def get_library_linked():
'''Print out info about scipy'''
# capture into a variable show_config output
capture = io.StringIO()
with contextlib.redirect_stdout(capture):
sp.show_config()
output = capture.getvalue()
# look for library in use
lines = output.split('\n')
print('scipy is using: ')
for line in lines:
if 'libraries' in line:
print('{}'.format(line))
print('you get the gist ;-)\n')
def cache_info(cls):
try:
# scipy is not mandatory requirement
import scipy
except ImportError:
cls._CACHE = 'SciPy is not installed'
return
f = io.StringIO()
with contextlib.redirect_stdout(f):
try:
scipy.show_config()
except Exception as e:
msg = ('Unable to collect scipy related information, '
'scipy.show_config raises {}({})')
logger.error(msg.format(e.__class__.__name__, e))
cls._CACHE = f.getvalue()
def main():
print('numpy config (python 3.7 image):')
print(numpy.__version__)
print(numpy.show_config())
print('')
print('scipy config:')
print(scipy.__version__)
print(scipy.show_config())
print('')
return 0
def main():
print('numpy config (miniconda:4.6.14 image):')
print(numpy.__version__)
print(numpy.show_config())
print('')
print('scipy config:')
print(scipy.__version__)
print(scipy.show_config())
print('')
return 0
#!/usr/bin/env python
__author__ = u"james.morris"
import scipy
if __name__ == u"__main__":
scipy.show_config()
config.PREPROCESS_REFERENCE_VIDEO_ONLY = True;
elif sys.argv[3] == "--process-query-and-align-videos":
config.PREPROCESS_REFERENCE_VIDEO_ONLY = False;
else:
config.PREPROCESS_REFERENCE_VIDEO_ONLY = "do_all";
print("config.PREPROCESS_REFERENCE_VIDEO_ONLY = %s" % str(config.PREPROCESS_REFERENCE_VIDEO_ONLY));
AskFirst();
# Inspired from https://stackoverflow.com/questions/1520234/how-to-check-which-version-of-numpy-im-using
print("numpy.version.version = %s" % str(np.version.version));
print("scipy.version.version = %s" % str(scipy.version.version));
#scipy.version.version
np.show_config();
scipy.show_config();
# See http://docs.scipy.org/doc/numpy/reference/generated/numpy.set_printoptions.html
# We use 7 digits precision and suppress using scientific notation.
np.set_printoptions(precision=7, suppress=True, \
threshold=70000, linewidth=4000);
#threshold=7000000, linewidth=4000);
#threshold=7000, linewidth=300);
#threshold=1000000, linewidth=3000);
# Inspired from \OpenCV2-Python-Tutorials-master\source\py_tutorials\py_core\py_optimization
# normally returns True - relates to using the SIMD extensions of x86: SSX, AVX
common.DebugPrint("cv2.useOptimized() is %s" % str(cv2.useOptimized()));
def show_scipy_config():
import scipy
return scipy.show_config()
def main(argv):
assert len(sys.argv) >= 3
if FLAGS.preprocess_ref:
config.PREPROCESS_REFERENCE_VIDEO_ONLY = True
elif FLAGS.process_query_and_align_videos:
config.PREPROCESS_REFERENCE_VIDEO_ONLY = False
else:
config.PREPROCESS_REFERENCE_VIDEO_ONLY = False
print("config.PREPROCESS_REFERENCE_VIDEO_ONLY = %s" % str(
config.PREPROCESS_REFERENCE_VIDEO_ONLY))
ask_first()
# Inspired from https://stackoverflow.com/questions/1520234/how-to-check-which-version-of-numpy-im-using
print("numpy.version.version = %s" % str(np.version.version))
print("scipy.version.version = %s" % str(scipy.version.version))
np.show_config()
scipy.show_config()
