OpenLLM-France/wikimedia huggingface dataset:
# SwisTrack/Components/BlobDetectionRedGreen
## Description

This component detects markers consisting of a red and a green blob on a color image. The background is supposed to be uniformly black, gray, blue, yellow, or white. The particle position is the point between the two blob centers, while the particle orientation is pointing towards the red blob.
The component was originally written to detect green and red LEDs mounted on a robot, and works reliably even if the green (resp. red) blob consists of various shades of green (resp. red). Best results were achieved in a dark room where the only visible light on the camera images were the red and green LEDs.
The algorithm compares the green (g) and red (r) value and classifies each pixel into one of the following categories:
* Red pixel, if $r-g>t_{r}$
* Green pixel, if $g-r>t_{g}$
* Gray pixel, otherwise
$t_{r}$ and $t_{g}$ denote the thresholds for the red resp. green blobs. The blue (b) channel is ignored.

## Input

File:SwisTrack RedGreenBlobs.png
A color image with red and green blobs. The background is supposed to be uniformly black, gray, blue, yellow, white, or any combination of these colors.

## Output

File:SwisTrack RedGreenParticles.png
Particles, with the center being between the red and green blobs, and the orientation pointing towards the red blob.

## Parameters
### Max. particles

Maximum number of particles to detect. Note that the algorithm first detects this many red and green blobs, and then matches them. Hence, if there are stray red or green areas in your image (not part of a marker), you should choose a number of particles that is slightly higher than the number of markers you deploy.

### Max. distance

The maximum distance between the center of the red blob and the center of the green blob. Blobs that are farther away from each other are considered not being part of the same marker.
This distance is also used to eliminate stray blobs in the neighborhood of red or green blobs. More precisely, within any disk of this diameter (not radius), only the biggest red (resp. green) blob is detected.

### Threshold (red and green blob)

The thresholds $t_{r}$ and $t_{g}$ (see description above).

### Selection by area (red and green blob)

Check this to base blob selection on the blob area.

### Min. blob size (red and green blob)

Minimum area in pixels of a blob to select.

### Max. blob size (red and green blob)

Maximum area in pixels of a blob to select.

### Selection by compactness (red and green blob)

Check this to base blobs section on the compactness of the blob.

### Min. compactness (red and green blob)

Minimum compactness (0..1) of a blob to select.

### Max. compactness (red and green blob)

Maximum compactness (0..1) of a blob to select.
# SwisTrack/Components/BlobDetectionTwoColors
## Description

This component detects markers consisting of two blobs (of different color) on a color image. The particle position is the point between the two blob centers, while the particle orientation is pointing towards color 1.
For both colors, the algorithm first selects all pixels falling in to the following cube in RGB color space:
$|r-c_{r}|<t_{r}$ $|g-c_{g}|<t_{g}$ $|b-c_{b}|<t_{b}$
where $(c_{r},c_{g},c_{b})$ denotes the reference color, and $t_{r}$, $t_{g}$ and $t_{b}$ the thresholds for the respective channels. In a second step, the algorithm applies blob detection on the thresholded image. Both the color subtracted image and the thresholded image can be visualized.
Once the blobs of both colors have been extracted, close lying blobs of different color are combined to particles.

## Input

File:SwisTrack TwoColorBlobs.png
A color image with markers consisting of two blobs of different color. Both colors need to be sufficiently different from any color appearing in the background. (A background subtraction component or a mask component may be useful to get rid of unwanted areas.)

## Output

File:SwisTrack TwoColorParticles.png
Particles, with the center being between the two blobs, and the orientation pointing towards the blob of color 1.

## Parameters
### Max. particles

Maximum number of particles to detect. Note that the algorithm first detects this many red and green blobs, and then matches them. Hence, if there are stray red or green areas in your image (not part of a marker), you should choose a number of particles that is slightly higher than the number of markers you deploy.

### Max. distance

The maximum distance between the center of the red blob and the center of the green blob. Blobs that are farther away from each other are considered not being part of the same marker.
This distance is also used to eliminate stray blobs in the neighborhood of red or green blobs. More precisely, within any disk of this diameter (not radius), only the biggest red (resp. green) blob is detected.

