Definitions: Let P be a point in 3D of coordinates X in the world reference frame stored in the matrix X The coordinate vector of P in the camera reference frame is:. Note that the function assumes the camera intrinsic matrix of the undistorted points to be identity. Computes undistortion and rectification maps for image transform by cv::remap. If D is empty zero distortion is used, if R or P is empty identity matrixes are used. The function computes projections of 3D points to the image plane given intrinsic and extrinsic camera parameters.

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument s it accepts. The function is simply a combination of fisheye::initUndistortRectifyMap with unity R and remap with bilinear interpolation.

See the former function for details of the transformation being performed. Pictures a and b almost the same. But if we consider points of image located far from the center of image, we can notice that on image a these points are distorted.

Namespaces Enumerations Functions. Fisheye camera model Camera Calibration and 3D Reconstruction. Parameters objectPoints vector of vectors of calibration pattern points in the calibration pattern coordinate space.

Otherwise, cx, cy is initially set to the image center imageSize is usedand focal distances are computed in a least-squares fashion. Balance is in range of [0, 1]. See convertMaps for details. In the old interface different components of the jacobian are returned via different output parameters. Parameters objectPoints Vector of vectors of the calibration pattern points. The parameter is similar to K1. The parameter is similar to D1. R Output rotation matrix between the 1st and the 2nd camera coordinate systems.

T Output translation vector between the coordinate systems of the cameras. Parameters K1 First camera intrinsic matrix. D1 First camera distortion parameters. K2 Second camera intrinsic matrix.

D2 Second camera distortion parameters. R Rotation matrix between the coordinate systems of the first and the second cameras. R1 Output 3x3 rectification transform rotation matrix for the first camera. R2 Output 3x3 rectification transform rotation matrix for the second camera.

P1 Output 3x4 projection matrix in the new rectified coordinate systems for the first camera. P2 Output 3x4 projection matrix in the new rectified coordinate systems for the second camera. If the flag is set, the function makes the principal points of each camera have the same pixel coordinates in the rectified views.

And if the flag is not set, the function may still shift the images in the horizontal or vertical direction depending on the orientation of epipolar lines to maximize the useful image area.

When 0,0 is passed defaultit is set to the original imageSize. Setting it to larger value can help you preserve details in the original image, especially when there is a big radial distortion.

Parameters distorted image with fisheye lens distortion.While there are few of these libraries available, the most popular and stable is mysql-connector-python library. Also, this library by itself is written in python program and does not have any other external dependencies, which makes it easier to maintain.

This tutorial explains how to install mysql-connector-python library.

camera matrix python

It is also available for Windows. You can download the mysql-connector-python from this download page. Next, use rpm command to install the mysql-connector-python library as shown below. This does not have any other additional package dependencies. All you need is just this package. Next, let us make sure the mysql library that we installed is valid, and we are able to import it without any issue.

If the following import mysql. From this point on-awards, you can start using any MySQL python library commands and get it going. Next, let us write a quick small sample python program that will connect to a specific MySQL database using an user name and password. Finally, we are using conn. If you are trying to connect to a MySQL database that is running on a different server, then change the ip-address in the host parameter accordingly.

If there is an error while making the connection, it will throw an error messages as shown below. The following example expands the above python program. The following is the output of the above sample python program, which connects to a MySQL database and retrieve rows from the given table.

How about you show us how to write a route to automatically handle basic errors? A function,subroutine, class I can say connect address,user,pass,database. Then I can issue and sql command. However, pretend there are errors. Say the connection timeouts. I would like to the program automatically retry and if that fails it tries to establish the connection to the mysql server.

How to Connect to MySQL Database from Python With Example

Instead of having to check for all that stuff after each command. An additional concern is security, I would like to keep the username and password away from the program in a private variable so that it is harder for hackers to exploit. Notify me of followup comments via e-mail. All rights reserved Terms of Service. A function,subroutine, class I can say connect address,user,pass,database Then I can issue and sql command. Shantanu Oak July 15,am. I am using pymysql module. Are there any advantages using mysql connector?

Francesco Ridolfi March 27,pm. Anonymous June 20,am.

Corona Virus Live Updates for India – Using Python

Pymysql module is for Pyhton 3. MySQldb module is for Python 2. Anonymous March 25,pm.This page lists libraries that may be useful when GameProgramming in Python.

It is written on top of the excellent SDL library. This allows you to create fully featured games and multimedia programs in the python language. It is the most popular, and portable game library for python, with over free and open source projects that use pygame to look at. The tutorial covers how to use the module, and the site also provides a few demo programs.

Layout facilities include rows, columns and grids. A theme system allows fonts and colours to be customised easily. Also provides some facilities for locating and managing resources and playing and controlling music. Also available from the same site is Humerusa game skeleton based on Albow.

