图像拼接融合_图像拼接拼一圈有什么障碍吗

import cv2
import numpy as np

def image_rotate(src, rotate=0):
  h,w,c = src.shape
  cos_val = np.cos(np.deg2rad(rotate))
  sin_val = np.sin(np.deg2rad(rotate))
  M = np.float32([[cos_val, sin_val, 0], [-sin_val, cos_val, 0]])
  img = cv2.warpAffine(src, M, (w,h))
  return img


 # Open the image files.
img1_color = image_rotate(cv2.imread("picb.png"),0)  # Image to be aligned.
img2_color = image_rotate(cv2.imread("pica.png"),5)   # Reference image.

 # Convert to grayscale.
img1 = cv2.cvtColor(img1_color, cv2.COLOR_BGR2GRAY)
img2 = cv2.cvtColor(img2_color, cv2.COLOR_BGR2GRAY)
height, width = img2.shape

 # Create ORB detector with 5000 features.
orb_detector = cv2.ORB_create(5000)

 # Find keypoints and descriptors.

# The first arg is the image, second arg is the mask

#  (which is not required in this case).
kp1, d1 = orb_detector.detectAndCompute(img1, None)
kp2, d2 = orb_detector.detectAndCompute(img2, None)

 # Match features between the two images.

# We create a Brute Force matcher with

# Hamming distance as measurement mode.
matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck = True)
 # Match the two sets of descriptors.
matches = list(matcher.match(d1, d2))
 # Sort matches on the basis of their Hamming distance.
matches.sort(key = lambda x: x.distance)
 # Take the top 90 % matches forward.
matches = matches[:int(len(matches)*0.9)]
no_of_matches = len(matches)

# Define empty matrices of shape no_of_matches * 2.
p1 = np.zeros((no_of_matches, 2))
p2 = np.zeros((no_of_matches, 2))
for i in range(len(matches)):
  p1[i, :] = kp1[matches[i].queryIdx].pt
  p2[i, :] = kp2[matches[i].trainIdx].pt
 # Find the homography matrix.
homography, mask = cv2.findHomography(p1, p2, cv2.RANSAC)
 # Use this matrix to transform the
# colored image wrt the reference image.


#image fusion
#create mask for fusion
img1_mask = np.zeros(img1.shape, np.uint8)
img1_mask.fill(255)
img1_mask = cv2.warpPerspective(img1_mask,
                    homography, (width*2, height))
img2_mask=np.zeros(img2.shape, np.uint8)
img2_mask.fill(255)
img2_mask=cv2.copyMakeBorder(img2_mask,0,0,0,img2.shape[1], cv2.BORDER_CONSTANT,value=0)
img_and = cv2.bitwise_and(img1_mask, img2_mask)
img_xor=cv2.bitwise_xor(img1_mask, img_and)
#distance transformer
img1_distance=cv2.distanceTransform(img1_mask, cv2.DIST_L2, 0)
t,img1_distance=cv2.threshold (img1_distance,255,255,cv2.THRESH_TRUNC)
img1_distance=np.uint8(img1_distance)
dst = cv2.bitwise_and(img1_distance, img_and)

#satisfied to constraints
img2_g=cv2.copyMakeBorder(img2,0,0,0,img2.shape[1], cv2.BORDER_CONSTANT,value=0)
img1_g = cv2.warpPerspective(img1,
                    homography, (width*2, height))
h=np.ones(img1_g.shape)
max=dst.max()
for i in range(dst.shape[0]):
    temp_max=dst[i,:].max()
    dst[i, :]=dst[i,:]/temp_max*max
dst=dst/max
img_xor=img_xor/img_xor.max()
dst=dst+img_xor
cv2.imwrite('mask.jpg', dst*max)

fusion_img=(h-dst)*img2_g+dst*img1_g
cv2.imwrite('stitch and fusion.jpg', fusion_img)