无法使用Python分割影像

Question

I need to split or crop my image into several boxes showing in the image. My code is given below which can split an Image but I am not be able to create 15 different boxes using my code.

My code is given below:

import math, cv2
from scipy import misc
import numpy

def getFactors(num):
    """
    Split the input number into factors nearest to its square root. May not be
    the most efficient for large numbers, but will do for numbers smaller than 1000.
    """
    sqt = int(math.sqrt(num))
    if (num % sqt) == 0:
        return (sqt,int(num/sqt))

    num1 = sqt
    num2 = sqt
    while True:
        num1 += 1
        num2 -= 1
        if (num1 >= num) or (num2 <= 0):
            return (num, 1)
        if (num % num1) == 0:
            return (num1, int(num/num1))
        if (num % num2) == 0:
            return (num2, int(num/num2))
    return

def splitImage(img, numsplits):
    """
    Split the input image into number of splits provided by the second argument.
    The results are stored in a numpy array res and returned. The last index of the
    res array indexes the individual parts.
    """
    # Get the factors for splitting. So if the number of splits is 9, then (3,3)
    # or if 6 then (2,3) etc.
    factors = getFactors(numsplits)
    # Height and width of each split
    h = int(img.shape[0] / factors[0])
    w = int(img.shape[1] / factors[1])
    # Handle both color and B&W images
    if img.ndim >= 3:
        size = (h,w,img.shape[2],numsplits)
    else:
        size = (h,w,numsplits)
    # Initialize the result array
    res = numpy.ndarray( size, dtype = img.dtype )
    # Iterate through the number of factors to split the source image horizontally
    # and vertically, and store the resultant chunks
    for i in range(factors[0]):
        for j in range(factors[1]):
            if img.ndim >= 3:
                res[:,:,:,((i*factors[1])+j)] = img[(i*h):((i+1)*h), (j*w):((j+1)*w),:]
            else:
                res[:,:,((i*factors[1])+j)] = img[(i*h):((i+1)*h), (j*w):((j+1)*w)]

    return res

def cropImage(img):
    """
    Detect lines in the image to crop it so that the resultant image can be split well.
    We use here Canny edge detection followed by Hough Line Transform.
    """
    # Convert image to grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # Detect edges and lines
    edges = cv2.Canny(gray, 50, 150, apertureSize = 3)
    lines = cv2.HoughLines(edges, 1, numpy.pi/90, 200)

    min_x = img.shape[0]
    max_x = 0
    min_y = img.shape[1]
    max_y = 0
    # Find the extremal horizontal and vertical coordinates to crop
    for i in range(len(lines[:,0,0])):
        rho = lines[i,0,0]
        theta = lines[i,0,1]
        a = numpy.cos(theta)
        b = numpy.sin(theta)
        x = a*rho
        y = b*rho

        if abs(a) < 1e-06 :
            if min_y > int(y):
                min_y = int(y)
            if max_y < int(y):
                max_y = int(y)
        if abs(b) < 1e-06 :
            if min_x > int(x):
                min_x = int(x)
            if max_x < int(x):
                max_x = int(x)

    return img[min_y:max_y, min_x:max_x, :]

# Read image     
img = misc.imread('tmp.png')
# Crop the image
img = cropImage(img)
# Call the splitter function
res = splitImage(img, 6)
# Save the results to files
for i in range(res.shape[-1]):
    if img.ndim >= 3:
        misc.imsave('res_{0:03d}.png'.format(i),res[:,:,:,i])
    else:
        misc.imsave('res_{0:03d}.png'.format(i),res[:,:,i])

Now I want to crop or split it 15 different boxes available in the image attached here. I am not be able to finding out any solution.

Answer 1

OpenCV: findContours

An easy solution could be to use the built-in function from OpenCV findContours

It will find all the contours present in your image (returned as a list of contour), in your example there are great chances that it will detect your boxes.
Then you can iterate through this list to get the bounding boxes of your contour. From there you can create your sub images by slicing the original images with the bounding box coordinates.

_, contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for contour in countours:
    (_x, _y, w, h) = cv2.boundingRect(contour)

Official tutorial to use cv2.findContours:
https://docs.opencv.org/3.3.1/d4/d73/tutorial_py_contours_begin.html