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.gitignore
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.gitignore
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# Created by https://www.gitignore.io/api/pycharm+all
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# Edit at https://www.gitignore.io/?templates=pycharm+all
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### PyCharm+all ###
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# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm
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# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
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# User-specific stuff
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.idea/**/workspace.xml
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.idea/**/tasks.xml
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.idea/**/usage.statistics.xml
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.idea/**/dictionaries
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.idea/**/shelf
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# Generated files
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.idea/**/contentModel.xml
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# Sensitive or high-churn files
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.idea/**/dataSources/
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.idea/**/dataSources.ids
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.idea/**/dataSources.local.xml
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.idea/**/sqlDataSources.xml
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.idea/**/dynamic.xml
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.idea/**/uiDesigner.xml
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.idea/**/dbnavigator.xml
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# Gradle
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.idea/**/gradle.xml
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.idea/**/libraries
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# Gradle and Maven with auto-import
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# When using Gradle or Maven with auto-import, you should exclude module files,
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# since they will be recreated, and may cause churn. Uncomment if using
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# auto-import.
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# .idea/modules.xml
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# .idea/*.iml
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# .idea/modules
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# CMake
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cmake-build-*/
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# Mongo Explorer plugin
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.idea/**/mongoSettings.xml
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# File-based project format
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*.iws
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# IntelliJ
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out/
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# mpeltonen/sbt-idea plugin
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.idea_modules/
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# JIRA plugin
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atlassian-ide-plugin.xml
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# Cursive Clojure plugin
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.idea/replstate.xml
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# Crashlytics plugin (for Android Studio and IntelliJ)
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com_crashlytics_export_strings.xml
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crashlytics.properties
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crashlytics-build.properties
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fabric.properties
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# Editor-based Rest Client
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.idea/httpRequests
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# Android studio 3.1+ serialized cache file
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.idea/caches/build_file_checksums.ser
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# JetBrains templates
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**___jb_tmp___
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### PyCharm+all Patch ###
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# Ignores the whole .idea folder and all .iml files
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# See https://github.com/joeblau/gitignore.io/issues/186 and https://github.com/joeblau/gitignore.io/issues/360
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.idea/
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# Reason: https://github.com/joeblau/gitignore.io/issues/186#issuecomment-249601023
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*.iml
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modules.xml
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.idea/misc.xml
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*.ipr
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# Sonarlint plugin
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.idea/sonarlint
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# End of https://www.gitignore.io/api/pycharm+all
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590
main.py
590
main.py
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@ -1,82 +1,59 @@
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from functools import lru_cache
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import cv2
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import runner
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from sklearn.externals import joblib
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import numpy as np
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import operator
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from matplotlib import pyplot as plt
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import glob
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import os
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import heapq
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import math
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from datetime import datetime
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import sys
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from collections import defaultdict
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from datetime import datetime
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import numpy as np
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import runner
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from util import load_classifier, PIECE, COLOR, POSITION, Board, Squares, PieceAndColor
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np.set_printoptions(threshold=sys.maxsize)
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pieces = ['rook', 'knight']
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#pieces = ['rook', 'knight']
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#piece_to_symbol = {'rook': 1, 'knight': 2, 'empty': 0}
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piece_to_symbol = {'rook': 1, 'knight': 2}
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colors = ['black', 'white']
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def classify(image, sift : cv2.xfeatures2d_SIFT, file, rank, empty_bias=False):
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def identify_piece(image: np.ndarray, sift : cv2.xfeatures2d_SIFT, empty_bias=False) -> PieceAndColor:
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centers = np.load("training_data/centers.npy")
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probs = {'rook': {'black': 0, 'white': 0}, 'knight': {'black': 0, 'white': 0}, 'empty': {'black': 0, 'white': 0}}
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#probs = {'rook': 0, 'knight': 0, 'empty': 0}
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for piece in pieces:
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for color in colors:
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probs = defaultdict(lambda: defaultdict(float))
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best = 0
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best_piece = best_color = None
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for piece in PIECE:
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for color in COLOR:
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#color = runner.compute_color(file, rank)
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classifier = joblib.load(f"classifiers/classifier_{piece}/{color}.pkl")
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classifier = load_classifier(f"classifiers/classifier_{piece}/{color}.pkl")
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features = runner.generate_bag_of_words(image, centers, sift)
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prob = classifier.predict_proba(features)
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probs[piece][color] = prob[0, 1]
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if prob[0, 1] > best:
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best_piece, best_color = piece, color
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print(probs)
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if empty_bias:
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probs['empty'] *= 1.2
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probs[PIECE.EMPTY] *= 1.2
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return probs
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return best_piece, best_color
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def pred_test(file, rank, mystery_image=None, empty_bias=False):
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def pred_test(position: POSITION, mystery_image=None, empty_bias=False):
