diff --git a/geometry_extraction/center_of_mass_detection.py b/geometry_extraction/center_of_mass_detection.py
index a53c62e..e6c77cc 100644
--- a/geometry_extraction/center_of_mass_detection.py
+++ b/geometry_extraction/center_of_mass_detection.py
@@ -1,6 +1,85 @@
-def main():
+import cv2
+import numpy as np
+import sys, argparse
+
+# needs work to find optimal algorithms and parameters
+# find ways to improve image quality before contouring and finding the center
+
+def find_center_of_gate(img):
     pass
 
 
+def find_center_of_blob():
+
+    # print("new function call with threshhold set to {}".format(thresh))
+    img_copy = src_img.copy()
+
+    img_gray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
+    img_gray = cv2.blur(img_gray, (3,3))
+
+    img_thresh = cv2.adaptiveThreshold(img_gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 199, 5)
+    # canny_output = cv2.adaptiveThreshold(img_gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 199, 5)
+
+
+    # canny_output = cv2.Canny(img_gray, thresh, thresh * 2) # canny edge detection
+    contours,hierarchy = cv2.findContours(img_thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+    num_contours = len(contours)
+
+    if (num_contours > 0):
+        mu = [None]*num_contours
+        for i in range(num_contours):
+            mu[i] = cv2.moments(contours[i])
+
+        mass_centers = [None]*num_contours
+        for i in range(num_contours):
+            # add 1e-5 to avoid division by zero -> opencv tutorial method
+            mass_centers[i] = (mu[i]['m10'] / (mu[i]['m00'] + 1e-5), mu[i]['m01'] / (mu[i]['m00'] + 1e-5))
+
+        # print(hierarchy)
+
+        for i in range(num_contours):
+            if (hierarchy[0][i][3] == -1):
+                color_contour = (0, 0, 255)
+                color_center = (0, 255, 0)
+                cv2.drawContours(img_copy, contours, i, color_contour, 2)
+                cv2.circle(img_copy, (int(mass_centers[i][0]), int(mass_centers[i][1])), 2, color_center, -1)
+
+        cv2.imshow("Image", img_copy)
+
+def main(argv):
+
+    parser = argparse.ArgumentParser(description='Find center of a blob.')
+    parser.add_argument('--img', help='Image file name', default='test.jpg')
+    args = parser.parse_args()
+
+    try:
+        cv2.samples.findFile(args.img, required=True)
+    except cv2.error:
+        sys.exit()
+
+    global src_img
+    src_img = cv2.imread(args.img)
+
+    scale_percent = 50
+    width = int(src_img.shape[1] * 0.5)
+    height = int(src_img.shape[0] * 0.5)
+    new_size = (width, height)
+    src_img = cv2.resize(src_img, new_size)
+
+    src_window = 'Image'
+    cv2.namedWindow(src_window)
+    cv2.imshow(src_window, src_img)
+
+    # max_thresh = 255
+    # thresh = 100 # initial threshold
+    # cv2.createTrackbar('Canny Thresh:', src_window, thresh, max_thresh, find_center_of_blob)
+    # find_center_of_blob(thresh)
+
+    find_center_of_blob()
+
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+
 if __name__ == '__main__':
-    main()
+    main(sys.argv[1:])