原文地址：在Ubuntu中安裝Python科學(xué)計(jì)算環(huán)境 作者：HyryStudio

sudo apt-get install python-pip

sudo apt-get install python-dev

cd
sudo apt-get install git
git clone https://github.com/ipython/ipython.git
cd ipython
sudo python setup.py install

sudo apt-get install ipython

sudo pip install tornado
sudo apt-get install libzmq-dev
sudo pip install pyzmq
sudo pip install pygments

cd
mkdir notebook
cd notebook
ipython notebook

sudo ipython notebook

from IPython.external.mathjax import install_mathjax
install_mathjax()

sudo apt-get install python-numpy
sudo apt-get install python-scipy
sudo apt-get install python-matplotlib

sudo apt-get build-dep python-numpy
sudo apt-get build-dep python-scipy

sudo pip install numpy
sudo pip install scipy

sudo apt-get install python-qt4
sudo apt-get install qt4-designer
sudo apt-get install pyqt4-dev-tools
sudo apt-get install python-qt4-doc

/usr/share/doc/python-qt4-doc

sudo apt-get install spyder

sudo pip install spyder --upgrade

sudo pip install cython
sudo apt-get install swig

sudo apt-get install python-dev libxtst-dev scons python-vtk  pyqt4-dev-tools python2.7-wxgtk2.8 python-configobj
sudo apt-get install libgl1-mesa-dev libglu1-mesa-dev

mkdir ets
cd ets
wget https://github.com/enthought/ets/raw/master/ets.py
python ets.py clone
sudo python ets.py develop
#sudo python ets.py install    或者運(yùn)行install安裝

sudo apt-get install build-essential
sudo apt-get install cmake
sudo apt-get install cmake-gui
sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev
sudo apt-get install libjpeg-dev libpng-dev libtiff-dev libjasper-dev

mkdir release
cmake-gui

cd release
make
sudo make install

Google Earth坐標(biāo)－美國(guó)航空母艦坐標(biāo)

本列表收錄了美國(guó)海軍己退役或現(xiàn)役中的航空母艦，包括船級(jí)屬于CV、CVA、CVB、CVL或CVN的全部艦只。編號(hào)在CVA-58之后的都屬于超級(jí)航空母艦（排水量超過(guò)75,000噸），CVN-65和CVN-68以后的都屬于核動(dòng)力航空母艦。

排水量較小的護(hù)航航空母艦（Escort Aircraft Carriers，CVE），則另行收錄于美國(guó)海軍護(hù)航航空母艦列表中。

高光譜成像是新一代光電檢測(cè)技術(shù)，興起于2O世紀(jì)8O年代，目前仍在迅猛發(fā)展巾。高光譜成像是相對(duì)多光譜成像而言，通過(guò)高光譜成像方法獲得的高光譜圖像與通過(guò)多光譜成像獲取的多光譜圖像相比具有更豐富的圖像和光譜信息。如果根據(jù)傳感器的光譜分辨率對(duì)光譜成像技術(shù)進(jìn)行分類，光譜成像技術(shù)一般可分成3類。

(1)  多光譜成像——光譜分辨率在 delta_lambda/lambda=0．1數(shù)量級(jí)，這樣的傳感器在可見光和近紅外區(qū)域一般只有幾個(gè)波段。

(2)  高光譜成像—— 光譜分辨率在 delta_lambda/lambda=0．01數(shù)量級(jí)，這樣的傳感器在可見光和近紅外區(qū)域有幾卜到數(shù)百個(gè)波段，光譜分辨率可達(dá)nm級(jí)。

(3)  超光譜成像—— 光譜分辨率在delta_lambda/lambda =O．001數(shù)量級(jí)，這樣的傳感器在可見光和近紅外區(qū)域可達(dá)數(shù)千個(gè)波段。

眾所周知，光譜分析是自然科學(xué)中一種重要的研究手段，光譜技術(shù)能檢測(cè)到被測(cè)物體的物理結(jié)構(gòu)、化學(xué)成分等指標(biāo)。光譜評(píng)價(jià)是基于點(diǎn)測(cè)量，而圖像測(cè)量是基于空間特性變化，兩者各有其優(yōu)缺點(diǎn)。因此，可以說(shuō)光譜成像技術(shù)是光譜分析技術(shù)和圖像分析技術(shù)發(fā)展的必然結(jié)果，是二者完美結(jié)合的產(chǎn)物。光譜成像技術(shù)不僅具有光譜分辨能力，還具有圖像分辨能力，利用光譜成像技術(shù)不僅可以對(duì)待檢測(cè)物體進(jìn)行定性和定量分析，而且還能進(jìn)對(duì)其進(jìn)行定位分析。