### Output Parameters

The parameters of the output blobs can be choosen as the parameters of the first colored blob, the second one, or the combination of those (i.e. area added, centers averaged etc.)

### Color 1 and 2

The reference color ($c_{r},c_{g},c_{b}$, see description above).

### Threshold R, G and B (for color 1 and 2)

The thresholds $t_{r}$, $t_{g}$ and $t_{b}$ (see description above).

### Selection by area (for color 1 and 2)

Check this to base blob selection on the blob area.

### Min. blob size (for color 1 and 2)

Minimum area in pixels of a blob to select.

### Max. blob size (for color 1 and 2)

Maximum area in pixels of a blob to select.

### Selection by compactness (for color 1 and 2)

Check this to base blobs section on the compactness of the blob.

### Min. compactness (for color 1 and 2)

Minimum compactness (0..1) of a blob to select.

### Max. compactness (for color 1 and 2)

Maximum compactness (0..1) of a blob to select.
# SwisTrack/Components/BlobSelection
## Description

This component detects the blobs resulting of a thresholding step and keeps those with their area, orientation and compactness between specified boundaries. It is really close to the blob detection component. The only difference is that the result is a black and white image and not particles. Therefore, additional binary processing can be done after this method (Such as erosion or dilation).

## Input

A binary (black and white) image.

## Output

A binary (black and white) image.

## Parameters
### Area Selection

Choose if a selection based on the blobs area is done.

### Min. blob size

Minimal area of a selected blob in pixels.

### Max. blob size

Maximal area of a selected blob in pixels.

### Compactness Selection

Choose if a selection based on the blobs compactness is done.

### Min. compactness

Minimal compactness of a selected blob (between 0 and 1).

### Max. compactness

Maximal compactness of a selected blob (between 0 and 1).

### Orientation Selection

Choose if a selection based on the blobs orientation is done.

### Min. orientation

Minimal orientation of a selected blob in degrees.

### Max. orientation

Maximal orientation of a selected blob in degrees.
# SwisTrack/Components/CalibrationFileTSAI
## Description

Camera calibration using an algorithm proposed by Tsai. We use it to transform the image coordinates in world coordinates. More information about it can be found in his paper : "An Efficient and Accurate Camera Calibration Technique for 3D Machine Vision", Roger Y. Tsai, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Miami Beach, FL, 1986, pages 364-374." or on this website. The calibration code come from this website.
For a deformed image, TSAI is better than a purely linear second order calibration method. However, it is slower, and sometimes, when the camera axis is orthogonal the to setup plan, TSAI method can lead to error in computation and tremendous errors, and even crash of the method.
A good presentation of the parameters can be found here.
This component is different from CalibrationTSAI as it does not compute the parameters and load them from a xml file.
Tools to compute these parameters with matlab/octave are provided in calibrationTools/Matlab_Octave. The following files can be used:
* computeTsaiCalibrationParameters.m: Compute the Tsai parameters from calibration points coordinates (coplanar calibration)
* TsaiImage2World.m: Transform the image coordinates to world coordinates using the Tsai calibration parameters (It is the same implementation than the one used in this component)
* WriteTSAICalibrationParametersXMLFile.m: Write a Tsai calibration parameters in the right format for the component.
* WriteSwisTrackCalibrationXMLFile.m: Write calibration points to a xml file compatible with swisTrack
* readSwisTrackCalibrationXMLFile.m: Read calibration points from a xml file compatible with swisTrack
* optimizeSx.m: Search for the optimal value of sx for the calibration
* computeError.m: Compute the calibration errors

## Input

Particles

## Output

Particles

## Parameters
### Calib. Points

This file is not mandatory as they are not used to compute the calibration paramters. If provided, the component compute the calibration errors and write them in a log file. Path the XML file containing the calibration points. The file must have the following format:
More points can be added in the same way as the two points in this example. A minimum of 5 non-collinear points is needed to compute the calibration.

