本文实例为大家分享了python将两张图片生成全景图片的具体代码，供大家参考，具体内容如下

1、全景图片的介绍

全景图通过广角的表现手段以及绘画、相片、视频、三维模型等形式，尽可能多表现出周围的环境。360全景，即通过对专业相机捕捉整个场景的图像信息或者使用建模软件渲染过后的图片，使用软件进行图片拼合，并用专门的播放器进行播放，即将平面照片或者计算机建模图片变为360 度全观，用于虚拟现实浏览，把二维的平面图模拟成真实的三维空间，呈现给观赏者。

2、如何实现

2.1、实现原理

主要是利用sift的特征提取与匹配，参考链接

2.2、实现代码

# -*- coding:utf-8 -*-

u'''

Created on 2019年6月14日

@author: wuluo

'''

__author__ = 'wuluo'

__version__ = '1.0.0'

__company__ = u'重庆交大'

__updated__ = '2019-06-14'

import numpy as np

import cv2 as cv

from PIL import Image

from matplotlib import pyplot as plt

print('cv version: ', cv.__version__)

def pinjie():

top, bot, left, right = 100, 100, 0, 500

img1 = cv.imread('G:/2018and2019two/qianrushi/wuluo1.png')

cv.imshow("img1", img1)

img2 = cv.imread('G:/2018and2019two/qianrushi/wuluo2.png')

cv.imshow("img2", img2)

srcImg = cv.copyMakeBorder(

img1, top, bot, left, right, cv.BORDER_CONSTANT, value=(0, 0, 0))

testImg = cv.copyMakeBorder(

img2, top, bot, left, right, cv.BORDER_CONSTANT, value=(0, 0, 0))

img1gray = cv.cvtColor(srcImg, cv.COLOR_BGR2GRAY)

img2gray = cv.cvtColor(testImg, cv.COLOR_BGR2GRAY)

sift = cv.xfeatures2d_SIFT().create()

# find the keypoints and descriptors with SIFT

kp1, des1 = sift.detectAndCompute(img1gray, None)

kp2, des2 = sift.detectAndCompute(img2gray, None)

# FLANN parameters

FLANN_INDEX_KDTREE = 1

index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)

search_params = dict(checks=50)

flann = cv.FlannBasedMatcher(index_params, search_params)

matches = flann.knnMatch(des1, des2, k=2)

# Need to draw only good matches, so create a mask

matchesMask = [[0, 0] for i in range(len(matches))]

good = []

pts1 = []

pts2 = []

# ratio test as per Lowe's paper

for i, (m, n) in enumerate(matches):

if m.distance < 0.7 * n.distance:

good.append(m)

pts2.append(kp2[m.trainIdx].pt)

pts1.append(kp1[m.queryIdx].pt)

matchesMask[i] = [1, 0]

draw_params = dict(matchColor=(0, 255, 0),

singlePointColor=(255, 0, 0),

matchesMask=matchesMask,

flags=0)

img3 = cv.drawMatchesKnn(img1gray, kp1, img2gray,

kp2, matches, None, **draw_params)

#plt.imshow(img3, ), plt.show()

rows, cols = srcImg.shape[:2]

MIN_MATCH_COUNT = 10

if len(good) > MIN_MATCH_COUNT:

src_pts = np.float32(

[kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)

dst_pts = np.float32(

[kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)

M, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC, 5.0)

warpImg = cv.warpPerspective(testImg, np.array(

M), (testImg.shape[1], testImg.shape[0]), flags=cv.WARP_INVERSE_MAP)

for col in range(0, cols):

if srcImg[:, col].any() and warpImg[:, col].any():

left = col

break

for col in range(cols - 1, 0, -1):

if srcImg[:, col].any() and warpImg[:, col].any():

right = col

break

res = np.zeros([rows, cols, 3], np.uint8)

for row in range(0, rows):

for col in range(0, cols):

if not srcImg[row, col].any():

res[row, col] = warpImg[row, col]

elif not warpImg[row, col].any():

res[row, col] = srcImg[row, col]

else:

srcImgLen = float(abs(col - left))

testImgLen = float(abs(col - right))

alpha = srcImgLen / (srcImgLen + testImgLen)

res[row, col] = np.clip(

srcImg[row, col] * (1 - alpha) + warpImg[row, col] * alpha, 0, 255)

# opencv is bgr, matplotlib is rgb

res = cv.cvtColor(res, cv.COLOR_BGR2RGB)

# show the result

plt.figure()

plt.imshow(res)

plt.show()

else:

print("Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT))

matchesMask = None

if __name__ == "__main__":

pinjie()

3、运行效果

原始的两张图：

效果图：

原始图，水杯没有处理好，导致此处效果不好。

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持脚本之家。