diff --git a/adapter.py b/adapter.py
new file mode 100644
index 00000000..5cc70e78
--- /dev/null
+++ b/adapter.py
@@ -0,0 +1,25 @@
+import json
+
+import cv2
+import numpy as np
+
+from runner import warp_board
+
+# Run this script as: 'python3 adapter.py < ~/Pictures/board.png' or pipe a binary image to stdin
+
+# Load binary image from stdin
+with open(0, "rb") as stdin:
+    array = np.frombuffer(stdin.read(), dtype='uint8')
+camera_img = cv2.imdecode(array, flags=cv2.IMREAD_COLOR)
+
+# Warp board, saving the homography points as well
+points = []
+warped = warp_board(camera_img, dst_points=points)
+
+
+# Finally, output to stdout for unity to read
+result = {
+    "points": points
+}
+
+print(json.dumps(result))
diff --git a/runner.py b/runner.py
index a69ee945..7db425b0 100644
--- a/runner.py
+++ b/runner.py
@@ -131,7 +131,7 @@ def train_pieces_svm_canny() -> None:
         joblib.dump(classifier, f"classifiers/classifier_empty/white_piece_on_{square_color}_square.pkl")
 
 
-def warp_board(camera_image, debug_image=None) -> np.ndarray:
+def warp_board(camera_image, debug_image=None, dst_points: list = None) -> np.ndarray:
     baseline = cv2.imread("new_baseline_board.png")
 
     camera_image_gray = cv2.cvtColor(camera_image, cv2.COLOR_BGR2GRAY)
@@ -188,6 +188,9 @@ def warp_board(camera_image, debug_image=None) -> np.ndarray:
         points1[i, :] = camera_image_keypoints[m.queryIdx].pt
         points2[i, :] = baseline_keypoints[m.trainIdx].pt
 
+    if dst_points is not None:
+        dst_points.extend(zip(points1, points2))
+
     h, mask = cv2.findHomography(points1, points2, cv2.RANSAC)
 
     height, width, channels = baseline.shape