numpy

问题 I want to find the pixels of a video stream that are static. This way I can detect logos and other non-moving items on my video stream. My idea behind the script is as follows: collect a number of equally-sized and graysized frames in a list called previous if a certain amount of frames is collected, call the function np.std This function loops over all the x- and y-coordinates of a new image. Calculate the standard deviation of the grayvalues for all the coordinates based on the grayvalues

问题 I have a cloud point obtained from photogrammetry from a person's back. I'm trying to interpolate it to get a regular grid, and for that I'm using scipy.interpolate with good results so far. The problem is: the function I'm using ( scipy.interpolate.griddata ) uses the convex hull of the cloudpoint in the plane x,y, thus giving as result some values that don't exist in the original surface, which has a concave perimeter. The following illustration shows the original cloudpoint at the left

问题 I have a cloud point obtained from photogrammetry from a person's back. I'm trying to interpolate it to get a regular grid, and for that I'm using scipy.interpolate with good results so far. The problem is: the function I'm using ( scipy.interpolate.griddata ) uses the convex hull of the cloudpoint in the plane x,y, thus giving as result some values that don't exist in the original surface, which has a concave perimeter. The following illustration shows the original cloudpoint at the left

问题 If I have a list of images represented by 3D ndarray such as [[x,y,color],...] , what operations can I use to output an image with values that are median of all values? I am using a for loop and find it too slow. 回答1: You said your images in color, formatted as a list of 3d ndarrays . Let's say there are n images: imgs = [img_1, ..., img_n] Where imgs is a list and each img_i is a an ndarray with shape (nrows, ncols, 3) . Convert the list to a 4d ndarray, then take the median over the

问题 If I have a list of images represented by 3D ndarray such as [[x,y,color],...] , what operations can I use to output an image with values that are median of all values? I am using a for loop and find it too slow. 回答1: You said your images in color, formatted as a list of 3d ndarrays . Let's say there are n images: imgs = [img_1, ..., img_n] Where imgs is a list and each img_i is a an ndarray with shape (nrows, ncols, 3) . Convert the list to a 4d ndarray, then take the median over the

问题 I'm trying to code up a simple Simplex algorithm, the first step of which is to find a basic feasible solution: Choose a set B of linearly independent columns of A Set all components of x corresponding to the columns not in B to zero. Solve the m resulting equations to determine the components of x. These are the basic variables. I know the solution will involve using scipy.linalg.svd (or scipy.linalg.lu ) and some numpy.argwhere / numpy.where magic, but I'm not sure exactly how. Does anyone

问题 today I used the numpy array for some calculation and found a strange problem, for example, assume i already imported numpy.arange in Ipython, and I run some scripts as follows: In [5]: foo = arange(10) In [8]: foo1 = foo[arange(3)] In [11]: foo1[:] = 0 In [12]: foo Out[12]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) In [16]: foo2 = foo[0:3] In [19]: foo2[:]=0 In [21]: foo Out[21]: array([0, 0, 0, 3, 4, 5, 6, 7, 8, 9]) above shows that when i slice the array by foo[arange(3)], i got a copy of the

问题 I have a 4x3 boolean numpy array, and I'm trying to return a same-sized array which is all False, except for the location of the first True value on each row of the original. So if I have a starting array of all_bools = np.array([[False, True, True],[True, True, True],[False, False, True],[False,False,False]]) all_bools array([[False, True, True], # First true value = index 1 [ True, True, True], # First true value = index 0 [False, False, True], # First true value = index 2 [False, False,

问题 today I used the numpy array for some calculation and found a strange problem, for example, assume i already imported numpy.arange in Ipython, and I run some scripts as follows: In [5]: foo = arange(10) In [8]: foo1 = foo[arange(3)] In [11]: foo1[:] = 0 In [12]: foo Out[12]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) In [16]: foo2 = foo[0:3] In [19]: foo2[:]=0 In [21]: foo Out[21]: array([0, 0, 0, 3, 4, 5, 6, 7, 8, 9]) above shows that when i slice the array by foo[arange(3)], i got a copy of the

问题 I want to implement a classic martingale using Python and Pandas in a betting system. Let's say that this DataFrame is defined like this df = pd.DataFrame(np.random.randint(0,2,100)*2-1, columns=['TossResults']) so it contains toss results (-1=lose 1=win) I would like to change stake (the amount I bet every bet) using classic martingale. Initial stake is 1. If I lose stake will be 2 times previous stake (multiplier=2). If I win stake will be stake_initial I did a function def stake_martingale