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sift = cv2.xfeatures2d.SIFT_create()
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if mystery_image is None:
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mystery_image = cv2.imread("training_images/rook/white/rook_training_D4_2.png")
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probs = classify(mystery_image, sift, file, rank, empty_bias=empty_bias)
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probs = classify(mystery_image, sift, empty_bias=empty_bias)
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return probs
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def pre_process_and_train():
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def pre_process_and_train() -> None:
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runner.do_pre_processing()
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runner.train_pieces_svm()
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def build_board_from_dict(board_dict : dict):
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def build_board_from_squares(squares: Squares) -> Board:
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sift = cv2.xfeatures2d.SIFT_create()
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board = [[0]*8 for _ in range(8)]
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board = Board()
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counter = 0
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for idx, value in enumerate(board_dict.values()):
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probs = classify(value, sift)
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likely_piece = max(probs.items(), key=operator.itemgetter(1))[0]
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symbol = piece_to_symbol[likely_piece]
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column = idx // 8
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row = (idx % 7)
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board[row][column] = symbol
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print(probs)
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if likely_piece != 'empty':
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for position, square in squares.values():
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likely_piece = identify_piece(square, sift)
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board[position] = likely_piece
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if likely_piece != PIECE.EMPTY:
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counter += 1
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print(counter)
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@ -84,333 +61,30 @@ def build_board_from_dict(board_dict : dict):
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return board
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def test_entire_board() -> None:
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board_img = cv2.imread("homo_pls_fuck.jpg")
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warped = runner.warp_board(board_img)
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def detect_using_nn(spec_image):
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probs = {'rook': 0, 'knight': 0}
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for piece in pieces:
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piece_class = piece_to_symbol[piece]
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win_size = (64, 64)
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classifier = joblib.load("classifiers/neural_net_" + piece + ".pkl")
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spec_image = cv2.resize(spec_image, (64, 128))
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features = np.reshape(spec_image, (1, np.product(spec_image.shape)))
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prob = classifier.predict_proba(features)
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print(piece)
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print(prob[0,1])
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def test_entire_board():
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board = cv2.imread("homo_pls_fuck.jpg")
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warped = runner.warp_board(board)
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board_dict = runner.get_squares(warped)
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board = build_board_from_dict(board_dict)
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squares = runner.get_squares(warped)
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board = build_board_from_squares(squares)
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print(board)
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def lel_test():
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# img = cv2.imread('training_images/rook/white/rook_training_D4_2.png')
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def predict(square: np.ndarray, position: POSITION) -> PIECE:
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y, x = np.histogram(square.ravel(), bins=256, range=[0, 256])
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counter = 0
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for filename in glob.glob(os.path.join("training_images", "empty", "*", "*.png")):
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img = cv2.imread(filename)
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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ret = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 3, 3)
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# binarize the image
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#ret, bw = cv2.threshold(gray, 100, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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# find connected components
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connectivity = 4
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nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(ret, connectivity, cv2.CV_32S)
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sizes = stats[1:, -1]
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nb_components = nb_components - 1
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min_size = 250 # threshhold value for objects in scene
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img2 = np.zeros((img.shape), np.uint8)
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for i in range(0, nb_components + 1):
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# use if sizes[i] >= min_size: to identify your objects
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color = np.random.randint(255, size=3)
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# draw the bounding rectangele around each object
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cv2.rectangle(img2, (stats[i][0], stats[i][1]), (stats[i][0] + stats[i][2], stats[i][1] + stats[i][3]),
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(0, 255, 0), 2)
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img2[output == i + 1] = color
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#print(nb_components+1)
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if nb_components+1 >= 4:
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counter += 1
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print(filename)
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cv2.imshow("lel", img2)
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cv2.waitKey(0)
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print(counter)
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def selective_search(image, use_fast=False, use_slow=False):
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# speed-up using multithreads
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cv2.setUseOptimized(True)
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cv2.setNumThreads(4)
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if type(image) == str:
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# read image
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im = cv2.imread(image)
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else:
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im = image
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# resize image
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#newHeight = 200
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#newWidth = int(im.shape[1] * 150 / im.shape[0])
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#im = cv2.resize(im, (newWidth, newHeight))
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#im = cv2.imread(image)
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#lel, im = cv2.threshold(im, 128, 255, cv2.THRESH_BINARY)
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# create Selective Search Segmentation Object using default parameters
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ss = cv2.ximgproc.segmentation.createSelectiveSearchSegmentation()
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# set input image on which we will run segmentation
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ss.setBaseImage(im)
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# Switch to fast but low recall Selective Search method
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ss.switchToSingleStrategy()
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if (use_fast):
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ss.switchToSelectiveSearchFast()
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# Switch to high recall but slow Selective Search method
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elif (use_slow):
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ss.switchToSelectiveSearchQuality()
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# run selective search segmentation on input image
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rects = ss.process()
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#print('Total Number of Region Proposals: {}'.format(len(rects)))
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# number of region proposals to show
