* `png-32` supports different levels of transparency. Each pixel can have an opacity between 0 and 255, with 0 as completely transparent.

* `png-24` supports setting one color as fully transparent. Everything else will be opaque.

* `gif` uses a color palette. You can specify that one color in the palette is fully transparent.

* `png-8` also uses a color palette but each color in the palette has its own opacity value, so it supports varying levels of transparency too.

* `jpg` does not support transparency.

JPEG 2000 supports full transparency.  See [JPEG 2000 Side channel spatial information][1].


  [1]: https://en.wikipedia.org/wiki/JPEG_2000#Side_channel_spatial_information &quot;JPEG 2000 Side channel spatial inforamationquot;&quot;

[JPEG XR][1] (not the same as JPEG 2000) also supports transparency.  
However it is not a useful format for the web as only Internet Explorer and Edge support it.

[WebP][2] is another emerging image format with support for transparency.  
However, Internet Explorer lacks support for it.


  [1]: https://docs.microsoft.com/en-gb/windows/desktop/wic/jpeg-xr-codec
  [2]: https://developers.google.com/speed/webp/

From [Transparency (graphic) - Wikipedia](https://en.wikipedia.org/wiki/Transparency_(graphic) &quot;Transparency (graphic) - Wikipedia&quot;):

&gt;Raster file formats that support transparency include **GIF, PNG, BMP, TIFF, TGA** and **JPEG 2000**, through either a transparent color or an alpha channel. 

I&#39;m trying to understand the difference between a TCP segment with the flag `PSH` and with the flag `URG`.
I read the RFC but still couldn&#39;t get it, does one of them buffer the data before it sends it to the process and the other doesn&#39;t ?

Difference between push and urgent flags in TCP

I have two tags in my git in same branch. There are at least 5-6 commits between them. How can I create a single patch between the two tags so that it can be applied to a GitHub repo?

How to create patch between two tags with multiple commits between them?

I found JPG does not support transparency, the alpha value is always 255. I am wondering only png supports transparency?

Only PNG supports transparency, is that true?

<p>I found JPG does not support transparency, the alpha value is always 255. I am wondering only png supports transparency?</p>


I have a web server running with Jersey REST resources up and I wonder how to get an image/png reference for the browsers img tag; after submitting a Form or getting an Ajax response. The image processing code for adding graphics is working, just need to return it somehow.

Code:

    @POST
    @Path(&quot;{fullsize}&quot;)
    @Consumes(MediaType.MULTIPART_FORM_DATA)
    @Produces(&quot;image/png&quot;)
    // Would need to replace void
    public void getFullImage(@FormDataParam(&quot;photo&quot;) InputStream imageIS,
                             @FormDataParam(&quot;submit&quot;) String extra) {

          BufferedImage image = ImageIO.read(imageIS);

          // .... image processing
          //.... image processing

          return ImageIO.  ..  ?

    }


Cheers

How to return a PNG image from Jersey REST service method to the browser

I have recently watched  [this][1] video of a Finnish internet security expert. Somewhere around eleventh minute, he talks about a virus which is hidden in an image and executes when the image is about to be displayed.

I am wondering how do they technically do such a thing, I mean how come the **virus** is **executed**, when the picture should be **displayed** and how come the picture is not compromised in some way. I thought the computer first looks at the extension, then opens it with appropriate program and lets the program work itself (and I don&#39;t expect regular image viewer to be able to run a virus within itself). Obviously it doesn&#39;t work like that, but no one I asked could help me out with this.

So does anyone know how do they do this, the principle? Thank you very much.

  [1]: http://www.ted.com/talks/mikko_hypponen_fighting_viruses_defending_the_net.html

How can a virus exist in an image?

I want to convert a color tuple to a color name, like &#39;yellow&#39; or &#39;blue&#39;

    &gt;&gt;&gt; im = Image.open(&quot;test.jpg&quot;)
    &gt;&gt;&gt; n, color = max(im.getcolors(im.size[0]*im.size[1]))
    &gt;&gt;&gt; print color
    (119, 172, 152)

Is there a simple way in python to do this?


