detection.py

import numpy as np
import cv2
from matplotlib import pyplot as plt

MIN_MATCH_COUNT = 10

img1 = cv2.imread('./sample/pos/1.jpg',0)
# img2 = cv2.imread('./sample/neg/11.jpg',0)
img2 = cv2.imread('./sample/pos/2.jpg',0)

# 使用SIFT检测角点
sift = cv2.xfeatures2d.SIFT_create()
# kp将是一个关键点列表，des是一个numpy数组,是描述符
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)

# 定义FLANN匹配器
index_params = dict(algorithm=1, trees=5)
search_params = dict(checks=50)
flann = cv2.FlannBasedMatcher(index_params, search_params)
# 使用KNN算法匹配
matches = flann.knnMatch(des1, des2, k=2)

# 去除错误匹配
good = []
for m, n in matches:
    if m.distance < 0.7*n.distance:
        good.append(m)
print(len(good))

# 单应性
if len(good)>MIN_MATCH_COUNT:
    # 改变数组的表现形式，不改变数据内容，数据内容是每个关键点的坐标位置
    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)
    # findHomography 函数是计算变换矩阵
    # 参数cv2.RANSAC是使用RANSAC算法寻找一个最佳单应性矩阵H，即返回值M
    # 返回值：M 为变换矩阵，mask是掩模
    M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)
    # ravel方法将数据降维处理，最后并转换成列表格式
    matchesMask = mask.ravel().tolist()
    # 获取img1的图像尺寸
    h,w = img1.shape
    # pts是图像img1的四个顶点
    pts = np.float32([[0,0],[0,h-1],[w-1,h-1],[w-1,0]]).reshape(-1,1,2)
    # 计算变换后的四个顶点坐标位置
    dst = cv2.perspectiveTransform(pts,M)

    # 根据四个顶点坐标位置在img2图像画出变换后的边框
    img2 = cv2.polylines(img2,[np.int32(dst)],True,(255,0,0),3, cv2.LINE_AA)

else:
    matchesMask = None

# 显示匹配结果
draw_params = dict(matchColor = (0,255,0),
                   singlePointColor = None,
                   matchesMask = matchesMask,
                   flags = 2)
img3 = cv2.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params)
plt.imshow(img3, 'gray'),plt.show()