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numShowRects = 150
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# increment to increase/decrease total number
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# of reason proposals to be shown
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increment = 1
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best_proposals = []
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while True:
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# create a copy of original image
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# itereate over all the region proposals
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for i, rect in enumerate(rects):
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imOut = im.copy()
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# draw rectangle for region proposal till numShowRects
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if (i < numShowRects):
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x, y, w, h = rect
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# cv2.rectangle(imOut, (x, y), (x + w, y + h), (0, 255, 0), 1, cv2.LINE_AA)
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# size = (max(w, x) - min(w, x)) * ((max(h, y) - min(h, y)))
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top_left = (x,y)
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bottom_left = (x, y+h)
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top_right = (x+w, y)
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bottom_right = (x+w, y+h)
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rect_width = bottom_right[0] - bottom_left[0]
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rect_height = bottom_right[1] - top_right[1]
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size = rect_width * rect_height
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#print(f"({x}, {y}), ({w}, {h})\n Of size: { size }")
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#cv2.rectangle(imOut, (x, y), (x + w, y + h), (0, 255, 0), 1, cv2.LINE_AA)
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#cv2.imshow("lel", imOut)
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#cv2.waitKey(0)
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best_proposals.append((rect, size))
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#if size > biggest_size:
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# biggest_rect = (x, y, w, h)
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# biggest_size = size
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# print(f"New biggest: \n({x}, {y}), ({w}, {h})\nOf size: {biggest_size}")
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else:
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break
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height, width, channels = im.shape
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center_x = width // 2
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center_y = (height // 2)+5
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dists = []
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#print(f"Amount of best proposals:\n{len(best_proposals)}")
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#print(f"lel: {len(heapq.nlargest(10, best_proposals, key=lambda x: x[1]))}")
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for i in heapq.nlargest(10, best_proposals, key=lambda x: x[1]):
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width, height, channels = im.shape
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#print(width * height)
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#print(i[1])
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x, y, w, h = i[0]
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if i[1] <= (width*height)*0.8 and i[1] > (width*height)*0.25:
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imCop = imOut.copy()
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#cv2.rectangle(imCop, (x, y), (x + w, y + h), (0, 255, 0), 2, cv2.LINE_AA)
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#cv2.imshow("lel", imCop)
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#cv2.waitKey(0)
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#cv2.rectangle(imCop, (x, y), (x + w, y + h), (0, 255, 0), 4, cv2.LINE_AA)
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top_left = (x,y)
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bottom_left = (x, y+h)
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top_right = (x+w, y)
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bottom_right = (x+w, y+h)
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box_center_x = (top_left[0]+bottom_left[0]+top_right[0]+bottom_right[0]) // 4
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box_center_y = (top_left[1]+bottom_left[1]+top_right[1]+bottom_right[1]) // 4
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#print(f"{box_center_x}, {box_center_y}, {center_x}, {center_y}")
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dist = (center_x - box_center_x) ** 2 + (center_y - box_center_y) ** 2
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print(dist)
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dists.append([i, dist])
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cv2.drawMarker(imCop, position=(x+w, h+y), color=(255, 0, 0), thickness=3)
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cv2.drawMarker(imCop, position=(x+w, y), color=(255, 0, 0), thickness=3)
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cv2.drawMarker(imCop, position=(x, y), color=(255, 0, 0), thickness=3)
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cv2.drawMarker(imCop, position=(x, y+h), color=(255, 0, 0), thickness=3)
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cv2.drawMarker(imCop, position=(box_center_x, box_center_y), color=(0, 255, 0), thickness=3)
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cv2.drawMarker(imCop, position=(center_x, center_y), color=(0, 0, 255), thickness=3)
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#cv2.imshow("lel", imCop)
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#cv2.waitKey(0)
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#print("-------"*5)
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for pls in dists:
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imCop = imOut.copy()
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x, y, w, h = pls[0][0]
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#print(x,y,w,h)
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#print(pls[1])
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top_left = (x, y)
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bottom_left = (x, y + h)
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top_right = (x + w, y)
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bottom_right = (x + w, y + h)
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cv2.drawMarker(imCop, position=(x + w, h + y), color=(255, 0, 0), thickness=3)
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cv2.drawMarker(imCop, position=(x + w, y), color=(255, 0, 0), thickness=3)
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cv2.drawMarker(imCop, position=(x, y), color=(255, 0, 0), thickness=3)
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cv2.drawMarker(imCop, position=(x, y + h), color=(255, 0, 0), thickness=3)
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box_center_x = (top_left[0] + bottom_left[0] + top_right[0] + bottom_right[0]) // 4
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box_center_y = (top_left[1] + bottom_left[1] + top_right[1] + bottom_right[1]) // 4
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cv2.drawMarker(imCop, position=(box_center_x, box_center_y), color=(0, 255, 0), thickness=3)
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cv2.drawMarker(imCop, position=(center_y, center_x), color=(0, 0, 255), thickness=3)
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cv2.rectangle(imCop, (x, y), (x + w, y + h), (0, 255, 0), 2, cv2.LINE_AA)
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#cv2.imshow("lel", imCop)
|
||||
#cv2.waitKey(0)
|
||||
|
||||
imCop = imOut.copy()
|
||||
|
||||
|
||||
|
||||
|
||||
best = heapq.nsmallest(1, dists, key=lambda x: x[1])
|
||||
|
||||
if (len(best) == 0):
|
||||
return ((0, 0), (0, height), (width, 0), (width, height))
|
||||
|
||||
x, y, w, h = best[0][0][0]
|
||||
cv2.rectangle(imCop, (x, y), (x + w, y + h), (0, 255, 0), 4, cv2.LINE_AA)
|
||||
|
||||
top_left = (x, y)
|
||||
bottom_left = (x, y + h)
|
||||
top_right = (x + w, y)
|
||||
bottom_right = (x + w, y + h)
|
||||
|
||||
cv2.drawMarker(imCop, position=(x + w, h + y), color=(255, 0, 0), thickness=3)
|
||||
cv2.drawMarker(imCop, position=(x + w, y), color=(255, 0, 0), thickness=3)
|
||||
cv2.drawMarker(imCop, position=(x, y), color=(255, 0, 0), thickness=3)
|
||||
cv2.drawMarker(imCop, position=(x, y + h), color=(255, 0, 0), thickness=3)
|
||||
|
||||
box_center_x = (top_left[0] + bottom_left[0] + top_right[0] + bottom_right[0]) // 4
|
||||
box_center_y = (top_left[1] + bottom_left[1] + top_right[1] + bottom_right[1]) // 4
|
||||
|
||||
cv2.drawMarker(imCop, position=(box_center_x, box_center_y), color=(0, 255, 0), thickness=3)
|
||||
|
||||
cv2.drawMarker(imCop, position=(center_x, center_y), color=(0, 0, 255), thickness=3)
|
||||
|
||||
|
||||
#cv2.imshow("lel", imCop)
|
||||
#cv2.waitKey(0)
|
||||
return (top_left, bottom_left, top_right, bottom_right)
|
||||
|
||||
# show output
|
||||
cv2.imshow("Output", imOut)
|
||||
|
||||
# record key press
|
||||
k = cv2.waitKey(0) & 0xFF
|
||||
|
||||
# m is pressed
|
||||
if k == 109:
|
||||
# increase total number of rectangles to show by increment
|
||||
numShowRects += increment
|
||||
# l is pressed
|
||||
elif k == 108 and numShowRects > increment:
|
||||
# decrease total number of rectangles to show by increment
|
||||
numShowRects -= increment
|
||||
# q is pressed
|
||||
elif k == 113:
|
||||
break
|
||||
# close image show window
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
|
||||
def predict(square, file, rank):
|
||||
square_color = runner.compute_color(file, rank)
|
||||
|
||||
y, x = np.histogram(square.ravel(), bins=256, range=[0, 256]) # TODO: Maybe img.ravel() ?
