我一直在寻找有关如何删除文本更改侦听器的答案。
这是我当前的代码：
from PIL import Image
import numpy as np
import imutils
import cv2
# from panorama import Stitcher
import argparse
import imutils
import cv2
class Stitcher:
def __init__(self):
# determine if we are using OpenCV v3.X
self.isv3 = imutils.is_cv3()
def stitch(self, images, ratio=0.75, reprojThresh=4.0,
showMatches=False):
# unpack the images, then detect keypoints and extract
# local invariant descriptors from them
(imageB, imageA) = images
(kpsA, featuresA) = self.detectAndDescribe(imageA)
(kpsB, featuresB) = self.detectAndDescribe(imageB)
# match features between the two images
M = self.matchKeypoints(kpsA, kpsB,
featuresA, featuresB, ratio, reprojThresh)
# if the match is None, then there aren't enough matched
# keypoints to create a panorama
if M is None:
return None
# otherwise, apply a perspective warp to stitch the images
# together
(matches, H, status) = M
result = cv2.warpPerspective(imageA, H,
(imageA.shape[1] + imageB.shape[1], imageA.shape[0]))
result[0:imageB.shape[0], 0:imageB.shape[1]] = imageB
# check to see if the keypoint matches should be visualized
if showMatches:
vis = self.drawMatches(imageA, imageB, kpsA, kpsB, matches,
status)
# return a tuple of the stitched image and the
# visualization
return (result, vis)
# return the stitched image
return result
def detectAndDescribe(self, image):
# convert the image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# check to see if we are using OpenCV 3.X
if self.isv3:
# detect and extract features from the image
descriptor = cv2.xfeatures2d.SIFT_create()
(kps, features) = descriptor.detectAndCompute(image, None)
# otherwise, we are using OpenCV 2.4.X
else:
# detect keypoints in the image
detector = cv2.FeatureDetector_create("SIFT")
kps = detector.detect(gray)
# extract features from the image
extractor = cv2.DescriptorExtractor_create("SIFT")
(kps, features) = extractor.compute(gray, kps)
# convert the keypoints from KeyPoint objects to NumPy
# arrays
kps = np.float32([kp.pt for kp in kps])
# return a tuple of keypoints and features
return (kps, features)
def matchKeypoints(self, kpsA, kpsB, featuresA, featuresB,
ratio, reprojThresh):
# compute the raw matches and initialize the list of actual
# matches
matcher = cv2.DescriptorMatcher_create("BruteForce")
rawMatches = matcher.knnMatch(featuresA, featuresB, 2)
matches = []
# loop over the raw matches
for m in rawMatches:
# ensure the distance is within a certain ratio of each
# other (i.e. Lowe's ratio test)
if len(m) == 2 and m[0].distance < m[1].distance * ratio:
matches.append((m[0].trainIdx, m[0].queryIdx))
# computing a homography requires at least 4 matches
if len(matches) > 4:
# construct the two sets of points
ptsA = np.float32([kpsA[i] for (_, i) in matches])
ptsB = np.float32([kpsB[i] for (i, _) in matches])
# compute the homography between the two sets of points
(H, status) = cv2.findHomography(ptsA, ptsB, cv2.RANSAC,
reprojThresh)
# return the matches along with the homograpy matrix
# and status of each matched point
return (matches, H, status)
# otherwise, no homograpy could be computed
return None
def drawMatches(self, imageA, imageB, kpsA, kpsB, matches, status):
# initialize the output visualization image
(hA, wA) = imageA.shape[:2]
(hB, wB) = imageB.shape[:2]
vis = np.zeros((max(hA, hB), wA + wB, 3), dtype="uint8")
vis[0:hA, 0:wA] = imageA
vis[0:hB, wA:] = imageB
# loop over the matches
for ((trainIdx, queryIdx), s) in zip(matches, status):
# only process the match if the keypoint was successfully
# matched
if s == 1:
# draw the match
ptA = (int(kpsA[queryIdx][0]), int(kpsA[queryIdx][1]))
ptB = (int(kpsB[trainIdx][0]) + wA, int(kpsB[trainIdx][1]))
cv2.line(vis, ptA, ptB, (0, 255, 0), 1)
# return the visualization
return vis
if __name__ == '__main__':
images_folder = sys.argv[1]
images = ["0.png", "1.png", "2.png", "3.png"]
imageA = cv2.imread(images_folder+images[0])
imageB = cv2.imread(images_folder+images[1])
# stitch the images together to create a panorama
stitcher = Stitcher()
(result, vis) = stitcher.stitch([imageA, imageB], showMatches=True)
count = 0
imgRGB=cv2.cvtColor(result, cv2.COLOR_BGR2RGB)
img = Image.fromarray(imgRGB)
current_stiched_image = images_folder + "lol10{}.png".format(count)
img.save(current_stiched_image)
for image in images[2:]:
count+=1
print("image: {}".format(image))
print("count: {}".format(count))
print("current_stiched_image: {}".format(current_stiched_image))
imageA1 = cv2.imread(current_stiched_image)
imageB1 = cv2.imread(images_folder + image)
(result, vis) = stitcher.stitch([imageA1, imageB1], showMatches=True)
imgRGB=cv2.cvtColor(result, cv2.COLOR_BGR2RGB)
img = Image.fromarray(imgRGB)
current_stiched_image = images_folder + "lol10{}.png".format(count)
print("new current_stiched_image: {}".format(current_stiched_image))
img.save(current_stiched_image)
当我的布尔enableFormatting为False时，textchangelistener仍然存在。
如果您想更清楚地解释我的代码，可以提供NumberTextwatcherForThousands类。
答案 0 :(得分：2)
您需要保留对侦听器的引用，当您尝试删除旧的侦听器时，现在正在创建一个新的侦听器。 像这样：
/* get this into @sql */
select 'Y' as col1;
declare @SQL as nvarchar(max)
set @SQL = N'select ''Y'' as col1;'
select @SQL;
+---------------------+
| @SQL |
+---------------------+
| select 'Y' as col1; |
+---------------------+