Cv2 Draw matches Python

imageCorrespondence = cv2.drawMatches(imageLeft, kpLeft, imageRight, kpRight, [goodMatches[0]], None, flags=2) cv2.imwrite('imageCorrespondence.png', imageCorrespondence) gives expected output in jupyter notebook, but when I save the file using python script, it is drawing matches and flags=4 is drawing keypoints just fine, except everything is happening on a black image (of right size: left. Like we used cv2.drawKeypoints() to draw keypoints, cv2.drawMatches() helps us to draw the matches. It stacks two images horizontally and draw lines from first image to second image showing best matches. There is also cv2.drawMatchesKnn which draws all the k best matches. If k=2, it will draw two match-lines for each keypoint 기본적으로는 cv2.norm_l2이며 cv2.norm_l1과 함께 키포인트와 특징점 기술자를 연산 방식인 sift, surf에 좋습니다. ORB와 BRIEF, BRISK와 같은 2진 문자열 기반의 방식에서는 cv2.NORM_HAMMING가 사용되어져야 합니다 Mask determining which matches are drawn. If the mask is empty, all matches are drawn. flags. Flags setting drawing features. Possible flags bit values are defined by DrawMatchesFlags. This function draws matches of keypoints from two images in the output image. Match is a line connecting two keypoints (circles)

python - cv2 drawMatches draws on blank screen? - Stack Overflo

  1. flags: 매칭점 그리기 옵션 (cv2.draw_matches_flags_default: 결과 이미지 새로 생성(default값), cv2.draw_matches_flags_draw_over_outimg: 결과 이미지 새로 생성 안 함, cv2.draw_matches_flags_draw_rich_keypoints: 특징점 크기와 방향도 그리기, cv2.draw_matches_flags_not_draw_single_points: 한쪽만 있는.
  2. 그 다음 키포인트를 그리기 위해서 cv2.keypoints()를 사용했지만 cv2.drawMatches()를 사용해서 매치를 그릴수가 있겠습니다. 이걸 사용하면 두 이미지가 평행하게 놓여지고, 첫번쨰 이미지에서 두번째 이미지로 최적의 매치를 하는 선을 그려줄겁니다. k개의 최적 매치에 대해서 cv2.drawMatchsKnn도 사용할수.
  3. MaxLoc() to find the object position. In order to understand cv2.

Feature Matching — OpenCV-Python Tutorials beta documentatio

  1. Python03 cv2.findHomography()와 cv2.p⋯ 신사 Howl; Python 02 리스트 추가하는 방법 4가지⋯ 신사 Howl; Python 01 img2pdf 이미지 처리 - CodeLift 신사 Howl; 소화불량 예방하는 법 5가지 더부룩하고⋯ 신사 Howl; 약 없이 통증 줄이는 방법 5가지 신사 Howl; 심혈관질환 예방 식품 이것보다 좋은것⋯ 신사 How
  2. Python replacement for cv2.drawMatches(), for which there are no Python bindings in a release version. - draw_matches.p
  3. I have successfully matched 2 images using bf.Match and drawn matches as well. Now, I am trying to match multiple images from 2 different directories. For this, I am using 2 different array to store images from 2 different directories. Everything seems to be working fine and printing the array actually shows the points as well when printed
  4. The following are 8 code examples for showing how to use cv2.drawMatchesKnn().These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example

Feature matching between images in OpenCV can be done with Brute-Force matcher or FLANN based matcher. BF Matcher matches the descriptor of a feature from one image with all other features of another image and returns the match based on the distance. It is slow since it checks match with all the features OpenCV-Python is a library of Python bindings designed to solve computer vision problems.cv2.putText() method is used to draw a text string on any image. Syntax: cv2.putText(image, text, org, font, fontScale, color[, thickness[, lineType[, bottomLeftOrigin]]]) Parameters: image: It is the image on which text is to be drawn. text: Text string to be drawn This page shows Python examples of cv2.drawMatches. def visualize_homo(img1, img2, kp1, kp2, matches, homo, mask): h, w, d = img1.shape pts = [[0, 0], [0, h - 1], [w. python : cv2.geGoptimalNewCameramatrix는 일부 데이터 세트에서 [0,0,0,0]의 ROI를 반환합니다. python : opencv code 스 니펫 슬라우기 슬라우기 슬라우 멀티 프로세스; python : 헤드 포즈 추정을 계산하는 방법은 무엇입니까? python : CV2는 성공적으로 설치 한 후 가져 오지 않습니

[파이썬 OpenCV] 영상의 특징점 검출과 특징점 그리기 - KAZE, AKAZE, ORB - cv2

Python과 OpenCV - 38 : 이미지의 특징점 매칭(Feature Matching) - GIS

In this tutorial we are going to learn how to draw lines in an image, using Python and OpenCV. Being able to draw lines on an image might be useful to mark, for example, regions of interest on an image. This tutorial was tested with version 4.0.0 of OpenCV and version 3.7.2 of Python. The code. We will start our code by importing the cv2 module Now, we also have to convert the video capture into grayscale and by using appropriate matcher we have to match the points from image to the frame. Here, we may face exceptions when we draw matches because infinitely there will we many points on both planes

OpenCV: Drawing Function of Keypoints and Matche

Python drawMatches - 30 examples found. These are the top rated real world Python examples of cv2.drawMatches extracted from open source projects. You can rate examples to help us improve the quality of examples draw_matches_flags_draw_over_outimg:函数不创建输出的图像,而是直接在输出图像变量空间绘制,要求本身输出图像变量就是一个初始化好了的,size与type都是已经初始化好的变量。 draw_matches_flags_not_draw_single_points :单点的特征点不被绘制 # Standard imports import cv2 import numpy as np # Read image im = cv2.imread(petri.png, cv2.IMREAD_COLOR) # Setup SimpleBlobDetector parameters. params = cv2.SimpleBlobDetector_Params() # Change thresholds params.minThreshold = 0 params.maxThreshold = 255 # Set edge gradient params.thresholdStep = 5 # Filter by Area. params.filterByArea = True params.minArea = 10 # Set up the detector with. OpenCV-Python Tutorials 1 documentation # Sort them in the order of their distance. matches = sorted (matches, key = lambda x: x. distance) # Draw first 10 matches. img3 = cv2. drawMatches (img1, kp1, img2, kp2, matches [: 10], flags = 2) plt. imshow (img3), plt. show 以下に結果画像を示します

OpenCV - 28. 특징 매칭(Feature Matching

  1. OpenCV-Python Feature模块主要是实现一些经典 的 局部特征描述方法。. 在上一篇博客 特征匹配 中,为了画出匹配,一会 使用 了cv. drawMatch es (),一会 使用 了cv. drawMatch esKnn (),两者有什么区别吗?. 一、函数构造 // Draw s match es of keypints from two images on output image. void.
  2. In order to be able to draw the detected keypoints on a given image, we make use of a function called drawKeypoints () function in OpenCV. The drawKeypoints () function takes the input image, keypoints, color and flag as the input. The possible values for flag are: cv.DRAW_MATCHES_FLAGS_DEFAULT. cv.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS
  3. Python cv2.drawMatchesKnn怎么用?Python cv2.drawMatchesKnn使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类cv2的用法示例。 在下文中一共展示了cv2.drawMatchesKnn方法的7个代码示例,这些例子默认根据受欢迎程度.
  4. 本文整理匯總了python中cv2.draw_matches_flags_draw_rich_keypoints屬性的典型用法代碼示例。如果您正苦於以下問題:python cv2.draw_matches_flags_draw_rich_keypoints屬性的具體用法?python cv2.draw_matches_flags_draw_rich_keypoints怎麽用?python cv2.draw_matches_flags_draw_rich_keypoints使用的例子?那麽恭喜您, 這裏精選的屬性代碼示例.
  5. Python cv2 模块, drawKeypoints() 实例源码. 我们从Python开源项目中,提取了以下23个代码示例,用于说明如何使用cv2.drawKeypoints()
  6. Documentation: Python: cv2.matchTemplate(image, templ, method[, result]) → result Compares a template against overlapped image regions. Parameters: image - Image where the search is running.It must be 8-bit or 32-bit floating-point. templ - Searched template.It must be not greater than the source image and have the same data type

SIFT Keypoint Matching using Python OpenCV 18 Jan 2013 on Computer Vision . I have been working on SIFT based keypoint tracking algorithm and something happened on Reddit. Kat wanted this is Python so I added this feature in SimpleCV. Here's the pull request which got merged.. SIFT KeyPoints Matching using OpenCV-Python:. To match keypoints, first we need to find keypoints in the image and. flags(optional): 표시 방법 (cv2.DRAW_MATCHES_FLAGS_DEFAULT: 좌표 중심에 동그라미만 그림(default), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS: 동그라미의 크기를 size와 angle을 반영해서 그림) GFTTDetector. GFTTDetector는 앞서 살펴본 cv2.goodFeaturesToTrack() 함수로 구현된 특징점 검출기입니다

opencv-python 튜토리얼 - 26

python opencv blob. 답변 # 1. 코드 시작 부분에서 수행 한 작업을 잘 모르지만이 작업을 수행하면 제대로 작동하는 것 같습니다 : #!/usr/bin/env python3. import cv2. import numpy as np. # Read image. im = cv2.imread(blobs.png, cv2.IMREAD_GRAYSCALE python - Open CV (Python # Detect blobs. keypoints = detector.detect(im) # Draw detected blobs as red circles. # cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob im_with_keypoints = cv2.drawKeypoints(im,keypoints,np.array([]), (0,0,255),cv2. Added cv2.drawMatches to python bindings Minor doc changes in feature2d module: SIFT, SURF, drawKeypoints, drawMatches Minor change in grabcut.py sample due to new modification in grabcut function So we are ready with the setup, Now lets open your favourite python editor, finally we are using cv2.polylines() to draw the borders in the query image. import cv2 import numpy as np MIN_MATCH_COUNT=30 detector=cv2.SIFT().

Drawing Function of Keypoints and Matches, See details above in drawMatches . Note: For Python API, flags are modified as cv2.DRAW_MATCHES_FLAGS_DEFAULT, cv2. Draws the found matches of keypoints from two images. Its content depends on the flags value defining what is drawn in the output image. See possible flags bit values below python cv2.drawMatchesKnn examples Here are the examples of the python api cv2.drawMatchesKnn taken from open source projects. By voting up you can indicate which examples are most useful and appropriate

python : 배경과 다른 개체를 식별하고 계산합니다. Python, numpy 및 opencv를 사용하여 아래 이미지를 분석하고 발견 된 각 객체에 원을 그립니다. 여기에있는 아이디어는 버그가 배경과 다른 오브젝트 만 식별하는 것만 식별하는 것이 아닙니다. 여기에 내가 사용하는. To use with knnMatch from cv2.BFMatcher. I'd like to make a note where the above code only works if you assume that the matches appear in a 1D list. However, if you decide to use the knnMatch method from cv2.BFMatcher for example, what is returned is a list of lists. Specifically, given the descriptors in img1 called des1 and the descriptors in img2 called des2, each element in the list. SIFT stands for Scale Invariant Feature Transform, it is a feature extraction method (among others, such as HOG feature extraction) where image content is transformed into local feature coordinates that are invariant to translation, scale and other image transformations.. In this tutorial, you will learn the theory behind SIFT as well as how to implement it in Python using OpenCV library Learn to detect and match shapes and to use different methods to extract will be drawn, and all contours with an area smaller than 9000 pixels will not be drawn. To draw contours we will use the function cv2 This can be implemented in the Python code using the OpenCV function cv2.moments(). def get.

cv2.matchTemplate(): Object Detection From Image using Python OpenCV - Python OpenCV ..

Read both the input and the template image; Apply the template matching using cv2.matchTemplate() If the method is cv2.TM_SQDIFF or cv2.TM_SQDIFF_NORMED, take the minimum, otherwise, take the maximum. This can be done using the cv2.minMaxLoc() function which finds the minimum and maximum element values and their positions.; Once the min/max position is found, we can easily draw the rectangle. 3. Match Shapes¶ OpenCV comes with a function cv2.matchShapes() which enables us to compare two shapes, or two contours and returns a metric showing the similarity. The lower the result, the better match it is. It is calculated based on the hu-moment values. Different measurement methods are explained in the docs Code for SIFT Feature Extraction using OpenCV in Python Tutorial View on Github. sift.py. import cv2 # reading the image img = cv2.imread('table.jpg') # convert to greyscale gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # create SIFT feature extractor sift = cv2.xfeatures2d.SIFT_create() # detect features from the image keypoints, descriptors = sift.detectAndCompute(img, None) # draw the.

sift = cv2.SIFT() kp, des = sift.detectAndCompute(gray,None) 키포인트와 식별자를 얻었다면, 이 키포인트와 식별자를 조합하여 특징점을 사용하여 서로 다른 이미지 사이의 동일한 위치를 식별할 수 있습니다. 이는 다른 글에서 살펴 보겠습니다 OpenCV on Wheels. Pre-built CPU-only OpenCV packages for Python. Check the manual build section if you wish to compile the bindings from source to enable additional modules such as CUDA. Installation and Usage. If you have previous/other manually installed (= not installed via pip) version of OpenCV installed (e.g. cv2 module in the root of Python's site-packages), remove it before. 然后我们用Matcher.match()方法来获得两个图像里最匹配的。我们按他们距离升序排列,这样最匹配的(距离最小)在最前面。然后我们画出最开始的10个匹配(为了好看,你可以增加) # create BFMatcher object bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True) # Match descriptors Here we create matches of the descriptors, then we sort them based on their distances. img3 = cv2.drawMatches(img1,kp1,img2,kp2,matches[:10],None, flags=2) plt.imshow(img3) plt.show() Here, we've drawn the first 10 matches. The output: The next tutorial: MOG Background Reduction OpenCV Python Tutorial

Python03 cv2.findHomography()와 cv2.perspectiveTransform(

Python replacement for cv2

Introduction to OpenCV SIFT. In order to perform detection of features and matching, we make use of a function called sift function or Scale invariant Feature Transform function in OpenCV using which the vector representations from the image's visual content are extracted to perform mathematical operations on them and sift function is protected by patent and we are not allowed to use sift. Matches from the first image to the second one, which means that keypoints1[i] has a corresponding point in keypoints2[matches[i]] . outImg: Output image. Its content depends on the flags value defining what is drawn in the output image. See possible flags bit values below. matchColor: Color of matches (lines and connected keypoints) cv2.computeCorrespondEpilines. Here are the examples of the python api cv2.computeCorrespondEpilines taken from open source projects. By voting up you can indicate which examples are most useful and appropriate Cv2.rectangle draws a rectangle in a frame. Cv2.putText draws text in a frame. OpenCV Face Detection & Recognition Output. Summary. In this project, we built a face detection and recognition system using python OpenCV. We used the face_recognition library to perform all the tasks 1.cv2.drawMatches(imageA, kpsA, imageB, kpsB, matches[:10], None, flags=2) # 对两个图像关键点进行连线操作 参数说明:ima 机器学习进阶-案例实战-图像全景拼接-书籍SIFT特征点连接 1.cv2.drawMatches(对两个图像的关键点进行连线操作) - python我的最爱 - 博客

Images matching using bf

難しい話は抜きにして:. import numpy as np import cv2 def drawMatches(img1, kp1, img2, kp2, matches): My own implementation of cv2.drawMatches as OpenCV 2.4.9 does not have this function available but it's supported in OpenCV 3.0.0 This function takes in two images with their associated keypoints, as well as a list of DMatch data. We then use the cv2.rectangle() function to draw a rectangle around the match. We do this in a red color with a line thickness of 5. We then show the image. And this is how we can match an image embedded in another image in Python using OpenCV. Related Resource

This notebook is open with private outputs. Outputs will not be saved. You can disable this in Notebook setting タグ python-2.7, opencv3.0, sift. 私は約1週間SIFT検出器を使用しようとしていましたが、ついにそれが機能するようになると、 cv2.drawKeypoints()関数でエラーが発生します。コードは次のとおりです。 import cv2 import numpy as np img = cv2.imread('\\home\\gaiarsa\\matrix\\poste.jpg') gray

Python Examples of cv2

python字符 python浅拷贝 python入门 python教程 python课程 python进阶 python编程工具 python序列 python while python学习之 python注释 pythonpostgresql python运算符 python自动化运维 python open python内存分配 python3.6.1 python pycharm python3函数 python知识 Kornia-moons. Helper functions for navigating the space around planet Kornia ;) Install. pip install kornia_moons. How to use. Here is an example how to use kornia-moons for local feature conversion from OpenCV ORB keypoints % matplotlib inline import matplotlib.pyplot as plt import cv2 import torch import kornia as K from typing import List import matplotlib.pyplot as plt from kornia_moons. I have a little experience with OpenCV (Python) and just received a Jetson Nano and want to test OpenCV with CUDA. So I tried an ORB-example but am stuck with converting GPU-Keypoints to CPU-Keypoints, but I am unable to get any result. def orb_with_cuda(): MAX_FEATURES = 100 GOOD_MATCH_PERCENT = 0.15 # load images into numpy npMat1 = get. Recent Posts. discord.py Set a command to only work if the user doesn't have a certain role; Why is pydroid3 keep telling me there is no attribute.. in module..? Cannot key in bearer api token into Postman Santau.io api; ValueError: The truth value of a series is ambiguous. use a.empty(), a.boo() , a.item(), a.any() or a .all( Also, Read - Lambda Expression in Python. More than half of the first 10 matches were correct. In real applications, instead of using the first n matches, a match distance threshold is used to filter out spurious matches. I hope you liked this article on how we can extract image features using Machine Learning

Subscribe. Subscribe to this blo The first step in the image-processing pipeline is to resize the image, to speed up future processing steps. Add the following code inside the try block, then rerun the node. # resize image (half-size) for easier processing resized = cv2.resize(orig, None, fx=0.5, fy=0.5) drawImg = resized For drawing the key points we use cv2.drawKeypoints which takes the following arguments. cv2.drawKeypoints(input image,keypoints,blank_output_array,color,flags) where in the flags could be. cv2.DRAW_MATCHES_FLAGS_DEFAULT. cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS. cv2.DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG. cv2.DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE. My drawing: NAO's drawing: At this point I would like to write some conclusions about it, specifically I want to extract the contours from both pictures and match the contours using the OpenCV function cv2.matchShapes(). However, I wrote a small Python code script for this and it gives me some errors OpenCV-Python is a library of Python bindings designed to solve computer vision problems.cv2.rectangle() method is used to draw a rectangle on any image. Syntax: cv2.rectangle(image, start_point, end_point, color, thickness) Parameters: image: It is the image on which rectangle is to be drawn. start_point: It is the starting coordinates of rectangle

matching - inlier. ransac을 통해 골라낸 매칭이다. mtrx, mask = cv2.findHomography (srcPoints, dstPoints, method, ransacReprojThreshold, mask, maxIters, confidence) method=0 (optional): 근사 계산 알고리즘 선택 (0: 모든 점으로 최소 제곱 오차 계산, cv2.RANSAC, cv2.LMEDS, cv2.RHO) 매칭 되는 물체를 찾아서. 'Python/OpenCV' 카테고리의 다른 글 [OpenCV] 06-1. Meanshift and Camshift [OpenCV] 05-10. Feature Matching + Homography to find Objects [OpenCV] 05-8. ORB (Oriented FAST and Rotated BRIEF) [OpenCV] 05-7. BRIEF (Binary Robust Independent Elementary Features Pythonで音を録音して保存する方法 kaze = cv2.KAZE_create() kp = kaze.detect(img) img_kaze = cv2.drawKeypoints(img, kp, img, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) cv2.imwrite('img_kaze.png', img_kaze cv2.draw_matches_flags_draw_over_outimg:函数不创建输出的图像,而是直接在输出图像变量空间绘制,要求本身输出图像变量就是一个初始化好了的,size与type都是已经初始化好的变量。 cv2.draw_matches_flags_not_draw_single_points: 单点的特征点不被绘制 。 参 python - OpenCV Python으로 객체 감지. 기사 출처 python opencv image-processing. OpenCV 를 처음 접했습니다. 첫 작은 스크립트를 만들기 위해 튜토리얼과 문서를 읽고 있습니다. 이미지가 있는데 가로등, 쓰레기통에 detect objects 하고 싶습니다 내 이미지는 다음과 같습니다.

