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##Vectorization with NumPy

###I. Crop to remove all black rows and columns across entire image

###II. Crop while keeping the inner all black rows or columns

###Benchmarking

###Extend to generic 2D or 3D image data cases

Vectorization with NumPy

I. Crop to remove all black rows and columns across entire image

II. Crop while keeping the inner all black rows or columns

Benchmarking

Extend to generic 2D or 3D image data cases

def crop_image_only_outside(img,tol=0):
    # img is 2D image data
    # tol  is tolerance
    mask = img>tol
    m,n = img.shape
    mask0,mask1 = mask.any(0),mask.any(1)
    col_start,col_end = mask0.argmax(),n-mask0[::-1].argmax()-1
    row_start,row_end = mask1.argmax(),m-mask1[::-1].argmax()-1
    return img[row_start:row_end,col_start:col_end]

def crop_image(img,tol=0):
    # img is 2D or 3D image data
    # tol  is tolerance
    mask = img>tol
    if img.ndim==3:
        mask = mask.all(2)
    mask0,mask1 = mask.any(0),mask.any(1)
    return img[np.ix_(mask0,mask1)]

def crop_image_only_outside(img,tol=0):
    # img is 2D or 3D image data
    # tol  is tolerance
    mask = img>tol
    if img.ndim==3:
        mask = mask.all(2)
    m,n = mask.shape
    mask0,mask1 = mask.any(0),mask.any(1)
    col_start,col_end = mask0.argmax(),n-mask0[::-1].argmax()-1
    row_start,row_end = mask1.argmax(),m-mask1[::-1].argmax()-1
    return img[row_start:row_end,col_start:col_end]

def crop_image_only_outside(img,tol=0):
    # img is 2D image data
    # tol  is tolerance
    mask = img>tol
    m,n = img.shape
    mask0,mask1 = mask.any(0),mask.any(1)
    col_start,col_end = mask0.argmax(),n-mask0[::-1].argmax()
    row_start,row_end = mask1.argmax(),m-mask1[::-1].argmax()
    return img[row_start:row_end,col_start:col_end]

def crop_image(img,tol=0):
    # img is 2D or 3D image data
    # tol  is tolerance
    mask = img>tol
    if img.ndim==3:
        mask = mask.all(2)
    mask0,mask1 = mask.any(0),mask.any(1)
    return img[np.ix_(mask0,mask1)]

def crop_image_only_outside(img,tol=0):
    # img is 2D or 3D image data
    # tol  is tolerance
    mask = img>tol
    if img.ndim==3:
        mask = mask.all(2)
    m,n = mask.shape
    mask0,mask1 = mask.any(0),mask.any(1)
    col_start,col_end = mask0.argmax(),n-mask0[::-1].argmax()
    row_start,row_end = mask1.argmax(),m-mask1[::-1].argmax()
    return img[row_start:row_end,col_start:col_end]

To crop the image while keeping the inner all black rows or columns, the implementation would be close to the previous method. The basic idea here would be getting the start, stop indices along rows and columns that decide the bounding box. To get those indices,

We will start off with the same mask of ANY match along rows and columns as used in the previous one. Then, argmax would be useful to get start and stop indices of those matches along rows and cols. We will use these indices to slice the 2D input array, which is the desired cropped image output. The implementation would look something like this -

###Benchmarking

To crop the image while keeping the inner all black rows or columns, the implementation would be close to the previous method. The basic idea here would be getting the start, stop indices along rows and columns that decide the bounding box. We will start off with the same mask of ANY match along rows and columns as used in the previous one. Then, argmax would be useful to get start and stop indices of those matches along rows and cols. We will use these indices to slice the 2D input array, which is the desired cropped image output. The implementation would look something like this -

###Benchmarking

Since we are talking about performance in this Q&A, let's test out how this method works in comparison to others.