calibrate | Micro Library for providing a uniform JSON output | REST library
kandi X-RAY | calibrate Summary
kandi X-RAY | calibrate Summary
[Dependency Status] Feel free to raise an issue or contact me on twitter if you have any questions [@johnbrett_] Beginners, feature requests and bug reports are welcomed. License MIT @ John Brett.
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Lines of Code : 216License : Strong Copyleft (CC BY-SA 4.0)
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var data = [{
year: "2002",
population: 191207000
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year: "2003",
population: 192618000
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//var tCommunity Discussions
Trending Discussions on calibrate
QUESTION
I am trying to create a class for calibrating a classifier. I have been reading resources on probability calibration and I am a bit confused on which dataset should we calibrate the classifier. I created a class that split the training set to further train and validation the set. Then, the classifier is first fitted to the train set and predicts the uncalibrated probability on the validation set.
Then, I create a cal_model instance of the CalibrationCV class and then fit it to the validation set and predict calibrated probabilities of the validation set again.
Could someone take a look at the code below and correct the code for me?
...ANSWER
Answered 2021-Jun-11 at 14:06the calibration_curve code is correct. I am comparing the logistic regression calibration versus the xgboost calibration. the dataframes hold predict_proba[:,1] values or the probability of happening. see (https://github.com/dnishimoto/python-deep-learning/blob/master/Credit%20Loan%20Risk%20.ipynb)
QUESTION
Context: I am trying to find the directional heading from a small image of a compass. Directional heading meaning if the red (north) point is 90 degrees counter-clockwise from the top, the viewer is facing East, 180 degrees is south, 270 is west, 0 is north. etc. I understand there are limitations with such a small blurry image but I'd like to be as accurate as possible. The compass is overlaid on street view imagery meaning the background is noisy and unpredictable.
The first strategy I thought of was to find the red pixel that is furthest away from the center and calculate the directional heading from that. The math is simple enough.
The tough part for me is differentiating the red pixels from everything else. Especially because almost any color could be in the background.
My first thought was to black out the completely transparent parts to eliminate the everything but the white transparent ring and the tips of the compass.
True Compass Values: 35.9901, 84.8366, 104.4101
These values are taken from the source code.
I then used this solution to find the closest RGB value to a user given list of colors. After calibrating the list of colors I was able to create a list that found some of the compass's inner most pixels. This yielded the correct result within +/- 3 degrees. However, when I tried altering the list to include every pixel of the red compass tip, there would be background pixels that would be registered as "red" and therefore mess up the calculation.
I have manually found the end of the tip using this tool and the result always ends up within +/- 1 degree ( .5 in most cases ) so I hope this should be possible
The original RGB value of the red in the compass is (184, 42, 42) and (204, 47, 48) but the images are from screenshots of a video which results in the tip/edge pixels being blurred and blackish/greyish.
Is there a better way of going about this than the closest_color() method? If so, what, if not, how can I calibrate a list of colors that will work?
...ANSWER
Answered 2021-Jun-04 at 08:45If you don't have hard time constraints (e.g. live detection from video), and willing to switch to NumPy, OpenCV, and scikit-image, you might use template matching. You can derive quite a good template (and mask) from the image of the needle you provided. In some loop, you'll iterate angles from 0° to 360° with a desired resolution – the finer the longer takes the whole procedure – and perform the template matching. For each angle, you save the value of the best match, and finally search for the best score over all angles.
That'd be my code:
QUESTION
In reading about and experimenting with camera calibration I haven't seen any mention of the required tolerance for the placement of calibration targets. For example say I have a field of view of 200mm x 30mm and I want to be able to measure the position of objects in this field to within 1mm. I will calibrate my camera using a grid pattern and the OpenCV calibrateCamera flow. Say my calibration target is a printed chessboard grid with 5mm pitch. What is the tolerance on that 5mm spacing between corners on my target? Does a tighter tolerance result in more accurate pixel to real-world transformation? Does a tighter tolerance result in better distortion removal? Note I'm measuring objects on a 2D plane, no depth measurement, and unfortunately I don't have the ability to move the calibration targets around and take multiple views of it. So I'm talking specifically about calibrating using a single view.
...ANSWER
Answered 2021-Jun-02 at 21:22Calibration using a single view is a poor idea, generally speaking, because of the small number of independent samples it entails, so it is possible that tolerance on the calibration grid manufacture be the least of your worries. But if you must...
The controlling factor here is the sensor's dot pitch. Given the nominal focal length of your lens, and that you want your calibration RMSE to be order of a few tenths of pixel, you can work out the angle spanned by, say, 1/10 of a pixel along the sensor's horizontal axis. Back projecting that at the nominal distance between the lens's exit pupil and the target will give you a length in 3D world that measures the uncertainty in a target's corner location at the calibration optimum. Your physical target points should be known at least as accurately, and normally better.
Example: Setup: Dot pitch 5um, 16mm focal lens, 200mm working distance to target.
- Backprojected 1/10 pixel:
200/16*0.5um =~ 6um. - Backprojected 1/2 pixel :
200/16*2.5um =~ 31um.
