diff --git a/.image-extraction/ROI_0.png b/.image-extraction/ROI_0.png
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diff --git a/.image-extraction/img.jpeg b/.image-extraction/img.jpeg
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diff --git a/.image-extraction/main.py b/.image-extraction/main.py
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--- /dev/null
+++ b/.image-extraction/main.py
@@ -0,0 +1,27 @@
+import cv2
+import numpy as np
+
+image = cv2.imread('./img.jpeg')
+original = image.copy()
+gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+blurred = cv2.GaussianBlur(gray, (3, 3), 0)
+canny = cv2.Canny(blurred, 120, 255, 1)
+kernel = np.ones((5,5),np.uint8)
+dilate = cv2.dilate(canny, kernel, iterations=1)
+
+# Find contours
+cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+cnts = cnts[0] if len(cnts) == 2 else cnts[1]
+
+# Iterate thorugh contours and filter for ROI
+image_number = 0
+for c in cnts:
+    x,y,w,h = cv2.boundingRect(c)
+    cv2.rectangle(image, (x, y), (x + w, y + h), (36,255,12), 2)
+    ROI = original[y:y+h, x:x+w]
+    cv2.imwrite("ROI_{}.png".format(image_number), ROI)
+    image_number += 1
+
+cv2.imshow('canny', canny)
+cv2.imshow('image', image)
+cv2.waitKey(0)