问题遇到的现象和发生背景
想问如何建立book.csv
现在可以正常运行 就是无法识别
如何更改可以达到识别功能
代码来自https://blog.csdn.net/cliukai/article/details/102467502
问题相关代码，请勿粘贴截图
import numpy as np
import glob
import csv
import cv2
class CoverDescriptor:
def describe(self, image):
descriptor = cv2.BRISK_create()
(kps, descs) = descriptor.detectAndCompute(image, None)
kps = np.float32([kp.pt for kp in kps])
return (kps, descs)
class CoverMatcher:
def __init__(self, descriptor, coverPaths, ratio=0.7, minMatches=40,
useHamming=True):
self.descriptor = descriptor
self.coverPaths = coverPaths
self.ratio = ratio
self.minMatches = minMatches
self.distanceMethod = "BruteForce"
if useHamming:
self.distanceMethod += "-Hamming"
def search(self, queryKps, queryDescs):
# 创建results字典
results = {}
# 封面图片循环
for coverPath in self.coverPaths:
cover = cv2.imread(coverPath)
gray = cv2.cvtColor(cover, cv2.COLOR_BGR2GRAY)
(kps, descs) = self.descriptor.describe(gray)
# query是待查图片的特征信息，后面的是当前数据库里每一张图片的信息
score = self.match(queryKps, queryDescs, kps, descs)
# 把score存入字典，字典的键固定为50个，值为对应的匹配分
results[coverPath] = score
# if matches were found, sort them
if len(results) > 0:
results = sorted([(v, k) for (k, v) in results.items() if v > 0],
reverse=True)
return results
def match(self, kpsA, featuresA, kpsB, featuresB):
# 新建一个描述匹配的对象，方法是BruteForce-Hamming
matcher = cv2.DescriptorMatcher_create(self.distanceMethod)
# 对两幅图片的特征描述做knn近邻匹配
rawMatches = matcher.knnMatch(featuresB, featuresA, 2)
matches = []
for m in rawMatches:
if len(m) == 2 and m[0].distance < m[1].distance * self.ratio:
matches.append((m[0].trainIdx, m[0].queryIdx))
# check to see if there are enough matches to process
if len(matches) > self.minMatches:
# construct the two sets of points
ptsA = np.float32([kpsA[i] for (i, _) in matches])
ptsB = np.float32([kpsB[j] for (_, j) in matches])
# compute the homography between the two sets of points
# and compute the ratio of matched points
(_, status) = cv2.findHomography(ptsA, ptsB, cv2.RANSAC, 4.0)
# return the ratio of the number of matched keypoints
# to the total number of keypoints
return float(status.sum()) / status.size
# no matches were found
return -1.0
# 封面图片的路径
coverPath = r"C:\opencv\source"
# 待查询照片的路径
queryPath = r"C:\opencv\search\search1.jpg"
# 书本数据库，里面保存了照片-作者-书名
bookDatabasePath = "books.csv"
# 新建一个保存封面的字典
bookDatabase = {}
# 把csv里面的数据放入字典，字典的键就是照片名，值就是作者，书名信息
for book in csv.reader(open(bookDatabasePath)):
# book 读出来是“书名，作者，其他等信息”
bookDatabase[book[0]] = book[1:]
# 初始化封面描述和封面匹配对象
# 封面描述需要指定描述方法
cd = CoverDescriptor()
# 封面匹配需要指定距离度量方法，例如Hamming
cm = CoverMatcher(cd, glob.glob(coverPath + "*.jpg"), ratio=0.7, minMatches=40)
# 加载查询图像
queryImage = cv2.imread(queryPath)
# 降噪，转换成灰度图
gray = cv2.cvtColor(queryImage, cv2.COLOR_BGR2GRAY)
# 提取关键点和描述子
(queryKps, queryDescs) = cd.describe(gray)
# 注意这个cm是CoverMatcher，search是CoverMatcher里面的一个方法
# 在已有的数据库中进行匹配，返回匹配分数和匹配对象的路径，注意这里的路径表达里面有个转义字符
# 举例，某一个返回值是[(0.9871794871794872, 'covers\\cover016.png')]
results = cm.search(queryKps, queryDescs)
# print(cd.describe(gray))
print(bookDatabase)
# 显示出待查询的封面
cv2.imshow("query", queryImage)
# 如果在数据库里没有找到书的封面
if len(results) == 0:
print("没有找到")
cv2.waitKey(0)
else:
# results的格式是[(0.9871794871794872, 'covers\\cover016.png')]
for i, (score, coverPath) in enumerate(results):
# rfind和find不同，rfind找出最右边的字符
# 在字典中查询coverPath对应的值，首先需要提取'covers\\cover016.png'中的'cover016.png'，注意这里需要使用转义反斜杠
# 也可以这样使用coverPath.split('||')[-1]
(author, title) = bookDatabase[coverPath[coverPath.rfind("\\") + 1:]]
# (author,title) = bookDatabase[coverPath.split("\\")[-1]]
# 打印输出结果，百分比保留2位小数点
print("loop{} precision is {:.2f}%: author is {} - title is {}".format(i + 1, score * 100, author, title))
# 这个coverPath是对应数据库文件里的图片
result = cv2.imread(coverPath)
cv2.imshow("results", result)
cv2.waitKey(0)
cv2.destroyAllWindows()
