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我正在运行一个相当复杂的代码，所以我不会为细节而烦恼，因为我之前已经让它工作了，但现在我得到了这个错误。

Particle 是一个用 0 或 255 填充的 3D 元组，我正在使用 scipy 质心函数，然后尝试将值转换为最接近的整数（因为我正在处理数组）。在最后一行发现错误......谁能解释为什么会这样？

第二行填充粒子第三行删除任何周围带有不同标签的粒子（这是在所有标签的 for 循环中）

Particle = []
Particle = big_labelled_stack[x_start+20:x_stop+20,y_start+20:y_stop+20,z_start+20:z_stop+20]
Particle = np.where(Particle == i ,255,0)

CoM = scipy.ndimage.measurements.center_of_mass(Particle)
CoM = [ (int(round(x)) for x in CoM ]

提前致谢。如果您需要更多代码，请询问，但我认为它不会对您有所帮助，而且非常混乱。

################## 更多代码 
 border = 30

[labelled_stack,no_of_label] = label(labelled,structure_array,output_type)
# RE-LABEL particles now no. of seeds has been reduced! LAST LABELLING

#Increase size of stack by increasing borders and equal them to 0; to allow us to cut out particles into cube shape which else might lye outside the border
h,w,l = labelled.shape
big_labelled_stack = np.zeros(shape=(h+60,w+60,l+60),dtype=np.uint32)

 # Creates an empty border around labelled_stack full of zeros of size border

if (no_of_label > 0):       #Small sample may return no particles.. so this stage not neccesary

    info = np.zeros(shape=(no_of_label,19))          #Creates array to store coordinates of particles

    for i in np.arange(1,no_of_label,1):  


        coordinates = find_objects(labelled_stack == i)[0] #Find coordinates of label i.
        x_start = int(coordinates[0].start)
        x_stop = int(coordinates[0].stop)
        y_start = int(coordinates[1].start)
        y_stop = int(coordinates[1].stop)
        z_start = int(coordinates[2].start)
        z_stop = int(coordinates[2].stop)

        dx = (x_stop - x_start)  
        dy = (y_stop - y_start)
        dz = (z_stop - z_start)

        Particle = np.zeros(shape=(dy,dx,dz),dtype = np.uint16)                
        Particle = big_labelled_stack[x_start+30:x_start+dx+30,y_start+30:y_start+dy+30,z_start+30:z_start+dz+30]
        Particle = np.where(Particle == i ,255,0)

        big_labelled_stack[border:h+border,border:w+border,border:l+border] = labelled_stack   
        big_labelled_stack = np.where(big_labelled_stack == i , 255,0)
        CoM_big_stack = scipy.ndimage.measurements.center_of_mass(big_labelled_stack)
        C = np.asarray(CoM_big_stack) - border



        if dx > dy:
            b = dx
        else:                           #Finds the largest of delta_x,y,z and saves as b, so that we create 'Cubic_Particle' of size 2bx2bx2b (cubic box)
            b = dy
        if dz > b:
            b = dz

        CoM = scipy.ndimage.measurements.center_of_mass(Particle)
        CoM = [ (int(round(x))) for x in CoM ] 


        Cubic_Particle = np.zeros(shape=(2*b,2*b,2*b))
        Cubic_Particle[(b-CoM[0]):(b+dx-CoM[0]),(b-CoM[1]):(b+dy-CoM[1]),(b-CoM[2]):(b+dz-CoM[2])] = Particle

        volume = Cubic_Particle.size    # Gives volume of the box in voxels



        info[i-1,:] = [C[0],C[1],C[2],i,C[0]-b,C[1]-b,C[2]-b,C[0]+b,C[1]+b,C[2]+b,volume,0,0,0,0,0,0,0,0] # Fills an array with label.No., size of box, and co-ords            


else:
    print('No particles found, try increasing the sample size')
    info = []

好的，所以我有一个充满标记粒子的堆栈，我想做两件事，首先找到每个粒子相对于 labelled_stack 的质心，这就是 CoM_big_labelled_stack（和 C）所做的。并将坐标存储在一个名为 info 的列表（元组）中。我也试图在粒子周围创建一个立方体，以它的质心为中心（这与 CoM 变量有关），所以首先我使用 scipy 中的 find objects 函数来查找粒子，然后我使用这些坐标在粒子周围创建一个非立方体盒子，并找到它的质心。然后我找到盒子的最长尺寸并将其命名为 b，创建一个大小为 2b 的立方体盒子，并在正确的位置用粒子填充它。

抱歉，这段代码很乱，我对 Python 很陌生

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