高光譜成像系統(tǒng)的主要工作部件是成像光譜儀，它是一種新型傳感器，2O世紀(jì)8O年代初正式開始研制，研制這類儀器的目的是為獲取大量窄波段連續(xù)光譜圖像數(shù)據(jù)，使每個(gè)像元具有幾乎連續(xù)的光譜數(shù)據(jù)。它是一系列光波波長(zhǎng)處的光學(xué)圖像，通常包含數(shù)十到數(shù)百個(gè)波段，光譜分辨率一般為1～l0nm。由于高光譜成像所獲得的高光譜圖像能對(duì)圖像中的每個(gè)像素提供一條幾乎連續(xù)的光譜曲線，其在待測(cè)物上獲得空間信息的同時(shí)又能獲得比多光譜更為豐富光譜數(shù)據(jù)信息，這些數(shù)據(jù)信息可用來(lái)生成復(fù)雜模型，來(lái)進(jìn)行判別、分類、識(shí)別圖像中的材料。

通過(guò)高光譜成像獲取待測(cè)物的高光譜圖像包含了待測(cè)物的豐富的空間、光譜和輻射三重信息。這些信息不僅表現(xiàn)了

地物空間分布的影像特征，同時(shí)也可能以其中某一像元或像元組為目標(biāo)獲取它們的輻射強(qiáng)度以及光譜特征。影像、輻射與光譜是高光譜圖像中的3個(gè)重要特征，這3個(gè)特征的有機(jī)結(jié)合就是高光譜圖像。

高光譜圖像數(shù)據(jù)為數(shù)據(jù)立方體(cube)。通常圖像像素的橫坐標(biāo)和縱坐標(biāo)分別用z和Y來(lái)表示，光譜的波長(zhǎng)信息以(Z即軸)表示。該數(shù)據(jù)立方體由沿著光譜軸的以一定光譜分辨率間隔的連續(xù)二維圖像組成。

1. Introduction
2. The Idea
3. The Gaussian Case
4. Experiments with Black-and-White Images
5. Experiments with Color Images
6. References

Introduction

Filtering is perhaps the most fundamental operation of image processing and computer vision. In the broadest sense of the term "filtering", the value of the filtered image at a given location is a function of the values of the input image in a small neighborhood of the same location. For example, Gaussian low-pass filtering computes a weighted average of pixel values in the neighborhood, in which the weights decrease with distance from the neighborhood center. Although formal and quantitative explanations of this weight fall-off can be given, the intuition is that images typically vary slowly over space, so near pixels are likely to have similar values, and it is therefore appropriate to average them together. The noise values that corrupt these nearby pixels are mutually less correlated than the signal values, so noise is averaged away while signal is preserved.
The assumption of slow spatial variations fails at edges, which are consequently blurred by linear low-pass filtering. How can we prevent averaging across edges, while still averaging within smooth regions? Many efforts have been devoted to reducing this undesired effect. Bilateral filtering is a simple, non-iterative scheme for edge-preserving smoothing.

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The Idea

The basic idea underlying bilateral filtering is to do in the range of an image what traditional filters do in its domain. Two pixels can be close to one another, that is, occupy nearby spatial location, or they can be similar to one another, that is, have nearby values, possibly in a perceptually meaningful fashion.
Consider a shift-invariant low-pass domain filter applied to an image:

The bold font for f and h emphasizes the fact that both input and output images may be multi-band. In order to preserve the DC component, it must be

Range filtering is similarly defined:

In this case, the kernel measures the photometric similarity between pixels. The normalization constant in this case is

The spatial distribution of image intensities plays no role in range filtering taken by itself. Combining intensities from the entire image, however, makes little sense, since the distribution of image values far away from x ought not to affect the final value at x. In addition, one can show that range filtering without domain filtering merely changes the color map of an image, and is therefore of little use. The appropriate solution is to combine domain and range filtering, thereby enforcing both geometric and photometric locality. Combined filtering can be described as follows:

with the normalization

Combined domain and range filtering will be denoted as bilateral filtering. It replaces the pixel value at x with an average of similar and nearby pixel values. In smooth regions, pixel values in a small neighborhood are similar to each other, and the bilateral filter acts essentially as a standard domain filter, averaging away the small, weakly correlated differences between pixel values caused by noise. Consider now a sharp boundary between a dark and a bright region, as in figure 1(a).

(a)	(b)	(c)
	Figure 1

When the bilateral filter is centered, say, on a pixel on the bright side of the boundary, the similarity function s assumes values close to one for pixels on the same side, and values close to zero for pixels on the dark side. The similarity function is shown in figure 1(b) for a 23x23 filter support centered two pixels to the right of the step in figure 1(a). The normalization term k(x) ensures that the weights for all the pixels add up to one. As a result, the filter replaces the bright pixel at the center by an average of the bright pixels in its vicinity, and essentially ignores the dark pixels. Conversely, when the filter is centered on a dark pixel, the bright pixels are ignored instead. Thus, as shown in figure 1(c), good filtering behavior is achieved at the boundaries, thanks to the domain component of the filter, and crisp edges are preserved at the same time, thanks to the range component.