### Calib. Param.

Path the XML file containing the calibration parameters. The file must have the following format:
# SwisTrack/Components/ColorBlur
## Description

This component blurs a color image. It's expensive but reduces noise.

## Input

A grayscale or a color image.

## Parameters
### Mode

The type of blur. This is from the openCV documentation:

### Radius

The size of the square to average over.
# SwisTrack/Components/Calibration

Calibration components translate image coordinates (pixels) into world coordinates (m).
# SwisTrack/Components/CalibrationTSAI
## Description

Camera calibration using an algorithm proposed by Tsai. We use it to transform the image coordinates in world coordinates. More information about it can be found in his paper : "An Efficient and Accurate Camera Calibration Technique for 3D Machine Vision", Roger Y. Tsai, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Miami Beach, FL, 1986, pages 364-374." or on this website. The calibration code come from this website.
For a deformed image, TSAI is better than a purely linear second order calibration method. However, it is slower, and sometimes, when the camera axis is orthogonal the to setup plan, TSAI method can lead to error in computation and tremendous errors, and even crash of the method.
A good presentation of the parameters can be found here.

## Input

Particles

## Output

Particles

## Parameters
### Calib. Points

Path the XML file containing the calibration points. The file must have the following format:
More points can be added in the same way as the two points in this example. A minimum of 5 non-collinear points is needed to compute the calibration.

### Ncx

[sel] Number of sensor elements in the camera's x direction.

### Nfx

[pix] Number of pixels in the frame grabber's x direction. If the calibration is crashing, put a value 5% smaller than the Ncx.

### Ncy

[sel] Number of sensor elements in the camera's y direction.

### Nfy

[pix] Number of pixels in the frame grabber's y direction. If the calibration is crashing, put a value 5% smaller than the Ncy.

### dx

[mm/sel] X dimension of camera's sensor element (in mm).

### dy

[mm/sel] Y dimension of camera's sensor element (in mm).

### sx

 Scale factor to compensate for any error in dpx. Use 1 by default.
# SwisTrack/Components/CamShiftTracking
## Description

Uses CamShiftTracking. I always ran it on the post-background-subtracted video. That worked well, but it tended to lose the person when they moved too quickly. I track only using R. I recently added multidimensional histograms so it can use more than one channel but it doesn't work so well. I use motion templates for my tracking now.
On the other hand, I think it should be possible to get this working... cam shift is *supposed* to be good.

## Parameters
### HistogramDimensions

The number of histogram dimensions; the number of channels to use in tracking.

### VMin

Threshold for "almost black."

### SMin

Threshold for "almost grey."
These parameters screen out noise. Read "Seeing with OpenCV" from Servo magazine for details. Swistrack uses the library described in that article. (Google for camshift-Servo-OpenCV-part3.pdf.)

### InitialWindowSize

The length in pixels of the side of a square to make an initial window size. It will always use the same size. Really it should set this automatically from the particle...

### FrameKillThreshold

The number of frames to keep the track around before killing it, if it hasn't been updated.

### MinNewTrackDistance

The minimum distance a new track must be from existing tracks.

### TrackDistanceKillThreshold

If two tracks get less than this distance to each other, the shorter one will be destroyed.

### MaximumNumberOfTracks

The maximum number of simultaneous trackers.
# Veterinary Medicine/Introduction

Veterinary medicine is one of the most diverse of all universitarian studies. A veterinary student on his way to his approbation as a veterinary doctor will have at least the following subjects: Botany, Zoology, Veterinary Anatomy, Parasitology, Microbiology and Immunology ,Histology, Pathology, Histopathology, organic, inorganic and Bio-Chemistry, Molecular Pathology, Physiology, Pharmacology, animal nutrition and feeding, animal care and management,Dairy and poultry science,animal genetics and breeding,animal surgery,obstetrics and gynaecology,embryology and theriogenology,Veterinary medicine, Toxicology and vet jurisprudence, Fodder cultivation and agronomy,agricultural extension,statistics ,Wild life and zoo animal medicine ,vet public health ,Lab animal management,vet ethics and legal aspects, etc. This is a book with everything needed to get a degree in Veterinary Medicine, it's here to satisfy public interest in medical care for animals, with self-help advice how to keep your pet healthy for example. Nothing here is intended to replace professional health care by qualified Vets!
# Veterinary Medicine
## Contents

