[人工智能]SIFT特征提取

提取图像中的特征点，并在原图像中显示出来

import cv2
img_path = r'../image/paojie.jpg'
img = cv2.imread(img_path)
# print(img.shape)
# img = cv2.resize(img,(136 * 3,512 * 3))
cv2.imshow("original", img)

gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

# 使用SIFT
sift = cv2.SIFT_create()
keypoints, descriptor = sift.detectAndCompute(gray, None)

cv2.drawKeypoints(image=img,
                  outImage=img,
                  keypoints=keypoints,
                  flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS,
                  color=(51, 163, 236))
cv2.imshow("SIFT", img)

img = cv2.imread(img_path)
# img = cv2.resize(img,(136 * 3,76 * 3))

cv2.waitKey(0)
cv2.destroyAllWindows()

提取图像中的SIFT特征点，并利用PCA(主成分分析)进行降维，并提取特征值。注：SIFT提取出来的特征是一个128维的矩阵，我在这里利用PCA主成分分析将矩阵降为100维。

import cv2
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler

img_path = r'../image/paojie.jpg'
img = cv2.imread(img_path)
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# 使用SIFT
sift = cv2.SIFT_create()
keypoints, descriptor = sift.detectAndCompute(gray, None)
# print(descriptor.shape)
# m,n=descriptor.shape
# print(m,n)
descriptor = StandardScaler().fit_transform(descriptor)
pca = PCA(n_components=100)
pca.fit(descriptor)
print(pca.singular_values_)  # 查看特征值
print(pca.components_)  # 打印查看特征值对应的特征向量
# print(pca.components_.shape)

cv2.drawKeypoints(image=img,
                  outImage=img,
                  keypoints=keypoints,
                  flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS,
                  color=(255, 0, 255))
cv2.imshow("SIFT", img)

cv2.waitKey(0)
cv2.destroyAllWindows()

flags不同，flags为绘制点的模式

import cv2

img = cv2.imread('../image/paojie.jpg', cv2.IMREAD_COLOR)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow('origin', img);

# SIFT
detector = cv2.SIFT_create()
keypoints = detector.detect(gray, None)
img = cv2.drawKeypoints(image=gray,
                        keypoints=keypoints,
                        outImage=None,
                        color=(255, 0, 255))
# img = cv2.drawKeypoints(gray,keypoints,flags = cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
cv2.imshow('test', img)
cv2.waitKey(0)
cv2.destroyAllWindows()

opencv4.4版本由于sift专利原因已经无法使用，opencv4.2与python3.8不匹配，python版本需降到3.6，opencv版本需降到3.4.2.17

pip uninstall opencv-python
pip install opencv-python==3.4.2.17
pip install opencv-contrib-python==3.4.2.17
import pandas as pd
import pickle

给定两张图片，计算其SIFT特征匹配结果

# coding: utf-8
from matplotlib import pyplot as plt
from imagedt.decorator import time_cost
import cv2

print('cv version: ', cv2.__version__)
def bgr_rgb(img):
    (r, g, b) = cv2.split(img)
    return cv2.merge([b, g, r])


def orb_detect(image_a, image_b):
    # feature match
    orb = cv2.ORB_create()

    kp1, des1 = orb.detectAndCompute(image_a, None)
    kp2, des2 = orb.detectAndCompute(image_b, None)

    # create BFMatcher object
    bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)

    # Match descriptors.
    matches = bf.match(des1, des2)

    # Sort them in the order of their distance.
    matches = sorted(matches, key=lambda x: x.distance)

    # Draw first 10 matches.
    img3 = cv2.drawMatches(image_a, kp1, image_b, kp2, matches[:100], None, flags=2)

    return bgr_rgb(img3)


@time_cost
def sift_detect(img1, img2, detector='surf'):
    if detector.startswith('si'):
        print("sift detector......")
        sift = cv2.xfeatures2d.SURF_create()
    else:
        print("surf detector......")
        sift = cv2.xfeatures2d.SURF_create()

    # find the keypoints and descriptors with SIFT
    kp1, des1 = sift.detectAndCompute(img1, None)
    kp2, des2 = sift.detectAndCompute(img2, None)

    # BFMatcher with default params
    bf = cv2.BFMatcher()
    matches = bf.knnMatch(des1, des2, k=2)

    # Apply ratio test
    good = [[m] for m, n in matches if m.distance < 0.5 * n.distance]

    # cv2.drawMatchesKnn expects list of lists as matches.
    img3 = cv2.drawMatchesKnn(img1, kp1, img2, kp2, good, None, flags=2)

    return bgr_rgb(img3)


if __name__ == "__main__":
    # load image
    image_a = cv2.imread('../image/tower2.jpg')
    image_b = cv2.imread('../image/tower2.jpg')

    # ORB
    # img = orb_detect(image_a, image_b)

    # SIFT or SURF
    img = sift_detect(image_a, image_b)

    plt.imshow(img)
    plt.show()

首先获取全部图片的特征数据

import cv2
import numpy as np
from os import walk
from os.path import join

def create_descriptors(folder):
    files = []
    for (dirpath, dirnames, filenames) in walk(folder):
        files.extend(filenames)
    for f in files:
        if '.jpg' in f:
            save_descriptor(folder, f, cv2.xfeatures2d.SIFT_create())

def save_descriptor(folder, image_path, feature_detector):
    # 判断图片是否为npy格式
    if image_path.endswith("npy"):
        return
    # 读取图片并检查特征
    img = cv2.imread(join(folder,image_path), 0)
    keypoints, descriptors = feature_detector.detectAndCompute(img, None)
    # 设置文件名并将特征数据保存到npy文件
    descriptor_file = image_path.replace("jpg", "npy")
    np.save(join(folder, descriptor_file), descriptors)

if __name__=='__main__':
    path = 'D://PycharmProjects//pythonProject//image'
    create_descriptors(path)

将图片的特征数据保存在npy文件。下一步是根据选择的图域这些特征数据文件进行匹配，从而找出最佳匹配的图片。

from os.path import join
from matplotlib import pyplot as plt
from os import walk
import numpy as np
import cv2

query = cv2.imread('C://Users//Garfield//Desktop//007.png', 0)
folder = 'C://Users//Garfield//Desktop//dataunion'
descriptors = []
# 获取特征数据文件名
for (dirpath, dirnames, filenames) in walk(folder):
    for f in filenames:
        if f.endswith("npy"):
            descriptors.append(f)
    print(descriptors)

# 使用SIFT算法检查图像的关键点和描述符
sift = cv2.xfeatures2d.SIFT_create()
query_kp, query_ds = sift.detectAndCompute(query, None)

# 创建FLANN匹配器
index_params = dict(algorithm=0, trees=5)
search_params = dict(checks=50)
flann = cv2.FlannBasedMatcher(index_params, search_params)

potential_culprits = {}
for d in descriptors:
    # 将图像query与特征数据文件的数据进行匹配
    matches = flann.knnMatch(query_ds, np.load(join(folder, d)), k=2)
    # 清除错误匹配
    good = []
    for m, n in matches:
        if m.distance < 0.7 * n.distance:
            good.append(m)
    # 输出每张图片与目标图片的匹配数目
    print("img is %s ! matching rate is (%d)" % (d, len(good)))
    potential_culprits[d] = len(good)
    
max_matches = None
potential_suspect = None
for culprit, matches in potential_culprits.items():
    if max_matches == None or matches > max_matches:
        max_matches = matches
        potential_suspect = culprit


print("potential suspect is %s" % potential_suspect.replace("npy", "").upper())

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