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The Gaussian Case

A simple and important case of bilateral filtering is shift-invariant Gaussian filtering, in which both the closeness function c and the similarity function s are Gaussian functions of the Euclidean distance between their arguments. More specifically, c is radially symmetric:

where

is the Euclidean distance. The similarity function s is perfectly analogous to c :

where

is a suitable measure of distance in intensity space. In the scalar case, this may be simply the absolute difference of the pixel difference or, since noise increases with image intensity, an intensity-dependent version of it. Just as this form of domain filtering is shift-invariant, the Gaussian range filter introduced above is insensitive to overall additive changes of image intensity. Of course, the range filter is shift-invariant as well.

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Experiments with Black-and-White Images

Figure 2 (a) and (b) show the potential of bilateral filtering for the removal of texture. The picture "simplification" illustrated by figure 2 (b) can be useful for data reduction without loss of overall shape features in applications such as image transmission, picture editing and manipulation, image description for retrieval.

(a)		(b)
	Figure 2

Bilateral filtering with parameters sd =3 pixels and sr =50 intensity values is applied to the image in figure 3 (a) to yield the image in figure 3 (b). Notice that most of the fine texture has been filtered away, and yet all contours are as crisp as in the original image. Figure 3 (c) shows a detail of figure 3 (a), and figure 3 (d) shows the corresponding filtered version. The two onions have assumed a graphics-like appearance, and the fine texture has gone. However, the overall shading is preserved, because it is well within the band of the domain filter and is almost unaffected by the range filter. Also, the boundaries of the onions are preserved.

(a)		(b)
(c)		(d)
	Figure 3

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Experiments with Color Images

For black-and-white images, intensities between any two gray levels are still gray levels. As a consequence, when smoothing black-and-white images with a standard low-pass filter, intermediate levels of gray are produced across edges, thereby producing blurred images. With color images, an additional complication arises from the fact that between any two colors there are other, often rather different colors. For instance, between blue and red there are various shades of pink and purple. Thus, disturbing color bands may be produced when smoothing across color edges. The smoothed image does not just look blurred, it also exhibits odd-looking, colored auras around objects.

(a)		(b)
(c)		(d)
	Figure 4

Figure 4 (a) shows a detail from a picture with a red jacket against a blue sky. Even in this unblurred picture, a thin pink-purple line is visible, and is caused by a combination of lens blurring and pixel averaging. In fact, pixels along the boundary, when projected back into the scene, intersect both red jacket and blue sky, and the resulting color is the pink average of red and blue. When smoothing, this effect is emphasized, as the broad, blurred pink-purple area in figure 4 (b) shows.
To address this difficulty, edge-preserving smoothing could be applied to the red, green, and blue components of the image separately. However, the intensity profiles across the edge in the three color bands are in general different. Smoothing the three color bands separately results in an even more pronounced pink and purple band than in the original, as shown in figure 4 (c). The pink-purple band, however, is not widened as in the standard-blurred version of figure 4 (b).
A much better result can be obtained with bilateral filtering. In fact, a bilateral filter allows combining the three color bands appropriately, and measuring photometric distances between pixels in the combined space. Moreover, this combined distance can be made to correspond closely to perceived dissimilarity by using Euclidean distance in the CIE-Lab color space. This color space is based on a large body of psychophysical data concerning color-matching experiments performed by human observers. In this space, small Euclidean distances are designed to correlate strongly with the perception of color discrepancy as experienced by an "average" color-normal human observer. Thus, in a sense, bilateral filtering performed in the CIE-Lab color space is the most natural type of filtering for color images: only perceptually similar colors are averaged together, and only perceptually important edges are preserved. Figure 4 (d) shows the image resulting from bilateral smoothing of the image in figure 4 (a). The pink band has shrunk considerably, and no extraneous colors appear.

(a)	(b)	(c)
	Figure 5

Figure 5 (c) shows the result of five iterations of bilateral filtering of the image in figure 5 (a). While a single iteration produces a much cleaner image (figure 5 (b)) than the original, and is probably sufficient for most image processing needs, multiple iterations have the effect of flattening the colors in an image considerably, but without blurring edges. The resulting image has a much smaller color map, and the effects of bilateral filtering are easier to see when displayed on a printed page. Notice the cartoon-like appearance of figure 5 (c). All shadows and edges are preserved, but most of the shading is gone, and no "new" colors are introduced by filtering.

