我正在实施 RANSAC 算法来检测图像中的圆圈。我分析了执行情况，得到：
13699392 function calls in 799.981 seconds
Random listing order was used
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.000 0.000 {time.time}
579810 0.564 0.000 0.564 0.000 {getattr}
289905 2.343 0.000 8.661 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/linalg/blas.py:226(_get_funcs)
579810 0.124 0.000 0.124 0.000 {method 'get' of 'dict' objects}
289905 0.645 0.000 2.676 0.000 {map}
2954 0.005 0.000 0.005 0.000 {method 'transpose' of 'numpy.ndarray' objects}
2954 0.023 0.000 0.464 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/shape_base.py:179(vstack)
2954 2.373 0.001 2.373 0.001 {method 'read' of 'cv2.VideoCapture' objects}
579810 0.966 0.000 2.031 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/lib/function_base.py:550(asarray_chkfinite)
289905 10.164 0.000 24.316 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/linalg/basic.py:456(lstsq)
2954 1.090 0.000 1.090 0.000 {normalize}
1455433 3.827 0.000 3.827 0.000 {numpy.core.multiarray.array}
579810 2.899 0.000 3.148 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/numerictypes.py:949(_can_coerce_all)
1 0.000 0.000 0.000 0.000 {numpy.core.multiarray.empty}
2954 32.544 0.011 795.875 0.269 git/tra-python-processer/tra/ransac.py:31(image_search)
289905 0.714 0.000 38.644 0.000 git/tra-python-processer/tra/features.py:44(__init__)
289905 2.157 0.000 2.157 0.000 {method 'randint' of 'mtrand.RandomState' objects}
1 0.005 0.005 0.005 0.005 {VideoCapture}
289905 1.026 0.000 1.026 0.000 {method 'astype' of 'numpy.generic' objects}
2954 0.006 0.000 0.010 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/fromnumeric.py:495(transpose)
289905 11.303 0.000 37.930 0.000 git/tra-python-processer/tra/features.py:48(__gen)
3496584 0.343 0.000 0.343 0.000 {len}
2954 0.344 0.000 0.344 0.000 {numpy.core.multiarray.concatenate}
2954 3.214 0.001 3.214 0.001 {numpy.core.multiarray.where}
869715 0.575 0.000 0.575 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/fromnumeric.py:2514(size)
869715 0.778 0.000 2.278 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/numeric.py:394(asarray)
289905 716.946 0.002 716.946 0.002 git/tra-python-processer/tra/features.py:89(points_distance)
5908 0.015 0.000 0.031 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/numeric.py:464(asanyarray)
289905 0.275 0.000 0.275 0.000 {isinstance}
289905 0.342 0.000 9.003 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/linalg/lapack.py:255(get_lapack_funcs)
5908 0.058 0.000 0.097 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/shape_base.py:60(atleast_2d)
295813 0.089 0.000 0.089 0.000 {method 'append' of 'list' objects}
289905 0.645 0.000 3.793 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/numpy/core/numerictypes.py:970(find_common_type)
2954 0.221 0.000 0.221 0.000 {threshold}
1 0.000 0.000 0.000 0.000 {method 'get' of 'cv2.VideoCapture' objects}
1 0.000 0.000 0.000 0.000 git/tra-python-processer/tra/ransac.py:24(__init__)
2954 0.009 0.000 0.009 0.000 {numpy.core.multiarray.zeros}
579810 0.143 0.000 0.143 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/linalg/misc.py:126(_datacopied)
1 0.201 0.201 799.981 799.981 git/tra-python-processer/tra/ransac.py:122(video_processing)
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
2954 1.528 0.001 1.528 0.001 {cvtColor}
289905 1.280 0.000 5.346 0.000 /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/linalg/blas.py:182(find_best_blas_type)
289905 0.198 0.000 0.198 0.000 {method 'index' of 'list' objects}
这是我第一次使用分析器，但据我所知，最重的功能是features.py:89(points_distance)这是一个非常简单的实现：
def points_distance(self,points):
d = n.abs(\
n.sqrt(\
n.power(self.xc - points[:,0],2) + n.power(self.yc - points[:,1],2)
)\
- self.radius
)
return d
有什么建议么？或许cython?
Use scipy.spatial.distance.cdist用于距离计算points_distance.
首先，用纯 Python 和 numpy 优化代码。然后，如有必要，将关键部分移植到 Cython。由于许多函数被重复调用大约 100000 次，因此您应该从 Cython 中获得这些部分的一些速度。当然，除非计算瓶颈在于距离计算，这将限制整体执行时间。
顺便说一句，您应该按以下方式对分析器结果进行排序tottime所以它们更容易阅读。
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