Humerus provides a framework for managing and loading levels, starting and resuming play, saving and restoring game state, and implementing an in-game level editor. A customisable main menu screen ties all these activities together. The scripts are a tile editor and a level editor. The libraries include a state engine, a full featured gui, document layout, html rendering, text rendering, sprite and tile engine, and a timer. It is described completely in the book "Game Programming - the L Line" but it is available for free even if you do not purchase the book.

Some of the features of pyglet are: For most application and game requirements, pyglet needs nothing else besides Python, simplifying distribution and installation. Requires ctypes, and Opengl. Take advantage of multiple windows and multi-monitor desktops. Load images, sound, music and video in almost any format. It is ideal for people learning to program, or developers that want to code a 2D game without learning a complex framework. It is built on top of Pyglet and OpenGL.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I am using Python 3. When I open the Python shell, how can I know what the current directory is and how can I change it to another directory where my modules are? Then, the interpreter searches also at this place for import ed modules.

I guess the name would be the same under Windows, but don't know how to change. In fact, os. Here is a relevant bit from Standard Modules section:. The variable sys. The easiest way to change the current working directory in python is using the 'os' package.

Below there is an example for windows computer:. If you import os you can use os. Learn more. Ask Question. Asked 8 years, 11 months ago. Active 1 year, 2 months ago. Viewed k times.

This has already been discussed [here] [1]: stackoverflow. Active Oldest Votes. You can use the os module. Whatever, you need to double up backslashes if you use them in a regular non-raw Python string.

Python also lets you use forward slashes instead. Thus, either os.

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It would be good to note that you call os. The code to actually change the cwd is just os. Tim Cooper k 34 34 gold badges silver badges bronze badges. Changing the current directory is not the way to deal with finding modules in Python. Here is a relevant bit from Standard Modules section: The variable sys.

camera matrix python

Steven Rumbalski Steven Rumbalski This answer is gold. Just add your project directory like this: import sys sys. Below there is an example for windows computer: Import the os package import os Confirm the current working directory os.

Rem-D 5 5 silver badges 6 6 bronze badges. But I agree the accepted answer is more descriptive. Aditya N.

S Aditya N. Sign up or log in Sign up using Google.GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Work fast with our official CLI. Learn more. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again.

If nothing happens, download the GitHub extension for Visual Studio and try again. The py-motmetrics library provides a Python implementation of metrics for benchmarking multiple object trackers MOT.

While benchmarking single object trackers is rather straightforward, measuring the performance of multiple object trackers needs careful design as multiple correspondence constellations can arise see image below.

A variety of methods have been proposed in the past and while there is no general agreement on a single method, the methods of [1,2,3,4] have received considerable attention in recent years. Pictures courtesy of Bernardin, Keni, and Rainer Stiefelhagen [1].

Both metrics attempt to find a minimum cost assignment between ground truth objects and predictions. This blog-post by Ergys illustrates the differences in more detail. The metrics have been aligned with what is reported by MOTChallenge benchmarks. Python 3. If no binary packages are available for your platform and building source packages fails, you might want to try a distribution like Conda see below to install dependencies.

In case you are using Conda, a simple way to run py-motmetrics is to create a virtual environment with all the necessary dependencies. In case you already have an environment you install the dependencies from within your environment by.

camera matrix python

The code above updates an event accumulator with data from a single frame. Note np. It signals that object 1 cannot be paired with hypothesis 2. To inspect the current event history simple print the events associated with the accumulator. Meaning object 1 was matched to hypothesis 1 with distance 0. Similarily, object 2 was matched to hypothesis 2 with distance 0.

Hypothesis 3 could not be matched to any remaining object and generated a false positive FP. Possible assignments are computed by minimizing the total assignment distance Kuhn-Munkres algorithm. While object 1 was matched, object 2 couldn't be matched because no hypotheses are left to pair with.

Object 2 is now tracked by hypothesis 3 leading to a track switch. Note, although a pairing 1, 3 with cost less than 0. Once the accumulator has been populated you can compute and display metrics.

Continuing the example from above. For MOTChallenge py-motmetrics provides predefined metric selectors, formatters and metric names, so that the result looks alike what is provided via their Matlab devkit.

Usually this is not the case. You are mostly given either rectangles or points centroids of related objects. To compute a distance matrix from them you can use motmetrics. For large datasets solving the minimum cost assignment becomes the dominant runtime part. A comparison for different sized matrices is shown below taken from here. Please note that the x-axis is scaled logarithmically.Some pinhole cameras introduce significant distortion to images. Two major kinds of distortion are radial distortion and tangential distortion.

Web camera background mask \u0026 Matrix rain screen saver. #OpenCV+Python

Radial distortion causes straight lines to appear curved. Radial distortion becomes larger the farther points are from the center of the image. For example, one image is shown below in which two edges of a chess board are marked with red lines.

