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[~/Yahtzee vramakrishnan]$ bash test_yahtzee_manager.sh node_name
erlc yahtzee_manager.erl
yahtzee_manager.erl:237: Warning: variable 'LoginTicket' is unused
yahtzee_manager.erl:253: Warning: variable 'Username' is unused
erlc tournament_manager.erl
tournament_manager.erl:64: Warning: variable 'ActiveUsernames' is unused
tournament_manager.erl:87: Warning: function calcRoundSize/2 is unused
erlc referee.erl
referee.erl:77: Warning: variable 'PlayerAPid' is unused
referee.erl:80: Warning: variable 'PlayerBPid' is unused

vijay120

2014-05-02 22:40:11.479: Referee<0.48.0> > My node is node_name@hollyhock
2014-05-02 22:40:11.479: Referee<0.48.0> > My pid is <0.48.0>
2014-05-02 22:40:11.893: Referee<0.48.0> > In handle game
Is timeout is false
inside true block
2014-05-02 22:40:12.171: Referee<0.48.0> > in handle roll
2014-05-02 22:40:12.171: Referee<0.48.0> > Before message sent
2014-05-02 22:40:12.172: Referee<0.48.0> > Player a username is: "enugent1"
2014-05-02 22:40:12.172: Referee<0.48.0> > Player b username is: "enugent2"
2014-05-02 22:40:12.172: Referee<0.48.0> > After message sent

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2014-05-02 22:42:31.259: Referee<0.48.0> > Player A tuple is {<5378.2.0>,vijay1@ash,
                                                "enugent1","hunter1",
                                                #Ref<0.0.0.47>,true,0,0,0,0}
2014-05-02 22:42:31.259: Referee<0.48.0> > Player B tuple is {<5379.2.0>,vijay2@lothlorien,
                                                "enugent2","hunter2",
                                                #Ref<0.0.0.67>,true,0,0,0,0}
2014-05-02 22:42:31.260: Referee<0.48.0> > My node is node_name@hollyhock
2014-05-02 22:42:31.260: Referee<0.48.0> > My pid is <0.48.0>
2014-05-02 22:42:31.679: Referee<0.48.0> > In handle game
Is timeout is false

vijay120

#importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline

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#importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
import math
import sys
%matplotlib inline

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def grayscale(img):
    """Applies the Grayscale transform
    This will return an image with only one color channel
    but NOTE: to see the returned image as grayscale
    you should call plt.imshow(gray, cmap='gray')"""
    return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

image = mpimg.imread('test_images/solidYellowCurve2.jpg')
# grayscale the image
grayscaled = grayscale(image)

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def gaussian_blur(img, kernel_size):
    """Applies a Gaussian Noise kernel"""
    return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)
  
# apply gaussian blur
kernelSize = 5
gaussianBlur = gaussian_blur(grayscaled, kernelSize)

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def canny(img, low_threshold, high_threshold):
    """Applies the Canny transform"""
    return cv2.Canny(img, low_threshold, high_threshold)

# canny
minThreshold = 100
maxThreshold = 200
edgeDetectedImage = canny(gaussianBlur, minThreshold, maxThreshold)

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def region_of_interest(img, vertices):
    """
    Applies an image mask.
    
    Only keeps the region of the image defined by the polygon
    formed from `vertices`. The rest of the image is set to black.
    """
    #defining a blank mask to start with
    mask = np.zeros_like(img)   
    

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def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):
    """
    `img` should be the output of a Canny transform.
        
    Returns an image with hough lines drawn.
    """
    lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap)
    line_img = np.zeros((*img.shape, 3), dtype=np.uint8)
    
    draw_lines(line_img, lines)