Convert RGB color to English color name, like &#39;green&#39; with Python

I need to verify an image url to check whether the url is an image of any of these extensions:- jpeg, jpg, gif, png. Example:- when we verify this url http://www.example.com/asdf.jpg it should give us true value and with url like this http://www.example.com/asdf.php should return false. How can we do this in javascript and also i want to check the content type of url. So that we can say whether the url is an image or not.

Javascript Image Url Verify

The common method to store images in a database is to convert the image to `base64` data before storing the data. This process will increase the size by 33%. Alternatively it is possible to directly store the image as a `BLOB`; for example:

    $image = new Imagick(&quot;image.jpg&quot;);
    $data = $image-&gt;getImageBlob();
    $data = $mysqli-&gt;real_escape_string($data);
    $mysqli-&gt;query(&quot;INSERT INTO images (data) VALUES (&#39;$data&#39;)&quot;);

and then display the image with

    &lt;img src=&quot;data:image/jpeg;base64,&#39; .  base64_encode($data)  . &#39;&quot; /&gt;

With the latter method, we save 1/3 storage space. Why is it more common to store images as `base64` in MySQL databases?

**UPDATE:** There are many debates about advantages and disadvantages of storing images in databases, and most people believe it is not a practical approach. Anyway, here I assume we store image in database, and discussing the best method to do so.

Storing image in database directly or as base64 data?

I&#39;ve been using tesseract to convert documents into text. The quality of the documents ranges wildly, and I&#39;m looking for tips on  what sort of image processing might improve the results. I&#39;ve noticed that text that is highly pixellated - for example that generated by fax machines - is especially difficult for tesseract to process - presumably all those jagged edges to the characters confound the shape-recognition algorithms. 

What sort of image processing techniques would improve the accuracy? I&#39;ve been using a Gaussian blur to smooth out the pixellated images and seen some small improvement, but I&#39;m hoping that there is a more specific technique that would yield better results. Say a filter that was tuned to black and white images, which would smooth out irregular edges, followed by a filter which would increase the contrast to make the characters more distinct.

Any general tips for someone who is a novice at image processing? 

image processing to improve tesseract OCR accuracy

One of the most interesting projects I&#39;ve worked on in the past couple of years was a project about [image processing][11]. The goal was to develop a system to be able to recognize Coca-Cola **&#39;cans&#39;** (note that I&#39;m stressing the word &#39;cans&#39;, you&#39;ll see why in a minute). You can see a sample below, with the can recognized in the *green rectangle* with scale and rotation.

![Template matching][1]

Some constraints on the project:

 - The background could be very noisy.
 - The *can* could have any *scale* or *rotation* or even orientation (within reasonable limits).
 - The image could have some degree of fuzziness (contours might not be entirely straight).
 - There could be Coca-Cola bottles in the image, and the algorithm should only detect the *can*!
 - The brightness of the image could vary a lot (so you can&#39;t rely &quot;too much&quot; on color detection).
 - The *can* could be partly hidden on the sides or the middle and possibly partly hidden behind a bottle.
 - There could be no *can* at all in the image, in which case you had to find nothing and write a message saying so.

So you could end up with tricky things like this (which in this case had my algorithm totally fail):

![Total fail][2]

I did this project a while ago, and had a lot of fun doing it, and I had a decent implementation. Here are some details about my implementation:

**Language**: Done in C++ using [OpenCV][10] library.

**Pre-processing**: For the image pre-processing, i.e. transforming the image into a more raw form to give to the algorithm, I used 2 methods:

 1. Changing color domain from RGB to [HSV][3] and filtering based on &quot;red&quot; hue, saturation above a certain threshold to avoid orange-like colors, and filtering of low value to avoid dark tones. The end result was a binary black and white image, where all white pixels would represent the pixels that match this threshold. Obviously there is still a lot of crap in the image, but this reduces the number of dimensions you have to work with.
![Binarized image][4]  
 2. Noise filtering using median filtering (taking the median pixel value of all neighbors and replace the pixel by this value) to reduce noise.
 3. Using [Canny Edge Detection Filter][5] to get the contours of all items after 2 precedent steps.
![Contour detection][6]


**Algorithm**: The algorithm itself I chose for this task was taken from [this][7] awesome book on feature extraction and called [Generalized Hough Transform][8] (pretty different from the regular Hough Transform). It basically says a few things:

 - You can describe an object in space without knowing its analytical equation (which is the case here).
 - It is resistant to image deformations such as scaling and rotation, as it will basically test your image for every combination of scale factor and rotation factor.
 - It uses a base model (a template) that the algorithm will &quot;learn&quot;.
 - Each pixel remaining in the contour image will vote for another pixel which will supposedly be the center (in terms of gravity) of your object, based on what it learned from the model.

In the end, you end up with a heat map of the votes, for example here all the pixels of the contour of the can will vote for its gravitational center, so you&#39;ll have a lot of votes in the same pixel corresponding to the center, and will see a peak in the heat map as below:

![GHT][9]

Once you have that, a simple threshold-based heuristic can give you the location of the center pixel, from which you can derive the scale and rotation and then plot your little rectangle around it (final scale and rotation factor will obviously be relative to your original template). In theory at least...


**Results**: Now, while this approach worked in the basic cases, it was severely lacking in some areas:

 - It is **extremely slow**! I&#39;m not stressing this enough. Almost a full day was needed to process the 30 test images, obviously because I had a very high scaling factor for rotation and translation, since some of the cans were very small.
 - It was completely lost when bottles were in the image, and for some reason almost always found the bottle instead of the can (perhaps because bottles were bigger, thus had more pixels, thus more votes)
 - Fuzzy images were also no good, since the votes ended up in pixel at random locations around the center, thus ending with a very noisy heat map.
 - In-variance in translation and rotation was achieved, but not in orientation, meaning that a can that was not directly facing the camera objective wasn&#39;t recognized.

Can you help me improve my **specific** algorithm, using **exclusively OpenCV** features, to resolve the **four specific** issues mentioned?

I hope some people will also learn something out of it as well, after all I think not only people who ask questions should learn. :)

  [1]: http://i.stack.imgur.com/irQtR.png
  [2]: http://i.stack.imgur.com/Byw82.png
  [3]: http://en.wikipedia.org/wiki/HSL_and_HSV
  [4]: http://i.stack.imgur.com/ktdAB.png
  [5]: http://en.wikipedia.org/wiki/Canny_edge_detector
  [6]: http://i.stack.imgur.com/F9319.png
  [7]: http://www.amazon.com/Feature-Extraction-Processing-Computer-Edition/dp/0123725380
  [8]: http://en.wikipedia.org/wiki/Generalised_Hough_transform
  [9]: http://i.stack.imgur.com/wxrT1.png
  [10]: http://opencv.org
  [11]: https://en.wikipedia.org/wiki/Image_processing

Image Processing: Algorithm Improvement for &#39;Coca-Cola Can&#39; Recognition

So I have a set of data which I am able to convert to form separate numpy arrays of R, G, B bands. Now I need to combine them to form an RGB image. 

I tried &#39;Image&#39; to do the job but it requires &#39;mode&#39; to be attributed. 

I tried to do a trick. I would use Image.fromarray() to take the array to image but it attains &#39;F&#39; mode by default when Image.merge requires &#39;L&#39; mode images to merge. If I would declare the attribute of array in fromarray() to &#39;L&#39; at first place, all the R G B images become distorted.

But, if I save the images and then open them and then merge, it works fine. Image reads the image with &#39;L&#39; mode.

Now I have two issues.

