Now we works and we still have type hints so thats nice

README.md

@@ -1,6 +1,6 @@
 ## Test Adapter:
 ```bash
-base64 --wrap=0 < whole_boards/board_118_1554110522.620303_.png| python3 adapter.py
+base64 --wrap=0 < whole_boards/boards_for_empty/board_1554285167.655788_rank_5.png | python3 adapter.py
 ```
 
 ## Build OpenCV

adapter.py

@@ -5,21 +5,29 @@ import sys
 import cv2
 import numpy as np
 
-from runner import find_homography
+from main import find_occupied_squares
-
+from runner import find_homography, warp_board
 # Load base64 encoded image from stdin
+from tensor_classifier import predict_board
+
 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.COLOR_BGR2RGB)
 camera_img = cv2.cvtColor(camera_img, cv2.COLOR_BGR2RGB)
 
-# Find keypoints in image and pass them back to unity
+
+# def do_everything:
 homography = find_homography(camera_img)
+warped_board = warp_board(camera_img, homography)
+occupied_squares = find_occupied_squares(warped_board)
+board = predict_board(occupied_squares)
+
 
 # Finally, output to stdout for unity to read
 result = {
     "homography": homography.tolist(),
+    "board": board.to_array,
 }
 
 print(json.dumps(result))

main.py

@@ -102,6 +102,7 @@ def predict_empty(square: np.ndarray, position: POSITION) -> PIECE:
 
     return None
 
+
 def remove_most_empties(warped):
     empty = 0
     non_empties = []
@@ -123,12 +124,6 @@ def remove_most_empties(warped):
 
             y, x = np.where(segment == i)
 
-
-            if position == POSITION.E8:
-                print(np.max(segment))
-                print(len(y))
-
-
             pls.append(len(y))
             top, bottom, left, right = min(y), max(y), min(x), max(x)
 
@@ -146,13 +141,13 @@ def remove_most_empties(warped):
     return non_empties
 
 
-def find_occupied_squares(warped: np.ndarray) -> List[Tuple[POSITION, np.ndarray]]:
+def find_occupied_squares(warped: np.ndarray) -> Squares:
     non_empties = remove_most_empties(warped)
 
-    completely_non_empties = []
+    completely_non_empties = {}
     for position, square in non_empties:
         if predict_empty(square, position) != PIECE.EMPTY:
-            completely_non_empties.append((position, square))
+            completely_non_empties[position] = square
 
     return completely_non_empties
 

opencv_video.py

@@ -10,11 +10,11 @@ cap = cv2.VideoCapture(0)
 color = COLOR.BLACK
 rank = RANK.EIGHT
 pieces = {
-    PIECE.rook: [POSITION(FILE.A, rank), POSITION(FILE.F, rank)],
+    PIECE.rook: [POSITION((FILE.A, rank)), POSITION((FILE.F, rank))],
-    PIECE.knight: [POSITION(FILE.E, rank), POSITION(FILE.H, rank)],
+    PIECE.knight: [POSITION((FILE.E, rank)), POSITION((FILE.H, rank))],
-    PIECE.bishop: [POSITION(FILE.C, rank), POSITION(FILE.D, rank)],
+    PIECE.bishop: [POSITION((FILE.C, rank)), POSITION((FILE.D, rank))],
-    PIECE.queen: [POSITION(FILE.B, rank)],
+    PIECE.queen: [POSITION((FILE.B, rank))],
-    PIECE.king: [POSITION(FILE.G, rank)],
+    PIECE.king: [POSITION((FILE.G, rank))],
 }
 
 while True:

runner.py

@@ -89,6 +89,7 @@ def train_empty_or_piece_hist() -> None:
         X = []
         Y = []
         for piece in OUR_PIECES + (PIECE.EMPTY,):
+            print(f"training training_images/{piece}/{square_color}_square/*.png")
             for filename in glob.glob(f"training_images/{piece}/{square_color}_square/*.png"):
                 img = cv2.imread(filename)
                 y, x = np.histogram(img.ravel(), bins=32, range=[0, 256])
@@ -209,7 +210,8 @@ def find_homography(camera_image: np.ndarray,
 
