OpenCV - 将图像保存到选定的特定文件夹(OpenCV - Saving images to a particular folder of choice)

 我无法说出你正在做的计算，但似乎你只是想安排组合。 问题是它们是否是有向或无向的 - 也就是说，是否需要计算流量（A，B）和流量（B，A），还是只计算一个流量？ 
 
 如果只有一个，你可以这样做： 
 
for i,ward_list in enumerate(idgroups):
    for j,ward_list2 in enumerate(idgroups[i:],start=i):
 
 这将迭代i，j对，如下所示： 
 
0,0 0,1 0,2 ... 0,n
1,1 1,2 ... 1,n
2,2 ... 2,n
 
 这将在无向情况下发挥作用。 
 
 如果你需要计算流（A，B）和流（B，A），那么你可以简单地将你的代码放到一个称为flows的函数中，并用反向参数调用它，如图所示。 ;-) 
 
 更新 
 
 我们来定义一个名为flows的函数： 
 
def flows():
    pass
 
 现在，什么是参数？ 
 
 好吧，看着你的代码，它从DataFrame获取数据。 你需要两个不同的病房，所以我们从这些开始。 结果似乎是结果网格的总和。 
 
def flows(df, ward_a, ward_b):
    """Return the sum of all the cells in the row/column intersections
    of ward_a and ward_b."""

    return 0
 
 现在我要复制你的代码行： 
 
    ward_list = idgroups[index]
    print ward_list
    df6 = mergedcsv.loc[ward_list] #select rows with values in the list
    dfcols = mergedcsv.loc[ward_list, :] #select columns with values in list
    ward_liststr = map(str, ward_list) #convert ward_list to strings so that they can be used to select columns, won't work as integers.
    ward_listint = map(int, ward_list)
    #dfrowscols = mergedcsv.loc[ward_list, ward_listint]
    df7 = df6.loc[:, ward_liststr]
    print df7
    regflowsum = df7.values.sum() #sum all values in dataframe
    intflow = [regflowsum]
    print intflow
 
 我认为这是大部分flow功能。 我们看看吧。 
 
 
 
 ward_list显然是ward_a或ward_b参数。 
 
 
 我不确定df6是什么，因为你在df7中重新计算它。 所以需要澄清。 
 
 
 regflowsum是我们期望的输出，我想。 
 
 
 重写这个函数： 
 
def flows(df, ward_a, ward_b):
    """Return the sum of all the cells in the row/column intersections
    of ward_a and ward_b."""

    print "Computing flows from:"
    print "    ", ward_a
    print ""
    print "flows into:"
    print "    ", ward_b

    # Filter rows by ward_a, cols by ward_b:
    grid = df.loc[ward_a, ward_b]

    print "Grid:"
    print grid

    flowsum = grid.values.sum()

    print "Flows:", flowsum

    return flowsum
 
 现在，我认为ward_a和ward_b值已经是正确的格式。 所以我们必须强化它们或者其他功能之外的任何东西。 让我们这样做： 
 
for ax, group_a in enumerate(idgroups):
    ward_a = map(str, group_a)

    for bx, group_b in enumerate(idgroups[ax:], start=ax):
        ward_b = map(str, group_b)

        flow_ab = flows(mergedcsv, ward_a, ward_b)

        if ax != bx:
            flow_ba = flows(mergedcsv, ward_b, ward_a)
        else:
            flow_ba = flow_ab

        # Now what?
 
 此时，你有两个数字。 当病房相同时（内部流量？），它们将是平等的。 在这一点上，你原来的代码不再有用，因为它只处理内部流，而不是A-> B流，所以我不知道该怎么做。 但值是在变量中，所以... 

I can't speak to the computations you're doing, but it seems like you just want to arrange combinations of groups. The question is whether they are directed or undirected- that is, do you need to compute flows(A,B) and flows(B,A), or just one?
 
If just one, you could do this:
 
for i,ward_list in enumerate(idgroups):
    for j,ward_list2 in enumerate(idgroups[i:],start=i):
 
This would iterate over i,j pairs like:
 
0,0 0,1 0,2 ... 0,n
1,1 1,2 ... 1,n
2,2 ... 2,n
 
which would serve in the undirected case. 
 
If you need to compute both flows(A,B) and flows(B,A), then you can simply push your code into a function called flows, and call it with reversed args, as shown. ;-)
 
Update
 
Let's define a function called flows:
 
def flows():
    pass
 
Now, what are the parameters?
 
Well, looking at your code, it gets data from a DataFrame. And you want two different wards, so let's start with those. The result seems to be a sum of the resulting grid.
 
def flows(df, ward_a, ward_b):
    """Return the sum of all the cells in the row/column intersections
    of ward_a and ward_b."""

    return 0
 
Now I'm going to copy lines of your code:
 
    ward_list = idgroups[index]
    print ward_list
    df6 = mergedcsv.loc[ward_list] #select rows with values in the list
    dfcols = mergedcsv.loc[ward_list, :] #select columns with values in list
    ward_liststr = map(str, ward_list) #convert ward_list to strings so that they can be used to select columns, won't work as integers.
    ward_listint = map(int, ward_list)
    #dfrowscols = mergedcsv.loc[ward_list, ward_listint]
    df7 = df6.loc[:, ward_liststr]
    print df7
    regflowsum = df7.values.sum() #sum all values in dataframe
    intflow = [regflowsum]
    print intflow
 
I think this is most of the flow function right here. Let's look.
 
 
 
The ward_list will obviously be either the ward_a or ward_b parameters.
 
 
I'm not sure what df6 is, because you sort of recompute it in df7. So that need to be clarified.
 
 
regflowsum is our desired output, I think.
 
 
Rewriting this into the function:
 
def flows(df, ward_a, ward_b):
    """Return the sum of all the cells in the row/column intersections
    of ward_a and ward_b."""

    print "Computing flows from:"
    print "    ", ward_a
    print ""
    print "flows into:"
    print "    ", ward_b

    # Filter rows by ward_a, cols by ward_b:
    grid = df.loc[ward_a, ward_b]

    print "Grid:"
    print grid

    flowsum = grid.values.sum()

    print "Flows:", flowsum

    return flowsum
 
Now, I have assumed that the ward_a and ward_b values are already in the correct format. So we'll have to str-ify them or whatever outside the function. Let's do that:
 
for ax, group_a in enumerate(idgroups):
    ward_a = map(str, group_a)

    for bx, group_b in enumerate(idgroups[ax:], start=ax):
        ward_b = map(str, group_b)

        flow_ab = flows(mergedcsv, ward_a, ward_b)

        if ax != bx:
            flow_ba = flows(mergedcsv, ward_b, ward_a)
        else:
            flow_ba = flow_ab

        # Now what?
 
At this point, you have two numbers. They will be equal when the wards are the same (internal flow?). At this point your original code stops being helpful because it only deals with internal flows, and not A->B flows, so I don't know what to do. But the values are in the variables, so ...