First, I dont think it is an elegant way of doing the work. So if anyone knows the better way of doing it, please tell 

Secondly, Image.SAVE is not working properly. Following are the errors I face:

    In [7]: Image.SAVE(imagefile, &#39;JPEG&#39;)
    ----------------------------------------------------------------------------------

    TypeError                                 Traceback (most recent call last)

    /media/New Volume/Documents/My own works/ISAC/SAMPLES/&lt;ipython console&gt; in &lt;module&gt;()

    TypeError: &#39;dict&#39; object is not callable

Please suggest solutions.

And please mind that the image is around 4000x4000 size array.


Combine 3 separate numpy arrays to an RGB image in Python 

How can I apply mask to a color image in latest python binding (cv2)? In previous python binding the simplest way was to use `cv.Copy` e.g.

`cv.Copy(dst, src, mask)`

But this function is not available in cv2 binding. Is there any workaround without using boilerplate code?

OpenCV - Apply mask to a color image

I found a quite [large list of available libraries][1] on Node.JS wiki but I&#39;m not sure which of those are more mature and provide better performance. Basically I want to do the following:

 1. load some images to a server from external sources
 2. put them onto one big canvas
 3. crop and mask them a bit
 4. apply a filter or two
 5. Resize the final image and give a link to it

Big plus if the node package works on both Linux *and Windows*.

  [1]: https://github.com/joyent/node/wiki/modules#wiki-graphics

Which library should I use for server-side image manipulation on Node.JS?

Suppose you have any image (PNG or JPG).
This image has a white background and I need to make this background transparent.

I have tried with these examples:

 - `convert original.png -background none transparent.png`
 - `convert original.png -background white -flatten -alpha off transparent.png`

but with no desirable results.

How can I make it?

IMPORTANT: Using `convert` command-line.

Set transparent background using ImageMagick and commandline prompt

I&#39;m using PIL to convert a transparent PNG image uploaded with Django to a JPG file. The output looks broken.

###Source file

![transparent source file][1]

###Code

    Image.open(object.logo.path).save(&#39;/tmp/output.jpg&#39;, &#39;JPEG&#39;)

or

    Image.open(object.logo.path).convert(&#39;RGB&#39;).save(&#39;/tmp/output.png&#39;)

###Result

Both ways, the resulting image looks like this:

![resulting file][2]

Is there a way to fix this? I&#39;d like to have white background where the transparent background used to be.


----------

# Solution

Thanks to the great answers, I&#39;ve come up with the following function collection:

    import Image
    import numpy as np


    def alpha_to_color(image, color=(255, 255, 255)):
        &quot;&quot;&quot;Set all fully transparent pixels of an RGBA image to the specified color.
        This is a very simple solution that might leave over some ugly edges, due
        to semi-transparent areas. You should use alpha_composite_with color instead.

        Source: http://stackoverflow.com/a/9166671/284318

        Keyword Arguments:
        image -- PIL RGBA Image object
        color -- Tuple r, g, b (default 255, 255, 255)

        &quot;&quot;&quot; 
        x = np.array(image)
        r, g, b, a = np.rollaxis(x, axis=-1)
        r[a == 0] = color[0]
        g[a == 0] = color[1]
        b[a == 0] = color[2] 
        x = np.dstack([r, g, b, a])
        return Image.fromarray(x, &#39;RGBA&#39;)


    def alpha_composite(front, back):
        &quot;&quot;&quot;Alpha composite two RGBA images.

