From 241c4346725204a7c29f25d4b19a6095d253c9d4 Mon Sep 17 00:00:00 2001
From: "Casper V. Kristensen" <c@spervk.com>
Date: Wed, 24 Apr 2019 09:54:37 +0200
Subject: [PATCH] Maybe faster maybe not.

---
 main.py | 16 ++++------------
 1 file changed, 4 insertions(+), 12 deletions(-)

diff --git a/main.py b/main.py
index 6db9d81e..696bdc26 100644
--- a/main.py
+++ b/main.py
@@ -1,7 +1,5 @@
 import sys
 import warnings
-from datetime import datetime
-from typing import List, Tuple
 
 import cv2
 import matplotlib.pyplot as plt
@@ -80,7 +78,7 @@ def test_entire_board() -> None:
     print(board)
 
 
-def predict_empty(square: np.ndarray, position: POSITION) -> PIECE:
+def predict_empty(square: np.ndarray, position: POSITION) -> bool:
     y, x = np.histogram(square.ravel(), bins=32, range=[0, 256])
 
     left, right = x[:-1], x[1:]
@@ -91,16 +89,10 @@ def predict_empty(square: np.ndarray, position: POSITION) -> PIECE:
     plt.xlabel(f"{position}")
     #plt.show()
 
-
-    #for color in COLOR:
     empty_classifier = load_classifier(f"classifiers/classifier_empty/white_piece_on_{position.color}_square.pkl")
     prob = empty_classifier.predict_proba(np.array(y).reshape(1, -1))
     #print(f"{position}, {position.color}: {prob[0, 1]}")
-    if prob[0, 1] > 0.95:
-    #    print(f"{position} is empty")
-        return PIECE.EMPTY
-
-    return None
+    return prob[0, 1] > 0.95
 
 
 def remove_most_empties(warped):
@@ -128,7 +120,7 @@ def remove_most_empties(warped):
             top, bottom, left, right = min(y), max(y), min(x), max(x)
 
             dst = masked.filled()[top: bottom + 1, left: right + 1]
-            cv2.imwrite(f"tmp_seg/segment_{datetime.utcnow().timestamp()}_{position}.png", dst)
+            #cv2.imwrite(f"tmp_seg/segment_{datetime.utcnow().timestamp()}_{position}.png", dst)
 
         if np.max(segment) > 0 and not np.all([x < (164 ** 2) * 0.2 for x in pls]) and (
                 np.max(segment) >= 3 or np.all([x < (164 ** 2) * 0.9469 for x in pls])):
@@ -146,7 +138,7 @@ def find_occupied_squares(warped: np.ndarray) -> Squares:
 
     completely_non_empties = {}
     for position, square in non_empties:
-        if predict_empty(square, position) != PIECE.EMPTY:
+        if not predict_empty(square, position):
             completely_non_empties[position] = square
 
     return completely_non_empties