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References

[1] C. Tomasi and R. Manduchi, "Bilateral Filtering for Gray and Color Images", Proceedings of the 1998 IEEE International Conference on Computer Vision, Bombay, India.
[2] T. Boult, R.A. Melter, F. Skorina, and I. Stojmenovic,"G-neighbors", Proceedings of the SPIE Conference on Vision Geometry II, pages 96-109, 1993.
[3] R.T. Chin and C.L. Yeh, "Quantitative evaluation of some edge-preserving noise-smoothing techniques", Computer Vision, Graphics, and Image Processing, 23:67-91, 1983.
[4] L.S. Davis and A. Rosenfeld, "Noise cleaning by iterated local averaging", IEEE Transactions on Systems, Man, and Cybernetics, 8:705-710, 1978.
[5] R.E. Graham, "Snow-removal - a noise-stripping process for picture signals", IRE Transactions on Information Theory, 8:129-144, 1961.
[6] N. Himayat and S.A. Kassam, "Approximate performance analysis of edge preserving filters", IEEE Transactions on Signal Processing, 41(9):2764-77, 1993.
[7] T.S. Huang, G.J. Yang, and G.Y. Tang, "A fast two-dimensional median filtering algorithm", IEEE Transactions on Acoustics, Speech, and Signal Processing, 27(1):13-18, 1979.
[8] J.S. Lee, "Digital image enhancement and noise filtering by use of local statistics", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2(2):165-168, 1980.
[9] M. Nagao and T. Matsuyama, "Edge preserving smoothing", Computer Graphics and Image Processing, 9:394-407, 1979.
[10] P.M. Narendra, "A separable median filter for image noise smoothing", IEEE Transactions on Pattern Analysis and Machine Intelligence, 3(1):20-29, 1981.
[11] K.J. Overton and T.E. Weymouth, "A noise reducing preprocessing algorithm",Proceedings of the IEEE Computer Science Conference on Pattern Recognition and Image Processing, pages 498-507, Chicago, IL, 1979.
[12] P. Perona and J. Malik, "Scale-space and edge detection using anisotropic diffusion", IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7):629-639, 1990.
[13] G. Ramponi, "A rational edge-preserving smoother", Proceedings of the International Conference on Image Processing, volume 1, pages 151-154, Washington, DC, 1995.
[14] G. Sapiro and D.L. Ringach, "Anisotropic diffusion of color images", Proceedings of the SPIE, volume 2657, pages 471-382, 1996.
[15] D.C.C. Wang, A.H. Vagnucci, and C.C. Li, "A gradient inverse weighted smoothing scheme and the evaluation of its performance", Computer Vision, Graphics, and Image Processing, 15:167-181, 1981.
[16] G. Wyszecki and W. S. Styles, Color Science: Concepts and Methods, Quantitative Data and Formulae, John Wiley and Sons, New York, NY, 1982.
[17] L. Yin, R. Yang, M. Gabbouj, and Y. Neuvo, "Weighted median filters: a tutorial",IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 43(3):155-192, 1996.

我們?cè)趯懗绦驎r(shí)，常常會(huì)遇到類型轉(zhuǎn)換的問(wèn)題。現(xiàn)總結(jié)一些常見的類型轉(zhuǎn)換。

1，const char*（C風(fēng)格字符串）與string之間轉(zhuǎn)換：

             （1） const char*可以直接對(duì)string類型賦值，例如：

                       const char* pchar = "qwerasdf";

                       stringstr = pchar;

             （2） string通過(guò)c_str()函數(shù)轉(zhuǎn)換為C風(fēng)格字符串，例如：

                       string str = "qwerasdf";

                       const char* pchar = str.c_str();

2，const char*類型可以直接給CString類型賦值，例如：

               const char* pchar = "qwerasdf";

               CString str = pchar;

3，string類型變量轉(zhuǎn)為為Cstring類型變量

             CString類型變量可以直接給string類型變量賦值，但是string類型不能對(duì)CString類型直接賦值。通過(guò)前兩類

      轉(zhuǎn)換我們可以得到，string類型變量轉(zhuǎn)換為const char*類型，然后再直接賦值就可以了。例如：

       CString cstr；

       sring str = “asdasd”；

       cstr = str.c_str();

      同理，CStrng類型變量先轉(zhuǎn)換為string類型在調(diào)用c_str()函數(shù)就可以完成向const char*類型的轉(zhuǎn)換。例如：

      CString cStr = "adsad";   

      string str = cStr;  

      const char* pchar = str.c_str();
4，double，int轉(zhuǎn)string

      double temp;
   stringstream strStream;
   strStream<<temp;
   string ss = strStream.str() 

   string 轉(zhuǎn)double，int 
   string.atoi   ,   string.atof

     從上面我們可以上面看出，通過(guò)類型之間的相互轉(zhuǎn)化，會(huì)使本來(lái)要通過(guò)復(fù)雜的函數(shù)來(lái)完成的類型轉(zhuǎn)換變得簡(jiǎn)單易懂。