|
||||
|
||||
for color in ['black', 'white']:
|
||||
for color in COLOR:
|
||||
empty_classifier = load_classifier(f"classifiers/classifier_empty/white_piece_on_{color}_square.pkl")
|
||||
prob = empty_classifier.predict_proba(np.array(y).reshape(1, -1))
|
||||
print(f"{file}{rank}, {color}: {prob[0, 1]}")
|
||||
if prob[0, 1] > 0.5:
|
||||
return 'empty'
|
||||
return PIECE.EMPTY
|
||||
|
||||
return None
|
||||
|
||||
|
||||
|
||||
@lru_cache()
|
||||
def load_classifier(filename):
|
||||
print(f"loading {filename}")
|
||||
return joblib.load(filename)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
board = cv2.imread("whole_boards/boards_for_empty/board_1554286488.605142_.png")
|
||||
board = cv2.imread("whole_boards/boards_for_empty/board_1554286488.605142_rank_3.png")
|
||||
warped = runner.warp_board(board)
|
||||
|
||||
empty = 0
|
||||
|
|
@ -421,58 +95,53 @@ if __name__ == '__main__':
|
|||
|
||||
non_empties = []
|
||||
|
||||
for file in files:
|
||||
for rank in ranks:
|
||||
counter = 0
|
||||
for position in POSITION:
|
||||
counter = 0
|
||||
|
||||
src = runner.get_square(warped, file, rank)
|
||||
width, height, _ = src.shape
|
||||
src = 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)
|
||||
src = runner.get_square(warped, position)
|
||||
width, height, _ = src.shape
|
||||
src = 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)
|
||||
|
||||
mask = segment.reshape(list(segment.shape) + [1]).repeat(3, axis=2)
|
||||
masked = np.ma.masked_array(src, fill_value=0)
|
||||
pls = []
|
||||
for i in range(np.max(segment)):
|
||||
masked.mask = mask != i
|
||||
mask = segment.reshape(list(segment.shape) + [1]).repeat(3, axis=2)
|
||||
masked = np.ma.masked_array(src, fill_value=0)
|
||||
pls = []
|
||||
for i in range(np.max(segment)):
|
||||
masked.mask = mask != i
|
||||
|
||||
y, x = np.where(segment == i)
|
||||
y, x = np.where(segment == i)
|
||||
|
||||
top, bottom, left, right = min(y), max(y), min(x), max(x)
|
||||
top, bottom, left, right = min(y), max(y), min(x), max(x)
|
||||
|
||||
dst = masked.filled()[top: bottom + 1, left: right + 1]
|
||||
lel = (bottom - top) * (right - left)
|
||||
#print(f"this is lel: {lel} ")
|
||||
#print(f"this is meh: {np.sum(mask[:,:,0])} ")
|
||||
if f"{file}{rank}" == "H7":
|
||||
print("--"*20)
|
||||
print("H7")
|
||||
print(lel)
|
||||
print(len(y))
|
||||
print(np.max(segment))
|
||||
dst = masked.filled()[top: bottom + 1, left: right + 1]
|
||||
lel = (bottom - top) * (right - left)
|
||||
#print(f"this is lel: {lel} ")
|
||||
#print(f"this is meh: {np.sum(mask[:,:,0])} ")
|
||||
if position == POSITION.H7:
|
||||
print("--"*20)
|
||||
print("H7")
|
||||
print(lel)
|
||||
print(len(y))
|
||||
print(np.max(segment))
|
||||
# print(lel)
|
||||
# print(np.sum(mask[:, :, 0]))
|
||||
print("--"*20)
|
||||
print("--"*20)
|
||||
|
||||
|
||||
pls.append(len(y))
|
||||
if len(y) < (164**2)*0.65:
|
||||
counter += 1
|
||||
pls.append(len(y))
|
||||
if len(y) < (164**2)*0.65:
|
||||
counter += 1
|
||||
|
||||
cv2.imwrite(f"segment_test/segment_{datetime.utcnow().timestamp()}_{file}{rank}.jpg", dst)
|
||||
cv2.imwrite(f"segment_test/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])):
|
||||
print(f"{file}{rank} is nonempty")
|
||||
non_empties.append([f"{file}{rank}", src])
|
||||
print(counter)
|
||||
print(np.max(segment))
|
||||
empty += 1
|
||||
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])):
|
||||
print(f"{position} is nonempty")
|
||||
non_empties.append([f"{position}", src])
|
||||
print(counter)
|
||||
print(np.max(segment))
|
||||
empty += 1
|
||||
print("++"*20)
|
||||
print(counter)
|
||||
print(64-empty)
|
||||
|
|
@ -498,124 +167,35 @@ if __name__ == '__main__':
|
|||
|
||||
for file in files:
|
||||
for rank in ranks:
|
||||
square = runner.get_square(warped, file, rank)
|
||||
if predict(square, file, rank) == 'empty':
|
||||
square_img = runner.get_square(warped, file, rank)
|
||||
if predict(square_img, file, rank) == 'empty':
|
||||
counter += 1
|
||||
|
||||
print(counter)
|
||||
exit()
|
||||
|
||||
|
||||
square = runner.get_square(warped, "D", 2)
|
||||
square_img = runner.get_square(warped, "D", 2)
|
||||
|
||||
gray_square = cv2.cvtColor(square, cv2.COLOR_BGR2GRAY)
|
||||
print(cv2.meanStdDev(gray_square)[1])
|
||||
print(cv2.meanStdDev(square)[1])
|
||||
cv2.imshow("square", square)
|
||||
gray_square_img = cv2.cvtColor(square_img, cv2.COLOR_BGR2GRAY)
|
||||
print(cv2.meanStdDev(gray_square_img)[1])
|
||||
print(cv2.meanStdDev(square_img)[1])
|
||||
cv2.imshow("square", square_img)
|
||||
cv2.waitKey(0)
|
||||
|
||||
|
||||
|
||||
|
||||
print(pred_test("C", 2, square))
|
||||
print(pred_test("C", 2, square_img))
|
||||
|
||||
sift: cv2.xfeatures2d_SIFT = cv2.xfeatures2d.SIFT_create()