        Source: http://stackoverflow.com/a/9166671/284318

        Keyword Arguments:
        front -- PIL RGBA Image object
        back -- PIL RGBA Image object

        &quot;&quot;&quot;
        front = np.asarray(front)
        back = np.asarray(back)
        result = np.empty(front.shape, dtype=&#39;float&#39;)
        alpha = np.index_exp[:, :, 3:]
        rgb = np.index_exp[:, :, :3]
        falpha = front[alpha] / 255.0
        balpha = back[alpha] / 255.0
        result[alpha] = falpha + balpha * (1 - falpha)
        old_setting = np.seterr(invalid=&#39;ignore&#39;)
        result[rgb] = (front[rgb] * falpha + back[rgb] * balpha * (1 - falpha)) / result[alpha]
        np.seterr(**old_setting)
        result[alpha] *= 255
        np.clip(result, 0, 255)
        # astype(&#39;uint8&#39;) maps np.nan and np.inf to 0
        result = result.astype(&#39;uint8&#39;)
        result = Image.fromarray(result, &#39;RGBA&#39;)
        return result


    def alpha_composite_with_color(image, color=(255, 255, 255)):
        &quot;&quot;&quot;Alpha composite an RGBA image with a single color image of the
        specified color and the same size as the original image.

        Keyword Arguments:
        image -- PIL RGBA Image object
        color -- Tuple r, g, b (default 255, 255, 255)

        &quot;&quot;&quot;
        back = Image.new(&#39;RGBA&#39;, size=image.size, color=color + (255,))
        return alpha_composite(image, back)

    
    def pure_pil_alpha_to_color_v1(image, color=(255, 255, 255)):
        &quot;&quot;&quot;Alpha composite an RGBA Image with a specified color.

        NOTE: This version is much slower than the
        alpha_composite_with_color solution. Use it only if
        numpy is not available.

        Source: http://stackoverflow.com/a/9168169/284318

        Keyword Arguments:
        image -- PIL RGBA Image object
        color -- Tuple r, g, b (default 255, 255, 255)

        &quot;&quot;&quot; 
        def blend_value(back, front, a):
            return (front * a + back * (255 - a)) / 255
        
        def blend_rgba(back, front):
            result = [blend_value(back[i], front[i], front[3]) for i in (0, 1, 2)]
            return tuple(result + [255])
        
        im = image.copy()  # don&#39;t edit the reference directly
        p = im.load()  # load pixel array
        for y in range(im.size[1]):
            for x in range(im.size[0]):
                p[x, y] = blend_rgba(color + (255,), p[x, y])
        
        return im

    def pure_pil_alpha_to_color_v2(image, color=(255, 255, 255)):
        &quot;&quot;&quot;Alpha composite an RGBA Image with a specified color.

        Simpler, faster version than the solutions above.

        Source: http://stackoverflow.com/a/9459208/284318

        Keyword Arguments:
        image -- PIL RGBA Image object
        color -- Tuple r, g, b (default 255, 255, 255)

        &quot;&quot;&quot;
        image.load()  # needed for split()
        background = Image.new(&#39;RGB&#39;, image.size, color)
        background.paste(image, mask=image.split()[3])  # 3 is the alpha channel
        return background


## Performance

The simple non-compositing `alpha_to_color` function is the fastest solution, but leaves behind ugly borders because it does not handle semi transparent areas.

Both the pure PIL and the numpy compositing solutions give great results, but `alpha_composite_with_color` is much faster (8.93 msec) than `pure_pil_alpha_to_color` (79.6 msec). &lt;del&gt;If numpy is available on your system, that&#39;s the way to go.&lt;/del&gt; (Update: The new pure PIL version is the fastest of all mentioned solutions.)

    $ python -m timeit &quot;import Image; from apps.front import utils; i = Image.open(u&#39;logo.png&#39;); i2 = utils.alpha_to_color(i)&quot;
    10 loops, best of 3: 4.67 msec per loop
    $ python -m timeit &quot;import Image; from apps.front import utils; i = Image.open(u&#39;logo.png&#39;); i2 = utils.alpha_composite_with_color(i)&quot;
    10 loops, best of 3: 8.93 msec per loop
    $ python -m timeit &quot;import Image; from apps.front import utils; i = Image.open(u&#39;logo.png&#39;); i2 = utils.pure_pil_alpha_to_color(i)&quot;
    10 loops, best of 3: 79.6 msec per loop
    $ python -m timeit &quot;import Image; from apps.front import utils; i = Image.open(u&#39;logo.png&#39;); i2 = utils.pure_pil_alpha_to_color_v2(i)&quot;
    10 loops, best of 3: 1.1 msec per loop


