Align x-ray images: find rotation, rotate and crop

Question

I want in the x-ray image below to (by using Python):

1. identify the rotation of the (imperfect) rectangle block
2. rotate the image so that it is in vertical (portrait form)
3. remove by cropping the remaining white space

I guess this partly the reverse of this question where the tools are most likely identical with the addition of a corner detector. I'm not entirely sure of how to best approach this and it seems like a problem that someone has solved.

Answer 1

This can be done using the Python bindings to the OpenCV library. The following code has been adapted from something I had already, so it can probably be further optimised and improved.

The image you have given is not only rotated, but it is also not rectangular, as such the script works in two main stages. Firstly it determines the rotation on the image and rotates and crops it around a minimum rectangle. It then stretches the resulting image to fit the resulting rectangle.

Initial threshold image

Initial bounding rectangle

Rotated and cropped image

Polygon to stretch from

Final cropped image

import numpy as np
import cv2
import math

THRESHOLD = 240

def subimage(image, center, theta, width, height):
    if 45 < theta <= 90:
        theta = theta - 90
        width, height = height, width

    theta *= math.pi / 180 # convert to rad
    v_x = (math.cos(theta), math.sin(theta))
    v_y = (-math.sin(theta), math.cos(theta))
    s_x = center[0] - v_x[0] * (width / 2) - v_y[0] * (height / 2)
    s_y = center[1] - v_x[1] * (width / 2) - v_y[1] * (height / 2)
    mapping = np.array([[v_x[0],v_y[0], s_x], [v_x[1],v_y[1], s_y]])
    return cv2.warpAffine(image, mapping, (width, height), flags=cv2.WARP_INVERSE_MAP, borderMode=cv2.BORDER_REPLICATE)

def auto_crop(image_source):
    # First slightly crop edge - some images had a rogue 2 pixel black edge on one side
    init_crop = 5
    h, w = image_source.shape[:2]
    image_source = image_source[init_crop:init_crop+(h-init_crop*2), init_crop:init_crop+(w-init_crop*2)]

    # Add back a white border
    image_source = cv2.copyMakeBorder(image_source, 5,5,5,5, cv2.BORDER_CONSTANT, value=(255,255,255))

    image_gray = cv2.cvtColor(image_source, cv2.COLOR_BGR2GRAY)
    _, image_thresh = cv2.threshold(image_gray, THRESHOLD, 255, cv2.THRESH_BINARY)
    image_thresh2 = image_thresh.copy()
    image_thresh2 = cv2.Canny(image_thresh2, 100, 100, apertureSize=3)
    points = cv2.findNonZero(image_thresh2)

    centre, dimensions, theta = cv2.minAreaRect(points)
    rect = cv2.minAreaRect(points)

    width = int(dimensions[0])
    height = int(dimensions[1])

    box = cv2.boxPoints(rect)
    box = np.int0(box)

    temp = image_source.copy()
    cv2.drawContours(temp, [box], 0, (255,0,0), 2)

    M = cv2.moments(box)    
    cx = int(M['m10']/M['m00'])
    cy = int(M['m01']/M['m00'])

    image_patch = subimage(image_source, (cx, cy), theta+90, height, width)

    # add back a small border
    image_patch = cv2.copyMakeBorder(image_patch, 1,1,1,1, cv2.BORDER_CONSTANT, value=(255,255,255))

    # Convert image to binary, edge is black. Do edge detection and convert edges to a list of points.
    # Then calculate a minimum set of points that can enclose the points.
    _, image_thresh = cv2.threshold(image_patch, THRESHOLD, 255, 1)
    image_thresh = cv2.Canny(image_thresh, 100, 100, 3)
    points = cv2.findNonZero(image_thresh)
    hull = cv2.convexHull(points)

    # Find min epsilon resulting in exactly 4 points, typically between 7 and 21
    # This is the smallest set of 4 points to enclose the image.
    for epsilon in range(3, 50):
        hull_simple = cv2.approxPolyDP(hull, epsilon, 1)

        if len(hull_simple) == 4:
            break

    hull = hull_simple

    # Find closest fitting image size and warp/crop to fit
    # (ie reduce scaling to a minimum)

    x,y,w,h = cv2.boundingRect(hull)
    target_corners = np.array([[0,0],[w,0],[w,h],[0,h]], np.float32)

    # Sort hull into tl,tr,br,bl order. 
    # n.b. hull is already sorted in clockwise order, we just need to know where top left is.

    source_corners = hull.reshape(-1,2).astype('float32')
    min_dist = 100000
    index = 0

    for n in xrange(len(source_corners)):
        x,y = source_corners[n]
        dist = math.hypot(x,y)

        if dist < min_dist:
            index = n
            min_dist = dist

    # Rotate the array so tl is first
    source_corners = np.roll(source_corners , -(2*index))

    try:
        transform = cv2.getPerspectiveTransform(source_corners, target_corners)
        return cv2.warpPerspective(image_patch, transform, (w,h))

    except:
        print "Warp failure"
        return image_patch


cv2.namedWindow("Result")
image_src = cv2.imread("xray.png")
image_cropped = auto_crop(image_src)
cv2.imwrite("cropped xray.png", image_cropped)
cv2.imshow("Result", image_cropped) 
cv2.waitKey(0)

Thanks go to this StackOverflow answer for the subimage function.

Tested on Python 2.7 and OpenCV 3.0