(output, error, retz) = shellcmd(comm)
if retz:
print "\n\nBLAS: cannot create table of contents for blas library"
print "stderr:\n", "*" * 50, "\n", comm, '\n', error, "\n", "*" * 50
sys.exit()
print "done"
# write the log on a file
log = log + output + error
fulllog = os.path.join(savecwd, 'log/log.blas')
writefile(fulllog, log)
print 'Installation of netlib BLAS successful.'
print '(log is in ', fulllog, ')'
# move librefblas.a to the lib directory
shutil.copy('librefblas.a',
os.path.join(self.config.prefix, 'lib/librefblas.a'))
# set framework variables to point to the freshly installed BLAS library
self.config.blaslib = '-L' + os.path.join(self.config.prefix,
'lib') + ' -lrefblas '
os.chdir(savecwd)
if __name__ == '__main__':
sys.path.insert(0, '../')
import configure
from dmft_install import Dmft_install
config = configure.Config((1, 0, 0))
dmft_install = Dmft_install([], config)
blas = Blas(config, dmft_install)
freq = float(line.split()[1])
count_ratio = word_counts[name] / max(1, float(capitalized_counts[name]))
if name in remove and freq < 0.002:
print name
if name not in remove and freq > 0.002:
name_stats.append((name, word_counts[name], capitalized_counts[name],
count_ratio))
if count_ratio < 0.75:
names.add(name)
if count_ratio > 0.2:
wordlike_names.add(name)
for w, wc, cc, cr in sorted(name_stats, key=itemgetter(-1), reverse=True):
if cc > 500:
print w, wc, cc, cr
utils.write_pickle(names, config.names)
utils.write_pickle(wordlike_names, config.wordlike_names)
def main(config):
count_words(config, False)
preprocess_names(config)
count_words(config, True)
vocabulary.Vocabulary(config, load=False).write()
prep_data(config)
if __name__ == '__main__':
main(configure.Config())
def main(argv):
### History of executed commands will be stored in log.config
logdir = 'log'
if(not os.path.isdir(logdir)):
print"Creating directory", logdir
os.mkdir(logdir)
# os.chdir(logdir)
cmd = ""
for arg in argv:
cmd += arg+" "
cmd += "\n"
fp = open("log/log.config",'a')
fp.write(cmd)
fp.close()
try:
py_ver = sys.version_info
print("\nDetected Python version %s" % ".".join(["%s" % i for i in py_ver]))
if py_ver < (2, 7) or py_ver >= (2, 8):
print("Python version 2.7+ required. Download and install the necessary "
"python version from http://www.python.org/download/.")
sys.exit(-1)
except:
print("\n Python version 2.7+ required. Download and install the necessary "
"python version from http://www.python.org/download/.")
sys.exit(-1)
# try:
# import setuptools
# print("Detected setuptools version {}".format(setuptools.__version__))
# except ImportError:
# print("setuptools not detected. Get it from https://pypi.python"
# ".org/pypi/setuptools and follow the instructions to install first.")
# sys.exit(-1)
# try:
# gcc_ver = subprocess.Popen(["gcc", "--version"], stdout=subprocess.PIPE)\
# .communicate()[0]
# except:
# print("gcc not found in PATH. gcc is needed for installation of numpy "
# "and C extensions. For Mac users, please install Xcode and its "
# "corresponding command-line tools first.")
# sys.exit(-1)
# try:
# import pip
# print("Detected pip version {}".format(pip.__version__))
# except ImportError:
# print("pip not detected. Installing...")
# subprocess.call(["easy_install", "pip"])
#
try:
import numpy
#from numpy.distutils.misc_util import get_numpy_include_dirs
print("Detected numpy version {}".format(numpy.__version__))
except ImportError:
print("numpy.distutils.misc_util cannot be imported. Please install ...")