  [1]: http://i.stack.imgur.com/I2uNe.png
  [2]: http://i.stack.imgur.com/rmlPz.png

Convert RGBA PNG to RGB with PIL

I want to convert SVG images to PNG files with transparent background and anti-aliased edges (using semi-transparent pixels). Unfortunately I can&#39;t get ImageMagick to do the anti-aliasing, the edges always look terrible. Here&#39;s what I tried:

    convert +antialias -background transparent  in.svg -resize 25x25 out.png

Any ideas or a different command line tool I could use?

Convert SVG to transparent PNG with antialiasing, using ImageMagick

I was wondering if PNG contains data like the following?

What I did was to convert the jpg file to png format, and I was expecting to retrieve the same info I had on the jpg as below:

*Currently using this [as3 library][1] to read the data.*

- IDF0--- IDF @[134 - 248] (9 entries)
 - Orientation (SHORT) : 1 	
 - XResolution (RATIONAL) : 72/1 	
 - YResolution (RATIONAL) : 72/1
 - ResolutionUnit (SHORT) : 2 	
 - Software (ASCIIx16) : QuickTime 7.6.6 	
 - DateTime (ASCIIx20)  : 2011:10:02 22:43:37 	
 - HostComputer (ASCIIx16)  : Mac OS X 10.6.8
 - Exif IFD (LONG) : 8  34853	
 - GPS IFD (LONG) : 248

&amp;nbsp;

- EXIF IDF--- IDF @[8 - 134](10 entries)
 - ExifVersion (UNDEFINEDx4) : 0220
 - DateTimeOrigina (ASCIIx20)  : 2011:04:14 17:22:01
 - UserComment (UNDEFINEDx63)  : ASCII
 - FlashpixVersion (UNDEFINEDx4) : 0100
 - ColorSpace (SHORT) : 1
 - PixelXDimension (LONG) : 1022
 - PixelYDimension (LONG) : 486
 - Unknown (ASCIIx13)  : Image Tag-LOL
 - SceneCaptureType (SHORT) : 0

&amp;nbsp;

- GPS IDF--- IDF @[248 - 338](7 entries)
 - Interoperability Index (ASCIIx2) : N
 - Interoperability Version (RATIONALx3)  @425: 52/1, 1144/100, 0/1
 - Unknown (ASCIIx2) : W
 - Unknown (RATIONALx3)  : 1/1, 4392/100, 0/1
 - Unknown (RATIONAL)  : 5/1
 - Unknown (ASCIIx2) : T
 - Unknown (RATIONAL)  : 3694/117

&amp;nbsp;

  [1]: http://code.google.com/p/as3-xmp-file/

Does PNG contain EXIF data like JPG?

I have a SVG file that has a defined size of 16x16. When I use ImageMagick&#39;s convert program to convert it into a PNG, then I get a 16x16 pixel PNG which is way too small:

    convert test.svg test.png

I need to specify the pixel size of the output PNG. `-size` parameter seems to be ignored, `-scale` parameter scales the PNG **after** it has been converted to PNG. The best result up to now I got by using the `-density` parameter:

    convert -density 1200 test.svg test.png

But I&#39;m not satisfied, because I want to specify the output size in pixels without doing math to calculate the density value. So I want to do something like this:

    convert -setTheOutputSizeOfThePng 1024x1024 test.svg test.png

So what is the magic parameter I have to use here? 

How to convert a SVG to a PNG with ImageMagick?

How can I use the Windows command line to change the extensions of thousands of files to `*****.jpg`?

Changing all files&#39; extensions in a folder with one command on Windows

I have the following:

    ghostscript-fonts-5.50-24
    ImageMagick-6.7.2-1
    ghostscript-9.02-1

Which I use to create a series of JPGs for each page using:

    convert -density 175 -colorspace sRGB test.pdf -resize 50% -quality 95 test.jpg

When I run this on my windows machine all appears to work ok, but on our linux server we get the black background problem.