|
||||
gray = cv2.cvtColor(square, cv2.COLOR_BGR2GRAY)
|
||||
gray = cv2.cvtColor(square_img, cv2.COLOR_BGR2GRAY)
|
||||
|
||||
kp, desc = sift.detectAndCompute(gray, None)
|
||||
|
||||
cv2.drawKeypoints(square, kp, square)
|
||||
cv2.drawKeypoints(square_img, kp, square_img)
|
||||
|
||||
cv2.imshow("kp", square)
|
||||
cv2.imshow("kp", square_img)
|
||||
cv2.waitKey(0)
|
||||
|
||||
exit()
|
||||
|
||||
board = cv2.imread("whole_boards/board_202_1554154094.001122_.png")
|
||||
|
||||
runner.fetch_empty_fields(board)
|
||||
exit()
|
||||
|
||||
|
||||
warped = runner.warp_board(board)
|
||||
|
||||
counter = 0
|
||||
|
||||
#square = runner.get_square(warped, "A", 3)
|
||||
|
||||
#top_left, bottom_left, top_right, bottom_right = selective_search(square, use_fast=True)
|
||||
#cropped = square[top_left[1]:bottom_right[1], top_left[0]:bottom_right[0]]
|
||||
|
||||
|
||||
|
||||
for file in files:
|
||||
for rank in ranks:
|
||||
|
||||
square = runner.get_square(warped, file, rank)
|
||||
|
||||
|
||||
top_left, bottom_left, top_right, bottom_right = selective_search(square, use_fast=True)
|
||||
cropped = square[top_left[1]:bottom_right[1], top_left[0]:bottom_right[0]]
|
||||
|
||||
rect_width = bottom_right[0] - bottom_left[0]
|
||||
rect_height = bottom_right[1] - top_right[1]
|
||||
|
||||
size = rect_width * rect_height
|
||||
|
||||
square_height, square_width, channels = square.shape
|
||||
|
||||
empty_bias = (size == square_height*square_width)
|
||||
if size == square_height*square_width:
|
||||
print(f"{file}{rank} is likely empty")
|
||||
|
||||
res = pred_test(file, rank, mystery_image=square, empty_bias=empty_bias)
|
||||
print(res)
|
||||
if (max(res.items(), key=operator.itemgetter(1))[0] == 'empty'):
|
||||
counter += 1
|
||||
|
||||
|
||||
|
||||
print(f"Amount of empty fields: {counter}")
|
||||
#print("Non-cropped:\t",pred_test(square))
|
||||
#print("Cropped:\t",pred_test(cropped))
|
||||
|
||||
#cv2.imshow("square", square)
|
||||
#cv2.waitKey(0)
|
||||
|
||||
|
||||
#runner.do_pre_processing()
|
||||
#runner.train()
|
||||
|
||||
|
||||
#img = "warped_square_B5.png"
|
||||
#detect_using_nn(img)
|
||||
|
||||
#selective_search("training_images/empty/colorless/warped_square_A6.png", use_fast=True)
|
||||
#selective_search("warped_square_B5.png", use_fast=True)
|
||||
img = "training_images/rook/white/rook_training_D4_7.png"
|
||||
#img = "training_images/rook/white_square/rook_training_E4_10.png"
|
||||
#img = "training_images/knight/white_square/training_D5_134.png"
|
||||
|
||||
#top_left, bottom_left, top_right, bottom_right = selective_search(img, use_fast=True)
|
||||
#cropped = cv2.imread(img)[top_left[1]:bottom_right[1], top_left[0]:bottom_right[0]]
|
||||
|
||||
#cv2.imshow("output", cropped)
|
||||
#print(pred_test(cropped))
|
||||
#cv2.waitKey(0)
|
||||
|
||||
|
||||
|
||||
#lel_test()
|
||||
|
||||
|
||||
# test_entire_board()
|
||||
|
||||
#board = [[0, 0, 1, 2, 0, 0, 0, 2], [0, 1, 2, 2, 1, 0, 0, 1], [0, 0, 0, 0, 1, 0, 2, 0], [0, 2, 2, 1, 1, 2, 2, 0], [0, 1, 0, 0, 1, 2, 0, 0], [0, 0, 0, 0, 0, 2, 2, 0], [0, 0, 0, 2, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]]
|
||||
#for i in board:
|
||||
# print(i)
|
||||
|
||||
#warped = cv2.imread("homo_pls_fuck.jpg")
|
||||
#square = runner.get_square(warped, "D", 4)
|
||||
#print(pred_test(square))
|
||||
#cv2.imshow("lel", square)
|
||||
#cv2.waitKey(0)
|
||||
|
|
|
|||
|
|
@ -1,53 +1,41 @@
|
|||
import itertools
|
||||
from pathlib import Path
|
||||
from threading import Thread
|
||||
from time import sleep
|
||||
import numpy as np
|
||||
import cv2
|
||||
import runner
|
||||
from datetime import datetime
|
||||
import utils
|
||||
|
||||
import cv2
|
||||
|
||||
import runner
|
||||
from util import FILE, RANK, PIECE, COLOR, imwrite, POSITION
|
||||
|
||||
#cap = cv2.VideoCapture(0)
|
||||
#cap = cv2.VideoCapture("rtsp://10.192.49.108:8080/h264_ulaw.sdp")
|
||||
cap = cv2.VideoCapture(0)
|
||||
|
||||
|
||||
piece = "knight"
|
||||
color = "black"
|
||||
|
||||
|
||||
rank = 8
|
||||
|
||||
color = COLOR.BLACK
|
||||
rank = RANK.EIGHT
|
||||
pieces = {
|
||||
'knight': [("E", rank), ("H", rank)],
|
||||
'rook': [("A", rank), ("F", rank)],
|
||||
'bishop': [("C", rank), ("D", rank)],
|
||||
'king': [("G", rank)],
|
||||
'queen': [("B", rank)]
|
||||
PIECE.rook: [POSITION(FILE.A, rank), POSITION(FILE.F, rank)],
|
||||
PIECE.knight: [POSITION(FILE.E, rank), POSITION(FILE.H, rank)],
|
||||
PIECE.bishop: [POSITION(FILE.C, rank), POSITION(FILE.D, rank)],
|
||||
PIECE.queen: [POSITION(FILE.B, rank)],
|
||||
PIECE.king: [POSITION(FILE.G, rank)],
|
||||
}
|
||||
|
||||
while(True):
|
||||
while True:
|
||||
# Capture frame-by-frame
|
||||
ret, frame = cap.read()
|
||||
|
||||
