opencvコンピュータ視覚学習ノート5
59682 ワード
第六章画像検索及び画像記述子による検索
特徴を抽出して画像のマッチングと検索を行う.
1特徴検出アルゴリズム
一般的な特徴と抽出アルゴリズム:
Harris検出角点
Sift検出スポット(blob)には特許保護があります.
Surf検出スポットには特許保護があります.
Fast検出角点
Brief検出スポット
Orbバンド方向のfastアルゴリズムと回転不変性を持つbrifアルゴリズム
特徴の定義
サンプルコードは以下の通りです
サンプルコードは以下の通りです.
ORBはベースです
FAST(feature sfrom accersterated segment test)ポイント検出技術
ピクセルの周りに円を描き、16ピクセルを含む.
BRIF(binaryrobust independent element ary feature)記述子
暴力(bruute-force)マッチング法
二つのディスクリプタを比較して、マッチ結果を生成します.
ORBの特徴マッチング
サンプルコードは以下の通りです
cv 2.error:D:\Build\OpenCV\opencv-31.0\modules\python\src 2\cv 2.cpp:163:error:(-215)The data shound normally be NULL!in functionNumpyAllocator::allocate
解決方法は、次のコードを追加します.
Fast library for approximate nearesneighborsは最近隣の快速倉庫に近似します.
Flannシングルマッチ
サンプルコードは以下の通りです
タトゥーに基づいて証明書を取るアプリケーションの例
A画像記述子をファイルに保存する
特徴を抽出して画像のマッチングと検索を行う.
1特徴検出アルゴリズム
一般的な特徴と抽出アルゴリズム:
Harris検出角点
Sift検出スポット(blob)には特許保護があります.
Surf検出スポットには特許保護があります.
Fast検出角点
Brief検出スポット
Orbバンド方向のfastアルゴリズムと回転不変性を持つbrifアルゴリズム
特徴の定義
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2016/12/5 12:30
# @Author : Retacn
# @Site :
# @File : cornerHarris.py
# @Software: PyCharm
__author__ = "retacn"
__copyright__ = "property of mankind."
__license__ = "CN"
__version__ = "0.0.1"
__maintainer__ = "retacn"
__email__ = "[email protected]"
__status__ = "Development"
import cv2
import numpy as np
#
img = cv2.imread('../test1.jpg')
#
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = np.float32(gray)
#
dst = cv2.cornerHarris(gray,
2,
23, # sobel ,3-31
0.04)
#
img[dst > 0.01 * dst.max()] = [0, 0, 255]
while (True):
cv2.imshow("corners", img)
if cv2.waitKey(33) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()
dogとsiftを用いて特徴抽出と説明を行う.サンプルコードは以下の通りです
import cv2
import sys
import numpy as py
#
# imgpath=sys.argv[1]
imgpath = '../test1.jpg'
img = cv2.imread(imgpath)
#
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# sift , , dog
sift = cv2.xfeatures2d.SIFT_create()
keypoints, descriptor = sift.detectAndCompute(gray, None)
# print(keypoints)
#
# angle
# class_id id
# octave
# pt
# response
# size
img = cv2.drawKeypoints(image=img,
outImage=img,
keypoints=keypoints,
color=(51, 163, 236),
flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
#
cv2.imshow('sift_keypoints', img)
while (True):
if cv2.waitKey(int(1000 / 12)) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()
心には高速hessianアルゴリズムとSURFを使って特徴を抽出します.サンプルコードは以下の通りです.
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2016/12/10 17:30
# @Author : Retacn
# @Site : sift
# @File : sift.py
# @Software: PyCharm
__author__ = "retacn"
__copyright__ = "property of mankind."
__license__ = "CN"
__version__ = "0.0.1"
__maintainer__ = "retacn"
__email__ = "[email protected]"
__status__ = "Development"
import cv2
import sys
import numpy as py
#
# imgpath=sys.argv[1]
# alg=sys.argv[2]
# threshold=sys.argv[3]
imgpath = '../test1.jpg'
img = cv2.imread(imgpath)
# alg = 'SURF'
alg = 'SIFT'
# threshold = '8000'
#
threshold = '4000'
def fd(algorithm):
if algorithm == 'SIFT':
return cv2.xfeatures2d.SIFT_create()
if algorithm == 'SURF':
# return cv2.xfeatures2d.SURF_create(float(threshold) if len(sys.argv) == 4 else 4000)
return cv2.xfeatures2d.SURF_create(float(threshold))
#
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# sift , , dog
fd_alg = fd(alg)
keypoints, descriptor = fd_alg.detectAndCompute(gray, None)
# print(keypoints)
#
# angle
# class_id id
# octave
# pt
# response
# size
img = cv2.drawKeypoints(image=img,
outImage=img,
keypoints=keypoints,
color=(51, 163, 236),
flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
#
cv2.imshow('keypoints', img)
while (True):
if cv2.waitKey(int(1000 / 12)) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()
ORBに基づく特徴検出と特徴マッチングORBはベースです
FAST(feature sfrom accersterated segment test)ポイント検出技術
ピクセルの周りに円を描き、16ピクセルを含む.
