_default(sess, filters=filters)
# pairs, count = controllers.fetch_pairs(
# sess,
# by_shape=True,
# order_by=ExemplarShapePair.shape_id.asc(),
# )
print(f"Fetched {count} pairs")
pairs = [
pair for pair in pairs
if (pair.data_exists(config.SHAPE_REND_SEGMENT_MAP_NAME)
and not pair.data_exists(config.FLOW_DATA_NAME))
]
print(f"Computing flows for {len(pairs)} pairs.")
pbar = tqdm(pairs)
pair: ExemplarShapePair
for pair in pbar:
pbar.set_description(f'Pair {pair.id}')
# if not pair.exemplar.data_exists(config.EXEMPLAR_SUBST_MAP_NAME, type='numpy'):
# logger.warning('pair %d does not have substance map', pair.id)
# continue
if not pair.data_exists(config.SHAPE_REND_SEGMENT_MAP_NAME):
print(f'Pair {pair.id} does not have segment map')
continue
if pair.data_exists(config.FLOW_DATA_NAME):
continue
exemplar_im = transform.resize(pair.exemplar.load_cropped_image(),
config.SHAPE_REND_SHAPE,
anti_aliasing=True,
mode='reflect')
seg_vis = pair.load_data(config.SHAPE_REND_SEGMENT_VIS_NAME)
vx, vy = compute_silhouette_flow(engine, pair)
flow_vis = visualize_flow(vx, vy)
vis.image(flow_vis.transpose((2, 0, 1)),
win='sil-flow',
opts={'title': 'sil-flow'})
vis.image(
((exemplar_im + apply_flow(seg_vis, vx, vy)) / 2).transpose(
(2, 0, 1)),
win='sil-flow-applied',
opts={'title': 'sil-flow-applied'})
# vx, vy = compute_phong_flow(engine, exemplar_im, phong_im)
#
# flow_vis = visualize_flow(vx, vy)
# vis.image(flow_vis.transpose((2, 0, 1)),
# win='phong-flow',
# opts={'title': 'phong-flow'})
# vis.image(
# ((exemplar_im + apply_flow(seg_vis, vx, vy))/2).transpose((2, 0, 1)),
# win='phong-flow-applied',
# opts={'title': 'phong-flow-applied'})
with warnings.catch_warnings():
warnings.simplefilter("ignore")
pair.save_data(config.FLOW_VIS_DATA_NAME, flow_vis)
pair.save_data(config.FLOW_DATA_NAME, np.dstack((vx, vy)))