Neural net magicness

This commit is contained in:
Alexander Munch-Hansen 2019-04-10 22:32:30 +02:00
parent 3e4327daee
commit 2a9c9c06e4
15 changed files with 169 additions and 16 deletions

BIN
classifiers/classifier_empty/.DS_Store vendored Normal file

Binary file not shown.

Binary file not shown.

Binary file not shown.

Binary file not shown.

Binary file not shown.

Binary file not shown.

Binary file not shown.

Binary file not shown.

Binary file not shown.

45
main.py
View File

@ -24,9 +24,15 @@ def identify_piece(image: np.ndarray, position: POSITION, sift: cv2.xfeatures2d_
for piece in OUR_PIECES:
for color in COLOR:
#color = runner.compute_color(file, rank)
#classifier = load_classifier(f"classifiers/neural_net_{piece}/{color}.pkl")
classifier = load_classifier(f"classifiers/classifier_{piece}/{color}.pkl")
features = runner.generate_bag_of_words(image, centers, sift)
prob = classifier.predict_proba(features)
#image = cv2.resize(image, (172, 172))
#data = np.reshape(image, (1, np.product(image.shape)))
#prob = classifier.predict_proba(data)
probs[piece.name][color.name] = prob[0, 1]
print(f"{piece}, {color}, {prob[0, 1]}")
#if prob[0, 1] > best and color == position.color: # can only be best if correct color. Iterating through both colors for debugging only
@ -102,9 +108,9 @@ def remove_most_empties(warped):
for position in POSITION:
counter = 0
src = runner.get_square(warped, position)
width, height, _ = src.shape
src = src[width // 25:, height // 25:]
img_src = runner.get_square(warped, position)
width, height, _ = img_src.shape
src = img_src[width // 25:, height // 25:]
# src = src[:-width//200, :-height//200]
segmentator = cv2.ximgproc.segmentation.createGraphSegmentation(sigma=0.8, k=150, min_size=700)
segment = segmentator.processImage(src)
@ -116,16 +122,23 @@ def remove_most_empties(warped):
masked.mask = mask != i
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)
dst = masked.filled()[top: bottom + 1, left: right + 1]
cv2.imwrite(f"segment_test/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) * 0.942 for x in pls])):
np.max(segment) >= 3 or np.all([x < (164 ** 2) * 0.9469 for x in pls])):
#print(f"{position} is nonempty")
non_empties.append([position, src])
non_empties.append([position, img_src])
empty += 1
#print(64 - empty)
@ -148,21 +161,29 @@ if __name__ == '__main__':
#runner.train_pieces_svm()
#board = cv2.imread("whole_boards/boards_for_empty/board_1554286488.605142_rank_3.png")
board = cv2.imread("whole_boards/boards_for_empty/board_1554286515.323962_rank_3.png")
board = cv2.imread("whole_boards/boards_for_empty/board_1554288891.129901_rank_8.png")
#board = cv2.imread("whole_boards/boards_for_empty/board_1554286515.323962_rank_3.png")
warped = runner.warp_board(board)
tmp = find_occupied_squares(warped)
for pos, square in tmp:
cv2.imshow(f"{pos}", square)
cv2.waitKey(0)
exit()
"""
rook_square = runner.get_square(warped, POSITION.H3)
knight_square = runner.get_square(warped, POSITION.D3)
cv2.imshow("lel", rook_square)
cv2.imshow("lil", knight_square)
rook_out = cv2.Canny(rook_square, 50, 55, L2gradient=True)
#rook_out = cv2.Canny(rook_square, 50, 55, L2gradient=True)
knight_out = cv2.Canny(knight_square, 50, 55, L2gradient=True)
cv2.imshow("lal", rook_out)
cv2.imshow("lul", knight_out)
knight_out_l = cv2.Canny(knight_square, 50, 55, L2gradient=False)
cv2.imshow("lal", knight_out)
cv2.imshow("lul", knight_out_l)
cv2.waitKey(0)
exit()
"""
occupied = find_occupied_squares(warped)
sift = cv2.xfeatures2d.SIFT_create()

