Yay!

README.md

@@ -0,0 +1,22 @@
+## Test Adapter:
+```bash
+base64 --wrap=0 < whole_boards/board_118_1554110522.620303_.png| python3 adapter.py
+```
+
+## Build OpenCV
+```bash
+mkdir opencv && curl -L https://github.com/opencv/opencv/archive/4.1.0.tar.gz | tar -xzv --directory=opencv_contrib --strip-components=1
+mkdir opencv_contrib && curl -L https://github.com/opencv/opencv_contrib/archive/4.1.0.tar.gz | tar -xzv --directory=opencv_contrib --strip-components=1
+
+mkdir -p opencv/build
+cd opencv/build
+
+cmake -D CMAKE_BUILD_TYPE=Release -D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules/ -D CMAKE_INSTALL_PREFIX=/usr/local -D OPENCV_ENABLE_NONFREE=ON -D BUILD_PERF_TESTS=OFF -D BUILD_TESTS=OFF ..
+make -j $(nproc)
+
+sudo make install
+sudo ldconfig
+
+python3 -c "import cv2; print(cv2.__version__)"
+```
+

adapter.py

@@ -5,7 +5,7 @@ import sys
 import cv2
 import numpy as np
 
-from runner import find_keypoints
+from runner import find_homography
 
 # Load base64 encoded image from stdin
 stdin = sys.stdin.readline()
@@ -15,12 +15,11 @@ camera_img = cv2.imdecode(img_array, flags=cv2.COLOR_BGR2RGB)
 camera_img = cv2.cvtColor(camera_img, cv2.COLOR_BGR2RGB)
 
 # Find keypoints in image and pass them back to unity
-src_points, dst_points = find_keypoints(camera_img)
+homography = find_homography(camera_img)
 
 # Finally, output to stdout for unity to read
 result = {
-    "src_points": [p.tolist() for p in src_points],
-    "dst_points": [p.tolist() for p in dst_points],
+    "homography": homography.tolist(),
 }
 
 print(json.dumps(result))

runner.py

@@ -133,9 +133,7 @@ def train_pieces_svm_canny() -> None:
         joblib.dump(classifier, f"classifiers/classifier_empty/white_piece_on_{square_color}_square.pkl")
 
 
-def find_keypoints(camera_image: np.ndarray,
-                   baseline: np.ndarray = cv2.imread(str(here.joinpath("new_baseline_board.png"))),
-                   debug=False) -> Tuple[np.ndarray, np.ndarray]:
+def find_keypoints(camera_image: np.ndarray, baseline: np.ndarray, debug=False) -> Tuple[np.ndarray, np.ndarray]:
     """
     Find keypoints in raw camera image of board.
 
@@ -200,14 +198,21 @@ def find_keypoints(camera_image: np.ndarray,
     return src_points, dst_points
 
 
-def warp_board(camera_image: np.ndarray, debug=False) -> np.ndarray:
-    baseline = cv2.imread(str(here.joinpath("new_baseline_board.png")))
+def find_homography(camera_image: np.ndarray,
+                    baseline: np.ndarray = cv2.imread(str(here.joinpath("new_baseline_board.png"))),
+                    debug=False) -> np.ndarray:
     src_points, dst_points = find_keypoints(camera_image, baseline, debug=debug)
-
     h, mask = cv2.findHomography(src_points, dst_points, cv2.RANSAC)
-    height, width, channels = baseline.shape
 
-    return cv2.warpPerspective(camera_image, h, (width, height))
+    return h
+
+
+def warp_board(camera_image: np.ndarray, homography: np.ndarray = None, debug=False) -> np.ndarray:
+    baseline = cv2.imread(str(here.joinpath("new_baseline_board.png")))
+    homography = homography or find_homography(camera_image, baseline, debug=debug)
+
+    height, width, channels = baseline.shape
+    return cv2.warpPerspective(camera_image, homography, (width, height))
 
 
 def get_square(warped_board: np.ndarray, position: POSITION) -> np.ndarray: