Adapter works. Btw when you look in the commit graph to see how much Alexander did, remember that this was Casper's commit.

adapter.py

@@ -1,25 +1,28 @@
+import base64
 import json
 
 import cv2
+import sys
 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)
+# Load base64 encoded image from stdin
+stdin = sys.stdin.readline()
+stdin_decoded = base64.b64decode(stdin)
+img_array = np.frombuffer(stdin_decoded, dtype=np.uint8)
+camera_img = cv2.imdecode(img_array, flags=cv2.IMREAD_COLOR)
 
 # Warp board, saving the homography points as well
-points = []
-warped = warp_board(camera_img, dst_points=points)
+src_points = dst_points = []
+warped = warp_board(camera_img, src_points=src_points, dst_points=dst_points)
 
 
 # Finally, output to stdout for unity to read
 result = {
-    "points": points
+    "src_points": [p.tolist() for p in src_points],
+    "dst_points": [p.tolist() for p in dst_points],
 }
 
 print(json.dumps(result))

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, dst_points: list = None) -> np.ndarray:
+def warp_board(camera_image, debug_image=None, src_points: list = None, dst_points: list = None) -> np.ndarray:
     baseline = cv2.imread("new_baseline_board.png")
 
     camera_image_gray = cv2.cvtColor(camera_image, cv2.COLOR_BGR2GRAY)
@@ -188,8 +188,10 @@ def warp_board(camera_image, debug_image=None, dst_points: list = None) -> np.nd
         points1[i, :] = camera_image_keypoints[m.queryIdx].pt
         points2[i, :] = baseline_keypoints[m.trainIdx].pt
 
+    if src_points is not None:
+        src_points.extend(points1)
     if dst_points is not None:
-        dst_points.extend(zip(points1, points2))
+        dst_points.extend(points2)
 
     h, mask = cv2.findHomography(points1, points2, cv2.RANSAC)