 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)
+    if homography is None:
+        homography = find_homography(camera_image, baseline, debug=debug)
 
     height, width, channels = baseline.shape
     return cv2.warpPerspective(camera_image, homography, (width, height))
@@ -289,4 +291,5 @@ def train_nn():
 
 
 if __name__ == '__main__':
-    train_nn()
+    #train_nn()
+    train_empty_or_piece_hist()

tensor_classifier.py

@@ -1,71 +1,27 @@
-#from __future__ import absolute_import, division, print_function, unicode_literals
-
-import tensorflow as tf
-from tensorflow.python.keras import datasets, layers, models
-import glob
-import numpy as np
 import cv2
+import numpy as np
+from tensorflow.python.keras import models
 
-import runner
+from util import PIECE, Squares, Board
-from main import find_occupied_squares
-from util import POSITION
 
 new_model = models.load_model('chess_model_3_pieces.h5')
-new_model.summary()
+#new_model.summary()
 
 #board = cv2.imread("whole_boards/boards_for_empty/board_1554286488.605142_rank_3.png")
 #board = cv2.imread("whole_boards/boards_for_empty/board_1554285167.655788_rank_5.png")
 board = cv2.imread("whole_boards/boards_for_empty/board_1554288891.129901_rank_8.png")
 
 
-warped = runner.warp_board(board)
+def predict_board(occupied_squares: Squares) -> Board:
-
+    board = Board()
-occupied = find_occupied_squares(warped)
+    for pos, square in occupied_squares.items():
-
+        square = cv2.cvtColor(square, cv2.COLOR_BGR2GRAY)
-pos_1 = POSITION.C5
+        width, height = square.shape
-pos_2 = POSITION.D5
+        square = square / 255.0
-pos_3 = POSITION.G5
+        test = new_model.predict(np.array(square).reshape((-1, width, height, 1)))
-pos_4 = POSITION.H5
-
-square_1 = runner.get_square(warped, pos_1)
-square_2 = runner.get_square(warped, pos_2)
-square_3 = runner.get_square(warped, pos_3)
-square_4 = runner.get_square(warped, pos_4)
-
-square_1 = cv2.cvtColor(square_1, cv2.COLOR_BGR2GRAY)
-square_2 = cv2.cvtColor(square_2, cv2.COLOR_BGR2GRAY)
-square_3 = cv2.cvtColor(square_3, cv2.COLOR_BGR2GRAY)
-square_4 = cv2.cvtColor(square_4, cv2.COLOR_BGR2GRAY)
-
-width, height = square_1.shape
-square_1 = square_1 / 255.0
-square_2 = square_2 / 255.0
-square_3 = square_3 / 255.0
-square_4 = square_4 / 255.0
-
-
-pieces = ['knight', 'rook', 'bishop']
-
-for pos, square in occupied:
-    square = cv2.cvtColor(square, cv2.COLOR_BGR2GRAY)
-    width, height = square.shape
-    square = square / 255.0
-    test = new_model.predict(np.array(square).reshape((-1, width, height, 1)))
-    text_color = 255
-    cv2.putText(square, f"{pos} {pieces[int(np.argmax(test))]}", (0, 50), cv2.FONT_HERSHEY_SIMPLEX, fontScale=1,
-                color=(text_color,) * 3, thickness=3)
-    cv2.imwrite("lel", square)
-
-cv2.waitKey(0)
-
-"""
-for pos, square in [(pos_1, square_1), (pos_2, square_2), (pos_3, square_3), (pos_4, square_4)]:
-    test = new_model.predict(np.array(square).reshape((-1, width, height, 1)))
-    print(f"{pos}: {np.argmax(test)}")
-    text_color = 255
-    cv2.putText(square, f"{pos} {pieces[int(np.argmax(test))]}", (0, 50), cv2.FONT_HERSHEY_SIMPLEX, fontScale=1,
-                color=(text_color,) * 3, thickness=3)
-    cv2.imshow(f"{pos}", square)
-    """
 