* Introduction
* History of Veterinary Medicine

### Self-help

* Home-Diagnosis Guide
* First Aid
* Nutrition

### Anatomy of the domestic animals

A very broad topic, for which 2 years of intense study doesn't seem to be enough. Even seasoned practitioners have to consult their books from time to time. As new discoveries are made, various parts are rewritten.
* Comparative Anatomy
* Equine Anatomy
* Canine Anatomy
* Feline Anatomy
* Small Ruminant Anatomy - sheep and goats
* Bovine Anatomy
* Rodent Anatomy
* Avine Anatomy
* Reptile Anatomy
* Piscal Anatomy

### Pet psychology

* Deshying
* Domestication
* Danger Potential
# Veterinary Medicine/Feline Anatomy
## See also

* Washington State University has diagrams of feline anatomy at
# Veterinary Medicine/History of Veterinary Medicine

It all began when people tried to optimize their meat-production system. There's references to aiding cows in birth dating back to ancient Egypt, Veterinaries have been sent to war, to help maintain the horses of the cavalry, or replace the fallen human surgeons on the battlefield, especially because they had a better command of anatomy. Veterinaries are helping archaeologists in reconstructing the living conditions of ancient civilisations on the basis of animal bones found.
# Version Control/Bazaar

Bazaar is a distributed version control system.
# Veterinary Medicine/Domestication
## Rats

Rattus Norvegicus, the Norway rat, is also known as the Asian Brown Rat, the Brown Rat and the Barn Rat, and is the species of domesticated pet and laboratory rats as well as the common city "sewer" rat. These animals are not nocturnal but semi-nocturnal, or crepuscular, like cats. While they are "up and down" all the time, they are generally dozy during the day, becoming active at dusk. Their close relative, Rattus Rattus -- the Asian Black Rat, Ship Rat or Roof Rat -- is more truly nocturnal: nearly comatose during the day and has developed an automatic bite reflex if disturbed in the nest during sleep cycles.
# Veterinary Medicine/Comparative Anatomy

Comparative Anatomy means to describe anatomy by comparing different species or individuals. Concerning the mammals, much of the anatomy is very similar. Despite totally different outer appearance of the legs, the muscles present there are roughly the same, each tagged by a name from human anatomy. The inner organs tend to be a lot more different. While all animals have a digestive aparatus in the form of a long pipe, only the ruminants (cattle, buffalo, sheep, goat) have four rooms (compartments) in the stomach, viz., rumen, reticulum, omasum and abomasum.
# Veterinary Medicine/Avine Anatomy

The avian species are having peculiar anatomical variations as their structutes are modified as flying adaptation.
# The American School/Introduction

This book is the result work by undergraduate students in an introductory course labeled The American School offered at Northern Michigan University. The focus of the course was to engage non-educators in authentic discussions about American schooling. Our goal for the project was to develop a means to facilitate thought and reflection on what schools are for. The undergraduates involved in this project chose topics of interest to them and related to them to a school environment. They viewed the topics as members of the community who would like to support and question what schools do. These papers led to the formation of school mission and goal statements used to engage current and former principals and superintendents in a discussion on the “ideal” school environment. The work presented in writing and in the final presentations went through the peer review process. We encourage you to comment on the topics these undergraduates chose to research and present. Any additions to what the American school is or has to offer will only further help to facilitate our ongoing discussion of how the public can help shape our educational system.
Home Introduction The Role of Education in a Democratic Society
# Thai Music and Dance
## Music
### Folk Music

The website of Thailand's Ministry of Culture shows the accurate information on the folk musical instruments. At .
# The American School