# See http://docs.scipy.org/doc/numpy/reference/generated/numpy.set_printoptions.html
# We use 7 digits precision and suppress using scientific notation.
np.set_printoptions(precision=7, suppress=True,
threshold=70000, linewidth=4000)
# Inspired from \OpenCV2-Python-Tutorials-master\source\py_tutorials\py_core\py_optimization
# normally returns True - relates to using the SIMD extensions of x86:
# SSX, AVX
common.DebugPrint("cv2.useOptimized() is %s" % str(cv2.useOptimized()))
"""
From http://docs.opencv.org/modules/core/doc/utility_and_system_functions_and_macros.html#checkhardwaresupport
CV_CPU_MMX - MMX
CV_CPU_SSE - SSE
CV_CPU_SSE2 - SSE 2
CV_CPU_SSE3 - SSE 3
CV_CPU_SSSE3 - SSSE 3
CV_CPU_SSE4_1 - SSE 4.1
CV_CPU_SSE4_2 - SSE 4.2
CV_CPU_POPCNT - POPCOUNT
CV_CPU_AVX - AVX
"""
# TODO: Figure out the correct way to reference these in OpenCV 3.x
"""
# Need to setUseOptimized before calling checkHardwareSupport
cv2.setUseOptimized(True)
if config.OCV_OLD_PY_BINDINGS == False:
featDict = {cv2.CpuFeatures.CV_CPU_AVX: "AVX",
cv2.CPU_MMX: "MMX",
cv2.CPU_NEON: "NEON",
cv2.CPU_POPCNT: "POPCNT",
cv2.CPU_SSE: "SSE",
cv2.CPU_SSE2: "SSE2",
cv2.CPU_SSE3: "SSE3",
cv2.CPU_SSE4_1: "SSE4.1",
cv2.CPU_SSE4_2: "SSE4.2",
cv2.CPU_SSSE3: "SSSE3"}
for feat in featDict:
res = cv2.checkHardwareSupport(feat)
print("%s = %d" % (featDict[feat], res))
"""
# "Returns the number of logical CPUs available for the process."
common.DebugPrint("cv2.getNumberOfCPUs() (#logical CPUs) is %s" % str(
cv2.getNumberOfCPUs()))
common.DebugPrint(
"cv2.getTickFrequency() is %s" % str(cv2.getTickFrequency()))
video_file_q = sys.argv[1] # input/current video
video_file_r = sys.argv[2] # reference video
# TODO: use getopt() to run Evangelidis' or "Alex's" algorithm, etc
ReadVideo.main(video_file_q, video_file_r)
def run_scipy_tests(*args, **kwargs):
import scipy
scipy.show_config()
scipy.test()
config.PREPROCESS_REFERENCE_VIDEO_ONLY = True;
elif sys.argv[3] == "--process-query-and-align-videos":
config.PREPROCESS_REFERENCE_VIDEO_ONLY = False;
else:
config.PREPROCESS_REFERENCE_VIDEO_ONLY = "do_all";
print("config.PREPROCESS_REFERENCE_VIDEO_ONLY = %s" % str(config.PREPROCESS_REFERENCE_VIDEO_ONLY));
AskFirst();
# Inspired from https://stackoverflow.com/questions/1520234/how-to-check-which-version-of-numpy-im-using
print("numpy.version.version = %s" % str(np.version.version));
print("scipy.version.version = %s" % str(scipy.version.version));
#scipy.version.version
np.show_config();
scipy.show_config();
# See http://docs.scipy.org/doc/numpy/reference/generated/numpy.set_printoptions.html
# We use 7 digits precision and suppress using scientific notation.
np.set_printoptions(precision=7, suppress=True, \
threshold=70000, linewidth=4000);
#threshold=7000000, linewidth=4000);
#threshold=7000, linewidth=300);
#threshold=1000000, linewidth=3000);
# Inspired from \OpenCV2-Python-Tutorials-master\source\py_tutorials\py_core\py_optimization
# normally returns True - relates to using the SIMD extensions of x86: SSX, AVX
common.DebugPrint("cv2.useOptimized() is %s" % str(cv2.useOptimized()));