But, you can see that the border of the chess board is not a straight line and doesn't match with the red line. All the expected straight lines are bulged out. Visit Distortion optics for more details. Similarly, tangential distortion occurs because the image-taking lense is not aligned perfectly parallel to the imaging plane. So, some areas in the image may look nearer than expected.

The amount of tangential distortion can be represented as below:. In addition to this, we need to some other information, like the intrinsic and extrinsic parameters of the camera. Intrinsic parameters are specific to a camera. The focal length and optical centers can be used to create a camera matrix, which can be used to remove distortion due to the lenses of a specific camera. The camera matrix is unique to a specific camera, so once calculated, it can be reused on other images taken by the same camera.

It is expressed as a 3x3 matrix:. Extrinsic parameters corresponds to rotation and translation vectors which translates a coordinates of a 3D point to a coordinate system.

For stereo applications, these distortions need to be corrected first. To find these parameters, we must provide some sample images of a well defined pattern e. We find some specific points of which we already know the relative positions e. We know the coordinates of these points in real world space and we know the coordinates in the image, so we can solve for the distortion coefficients. For better results, we need at least 10 test patterns.

As mentioned above, we need at least 10 test patterns for camera calibration. Consider an image of a chess board. The important input data needed for calibration of the camera is the set of 3D real world points and the corresponding 2D coordinates of these points in the image. These image points are locations where two black squares touch each other in chess boards.

What about the 3D points from real world space? Those images are taken from a static camera and chess boards are placed at different locations and orientations. This consideration helps us to find only X,Y values. Now for X,Y values, we can simply pass the points as 0,01,02,0In this case, the results we get will be in the scale of size of chess board square.

But if we know the square size, say 30 mmwe can pass the values as 0,030,060,0Thus, we get the results in mm. In this case, we don't know square size since we didn't take those images, so we pass in terms of square size. So to find pattern in chess board, we can use the function, cv.

We also need to pass what kind of pattern we are looking for, like 8x8 grid, 5x5 grid etc. In this example, we use 7x6 grid.For instance, we anticipated that impulse buying would remain lower than in other countries and that value for money would continue to be an important consideration when choosing products and services.

Interestingly, Chinese consumers across all age groups have, in some ways, become even more pragmatic. The individual consumer We also predicted that as Chinese consumers aspire to a better life and trade up their purchases, they would become more discerning and gradually more individualistic. This would lead, for example, to a shift toward more healthy choices, more user-friendly products, and products and brands that better fit their personality. This could be a big opportunity for niche brandsand a threat to the mass-market brands that had won big in previous years by using scale and ubiquitous availability, supported by the trust gained by heavy advertising.

Our latest research certainly shows a decrease in consumption in categories deemed less healthy and a willingness to spend significantly more on health and more environmentally conscious categories.

It also shows consumers are more likely to spend more to indulge themselves and more likely to try new technology. While their consumption choices have become more individualistic, though, it is important to note that family values continue to be at the top of their priorities (Exhibit 3). One area our predictions missed, however, was by anticipating that consumers, as they became more individualistic in their choices, might focus less on basic product reliability and safety.

Perhaps in part because of a number of more recent food scandals, however, consumers seemed more concerned with these issues in 2015 than they were before.

When our team first started researching Chinese consumers, nearly ten years ago, many of us were surprised by their fickle attitude toward brands. Fewer than half of consumers tended to stick with their favorite brands, compared, for example, with almost three quarters of US consumers. As we debated this tendency while making our predictions, we wondered if, in the clash between pragmatism and individualism, brand loyalty would stay low, increase, or even decline. Ultimately, we decided it would increase as the emotional benefits of brands became more important to consumers and as increased choice and availability of branded products (online and off) would allow consumers to optimize for price and convenience without changing choices too often.

Our recent research confirmed the changes we anticipated. Consumers are now significantly less likely to buy a brand that is not already among their favorites, continuing the upward trend we observed in 2011 (Exhibit 4). The modern shopper Our 2011 predictions were bullish on e-commerce, predicting that Chinese consumers would adapt their channel choices even faster than has occurred in developed markets.

camera matrix python

We estimated that by 2020, online consumer-electronics purchases would jump to 40 percent, from about 10 percent. More mainstream categories would rise to 15 percent, and some categories, such as groceries (now below 1 percent), could reach about 10 percent. These changes are occurring even as the enduring pragmatism and diligence of the Chinese consumer continue to be in place. Our latest research shows that consumers of all age groups are much more likely to collect information online, even on fast-moving consumer goods, than they were just three years ago.

In 2015, online food and beverages sales (excluding fresh) reached 7. The online share of consumer-electronic purchases, meanwhile, has reached a whopping 39 percent in 2015, and it now looks possible that by 2020 it will be about 50 percent of overall sales. Making predictions may be difficult, especially about the futureas US Baseball Hall of Famer Yogi Berra famously observed. But they can still provide valuable foresight for executives.

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