diff --git a/Sift_Distance.py b/Sift_Distance.py
deleted file mode 100644
index dad454b..0000000
--- a/Sift_Distance.py
+++ /dev/null
@@ -1,24 +0,0 @@
-sift = cv.xfeatures2d.SIFT_create()
-original_kp, original_des = sift.detectAndCompute(left,None)
-new_kp, new_des = sift.detectAndCompute(right,None)
-
-bf = cv2.BFMatcher()
-matches = bf.knnMatch(original_des, new_des, k=2)
-
-# Apply ratio test
-good = []
-for m, n in matches:
-    if m.distance < 0.3 * n.distance:
-        good.append([m])
-
-# Featured matched keypoints from images 1 and 2
-pts1 = np.float32([original_kp[m.queryIdx].pt for m in good])
-pts2 = np.float32([new_kp[m.trainIdx].pt for m in good])
-
-#convert to complex number
-z1 = np.array([[complex(c[0],c[1]) for c in pts1]])
-z2 = np.array([[complex(c[0],c[1]) for c in pts2]])
-
-# Distance between featured matched keypoints
-FM_dist = abs(z2 - z1)
-
diff --git a/__main__.py b/__main__.py
new file mode 100644
index 0000000..699c956
--- /dev/null
+++ b/__main__.py
@@ -0,0 +1,108 @@
+import numpy as np
+import cv2
+from matplotlib import pyplot as plt
+import os
+
+def main():
+
+	#Read in all images
+	images = readImages()
+
+	#Read in data from Section001_Data.txt
+	loads, displacements, surface_area, length = readSpecimenData()
+	
+	#Keep track of Stress and Strains
+	stresses = []
+	strains = []
+	#Get distances using sift
+	distances = getSiftDistance(images[0], images[1])
+
+	# These distances are coming out as zero for some reason.
+	# Still trying to figure out if it's a bug in the code I
+	# wrote, or if SIFT won't work for our case.
+	print("DISTANCES:")
+	print(distances)
+	print("MAX DISTANCE:")
+	print(max(distances))
+	print("MIN DISTANCE:")
+	print(min(distances))
+
+	#Eventually we'll find the distances, stress, strain for all images
+	"""
+	for idx in range(0, len(images)-1):
+		distances = getSiftDistance(images[idx], images[idx+1])
+		strains.append(getStrain(loads[idx], surface_area))
+		#stresses.append(getStress(length, ______))
+		youngs_mod = getYoungsModulus(strains[idx] / stress[idx])
+	"""
+
+
+def readImages():
+	my_dir = os.path.dirname(os.path.realpath(__file__))
+	image_dir = os.path.join(my_dir, "Images")
+	filenames = os.listdir(image_dir)
+
+	images = []
+	for file in filenames:
+		images.append(cv2.imread(os.path.join(image_dir,file)))
+	return images
+
+def readSpecimenData():
+	# TODO
+	return None, None, None, None
+
+def getStrain(load, surface_area):
+	return load / surface_area
+
+def getStress(length, change_in_length):
+	return change_in_length / length
+
+def getYoungsModulus(strain, stress):
+	return strain / stress
+
+def getSiftDistance(img1, img2):
+	""" Gets distance between matching pts in
+		img1 and img2.
+	"""
+	sift = cv2.xfeatures2d.SIFT_create()
+	original_kp, original_des = sift.detectAndCompute(img1,None)
+	new_kp, new_des = sift.detectAndCompute(img2,None)
+
+	bf = cv2.BFMatcher()
+	matches = bf.knnMatch(original_des, new_des, k=2)
+
+	# Ratio test
+	good = []
+
+	for m, n in matches:
+	    if m.distance < 0.3 * n.distance:
+	        good.append(m)
+
+	# Draw matches 
+	"""
+	# Uncomment to print matches between img1 and img2.
+	# SIFT may not be the best method based off the matched
+	# images.
+	matches = cv2.drawMatchesKnn(img1, original_kp, img2, new_kp, good, None, flags=2)
+	plt.imshow(matches)
+	plt.show()
+	"""
+
+	# Featured matched keypoints from images 1 and 2
+	pts1 = np.float32([original_kp[m.queryIdx].pt for m in good])
+	pts2 = np.float32([new_kp[m.trainIdx].pt for m in good])
+
+	#convert to complex number
+	z1 = np.array([[complex(c[0],c[1]) for c in pts1]])
+	z2 = np.array([[complex(c[0],c[1]) for c in pts2]])
+
+	# Distance between featured matched keypoints
+	distances = abs(z2 - z1)
+
+	return distances
+
+
+
+
+if __name__ == '__main__':
+	main()
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