You can loosen that if you assume perfect Chi-square scaling of the errors with the square root of the number of the data points. If you have, say, 100 corners, you can multiply that by 10, i.e. ~ 300um for 1/2 pixel
Note that with this kind of tolerances temperature control (for camera and target) may become a factor to keep into account.
QUESTION
I am trying to calibrate a model using pykalman and the scipy optimiser. For some reasons scipy seem to think that my input is a masked array, but it is not. I have added the code below:
...ANSWER
Answered 2021-May-25 at 07:20I found the solution, which involves a small change in the utils.py file in the pykalman library (line 73):
QUESTION
I am very new to Elasticsearch and I am trying to create a search engine with Fuzzy query.
I can get results with Fuzzy search with this code :
...ANSWER
Answered 2021-May-20 at 11:52You can use a combination of bool/must/filter clause
Adding a working example with index data, mapping, search query and search result
Index Data:
QUESTION
On a particular STM32 microcontroller, the system clock is driven by a PLL whose frequency F is given by the following formula:
ANSWER
Answered 2021-May-19 at 15:16I took your program (your first parentheses is redundant, so I removed):
QUESTION
I am trying to solve an economic problem using the sympy package in Julia. In this economic problem I have exogenous variables and endogenous variables and I am indexing them all. I have two questions:
- How to access the indexed variables to pass: calibrated values ( to exogenous variables, calibrated in other enveiroment) or formula (to endogenous variables, determined by the first order conditions of the agents' maximalization problem using pencil and paper). This will also allow me to study the behavior of equilibrium when I disturb exogenous variables. First, consider my attempto to pass calibrated values on exogenous variables.
ANSWER
Answered 2021-May-16 at 11:35There isn't any direct support for the IndexedBase feature of SymPy. As such, the syntax alpha[n] is not available. You can call the method __getitem__ directly, as with
QUESTION
I am trying to send some commands to a Terminal through UART, so in order for the MSP430 to know which command he got, I wrote some if-conditions in case cREC_BUFFER contains a certain word, the microcontroller should controller it then, for example if the string cREC_BUFFER contains the word "ENDE" at the end, he should go into the if condition inside. The problem that I am facing, is that when I check what the string empty string cREC_BUFFER has after debugging, it contains only the last character "E" of the word "ENDE". Can someone tell me what mistakes I am making here? Thanks a lot for the help in advance! (I reduced the length of the code in here by deleting the content of the other functions, since they do not cause the problem)
...ANSWER
Answered 2021-May-09 at 14:35 j= 0;
cREC_BUFFER[j++]=UCA0RXBUF;
QUESTION
I am working on a Raspberry Pi 4B and have a BME680 Air Quality sensor hooked up. My reading are taken every second and written to a MySQL database.
I want to be able to alert if the air quality, temp, etc. gets out of optimal range. The issue I am having is the sensor takes a reading every second so if I try to build an alert it goes off every second until the range is back to optimal. I am wondering how to alert only if the values change outside of a range.
...ANSWER
Answered 2021-Apr-28 at 00:06You can add a function to get the range in which the temperature is present. If the range has changed, send the alert again. Your state is the range in which your temperature falls.
Please see below:
QUESTION
This is a problem concerning stereo calibration and rectification using openCV (vers. 4.5.1.48) and Python (vers. 3.8.5).
I have two cameras placed on the same axis as shown on the image below:
The left (upper) camera is taking pictures with 640x480 resolution, while the right (lower) camera is taking pictures with 320x240 resolution. The goal is to find an object on the right image (320x240) and crop out the same object on the left image (640x480). In other words; To transfer the rectangle that makes up the object in the right image, to the left image. This idea is sketched below.
A red object is found on the right image and I need to transfer it's location to left image and crop it out. The objects is placed on a flat plane 30cm from the camera lenses. In other words; The distance (depth) from the two cameras lenses to the flat plane is constant (30cm).
This main question is about how transfer a location from one image to another, when two cameras are placed side by side, when the images are of different resolutions and when the depth is (fairly) constant. It's not a question about finding objects.
To solve this problem, as far as I know, stereo calibration must be used, and I have found the following articles/code, among other things:
- https://github.com/bvnayak/stereo_calibration/blob/master/camera_calibrate.py
- https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_calib3d/py_calibration/py_calibration.html
- https://python.plainenglish.io/the-depth-i-stereo-calibration-and-rectification-24da7b0fb1e0
Below are an example of a calibration pattern that I used:
I have 25 photos of the calibration pattern with the left and right camera. The pattern is 5x9 and the square sizes is 40x40 mm.
Based on my knowledge, I have written the following code:
...ANSWER
Answered 2021-Apr-26 at 19:02I solved this problem by using the following openCV functions:
cv2.findChessboardCorners()cv2.cornerSubPix()cv2.findHomography()cv2.warpPerspective()
I used the calibration plate at a distance of 30cm to calculate the perspective transformation matrix, H. Because of this, I can map an object from the right image to the left image. The depth has to be constant (30 cm) though, which is a bit problematic, but it is acceptable in my case.
Thanks to @Micka for the great answers.
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