Saturday, April 25, 2015

Using Python's PIL library to remove "green screen" background.

For some of the sprite sheets used in the game, the renders use a solid background color; these need to be removed to show only the object that was meant to be rendered. An easy way to do this is using Python's image editing library, PIL. Check out the source below.

 from PIL import Image  
 from PIL import ImageFilter  
 import os  
 for filename in os.listdir("."): # parse through file list in the current directory  
      if filename[-3:] == "png":  
           img =  
           img = img.convert("RGBA")  
           pixdata = img.load()  
           for y in xrange(img.size[1]):  
                for x in xrange(img.size[0]):  
                     r, g, b, a = img.getpixel((x, y))  
                     if (r < 130) and (g < 60) and (b < 60):  
                          pixdata[x, y] = (255, 255, 255, 0)  
                     #Remove anti-aliasing outline of body.  
                     if r == 0 and g == 0 and b == 0:  
                          pixdata[x, y] = (255, 255, 255, 0)  
           img2 = img.filter(ImageFilter.GaussianBlur(radius=1))  
 , "PNG")  

The basic idea is to load each frame of the animation, loop through each pixel of each frame, and remove the color of the "green screen" background color, including a small range to account for the semi-transparent pixels on the edge of the rendered object that are mixed with the green screen. Finally, a small blurring filter is added to soften the edges of the image, otherwise at the edge where the green screen is removed, it looks more obvious that it was "cut out". As a final touch, after creating the sprite sheet you can use a program like GIMP to add some additional filters to adjust the color and contrast. You can also use GIMP to get the exact RGBA numbers to specify which color to filter out in your program.

Wednesday, April 22, 2015

Concurrent Map

Before moving on, I decided to add one more concurrent container to the library I mentioned in my last post. Stack and queue are relatively primitive in that they are a singly linked list of data with either one or two pointers held by the class. Operations such as push and pop can be simplified to just changing the head or tail of the data structure. I really wanted to do one more that is an especially useful data structure so I wrote a pretty basic implementation of std::map but concurrent.

The concurrent map was implemented using a combination of buckets for a very small and basic hash, and then for any collisions in these buckets were stored in a balanced binary tree (AVL, which is contrary to the STL's red-black tree implementation). A simple hash was used because it allowed me to partition the values into "thread-safe zones" that I could wrap a mutex around. It's a pretty naive/basic way of adding concurrency to a map compared to more advanced lock and wait-free methods but it does work.

At this point I'm very anxious to get back to programming for my game and have already started reading a new book on ZeroMQ, which is a very robust library for handling many network connections in a simplified manner. I'm not sure if this is the library I will end up using for networking in the game, but it's extremely interesting none-the-less and I look forward to learning more about it.

Anyways, for those interested in checking out the source for the new concurrent map, check it out at:

Friday, April 17, 2015

Concurrent Data Structures

For the past 6 weeks I've been on a different schedule due to training at work that has unfortunately restricted much of my time that can be devoted towards programming. Although I've been busy with work, that doesn't mean I haven't had any free time left! On my days off I've been studying concurrency in a much more in-depth manner and even wrote a basic concurrent data structures library. It's published on github and supports a concurrent stack, queue, and a third data structure similar to stack and queue (in that it supports push and pop) but the pop order is not guaranteed (it must be assumed that the pop order can be random).

 #include "ccl.hpp"  
 #include <iostream>  
 int main() {  
   std::cout << "Starting concurrent data pool test" << std::endl;  
   ccl::data_pool<std::string> pool;  
   std::string popped_value = "Empty";  
   if(pool.try_pop(popped_value)) {  
     std::cout << "Popped value: " << popped_value << std::endl;  
   } else {  
     std::cout << "Value did not pop, variable is still: " << popped_value << std::endl;  

For the concurrent stack and queue, I used a technique called "Flat-Combining" to maintain thread-safety when accessing the containers. The idea behind flat-combining is that each thread gets access to a special record that they can publish their desired action to. They then wait for a response on that record indicating that the action has been processed on the data structure. A global lock exists that limits only 1 thread to be able to process the requests of the other threads. If the lock is available, any thread that is waiting for a response will try to take the lock and take it upon themselves to publish the actions of all the threads to the data structure. I found this data structure to not only be more performant but also much easier to implement compared to a traditional lock-free data structure.

For the last data structure (which I called "data_pool" since I'm not sure what the right name is for it), it is what I consider more concurrent friendly because it compromises by allowing any order for data popping. Data is stored in vectors of successively growing size (larger vectors are added as more data is pushed to an already full data structure), which helps with spatial locality, and since the the same vectors are used repeatedly, temporal locality is improved (instead of allocating a new node on the heap every time a value is pushed).

If interested in checking out the source code and some examples, the project's page can be found at: It should be noted that these are naive implementations and are neither guaranteed nor fully tested.