對齊的X射線圖像：找到旋轉，旋轉和裁剪

Question

我想要的x射線圖像中的下面（通過使用Python）：

1. 識別（不完善）矩形塊的旋轉
2. 旋轉圖像，使其垂直（縱向形式）
3. 刪除通過裁剪剩餘的空白

我想這部分的this question相反，其中工具最有可能與另外的相同一個corner detector。我不完全確定如何最好地解決這個問題，而且這似乎是某個人已經解決的問題。

Answer 1

這可以使用Python綁定到OpenCV庫來完成。下面的代碼已經從我以前的東西改編而來，所以它可能會進一步優化和改進。

您給出的圖像不僅是旋轉的，它也不是矩形的，因爲這樣的腳本在兩個主要階段中起作用。首先它確定圖像上的旋轉並旋轉並在最小矩形周圍進行裁剪。然後拉伸產生的圖像以適應所得到的矩形。

初始閾值圖像

初始邊界矩形

旋轉並且裁剪圖像

多邊形舒展從

最終裁剪圖像

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) 

由於去這個StackOverflow answer爲subimage功能。

測試Python 2.7和OpenCV 3.0