BRIF(binaryrobust independent element ary feature)記述子
暴力(bruute-force)マッチング法
二つのディスクリプタを比較して、マッチ結果を生成します.
ORBの特徴マッチング
サンプルコードは以下の通りです
import numpy as np
import cv2
from matplotlib import pyplot as plt
cv2.ocl.setUseOpenCL(False)
#
img1 = cv2.imread('../test2_part.jpg', cv2.IMREAD_GRAYSCALE)
img2 = cv2.imread('../test2.jpg', cv2.IMREAD_GRAYSCALE)
# orb
orb = cv2.ORB_create()
kp1, des1 = orb.detectAndCompute(img1, None)
kp2, des2 = orb.detectAndCompute(img2, None)
#
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
matches = bf.match(des1, des2)
matches = sorted(matches, key=lambda x: x.distance)
#
img3 = cv2.drawMatches(img1, kp1, img2, kp2, matches[:40], img2, flags=2)
plt.imshow(img3), plt.show()
以下のエラーを報告しますcv 2.error:D:\Build\OpenCV\opencv-31.0\modules\python\src 2\cv 2.cpp:163:error:(-215)The data shound normally be NULL!in functionNumpyAllocator::allocate
解決方法は、次のコードを追加します.
cv2.ocl.setUseOpenCL(False)
k最近接配匹
import numpy as np
import cv2
from matplotlib import pyplot as plt
cv2.ocl.setUseOpenCL(False)
#
img1 = cv2.imread('../test2_part.jpg', cv2.IMREAD_GRAYSCALE)
img2 = cv2.imread('../test2.jpg', cv2.IMREAD_GRAYSCALE)
# orb
orb = cv2.ORB_create()
kp1, des1 = orb.detectAndCompute(img1, None)
kp2, des2 = orb.detectAndCompute(img2, None)
# knn , k
bf = cv2.BFMatcher(cv2.NORM_L1, crossCheck=False)
matches = bf.knnMatch(des1, des2, k=2)
#
img3 = cv2.drawMatchesKnn(img1, kp1, img2, kp2, matches, img2, flags=2)
plt.imshow(img3), plt.show()
Flannマッチング法Fast library for approximate nearesneighborsは最近隣の快速倉庫に近似します.
import numpy as np
import cv2
from matplotlib import pyplot as plt
#
queryImage = cv2.imread('../test2_part.jpg', cv2.IMREAD_GRAYSCALE)
trainingImage = cv2.imread('../test2.jpg', cv2.IMREAD_GRAYSCALE)
# sift
sift = cv2.xfeatures2d.SIFT_create()
kp1, des1 = sift.detectAndCompute(queryImage, None)
kp2, des2 = sift.detectAndCompute(trainingImage, None)
FLANN_INDEX_KDTREE = 0
#
indexParams = dict(algorithm=FLANN_INDEX_KDTREE, trees=5) #
searchParams = dict(checks=50) # ,
flann = cv2.FlannBasedMatcher(indexParams, searchParams)
matches = flann.knnMatch(des1, des2, k=2)
matchesMask = [[0, 0] for i in range(len(matches))]
for i, (m, n) in enumerate(matches):
if m.distance < 0.7 * n.distance:
matchesMask[i] = [1, 0]
drawParams = dict(matchColor=(0, 255, 0),
singlePointColor=(255, 0, 0),
matchesMask=matchesMask,
flags=0)
resultImage = cv2.drawMatchesKnn(queryImage, kp1, trainingImage, kp2, matches, None, **drawParams)
plt.imshow(resultImage), plt.show()
