对齐X射线图像：查找旋转，旋转和裁剪

Question

我想在下面的X射线图片中（通过使用Python）：

1. 识别（不完美）矩形块的旋转
2. 旋转图像使其垂直（纵向）
3. 通过裁剪剩余的空白区域删除

我猜这部分与this question相反，其中工具很可能与添加corner detector相同。我不完全确定如何最好地解决这个问题，这似乎是某个人已经解决的问题。

Answer 1

这可以使用到sendSynchronousRequest库的Python绑定来完成。以下代码已根据我已经改编的内容进行了改编，因此可以进一步优化和改进。

您提供的图像不仅旋转，而且也不是矩形，因此脚本在两个主要阶段工作。首先，它确定图像上的旋转并旋转并围绕最小矩形裁剪。然后它会拉伸生成的图像以适合生成的矩形。

初始阈值图像

初始边界矩形

旋转和裁剪的图像

从

拉伸的多边形

最终裁剪图像

OpenCV

感谢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) 函数的StackOverflow answer。

在Python 2.7和OpenCV 3.0上测试