# Display the resulting frame
|
||||
cv2.imshow('frame', frame)
|
||||
cv2.imshow("frame", frame)
|
||||
|
||||
if cv2.waitKey(100) & 0xFF == ord('c'):
|
||||
if cv2.waitKey(100) & 0xFF == ord("c"):
|
||||
print(f"capturing frame")
|
||||
# cv2.imwrite(f"single_frame_{counter}.png", frame)
|
||||
utils.imwrite(f"whole_boards/boards_for_empty/board_{datetime.utcnow().timestamp()}_.png", frame)
|
||||
imwrite(f"whole_boards/boards_for_empty/board_{datetime.utcnow().timestamp()}_.png", frame)
|
||||
warped = runner.warp_board(frame)
|
||||
|
||||
runner.save_empty_fields(warped, skip_rank=rank)
|
||||
|
||||
for piece, positions in pieces.items():
|
||||
for position in positions:
|
||||
square = runner.get_square(warped, position[0], position[1])
|
||||
square = runner.get_square(warped, *position)
|
||||
x, y = position
|
||||
utils.imwrite(f"training_images/{piece}/{runner.compute_color(x, y)}_square/training_{x}{str(y)}_{datetime.utcnow().timestamp()}.png", square)
|
||||
imwrite(f"training_images/{piece}/{position.color}_square/training_{x}{str(y)}_{datetime.utcnow().timestamp()}.png", square)
|
||||
|
||||
|
||||
# When everything done, release the capture
|
||||
|
|
|
|||
384
runner.py
384
runner.py
|
|
@ -1,173 +1,36 @@
|
|||
from functools import lru_cache
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import glob
|
||||
import os
|
||||
from sklearn import cluster
|
||||
from sklearn import metrics
|
||||
from sklearn import svm
|
||||
from sklearn.externals import joblib
|
||||
from sklearn import neural_network
|
||||
import heapq
|
||||
from datetime import datetime
|
||||
from typing import Tuple
|
||||
|
||||
import numpy as np
|
||||
from sklearn import cluster, metrics, svm
|
||||
from sklearn.externals import joblib
|
||||
from sklearn.pipeline import make_pipeline
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
|
||||
import utils
|
||||
|
||||
import random
|
||||
|
||||
pieces = ["rook", "knight"]
|
||||
colors = ['black', 'white']
|
||||
from util import RANK, POSITION, imwrite, PIECE, COLOR, Squares
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def selective_search(image, use_fast=False, use_slow=False, image_name = None):
|
||||
# speed-up using multithreads
|
||||
cv2.setUseOptimized(True)
|
||||
cv2.setNumThreads(4)
|
||||
|
||||
im = image
|
||||
|
||||
img_out = im.copy()
|
||||
|
||||
# create Selective Search Segmentation Object using default parameters
|
||||
ss = cv2.ximgproc.segmentation.createSelectiveSearchSegmentation()
|
||||
|
||||
# set input image on which we will run segmentation
|
||||
ss.setBaseImage(im)
|
||||
|
||||
ss.switchToSingleStrategy()
|
||||
|
||||
if (use_fast):
|
||||
ss.switchToSelectiveSearchFast()
|
||||
|
||||
elif (use_slow):
|
||||
ss.switchToSelectiveSearchQuality()
|
||||
|
||||
# run selective search segmentation on input image
|
||||
rects = ss.process()
|
||||
|
||||
# number of region proposals to show
|
||||
numShowRects = 150
|
||||
|
||||
best_proposals = []
|
||||
|
||||
while True:
|
||||
# create a copy of original image
|
||||
|
||||
# itereate over all the region proposals
|
||||
for i, rect in enumerate(rects):
|
||||
imOut = im.copy()
|
||||
|
||||
# draw rectangle for region proposal till numShowRects
|
||||
if (i < numShowRects):
|
||||
x, y, w, h = rect
|
||||
|
||||
top_left = (x,y)
|
||||
bottom_left = (x, y+h)
|
||||
top_right = (x+w, y)
|
||||
bottom_right = (x+w, y+h)
|
||||
|
||||
rect_width = bottom_right[0] - bottom_left[0]
|
||||
rect_height = bottom_right[1] - top_right[1]
|
||||
|
||||
size = rect_width * rect_height
|
||||
|
||||
best_proposals.append((rect, size))
|
||||
|
||||
|
||||
else:
|
||||
break
|
||||
|
||||
height, width, channels = im.shape
|
||||
center_x = width // 2
|
||||
center_y = (height // 2)+5
|
||||
dists = []
|
||||
|
||||
for i in heapq.nlargest(10, best_proposals, key=lambda x: x[1]):
|
||||
width, height, channels = im.shape
|
||||
|
||||
x, y, w, h = i[0]
|
||||
|
||||
if i[1] < (width*height)*0.90 and i[1] > (width*height)*0.25:
|
||||
|
||||
|
||||
top_left = (x,y)
|
||||
bottom_left = (x, y+h)
|
||||
top_right = (x+w, y)
|
||||
bottom_right = (x+w, y+h)
|
||||
|
||||
box_center_x = (top_left[0]+bottom_left[0]+top_right[0]+bottom_right[0]) // 4
|
||||
box_center_y = (top_left[1]+bottom_left[1]+top_right[1]+bottom_right[1]) // 4
|
||||
|
||||
dist = (center_x - box_center_x) ** 2 + (center_y - box_center_y) ** 2
|
||||
dists.append([i, dist])
|
||||
|
||||
|
||||
imCop = imOut.copy()
|
||||
|
||||
|
||||
print(image_name)
|
||||
best = heapq.nsmallest(1, dists, key=lambda x: x[1])
|
||||
x, y, w, h = best[0][0][0]
|
||||
cv2.rectangle(imCop, (x, y), (x + w, y + h), (0, 255, 0), 4, cv2.LINE_AA)
|
||||
|
||||
top_left = (x, y)
|
||||
