Pownie/advancedskrald
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

pls

Browse Source
This commit is contained in:
= 2019-04-11 09:41:37 +02:00
parent deb083224f
commit 2c94c9a2d5
3 changed files with 16 additions and 17 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

BIN
chess_model_3_pieces.h5 Normal file
View File

Binary file not shown.

8
tensor_classifier.py
View File

@ -1,4 +1,4 @@
from __future__ import absolute_import, division, print_function, unicode_literals #from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf import tensorflow as tf
from tensorflow.python.keras import datasets, layers, models from tensorflow.python.keras import datasets, layers, models
@ -10,7 +10,7 @@ import runner
from main import find_occupied_squares from main import find_occupied_squares
from util import POSITION from util import POSITION
new_model = models.load_model('test_chess_model.h5') 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_1554286488.605142_rank_3.png")
@ -44,7 +44,7 @@ square_3 = square_3 / 255.0
square_4 = square_4 / 255.0 square_4 = square_4 / 255.0
pieces = ['knight', 'rook'] pieces = ['knight', 'rook', 'bishop']
for pos, square in occupied: for pos, square in occupied:
square = cv2.cvtColor(square, cv2.COLOR_BGR2GRAY) square = cv2.cvtColor(square, cv2.COLOR_BGR2GRAY)
@ -54,7 +54,7 @@ for pos, square in occupied:
text_color = 255 text_color = 255
cv2.putText(square, f"{pos} {pieces[int(np.argmax(test))]}", (0, 50), cv2.FONT_HERSHEY_SIMPLEX, fontScale=1, cv2.putText(square, f"{pos} {pieces[int(np.argmax(test))]}", (0, 50), cv2.FONT_HERSHEY_SIMPLEX, fontScale=1,
color=(text_color,) * 3, thickness=3) color=(text_color,) * 3, thickness=3)
cv2.imshow(f"{pos}", square) cv2.imwrite("lel", square)
cv2.waitKey(0) cv2.waitKey(0)

25
tmp/tensor.py
View File

@ -1,7 +1,6 @@
from __future__ import absolute_import, division, print_function, unicode_literals from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf import tensorflow as tf
from tensorflow.python.keras import datasets, layers, models
import glob import glob
import numpy as np import numpy as np
import cv2 import cv2
@ -58,18 +57,18 @@ test_img = np.array(test_img).reshape((len(test_img),width, height, 1))
# Normalize pixel values to be between 0 and 1 # Normalize pixel values to be between 0 and 1
train_images, test_images = training_img / 255.0, test_img / 255.0 train_images, test_images = training_img / 255.0, test_img / 255.0
model = models.Sequential() model = tf.keras.models.Sequential()
model.add(layers.Conv2D(256, (3, 3), activation='relu', input_shape=(width, height, 1))) model.add(tf.keras.layers.Conv2D(256, (3, 3), activation='relu', input_shape=(width, height, 1)))
model.add(layers.MaxPooling2D((2, 2))) model.add(tf.keras.layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu')) model.add(tf.keras.layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2))) model.add(tf.keras.layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu')) model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2))) model.add(tf.keras.layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu')) model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten()) model.add(tf.keras.layers.Flatten())
model.add(layers.Dense(64, activation='relu')) model.add(tf.keras.layers.Dense(64, activation='relu'))
model.add(layers.Dense(3, activation='softmax')) model.add(tf.keras.layers.Dense(3, activation='softmax'))
model.summary() model.summary()
@ -77,7 +76,7 @@ model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy', loss='sparse_categorical_crossentropy',
metrics=['accuracy']) metrics=['accuracy'])
model.fit(train_images, training_labels, epochs=10) model.fit(train_images, training_labels, epochs=3)
test_loss, test_acc = model.evaluate(test_images, test_labels_) test_loss, test_acc = model.evaluate(test_images, test_labels_)