【OpenCV实战】 图像拼接

import cv2
import numpy as np

def stitch_image(img1, img2, H):
    # 1. 获得每张图的4个角点
    h1, w1 = img1.shape[:2]
    h2, w2 = img2.shape[:2]
    img1_dims = np.float32([[0, 0], [0, h1], [w1, h1], [w1, 0]]).reshape(-1, 1, 2)
    img2_dims = np.float32([[0, 0], [0, h2], [w2, h2], [w2, 0]]).reshape(-1, 1, 2)

    # 2. 变换图片（旋转、平移）
    img1_transform = cv2.perspectiveTransform(img1_dims, H)

    # 3. 创建一张大图，拼接
    result_dims = np.concatenate((img2_dims, img1_transform), axis=0)

    # 4. 输出
    [x_min, y_min] = np.int32(result_dims.min(axis=0).ravel() - 0.5)
    [x_max, y_max] = np.int32(result_dims.max(axis=0).ravel() + 0.5)

    # 平移的距离
    transform_dist = [-x_min, -y_min]
    transform_array = np.array([[1, 0, transform_dist[0]],
                                [0, 1, transform_dist[1]],
                                [0, 0, 1]])

    result_img = cv2.warpPerspective(img1, transform_array.dot(H), (x_max-x_min, y_max-y_min))
    result_img[transform_dist[1]:transform_dist[1]+h2,
               transform_dist[0]:transform_dist[0]+w2] = img2

    return result_img

# 计算单应性矩阵
def get_homo(img1, img2):
    # 1 创建特征转换对象
    sift = cv2.xfeatures2d.SIFT_create()

    # 2 通过特征转换对象获得特征点和描述子
    k1, d1 = sift.detectAndCompute(img1, None)
    k2, d2 = sift.detectAndCompute(img2, None)
    
    # 3 创建特征匹配器
    bf = cv2.BFMatcher()
    
    # 4 进行特征匹配
    matches = bf.knnMatch(d1, d2, k=2)

    # 5 过滤特征，找出有效的特征匹配点
    verify_ratio = 0.8
    verify_matches = []
    for m1, m2 in matches:
        if m1.distance < 0.8 * m2.distance:
            verify_matches.append(m1)
    
    min_matches = 8
    if len(verify_matches) > min_matches:
        img1_pts = []
        img2_pts = []

        for m in verify_matches:
            img1_pts.append(k1[m.queryIdx].pt)
            img2_pts.append(k2[m.trainIdx].pt)
        
        img1_pts = np.float32(img1_pts).reshape(-1, 1, 2)
        img2_pts = np.float32(img2_pts).reshape(-1, 1, 2)

        H, mask  =cv2.findHomography(img1_pts, img2_pts, cv2.RANSAC, 5.0)

        return H
    else:
        print("error: no enough matches")
        exit()


# 第一步：读取文件，设为一样大小 640x480

img1 = cv2.imread('img/map2.jpg')
img2 = cv2.imread('img/map1.jpg')

img1 = cv2.resize(img1, (640, 480))
img2 = cv2.resize(img2, (640, 480))

inputs = np.hstack((img1, img2))

# 获得单应性矩阵
H = get_homo(img1, img2)

# 根据单应性矩阵对图像进行变换，然后平移, 拼接并输出结果
result_img  = stitch_image(img1, img2, H)

cv2.imshow('1', img1)
cv2.imshow('2', img2)
cv2.imshow('result', result_img)
cv2.waitKey(0)