bottom_right = (x + w, y + h)
|
||||
|
||||
|
||||
cropped = img_out[top_left[1]:bottom_right[1], top_left[0]:bottom_right[0]]
|
||||
return cropped
|
||||
|
||||
# show output
|
||||
cv2.imshow("Output", imOut)
|
||||
|
||||
# record key press
|
||||
k = cv2.waitKey(0) & 0xFF
|
||||
|
||||
# m is pressed
|
||||
if k == 109:
|
||||
# increase total number of rectangles to show by increment
|
||||
numShowRects += increment
|
||||
# l is pressed
|
||||
elif k == 108 and numShowRects > increment:
|
||||
# decrease total number of rectangles to show by increment
|
||||
numShowRects -= increment
|
||||
# q is pressed
|
||||
elif k == 113:
|
||||
break
|
||||
# close image show window
|
||||
cv2.destroyAllWindows()
|
||||
|
||||
|
||||
def generate_centers(number_of_clusters, sift : cv2.xfeatures2d_SIFT):
|
||||
features = None
|
||||
for piece in pieces:
|
||||
for color in colors:
|
||||
def generate_centers(number_of_clusters, sift: cv2.xfeatures2d_SIFT):
|
||||
features = []
|
||||
for piece in PIECE:
|
||||
for color in COLOR:
|
||||
for filename in glob.glob(os.path.join("training_images", piece, f"{color}_square", "*.png")):
|
||||
image = cv2.imread(filename)
|
||||
#image = selective_search(image, use_fast=True)
|
||||
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
|
||||
kp, desc = sift.detectAndCompute(gray, None)
|
||||
|
||||
if features is None:
|
||||
features = np.array(desc)
|
||||
else:
|
||||
print(f"{piece}, {color}, {filename}")
|
||||
features = np.vstack((features, desc))
|
||||
|
||||
print(f"{piece}, {color}, {filename}")
|
||||
features.append(desc)
|
||||
features = np.array(features)
|
||||
k_means = cluster.KMeans(number_of_clusters)
|
||||
k_means.fit(features)
|
||||
return k_means.cluster_centers_
|
||||
|
||||
|
||||
def generate_bag_of_words(image, centers, sift : cv2.xfeatures2d_SIFT):
|
||||
def generate_bag_of_words(image, centers, sift: cv2.xfeatures2d_SIFT):
|
||||
num_centers = centers.shape[0]
|
||||
histogram = np.zeros((1, num_centers))
|
||||
|
||||
|
|
@ -180,73 +43,58 @@ def generate_bag_of_words(image, centers, sift : cv2.xfeatures2d_SIFT):
|
|||
distances = metrics.pairwise.pairwise_distances(desc, centers)
|
||||
best_centers = np.argmin(distances, axis=1)
|
||||
|
||||
for i in best_centers:
|
||||
histogram[0,i] = histogram[0,i] + 1
|
||||
histogram = histogram / np.sum(histogram)
|
||||
for i in best_centers: # TODO: Could do this way faster in one line with numpy somehow
|
||||
histogram[0, i] += + 1
|
||||
|
||||
return histogram
|
||||
return histogram / np.sum(histogram)
|
||||
|
||||
|
||||
def do_pre_processing():
|
||||
def do_pre_processing() -> None:
|
||||
sift = cv2.xfeatures2d.SIFT_create()
|
||||
centers = generate_centers(8, sift)
|
||||
|
||||
np.save("training_data/centers", centers)
|
||||
|
||||
for piece in pieces:
|
||||
for color in colors:
|
||||
for piece in PIECE:
|
||||
for color in COLOR:
|
||||
for filename in glob.glob(os.path.join("training_images", piece, f"{color}_square", "*.png")):
|
||||
image = cv2.imread(filename)
|
||||
#image = selective_search(image, image_name=filename, use_fast=True)
|
||||
bow_features = generate_bag_of_words(image, centers, sift)
|
||||
np.save(f"training_data/{piece}/{color}_square/" + os.path.basename(filename), bow_features)
|
||||
np.save(f"training_data/{piece}/{color}_square/{os.path.basename(filename)}", bow_features)
|
||||
|
||||
|
||||
def load_training_data(spec_piece, color):
|
||||
X = None
|
||||
Y = None
|
||||
|
||||
for piece in pieces:
|
||||
piece_class = int(spec_piece == piece)
|
||||
def load_training_data(piece: PIECE, color: COLOR) -> Tuple[np.array, np.array]:
|
||||
X = []
|
||||
Y = []
|
||||
for p in PIECE:
|
||||
for filename in glob.glob(os.path.join("training_data", piece, f"{color}_square", "*.npy")):
|
||||
data = np.load(filename)
|
||||
if X is None:
|
||||
X = np.array(data)
|
||||
Y = np.array([piece_class])
|
||||
else:
|
||||
X = np.vstack((X, data))
|
||||
Y = np.vstack((Y, [piece_class]))
|
||||
return X, Y
|
||||
X.append(data)
|
||||
Y.append(p == piece)
|
||||
return np.array(X), np.array(Y)
|
||||
|
||||
|
||||
def train_empty_or_piece_var():
|
||||
for square_color in ["black", "white"]:
|
||||
X = None
|
||||
Y = None
|
||||
for piece in ['empty', 'rook', 'knight']:
|
||||
|
||||
def train_empty_or_piece_hist() -> None:
|
||||
for square_color in COLOR:
|
||||
X = []
|
||||
Y = []
|
||||
for piece in (PIECE.EMPTY, PIECE.ROOK, PIECE.KNIGHT):
|
||||
for filename in glob.glob(os.path.join("training_images", f"{piece}", f"{square_color}_square", "*.png")):
|
||||
piece_class = 'empty' == piece
|
||||
img = cv2.imread(filename)
|
||||
|
||||
y, x = np.histogram(img.ravel(), bins=256, range=[0, 256]) # TODO: Maybe img.ravel() ?