This book is the result work by undergraduate students in an introductory course labeled The American School offered at Northern Michigan University. The focus of the course was to engage non-educators in authentic discussions about American schooling. Our goal for the project was to develop a means to facilitate thought and reflection on what schools are for. The undergraduates involved in this project chose topics of interest to them and related to them to a school environment. They viewed the topics as members of the community who would like to support and question what schools do. These papers led to the formation of school mission and goal statements used to engage current and former principals and superintendents in a discussion on the “ideal” school environment. The work presented in writing and in the final presentations went through the peer review process. We encourage you to comment on the topics these undergraduates chose to research and present. Any additions to what the American school is or has to offer will only further help to facilitate our ongoing discussion of how the public can help shape our educational system.

## Table of Contents

    Introduction
* The Role of Education in a Democratic Society
* The Cultural Diversity of our Society and the Implications for Education
* Cultural Differences in Schools
* Public vs. Private Schools
* Charter vs. Public Schools
* Controversial Issues in America's Schools: Sex Education
* The Forces That Shape Curricula
* Sports in Public and Private Schools
* The Role of Media in Education
* Complexity and Controversy Surrounding The No Child Left Behind Act
# The Strengths and Weaknesses of Thailand

"Thailand" or the land of the Thai people, formerly known as Siam, has several strengths and weaknesses.
==Weaknesses== olok

## Strengths

The Strengths of Thai Culture
Like any other nation states, Thailand has both strengths and weaknesses. There are several strengths, and some of which are:
1. Thai people are morally civilized. They have built their civilization based on the teachings of Lord Buddha. Since the influence of Buddhism, the teachings of Lord Buddha and His disciples (Buddhist monks and believers) have been implemented, it can safely be said that today Buddhism has permeated into almost all fabrics of Thai society.
Many aspects of the teachings are eco-friendly, as discussed by Rom Avudhkom. Many articles of Pra Vinai (the Disciplinary Text) states what should be done to live harmony with nature.
They are non-violent people as they prefer to live in peace.
2. Thais are people who care about their cultural identities. Another strength lies in the practice of the Thai people passed down generation after generation. Cam Troung has noted that:
" However, wet paddy cultivation has been the driving force of the long historical development of the Thai community. It must not be forgotten on the other hand, that they have continued to maintain their links with upland forest areas through hunting and gathering activities. Their settlement and migration patterns are determined by this factor".
"One of the interesting features of their (The Thais) use of land resources is the division of the territory controlled by each village into reserved sections, reflecting a strong sense of ecological management, something that has been an intergral part of community traditions for a long time" (p. 2).
"The ancient laws of the Thai establish a classification of special areas which include sacred forests (where the Deities of Land and Water are warshipped); rivers and streams which are essential for community life and agriculture areas..."
3. Thailand has several c****** geographical advantages. In the past, the land was called "Suwanna Bhumi" or "The Land of Gold", reflecting the richness and fertility of the land.
Evidence of the land of plenty can ne found inscribed on the stone discovered by King Monkut, which says that Siam is a land of plenty and bountiful. "In the water there are fish' There is rice in the paddy."

### Positive 'Good' Findings on Thailand

A recent report released by the OECD finds that Thai people are satisfied with their lives. With a score of 7.2 on a scale of 1 to 10 (low to high satisfaction), "Thai people (citizens) rank the country among the top four countries in terms of quality of life, just behind high-income countries such as Singapore, Australia, and New Zealand. The average life satisfaction score for 30 OECD countries is 6.7".
# Thai Civilization/Women's Status in Thai Society

Traditionally, Thai women did not have the same rights as men's. They were not allowed to enter monkhoods, which means that they did not have access to education. their duties were limited to doing household chores such as cooking and doing dishes.
Thai men enjoyed better opportunities to develop themselves. They had access through monastic education.
Questions for Discussions
What were the key roles of Siamese women in the Ayudthaya and early Bangkok period?
Were the women's roles different from the men's roles? Discuss.
In the past, did Thai men and women have equal rights? Why?/Why not?
What were the key factors leading to changes in the status of Thai women during the early Rattanakorin (Bangkok) period?