The resulting JPGs have a black background rendering the image un-readable, what am I missing or is there something I should be doing to correct this?

I&#39;ve been all over google for days but each suggestion doesnt seem to work for me.

Any help is much appreciated, thanks in advance :)

**EDIT**

Just noticed this output when converting one of the PDFs that produces the black background:

    **** Warning: Fonts with Subtype = /TrueType should be embedded.
                 The following fonts were not embedded:
                        Arial
                        Arial,Bold
                        Arial,BoldItalic
    **** This file had errors that were repaired or ignored.
    **** The file was produced by:
    **** &gt;&gt;&gt;&gt; Microsoft&#171; Word 2010 &lt;&lt;&lt;&lt;
    **** Please notify the author of the software that produced this
    **** file that it does not conform to Adobe&#39;s published PDF
    **** specification.

This seems related but as we don&#39;t have control over how the PDFs are produced we need some way of fixing this server side.

Thanks again

ImageMagick PDF to JPGs sometimes results in black background

Why am I getting the message &quot;Not a JPEG file: starts with 0x89 0x50&quot; when I try to open my jpg file?


Why am I getting the error: &quot;Not a JPEG file: starts with 0x89 0x50&quot;

How do I make the background of a `Textview` about 20% transparent (not fully transparent), where there is a color in the background (i.e. white)?


How to make a background 20% transparent on Android

    convert input.png -extent 100x100 -gravity center -background white output.png

If the input is 50x50 the surrounding background is white. Can I somehow set this to transparent without declaring any color within input as transparent?

ImageMagick extend canvas with transparent background

I have some PNG images which consist of a black shape and a transparent background.
Unfortunately, IrfanView shows transparent background as black color, so I see just black on black. I&#39;ve found in Irfan settings that I can change the window&#39;s background color, but it changes only around the image, the transparent color is still black.

Is there any way to change the color displayed as transparent, or, optimally, use white-grey squares like Photoshop?

Setting transparent images background in IrfanView

I&#39;m working on implementing a widget transparency option for my app widget although I&#39;m having some trouble getting the hex color values right. Being completely new to hex color transparency I searched around a bit although I couldn&#39;t find a specific answer to my question. 

I want to set transparency by hex color so let&#39;s say my hex color id &quot;#33b5e5&quot; and I want it to be 50% transparent. Then I&#39;ll use &quot;#8033b5e5&quot; because 80 is 50%. 

I found a useful chart here: http://www.dtp-aus.com/hexadeci.htm . With this data I managed to come up with this:

    0% = #00
    10% = #16
    20% = #32
    30% = #48
    40% = #64
    50% = #80
    60% = #96
    70% = #112
    80% = #128
    90% = #144

Now the issues start appearing when I get higher than 100 in hex. Hex color codes can only be 8 symbols long right? For example #11233b5e5 (80%) crashes.

What can I do to enable me to use the higher numbers aswell?

Hex transparency in colors

I am using matplotlib to make some graphs and unfortunately I cannot export them without the white background. 

![sample plot with solid white background][1]

In other words, when I export a plot like this and position it on top of another image, the white background hides what is behind it rather than allowing it to show through. How can I export plots with a transparent background instead?

  [1]: http://i.stack.imgur.com/wfysS.png



Content Type	Original Author	Original Content on Stackoverflow
Question	Adam Lee	View Question on Stackoverflow
Solution 1 - Image	Richard G. Nielsen	View Answer on Stackoverflow
Solution 2 - Image	Neil Steiner	View Answer on Stackoverflow
Solution 3 - Image	ᅙᄉᅙ	View Answer on Stackoverflow
Solution 4 - Image	Ooker	View Answer on Stackoverflow

Only PNG supports transparency, is that true?

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