+        #cv2.putText(square, f"{pos} {PIECE(int(np.argmax(test)))}", (0, 50), cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, color=(255,) * 3, thickness=3)
+        #cv2.imwrite("lel", square)
+        board[pos] = PIECE(int(np.argmax(test)))
 
+    return board

tmp/tensor.py

@@ -1,52 +1,35 @@
 from __future__ import absolute_import, division, print_function, unicode_literals
 
-import tensorflow as tf
 import glob
-import numpy as np
+
 import cv2
+import numpy as np
+import tensorflow as tf
 
-#(train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data()
+# exit()
+from util import OUR_PIECES
 
-#print(train_images[0])
+# (train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data()
+# print(train_images[0])
 
-#exit()
 training_img = []
 training_labels = []
 
 test_img = []
 test_labels_ = []
 
+for piece in OUR_PIECES:
+    # training set
+    for _ in range(10):
+        for filename in glob.glob(f"../training_images/{piece}/*_square/*.png")[:-50]:
+            training_img.append(cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2GRAY))
+            training_labels.append(piece)
 
-for _ in range(10):
+    # test set
-    for filename in glob.glob(f"../training_images/rook/*_square/*.png")[:-50]:
+    for _ in range(5):
-        training_img.append(cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2GRAY))
+        for filename in glob.glob(f"../training_images/{piece}/*_square/*.png")[-50:]:
-        training_labels.append(1)
+            test_img.append(cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2GRAY))
-
+            test_labels_.append(piece)
-
-for _ in range(10):
-    for filename in glob.glob(f"../training_images/knight/*_square/*.png")[:-50]:
-        training_img.append(cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2GRAY))
-        training_labels.append(0)
-
-for _ in range(10):
-    for filename in glob.glob(f"../training_images/bishop/*_square/*.png")[:-50]:
-        training_img.append(cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2GRAY))
-        training_labels.append(2)
-
-for _ in range(5):
-    for filename in glob.glob(f"../training_images/rook/*_square/*.png")[-50:]:
-        test_img.append(cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2GRAY))
-        test_labels_.append(1)
-
-for _ in range(5):
-    for filename in glob.glob(f"../training_images/bishop/*_square/*.png")[-50:]:
-        test_img.append(cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2GRAY))
-        test_labels_.append(2)
-
-for _ in range(5):
-    for filename in glob.glob(f"../training_images/knight/*_square/*.png")[-50:]:
-        test_img.append(cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2GRAY))
-        test_labels_.append(0)
 
 
 width, height = training_img[0].shape

util.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 from enum import Enum
 from functools import lru_cache
 from pathlib import Path
-from typing import NamedTuple, Dict, Tuple
+from typing import NamedTuple, Dict, Tuple, List
 
 import cv2
 import numpy as np
@@ -19,23 +19,24 @@ class COLOR(Enum):
 
 
 class PIECE(Enum):
-    PAWN = "pawn"
+    KNIGHT = 0
-    ROOK = "rook"
+    ROOK = 1
-    KNIGHT = "knight"
+    BISHOP = 2
-    BISHOP = "bishop"
+    PAWN = 3
-    QUEEN = "queen"
+    QUEEN = 4
-    KING = "king"
+    KING = 5
-    EMPTY = "empty"
+    EMPTY = 6
 
     def __str__(self) -> str:
-        return self.value
+        return self.name.lower()
 
 
 PieceAndColor = Tuple[PIECE, COLOR]
 
 OUR_PIECES = (
-    PIECE.ROOK,
     PIECE.KNIGHT,
+    PIECE.ROOK,
+    PIECE.BISHOP,
 )
 
 
@@ -85,6 +86,11 @@ Squares = Dict[POSITION, np.ndarray]
 class Board(Dict[POSITION, PIECE]):
     """Board is a dict mapping positions to a piece, i.e. a board configuration after all image processing"""
 
+    @property
+    def to_array(self) -> List[List[int]]:
+        return [[self.get(POSITION((file, rank)), PIECE.EMPTY).value for file in FILE]
+                for rank in RANK]
+
 
 def imwrite(*args, **kwargs):
     Path(args[0]).parent.mkdir(parents=True, exist_ok=True)