方案一:采用sift算法提取特征并保存，用最近邻算法匹配最合适的图片
1.提取特征值并保存.npy文件, feature.py
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 = './test' # 文件夹路径
create_descriptors(path)
2.对比图片，匹配最佳图片 main.py
from os.path import join
from os import walk
import numpy as np
import cv2
query = cv2.imread('a.jpg', 0)
folder = './test'
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)
# 获取最多匹配数目的图片
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())
方案二:采用orb算法提取保存特征，并用最近邻匹配最合适的图片
1.提取特征值并保存.npy文件, feature.py
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.ORB_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 = './test' # 文件夹路径
create_descriptors(path)
2.对比图片，匹配最佳图片 main.py
from os.path import join
from os import walk
import numpy as np
import cv2
query = cv2.imread('a.jpg', 0)
folder = './test'
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ORB算法检查图像的关键点和描述符
sift = cv2.ORB_create()
query_kp, query_ds = sift.detectAndCompute(query, None)
# 创建FLANN匹配器
index_params = dict(algorithm=6, table_number=12, key_size=12, multi_probe_level=2)
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 < 0.4 * 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)
# 获取最多匹配数目的图片
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())