cleaned up code and added doc

Author: Pluto8195

README.md

@@ -1,5 +1,16 @@
 # TriloBits
-
 <p>Team Name: Team P
 <p>Challenge: Trilobite Bits
-<p>Subchallenge: 1) find the # of segments
+
+## Progress
+	<p> Focused only on the identification of Segments and differentiation between head, body, and tail
+	<p> Further modules will have to be developed to detect triolobyte in an image if not centered and either
+	1. rectify image to center
+	2. provide coordinates of start and end
+	<p> Using Derivative filtering as the main player in detecting segments we look for drastic changes in intensity along the center of the image and draw lines on those peaks yeilding optimistic results.  Details on how we might improve the accuracy and remove false positives aswell as how we can actually use this information in solving the problem are documented in the file segment.py
+	<p> Included is also a scraper that will collect the entire catalog of images from the museam website
+## Within
+	*. scraper.py use: "python scraper.py"
+	*. segment.py use: "python segment.py [filename]"  output:[output.jpg]
+
+

binary.py

@@ -0,0 +1,99 @@
+from sys import argv
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+import operator
+
+
+# This file will take a "sufficiently" centered image of
+# a well preserved trilobite and find areas of sharp change
+# to find boundry points between parts of the trilobites body
+
+# use "python segment.py [filename]"
+
+# results on this are not 100% accurate but show that this
+# might be the right path to a solution and should be expanded upon
+
+# TODO:
+# Come up with a metric to dynamically pick the window size to get
+#		the best results
+# Possiblity of passing over data a second time after peaks have
+# 	Been found to eliminate false positives recognize patters to 
+#		determine segments and differentation btwn head thorax and tail
+
+
+
+
+
+
+# Helper function to use matplot lib with scanlines
+def plot_graph(line, height, subcol, subrow, plotindex, color):
+	i = 0
+	prevx = 0
+	prevy = 0
+	plt.subplot(subcol,subrow,plotindex)
+	for x in np.nditer(line):
+		plt.plot([i,prevy], [x,prevx], color)
+		plt.axis([0, height, 0, 255])
+		prevx = x
+		prevy = i
+		i+=1	
+
+# Draw original image with detected segments based on
+# Scan Line derivative and window size
+#		Input		#
+# orignal image
+# derivative of scanline
+# window size
+def find_local_max(img, line, window_size):
+	height, width = img.shape
+	start = 0
+	end = window_size
+
+	# get a local maxima within in a window
+	# simple approach to find peaks in derivative
+	for i in range(0,height,window_size):
+		index, value = max(enumerate(line[start:end]), key=operator.itemgetter(1))
+		img = cv2.line(img,(0,index+start),(width,index+start),(255,255,255))
+		start += window_size
+		end += window_size
+	return img
+
+
+def main():
+	image_name = argv[1]
+
+	# scale down image size
+	img = cv2.pyrDown(cv2.pyrDown(cv2.imread(argv[1],0)))
+	height, width = img.shape
+
+	# get the center of the image
+	middle = width/2
+	middle = int(middle)
+
+	# get a 1D scanline of the center of the image
+	proc = img[0:height-1, middle:middle+1]
+
+	blur = cv2.GaussianBlur(proc,(5,5),0)
+
+	# compute first derivative of the scanline
+	sobelx64f = cv2.Sobel(blur,cv2.CV_64F,0,1,ksize=1)
+	# normalize values for representation
+	abs_sobel64f = np.absolute(sobelx64f)
+	sobel_8u = np.uint8(abs_sobel64f)
+	# t = np.transpose(blur)
+
+	#draw detected segements on image
+	img = find_local_max(img, sobel_8u, 20)
+
+
+
+	plot_graph(blur, height, 3, 1, 2 ,'k-')
+	plot_graph(sobel_8u, height, 3, 1,2,'r-')
+
+	cv2.imwrite('output.jpg',img)
+
+	plt.show()
+
+if __name__ == '__main__':
+	main()