Я не уверен, что это стандартный или самый эффективный способ сделать это, но, похоже, он работает:
# Load image as grayscale (since it's b&w to start with)
im = cv2.imread('im.jpg', cv2.IMREAD_GRAYSCALE)
# Threshold it. I tried a few pixel values, and got something reasonable at min = 5
_,thresh = cv2.threshold(im,5,255,cv2.THRESH_BINARY)
# Find contours:
im2, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
# Put all contours together and reshape to (_,2).
# The first "column" will be your x values of your contours, and second will be y values
c = np.vstack(contours).reshape(-1,2)
# Extract the most left, most right, uppermost and lowermost point
xmin = np.min(c[:,0])
ymin = np.min(c[:,1])
xmax = np.max(c[:,0])
ymax = np.max(c[:,1])
# Use those as a guide of where to crop your image
crop = im[ymin:ymax, xmin:xmax]
cv2.imwrite('cropped.jpg', crop)
В итоге вы получите следующее: