projects for python – Quality Score Based Image Corner Detection using Python – image processing in python
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Abstract
Quality Score Based Image Corner Detection using Python the corner detection methodology is defined in OpenCV python library. No feature-based vision system can work until good features can be identified and tracked from frame to frame. Although tracking itself is by and large a solved problem, selecting features that can be tracked well and correspond to physical points in the world is still an open problem. We propose a feature selection criterion that is optimal by construction because is based on how the tracker works, as well as a feature monitoring method that can detect occlusions, dis-occlusions, and features that do not correspond to points in the world. These methods are based on a new tracking algorithm that extends previous Newton-Raphson style search methods to work under affine image transformations. We test performance with several simulations and experiments on real images.
Introduction
image processing in python – Feature tracking is an important issue in computer vision, as many algorithms rely on the accurate computation of correspondences through a sequence of images. When an image sequence is acquired and sampled at a sufficiently high time frequency, frame-to-frame disparities are small enough to make optical-flow techniques viable. If frame-to-frame disparities are large (e.g., the images are taken from quite different viewpoints), stereo matching techniques are used instead, often in combination with Kalman filtering. Robust tracking means detecting automatically unreliable matches, or outliers, over an image sequence (for a survey of robust methods in computer vision). Recent examples of such robust algorithms include, which identifies tracking outliers while estimating the fundamental matrix, and which adopts a RANSAC approach to eliminate outliers for estimating the trifocal tensor. Such approaches increase the computational cost of tracking significantly. This paper concentrates on the well-known Shi-TomasiKanade tracker, and proposes a robust version based on an efficient outlier rejection scheme. Building on results from, Tomasi and Kanade introduced a feature tracker based on SSD matching and assuming translational frameto-frame displacements. Subsequently, Shi and Tomasi proposed an affine model, which proved adequate for region matching over longer time spans. Their system classified a tracked feature as good (reliable) or bad (unreliable) according to the residual of the match between the associated image region in the first and current frames; if the residual exceeded a user-defined threshold, the feature was rejected. Visual inspection of results demonstrated good discrimination between good and bad features, but the authors did not specify how to reject bad features automatically. This is the problem that our paper solves. We extend the Shi-Tomasi-Kanade tracker by introducing an automatic scheme for rejecting spurious features. We employ a simple, efficient, model-free outlier rejection rule, called X84, and prove that its assumptions are satisfied in the feature tracking scenario. Experiments with real and synthetic images confirm that our algorithm makes good features to track better, in the sense that outliers are located reliably. We illustrate quantitatively the benefits introduced by the algorithm with the example of fundamental matrix estimation. This project is one of the best example for image processing in python .
Formalization
A corner can be defined as the intersection of two edges. A corner can also be defined as a point for which there are two dominant and different edge directions in a local neighbourhood of the point.
An interest point is a point in an image which has a well-defined position and can be robustly detected. This means that an interest point can be a corner but it can also be, for example, an isolated point of local intensity maximum or minimum, line endings, or a point on a curve where the curvature is locally maximal.
In practice, most so-called corner detection methods detect interest points in general, and in fact, the term “corner” and “interest point” are used more or less interchangeably through the literature. As a consequence, if only corners are to be detected it is necessary to do a local analysis of detected interest points to determine which of these real corners are. Examples of edge detection that can be used with post-processing to detect corners are the Kirsch operator and the Frei-Chen masking set.
“Corner”, “interest point” and “feature” are used interchangeably in literature, confusing the issue. Specifically, there are several blob detectors that can be referred to as “interest point operators”, but which are sometimes erroneously referred to as “corner detectors”. Moreover, there exists a notion of ridge detection to capture the presence of elongated objects.
Corner detectors are not usually very robust and often require large redundancies introduced to prevent the effect of individual errors from dominating the recognition task.
One determination of the quality of a corner detector is its ability to detect the same corner in multiple similar images, under conditions of different lighting, translation, rotation and other transforms.
A simple approach to corner detection in images is using correlation, but this gets very computationally expensive and suboptimal. An alternative approach used frequently is based on a method proposed by Harris and Stephens (below), which in turn is an improvement of a method by Moravec.
Robust Monitoring
To monitor the quality of the features tracked, the tracker checks the residuals between the first and the current frame: high residuals indicate bad features which must be rejected. Following, we adopt the affine model, as a pure translational model would not work well with long sequences: too many good features are likely to undergo significant rotation, scaling or shearing, and would be incorrectly discarded. Non-affine warping, which will yield high residuals, is caused by occlusions, perspective distorsions and strong intensity changes (e.g. specular reflections). This section introduces our method for selecting a robust rejection threshold automatic.
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image processing in python – HARDWARE REQUIREMENTS
- System : Intel inside i3
- System Type : 64-bit Operating System
- Storage :500GB
- RAM :4 GB
image processing in python – SOFTWARE REQUIREMENTS
- Operating system : Windows 10
- Software : Anaconda , Python
- Python Libraries: OpenCV
Python is a general purpose, dynamic, high-level, and interpreted programming language. It supports Object Oriented programming approach to develop applications. It is simple and easy to learn and provides lots of high-level data structures.
Python is easy to learn yet powerful and versatile scripting language, which makes it attractive for Application Development.
Python’s syntax and dynamic typing with its interpreted nature make it an ideal language for scripting and rapid application development.
Python supports multiple programming pattern, including object-oriented, imperative, and functional or procedural programming styles.
Python is not intended to work in a particular area, such as web programming. That is why it is known as multipurpose programming language because it can be used with web, enterprise, 3D CAD, etc.
Python Features
Python provides many useful features which make it popular and valuable from the other programming languages. It supports object-oriented programming, procedural programming approaches and provides dynamic memory allocation. We have listed below a few essential features.
1) Easy to Learn and Use
Python is easy to learn as compared to other programming languages. Its syntax is straightforward and much the same as the English language. There is no use of the semicolon or curly-bracket, the indentation defines the code block. It is the recommended programming language for beginners.
2) Expressive Language
Python can perform complex tasks using a few lines of code. A simple example, the hello world program you simply type print(“Hello World”). It will take only one line to execute, while Java or C takes multiple lines.
3) Interpreted Language
Python is an interpreted language; it means the Python program is executed one line at a time. The advantage of being interpreted language, it makes debugging easy and portable.
4) Cross-platform Language
Python can run equally on different platforms such as Windows, Linux, UNIX, and Macintosh, etc. So, we can say that Python is a portable language. It enables programmers to develop the software for several competing platforms by writing a program only once.
5) Free and Open Source
Python is freely available for everyone. It is freely available on its official website www.python.org. It has a large community across the world that is dedicatedly working towards make new python modules and functions. Anyone can contribute to the Python community. The open-source means, “Anyone can download its source code without paying any penny.”
6) Object-Oriented Language
Python supports object-oriented language and concepts of classes and objects come into existence. It supports inheritance, polymorphism, and encapsulation, etc. The object-oriented procedure helps to programmer to write reusable code and develop applications in less code.
7) Extensible
It implies that other languages such as C/C++ can be used to compile the code and thus it can be used further in our Python code. It converts the program into byte code, and any platform can use that byte code.
8) Large Standard Library
It provides a vast range of libraries for the various fields such as machine learning, web developer, and also for the scripting. There are various machine learning libraries, such as Tensor flow, Pandas, Numpy, Keras, and Pytorch, etc. Django, flask, pyramids are the popular framework for Python web development.
9) GUI Programming Support
Graphical User Interface is used for the developing Desktop application. PyQT5, tkinter, kivy are the libraries which are used for developing the web application.
10) Integrated
It can be easily integrated with languages like C, C++, and JAVA, etc. Python runs code line by line like C,C++ Java. It makes easy to debug the code.
- Embeddable
The code of the other programming language can use in the Python source code. We can use Python source code in another programming language as well. It can embed other language into our code.
- Dynamic Memory Allocation
In Python, we don’t need to specify the data-type of the variable. When we assign some value to the variable, it automatically allocates the memory to the variable at run time.
Anaconda for, image processing in python
Anaconda distribution comes with over 250 packages automatically installed, and over 7,500 additional open-source packages can be installed from PyPI as well as the conda package and virtual environment manager. It also includes a GUI, Anaconda Navigator], as a graphical alternative to the command line interface (CLI).
The big difference between conda and the pip package manager is in how package dependencies are managed, which is a significant challenge for Python data science and the reason conda exists.
OpenCV Works
Computer recognize the image – image processing in python
Human eyes provide lots of information based on what they see. Machines are facilitated with seeing everything, convert the vision into numbers and store in the memory. Here the question arises how computer convert images into numbers. So the answer is that the pixel value is used to convert images into numbers. A pixel is the smallest unit of a digital image or graphics that can be displayed and represented on a digital display device.
The picture intensity at the particular location is represented by the numbers. In the above image, we have shown the pixel values for a grayscale image consist of only one value, the intensity of the black color at that location.
There are two common ways to identify the images:
- Grayscale
Grayscale images are those images which contain only two colors black and white. The contrast measurement of intensity is black treated as the weakest intensity, and white as the strongest intensity. When we use the grayscale image, the computer assigns each pixel value based on its level of darkness.
- RGB
An RGB is a combination of the red, green, blue color which together makes a new color. The computer retrieves that value from each pixel and puts the results in an array to be interpreted.
image processing in python – OpenCV is used for Computer Vision
- OpenCV is available for free of cost.
- Since the OpenCV library is written in C/C++, so it is quit fast. Now it can be used with Python.
- It require less RAM to usage, it maybe of 60-70 MB.
- Computer Vision is portable as OpenCV and can run on any device that can run on C.
Sample Coding for, image processing in python
# Load image using imread
image_bgr = cv.imread(‘kaashiv_plane.png’)
image_bgr
# Sharpening the input image using python program
image_sharp = cv2.filter2D(image_bgr, kernel)
# Matplotlib library to show the processed image
plt.imshow(image_sharp, cmap=’gray’), plt.axis(“off”)
plt.show()
corners = np.float32(corners)
corners
array([[[129., 110.]], [[169., 150.]], [[ 26., 61.]], [[218., 253.]], [[127., 52.]], [[227., 152.]], [[ 85., 268.]], [[ 11., 194.]], [[ 89., 45.]],
Sample Output for, image processing in python
Request for Project – [email protected] ; [email protected]
Contact Number – 7667668009 / 7667664842
Whatsapp Number – 9840678906 / 7667662428
For complete project lists – final year project for cse
For internship – internship in chennai