View File

@ -9,6 +9,7 @@ from sklearn import cluster, metrics, svm
from sklearn.externals import joblib
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn import neural_network
from util import RANK, POSITION, imwrite, PIECE, COLOR, Squares, OUR_PIECES
@ -108,11 +109,28 @@ def train_pieces_svm() -> None:
current_weight = len(glob.glob(f"training_images/{piece}/{color}_square/*.png"))
print(f"Training for piece: {piece}")
X, Y = load_training_data(piece, color)
classifier = svm.SVC(C=10, gamma=0.01, class_weight={0: 45, 1: 1}, probability=True)
classifier = svm.SVC(C=10, gamma=0.01, class_weight={0: 15, 1: 0.8}, probability=True)
classifier.fit(X, Y)
joblib.dump(classifier, f"classifiers/classifier_{piece}/{color}.pkl")
def train_pieces_svm_canny() -> None:
for square_color in COLOR:
X = []
Y = []
for piece in OUR_PIECES + (PIECE.EMPTY,):
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])
X.append(y)
Y.append(piece == PIECE.EMPTY)
classifier = make_pipeline(StandardScaler(),
svm.SVC(C=10.0, gamma=0.01, probability=True))
classifier.fit(np.array(X), np.array(Y))
joblib.dump(classifier, f"classifiers/classifier_empty/white_piece_on_{square_color}_square.pkl")
def warp_board(camera_image, debug_image=None) -> np.ndarray:
baseline = cv2.imread("new_baseline_board.png")
@ -181,10 +199,10 @@ def get_square(warped_board: np.ndarray, position: POSITION) -> np.ndarray:
side = int(width * 0.045)
size = width - 2 * side
square_size = size // 8
padding = 0
padding = 2
x1 = side + (square_size * (position.file - 1))
x2 = x1 + square_size
x2 = x1 + square_size + padding
y1 = max(0, side + (square_size * (8 - position.rank)) - padding) # 8 - rank because chessboard is from 8 to 1
y2 = min(width, y1 + square_size + padding)
@ -207,5 +225,47 @@ def save_empty_fields(warped_board: np.ndarray, skip_rank: RANK = None) -> None:
imwrite(f"training_images/empty/{position.color}_square/training_{position}_{datetime.utcnow().timestamp()}.png", square)
def load_data_nn(spec_piece, color):
X = None
Y = None
for piece in OUR_PIECES:
piece_class = int(spec_piece == piece)
for filename in glob.glob(f"training_images/{piece}/{color}_square/*.png"):
image = cv2.imread(filename)
data = np.reshape(image, (1, np.product(image.shape)))
if X is None:
if piece_class == 1:
for _ in range(10):
X = np.array(data)
Y = np.array([piece_class])
else:
for _ in range(5):
X = np.array(data)
Y = np.array([piece_class])
else:
if piece_class == 1:
for _ in range(10):
X = np.vstack((X, data))
Y = np.vstack((Y, [piece_class]))
else:
for _ in range(5):
X = np.vstack((X, data))
Y = np.vstack((Y, [piece_class]))
return X, Y
def train_nn():
for piece in OUR_PIECES:
for color in COLOR:
X, Y = load_data_nn(piece, color)
classifier = neural_network.MLPClassifier(hidden_layer_sizes=256)
classifier.fit(X, Y)
joblib.dump(classifier, f"classifiers/neural_net_{piece}/{color}.pkl")
if __name__ == '__main__':
train_empty_or_piece_hist()
train_nn()

72
tensor_classifier.py Normal file
View File

@ -0,0 +1,72 @@
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow.python.keras import datasets, layers, models
import glob
import numpy as np
import cv2
import runner
from main import find_occupied_squares
from util import POSITION
new_model = models.load_model('test_chess_model.h5')
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)
occupied = find_occupied_squares(warped)
pos_1 = POSITION.C5
pos_2 = POSITION.D5
pos_3 = POSITION.G5
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']
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.imshow(f"{pos}", 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)
"""

BIN
test_chess_model.h5 Normal file

Binary file not shown.