我正在使用Ubuntu-16.04。我正在使用OpenCV 3.4和Python 3缝合两个矩形图像。
我的拼接代码在这里：stitching.py和panaroma.py
stitching.py ：
from paranoma_old import Panaroma
import imutils
import cv2
#Take picture from folder like: Hill1 & Hill2, scene1 & scene2, my1 & my2, taj1 & taj2, lotus1 & lotus2, beach1 & beach2, room1 & room2
print("Enter the number of images you want to concantenate:")
no_of_images = int(input())
print("Enter the image name in order of left to right in way of concantenation:")
#like taj1.jpg, taj2.jpg, taj3.jpg .... tajn.jpg
filename = []
for i in range(no_of_images):
print("Enter the %d image:" %(i+1))
filename.append(input())
images = []
for i in range(no_of_images):
images.append(cv2.imread(filename[i]))
#We need to modify the image resolution and keep our aspect ratio use the function imutils
for i in range(no_of_images):
images[i] = imutils.resize(images[i], width=999)
for i in range(no_of_images):
images[i] = imutils.resize(images[i], height=700)
panaroma = Panaroma()
if no_of_images == 2:
(result, matched_points) = panaroma.image_stitch([images[1], images[0]], match_status=True)
else:
(result, matched_points) = panaroma.image_stitch([images[no_of_images-2], images[no_of_images-1]], match_status=True)
for i in range(no_of_images - 2):
(result, matched_points) = panaroma.image_stitch([images[no_of_images-i-3],result], match_status=True)
#if matched_points==all():
#(result, matched_points) = panaroma.image_stitch([images[0], images[1]], matched_points)
#to show the got panaroma image and valid matched points
for i in range(no_of_images):
cv2.imshow("Image {k}".format(k=i+1), images[i])
cv2.imshow("Keypoint Matches", matched_points)
cv2.imshow("Panorama", result)
#to write the images
cv2.imwrite("Matched_points.jpg",matched_points)
cv2.imwrite("XYZ.jpg",result)
cv2.waitKey(8000)
cv2.destroyAllWindows()
paranoma.py ：
import numpy as np
import imutils
import cv2
class Panaroma:
def image_stitch(self, images, lowe_ratio=0.8, max_Threshold=4.0,match_status=True):
#detect the features and keypoints from SIFT
(imageB, imageA) = images
(KeypointsA, features_of_A) = self.Detect_Feature_And_KeyPoints(imageA)
(KeypointsB, features_of_B) = self.Detect_Feature_And_KeyPoints(imageB)
#got the valid matched points
Values = self.matchKeypoints(KeypointsA, KeypointsB,features_of_A, features_of_B, lowe_ratio, max_Threshold)
if Values is None:
return None
#to get perspective of image using computed homography
(matches, Homography, status) = Values
result_image = self.getwarp_perspective(imageA,imageB,Homography)
result_image[0:imageB.shape[0], 0:imageB.shape[1]] = imageB
# check to see if the keypoint matches should be visualized
if match_status:
vis = self.draw_Matches(imageA, imageB, KeypointsA, KeypointsB, matches,status)
return (result_image, vis)
return result_image
def getwarp_perspective(self,imageA,imageB,Homography):
val = imageA.shape[1] + imageB.shape[1]
result_image = cv2.warpPerspective(imageA, Homography, (val , imageA.shape[0]))
return result_image
def Detect_Feature_And_KeyPoints(self, image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# detect and extract features from the image
descriptors = cv2.xfeatures2d.SIFT_create()
(Keypoints, features) = descriptors.detectAndCompute(image, None)
Keypoints = np.float32([i.pt for i in Keypoints])
return (Keypoints, features)
def get_Allpossible_Match(self,featuresA,featuresB):
# compute the all matches using euclidean distance and opencv provide
#DescriptorMatcher_create() function for that
match_instance = cv2.DescriptorMatcher_create("BruteForce")
All_Matches = match_instance.knnMatch(featuresA, featuresB, 2)
return All_Matches
def All_validmatches(self,AllMatches,lowe_ratio):
#to get all valid matches according to lowe concept..
valid_matches = []
for val in AllMatches:
if len(val) == 2 and val[0].distance < val[1].distance*lowe_ratio:
valid_matches.append((val[0].trainIdx, val[0].queryIdx))
return valid_matches
def Compute_Homography(self,pointsA,pointsB,max_Threshold):
#to compute homography using points in both images
(H, status) = cv2.findHomography(pointsA, pointsB, cv2.RANSAC, max_Threshold)
return (H,status)
def matchKeypoints(self, KeypointsA, KeypointsB, featuresA, featuresB,lowe_ratio, max_Threshold):
AllMatches = self.get_Allpossible_Match(featuresA,featuresB);
valid_matches = self.All_validmatches(AllMatches,lowe_ratio)
if len(valid_matches) > 4:
# construct the two sets of points
pointsA = np.float32([KeypointsA[i] for (_,i) in valid_matches])
pointsB = np.float32([KeypointsB[i] for (i,_) in valid_matches])
(Homograpgy, status) = self.Compute_Homography(pointsA, pointsB, max_Threshold)
return (valid_matches, Homograpgy, status)
else:
return None
def get_image_dimension(self,image):
(h,w) = image.shape[:2]
return (h,w)
def get_points(self,imageA,imageB):
(hA, wA) = self.get_image_dimension(imageA)
(hB, wB) = self.get_image_dimension(imageB)
vis = np.zeros((max(hB, hA), wA +wB, 3), dtype="uint8")
vis[0:hA, 0:wA] = imageA
vis[0:hB, wA:] = imageB
return vis
def draw_Matches(self, imageA, imageB, KeypointsA, KeypointsB, matches, status):
(hA,wA) = self.get_image_dimension(imageA)
vis = self.get_points(imageA,imageB)
# loop over the matches
for ((trainIdx, queryIdx), s) in zip(matches, status):
if s == 1:
ptA = (int(KeypointsA[queryIdx][0]), int(KeypointsA[queryIdx][1]))
ptB = (int(KeypointsB[trainIdx][0]) + wA, int(KeypointsB[trainIdx][1]))
cv2.line(vis, ptA, ptB, (0, 255, 0), 1)
return vis
我的代码在CASE 1中正常工作。为这两个图像获得完美的拼接图像：
当我在情况2中尝试这两个图像时，无法获得正确的拼接图像。
所有图像：enter link description here
情况2：错误
Traceback (most recent call last):
File "stich_old.py", line 39, in <module>
(result, matched_points) = panaroma.image_stitch([images[0], images[1]], match_status=True)
TypeError: 'NoneType' object is not iterable
请问我什么时候错了？
谢谢。