運転結果は以下の通りですFlannシングルマッチ
サンプルコードは以下の通りです
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2016/12/11 12:02
# @Author : Retacn
# @Site : flann
# @File : flann_homography.py
# @Software: PyCharm
__author__ = "retacn"
__copyright__ = "property of mankind."
__license__ = "CN"
__version__ = "0.0.1"
__maintainer__ = "retacn"
__email__ = "[email protected]"
__status__ = "Development"
import numpy as np
import cv2
from matplotlib import pyplot as plt
MIN_MATCH_COUNT = 10
#
img1 = cv2.imread('../test3_part.jpg', cv2.IMREAD_GRAYSCALE)
img2 = cv2.imread('../test3.jpg', cv2.IMREAD_GRAYSCALE)
# sift
sift = cv2.xfeatures2d.SIFT_create()
#
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)
FLANN_INDEX_KDTREE = 0
#
indexParams = dict(algorithm=FLANN_INDEX_KDTREE, trees=5) #
searchParams = dict(checks=50) # ,
flann = cv2.FlannBasedMatcher(indexParams, searchParams)
matches = flann.knnMatch(des1, des2, k=2)
good = []
for m, n in matches:
if m.distance < 0.7 * n.distance:
good.append(m)
if len(good) > MIN_MATCH_COUNT:
#
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)
#
M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
matchesMask = mask.ravel().tolist()
# ,
h, w = img1.shape
pts = np.float32([[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]).reshape(-1, 1, 2)
dst = cv2.perspectiveTransform(pts, M)
img2 = cv2.polylines(img2, [np.int32(dst)], True, 255, 3, cv2.LINE_AA)
else:
print("Not enough matches are found -%d/%d" % (len(good), MIN_MATCH_COUNT))
matchesMask = None
#
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()
運転結果は以下の通りですタトゥーに基づいて証明書を取るアプリケーションの例
A画像記述子をファイルに保存する
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2016/12/11 13:52
# @Author : Retacn
# @Site :
# @File : generate_descriptors.py
# @Software: PyCharm
__author__ = "retacn"
__copyright__ = "property of mankind."
__license__ = "CN"
__version__ = "0.0.1"
__maintainer__ = "retacn"
__email__ = "[email protected]"
__status__ = "Development"
import cv2
import numpy as np
from os import walk
from os.path import join
import sys
#
def create_descriptors(folder):
files = []
for (dirpath, dirnames, filenames) in walk(folder):
files.extend(filenames)
for f in files:
save_descriptor(folder, f, cv2.xfeatures2d.SIFT_create())
#
def save_descriptor(folder, image_path, feature_detector):
print("reading %s" % image_path)
if image_path.endswith("npy") or image_path.endswith("avi"):
return
img = cv2.imread(join(folder, image_path), cv2.IMREAD_GRAYSCALE)
keypoints, descriptors = feature_detector.detectAndCompute(img, None)
descriptor_file = image_path.replace("jpg", "npy")
np.save(join(folder, descriptor_file), descriptors)
#
dir = sys.argv[1]
create_descriptors(dir)
Bスキャンマッチング#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2016/12/11 14:05
# @Author : Retacn
# @Site :
# @File : scan4matches.py
# @Software: PyCharm
__author__ = "retacn"
__copyright__ = "property of mankind."
__license__ = "CN"
__version__ = "0.0.1"
__maintainer__ = "retacn"
__email__ = "[email protected]"
__status__ = "Development"
from os.path import join
from os import walk
import numpy as np
import cv2
from sys import argv
from matplotlib import pyplot as plt
#
folder = argv[1]
query = cv2.imread(join(folder, 'part.jpg'), cv2.IMREAD_GRAYSCALE)
# , ,
files = []
images = []
descriptors = []
for (dirpath, dirnames, filenames) in walk(folder):
files.extend(filenames)
for f in files:
if f.endswith('npy') and f != 'part.npy':
descriptors.append(f)
print(descriptors)
# sift
sift = cv2.xfeatures2d.SIFT_create()
query_kp, query_ds = sift.detectAndCompute(query, None)
# flann
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)
flann = cv2.FlannBasedMatcher(index_params, search_params)
#
MIN_MATCH_COUNT = 10
potential_culprits = {}
print(">> Initiating picture scan...")
for d in descriptors:
print("--------- analyzing %s for matches ------------" % d)
matches = flann.knnMatch(query_ds, np.load(join(folder, d)), k=2)
good = []
for m, n in matches:
if m.distance < 0.7 * n.distance:
good.append(m)
if len(good) > MIN_MATCH_COUNT:
print('%s is a match! (%d)' % (d, len(good)))
else:
print('%s is not a match ' % d)
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())