#subprocess.call(["pip", "install", "-q", "numpy>=1.8.0"])
#from numpy.distutils.misc_util import get_numpy_include_dirs
try:
import scipy
#from numpy.distutils.misc_util import get_numpy_include_dirs
print("Detected scipy version {}".format(scipy.__version__))
except ImportError:
print("scipy module cannot be imported. Please install ...")
subprocess.call(["pip", "install", "-q", "scipy>=0.14.0"])
#from numpy.distutils.misc_util import get_numpy_include_dirs
print "\n"
config = configure.Config((VERSION_MAJOR, VERSION_MINOR, VERSION_MICRO))
dmft_install = Dmft_install(argv, config)
# if dmft_install.downblas :
Blas(config, dmft_install)
# if dmft_install.downlapack :
Lapack(config, dmft_install)
# if dmft_install.downfftw :
Fftw(config, dmft_install)
# if dmft_install.downgsl :
Gsl(config, dmft_install)
dmft_install.resume()
return 0
y_pts /= y_max
e_pts /= y_max
lines.append(
plt.plot(x_pts,
y_pts,
linestyle='dashed',
marker='x',
markersize=3,
label='loss %d, max: %3.0f' % (idx, y_max))[0])
plt.errorbar(x_pts, y_pts, e_pts, linestyle='None')
plt.legend(lines, loc='upper right')
plt.show()
C = configure.Config()
T = Tools()
def mat2img(mats):
mats = np.clip(mats, 0.0, 1.0)
out = []
for mat in mats:
aerial = mat[:, :, 0:3]
cloud1I = mat[:, :, 3, np.newaxis].repeat(3, axis=2)
cloud1T = mat[:, :, 4, np.newaxis].repeat(3, axis=2)
groundI = mat[:, :, 5:8]
groundT = mat[:, :, 8:11]
groundO = mat[:, :, 11:14]
img_show = (aerial, cloud1I, cloud1T, groundI, groundT, groundO)
mat = mat.transpose((2, 0, 1, 3))
mat = np.concatenate(mat, axis=0)
mat = (mat * 255.0).astype(np.uint8)
out.append(mat)
img = np.concatenate(out, axis=1)
img = np.rot90(img)
cv2.imwrite(img_path, img)
out = []
for mat in mats:
mat = mat.reshape((C.size, C.size, -1, 3))
mat = mat.transpose((2, 0, 1, 3))
C = configure.Config('mod_WGAN')
T = Tools()
def model_save_npy(sess, print_info):
tf_vars = tf.global_variables()
'''save as singal npy'''
npy_dict = dict()
for var in tf_vars:
npy_dict[var.name] = var.eval(session=sess)
print("| FETCH: %s" % var.name) if print_info else None
np.savez(C.model_npz, npy_dict)
with open(C.model_npz + '.txt', 'w') as f:
f.writelines(["%s\n" % key for key in npy_dict.keys()])
mat = mat.transpose((2, 0, 1, 3))
mat = np.concatenate(mat, axis=0)
mat = (mat * 255.0).astype(np.uint8)
out.append(mat)
img = np.concatenate(out, axis=1)
img = np.rot90(img)
cv2.imwrite(img_path, img)
out = []
for mat in mats:
mat = mat.reshape((C.size, C.size, -1, 3))
mat = mat.transpose((2, 0, 1, 3))
C = configure.Config('mod_mend')
T = Tools()
def model_save_npy(sess, print_info):
tf_vars = tf.global_variables()
'''save as singal npy'''
npy_dict = dict()
for var in tf_vars:
npy_dict[var.name] = var.eval(session=sess)
print("| FETCH: %s" % var.name) if print_info else None
np.savez(C.model_npz, npy_dict)
with open(C.model_npz + '.txt', 'w') as f:
f.writelines(["%s\n" % key for key in npy_dict.keys()])
'''save as several npy'''
# shutil.rmtree(C.model_npy, ignore_errors=True)
from tensorflow.python.framework.graph_util import convert_variables_to_constants
from yolov3.utils.utils import get_yolo_boxes, get_yolo_boxes_by_tf, crop_boxes, freeze_session
import configure as config_obj
def image_reader(img_path):
input_image = misc.imread(img_path)
if input_image.shape[2] > 3:
input_image = input_image[:, :, :3]
return input_image
if __name__ == "__main__":
config = config_obj.Config(
root_path=os.path.abspath(os.path.join(os.getcwd(), "..")))
yolo_weights_path = config.weights_path
yolo_model = load_model(yolo_weights_path)
yolo_config_path = config.config_path
with open(yolo_config_path, 'r') as config_buffer:
yolo_config = json.load(config_buffer)
filepath = "../testing/demo_car_org.png"
filename = filepath.split('.')[-2].split('/')[-1]
input_image = image_reader(filepath)
input_image_list = list()
input_image_list.append(input_image)