When read with cv2.imread, you would already have the image data as a NumPy array. Now, NumPy supports various vectorization capabilities, which we can use to speed up things quite a bit.
To solve our case, one method would be to look for rows and columns that have at least one pixel along rows and columns that is greater than some lower limit or threshold. So, if you are sure that the black areas are absolutely zeros, you can set that threshold as 0. Thus, if img represents the image data, you would have correspondingly two boolean arrays : (img>tol).any(1) and (img>tol).any(0).
Next up, you can use these boolean arrays to index into the image data for extraction of valid bounding box using broadcasted indexing with np._ix -
np.ix_((img>tol).any(1),(img>tol).any(0))
Finally, we index into image data with it for the final extracted data, which is the required bounding box data.
To sum up, the final implementation would be -
def crop_image(img,tol=0):
# img is image data
# tol is tolerance
mask = img>tol
return img[np.ix_(mask.any(1),mask.any(0))]