Hm

tmp/tensor.py

@@ -18,19 +18,51 @@ 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)
 
-    # test set
-    for _ in range(5):
-        for filename in glob.glob(f"../training_images/{piece}/*_square/*.png")[-50:]:
-            test_img.append(cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2GRAY))
-            test_labels_.append(piece)
+# training set
+for _ in range(10):
+    for filename in glob.glob(f"../training_images/rook/*_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/knight/*_square/*.png")[:-50]:
+        training_img.append(cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2GRAY))
+        training_labels.append(1)
+
+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(10):
+    for filename in glob.glob(f"../training_images/empty/*_square/*.png")[:-7300]:
+        training_img.append(cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2GRAY))
+        training_labels.append(3)
+
+# test set
+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(0)
+
+# test set
+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(1)
+
+# test set
+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)
+
+# test set
+for _ in range(5):
+    for filename in glob.glob(f"../training_images/empty/*_square/*.png")[-50:]:
+        test_img.append(cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2GRAY))
+        test_labels_.append(3)
 
 width, height = training_img[0].shape
 
@@ -51,7 +83,7 @@ model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='relu'))
 
 model.add(tf.keras.layers.Flatten())
 model.add(tf.keras.layers.Dense(64, activation='relu'))
-model.add(tf.keras.layers.Dense(3, activation='softmax'))
+model.add(tf.keras.layers.Dense(4, activation='softmax'))
 
 model.summary()
 
@@ -66,5 +98,5 @@ test_loss, test_acc = model.evaluate(test_images, test_labels_)
 print(test_acc)
 
 # Save entire model to a HDF5 file
-model.save('chess_model_3_pieces.h5')
+model.save('pls_model.h5')