#sift 特征点检测 import cv2 img = cv2.imread('test.jpg') gray= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) sift = cv2.xfeatures2d.SIFT_create() kp = sift.detect(gray. Figure 2: Drawing lines with OpenCV. As you can see, using the cv2.line function is quite simple! But there is one other important argument to consider in the cv2.line method: the thickness.. On Lines 18-21, we define the color red as a tuple (again, in BGR rather than RGB format). We then draw a red line from the top-right corner of the image to the bottom-left

Video: Feature detection and matching with OpenCV by Vino Mahendran Francium Tec

img3 = cv2.drawMatches(img1,kp1,img2,kp2,None,matches[:10], flags=2) TypeError: outImg is not a numpy array, neither a scalar 누구든지 이것에 대해 나를 도울 수 있습니까? 프로토 타입에 주목하십시오 Template matching is a technique for finding areas of an image that are similar to a patch (template). A patch is a small image with certain features. The goal of template matching is to find the patch/template in an image. To find it, the user has to give two input images: Source Image (S) - The image to find the template in and Template Image (T) - The image that is to be found in the. cv2.drawMatchesKnn という関数を使うと,上位k個の対応点を描画する.例えば k=2 と設定すれば,各 , singlePointColor = (255, 0, 0), matchesMask = matchesMask, flags = 0) img3 = cv2. drawMatchesKnn (img1, kp1, img2, kp2, matches, None, ** draw OpenCV 3とPython 3の組み合わせで動かす場合. 2. Template matching in OpenCV with Python. First, we are going to import the necessary libraries and load the input image and the template image. We will also correct the color order because we will plot these images with matplotlib. import cv2 import numpy as np from matplotlib import pyplot as plt from google.colab.patches import cv2_imsho OpenCV와 Python으로 Image processing import os import sys import math from platform import python_version import cv2 import matplotlib.pyplot as plt import np.array([]), (0, 0, 255), cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS) # 원본 이미지에 찾은 blob 그리기 plt.figure (figsize=(15,15.

Python OpenCV cv2.putText() method - GeeksforGeek

Drawing with Python OpenCV. Let's start drawing with the OpenCV module. Setting it all up. In order to get started, we'll first need to import the modules that we'll be using for drawing in OpenCV. This, of course, consists of the cv2 module, but, another module we'll be using is the numpy module It was very easy to match keypoints in C++ using FlannMatcher, but it's a bit difficult to that in Python. flann = cv2.flann_Index (sd, flann_params) idx, dist = flann.knnSearch (td, 1, params={}) This method does a fast local approximate nearest neighbors (FLANN) calculation between two sets of feature vectors Opencv Python实现两幅图像匹配. 更新时间:2021年06月22日 14:45:11 作者:Scarlett2025. 这篇文章主要为大家详细介绍了Opencv Python实现两幅图像匹配,文中示例代码介绍的非常详细,具有一定的参考价值,感兴趣的小伙伴们可以参考一下. 本文实例为大家分享了Opencv Python. Anaconda 환경에는 opencv 및 numpy 패키지가 설치되어 있으므로 따로 설치할 필요가 없습니다. Windows에서 이 프로그램을 실행하는 경우 아래 설명에 따라 USB 카메라를 사용하도록 프로그램을 변경해야 합니다. 카드 판독에 PiCamera 또는 USB 카메라를 사용할 수 있습니다. 예제 - sift matching python 여러 이미지에 대한 OpenCV 기능 일치 (2) 내 조언 몇 가지 있습니다

OpenCV Python을 사용하여 자른 이미지를 참조 이미지와 동일한 방향

Pythonでブロブ検出パラメータを設定するBasic SIFT in Python – Stamford ResearchPython OpenCV cv2