|
||||
|
||||
if X is None:
|
||||
X = np.array(y)
|
||||
Y = np.array([piece_class])
|
||||
else:
|
||||
X = np.vstack((X, y))
|
||||
Y = np.vstack((Y, [piece_class]))
|
||||
y, x = np.histogram(img.ravel(), bins=256, 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(X, Y)
|
||||
classifier.fit(np.array(X), np.array(Y))
|
||||
joblib.dump(classifier, f"classifiers/classifier_empty/white_piece_on_{square_color}_square.pkl")
|
||||
|
||||
|
||||
def train_pieces_svm():
|
||||
for piece in pieces:
|
||||
for color in colors:
|
||||
def train_pieces_svm() -> None:
|
||||
for piece in PIECE:
|
||||
for color in COLOR:
|
||||
# TODO: Consider removing empty from total_weights, so all classifiers do not consider empty pieces
|
||||
total_weights = len(glob.glob(os.path.join("training_images", "*", f"{color}_square", "*.png")))
|
||||
current_weight = len(glob.glob(os.path.join("training_images", piece, f"{color}_square", "*.png")))
|
||||
|
|
@ -258,21 +106,7 @@ def train_pieces_svm():
|
|||
joblib.dump(classifier, f"classifiers/classifier_{piece}/{color}.pkl")
|
||||
|
||||
|
||||
|
||||
def compute_features(training_image):
|
||||
sift = cv2.xfeatures2d.SIFT_create()
|
||||
gray_training_image = cv2.cvtColor(training_image, cv2.COLOR_BGR2GRAY)
|
||||
kp = sift.detect(gray_training_image)
|
||||
kp, desc = sift.compute(gray_training_image, kp)
|
||||
|
||||
cv2.drawKeypoints(training_image, kp, training_image)
|
||||
|
||||
return training_image
|
||||
|
||||
|
||||
def warp_board(camera_image, debug_image=None):
|
||||
#cv2.imwrite('camera_image.png', camera_image)
|
||||
|
||||
def warp_board(camera_image, debug_image=None) -> np.ndarray:
|
||||
baseline = cv2.imread("new_baseline_board.png")
|
||||
|
||||
camera_image_gray = cv2.cvtColor(camera_image, cv2.COLOR_BGR2GRAY)
|
||||
|
|
@ -287,7 +121,7 @@ def warp_board(camera_image, debug_image=None):
|
|||
|
||||
if debug_image is not None:
|
||||
cv2.drawKeypoints(camera_image, keypoints=camera_image_keypoints, outImage=debug_image)
|
||||
cv2.imwrite('keypoints_img.jpg', camera_image)
|
||||
cv2.imwrite("keypoints_img.jpg", camera_image)
|
||||
|
||||
# FLANN parameters
|
||||
FLANN_INDEX_KDTREE = 0
|
||||
|
|
@ -298,28 +132,27 @@ def warp_board(camera_image, debug_image=None):
|
|||
matches = flann.knnMatch(des, des2, k=2)
|
||||
|
||||
# Need to draw only good matches, so create a mask
|
||||
matchesMask = [[0,0] for i in range(len(matches))]
|
||||
matchesMask = [[0, 0] for _ in range(len(matches))]
|
||||
|
||||
# Ratio test as per Lowe's paper
|
||||
good_matches = []
|
||||
|
||||
# ratio test as per Lowe's paper
|
||||
for i,(m,n) in enumerate(matches):
|
||||
for i, (m, n) in enumerate(matches):
|
||||
if m.distance < 0.55*n.distance:
|
||||
matchesMask[i]=[1,0]
|
||||
good_matches.append([m,n])
|
||||
matchesMask[i] = [1, 0]
|
||||
good_matches.append([m, n])
|
||||
|
||||
draw_params = dict(matchColor=(0,255,0),
|
||||
singlePointColor=(255,0,0),
|
||||
matchesMask=matchesMask,
|
||||
flags=0)
|
||||
|
||||
img3 = cv2.drawMatchesKnn(camera_image,
|
||||
camera_image_keypoints,
|
||||
baseline,
|
||||
baseline_keypoints,
|
||||
matches,
|
||||
None,
|
||||
**draw_params)
|
||||
img3 = cv2.drawMatchesKnn(
|
||||
camera_image,
|
||||
camera_image_keypoints,
|
||||
baseline,
|
||||
baseline_keypoints,
|
||||
matches,
|
||||
None,
|
||||
matchColor=(0, 255, 0),
|
||||
singlePointColor=(255, 0, 0),
|
||||
matchesMask=matchesMask,
|
||||
flags=0
|
||||
)
|
||||
cv2.imwrite("matches.jpg", img3)
|
||||
|
||||
# Extract location of good matches
|
||||
|
|
@ -330,112 +163,41 @@ def warp_board(camera_image, debug_image=None):
|
|||
points1[i, :] = camera_image_keypoints[m.queryIdx].pt
|
||||
points2[i, :] = baseline_keypoints[m.trainIdx].pt
|
||||
|
||||
# print(len(points2))
|
||||
|
||||
h, mask = cv2.findHomography(points1, points2, cv2.RANSAC)
|
||||
|
||||
height, width, channels = baseline.shape
|
||||
im1Reg = cv2.warpPerspective(camera_image, h, (width, height))
|
||||
|
||||
# cv2.imwrite('homo_pls_fuck.jpg', im1Reg)
|
||||
return im1Reg
|
||||
return cv2.warpPerspective(camera_image, h, (width, height))
|
||||
|
||||
|
||||
def get_square(warped_board, file, rank):
|
||||
files = "ABCDEFGH"
|
||||
file = files.index(file)
|
||||
rank = 8 - rank
|
||||
def get_square(warped_board: np.ndarray, position: POSITION) -> np.ndarray:
|
||||
width, _, _ = warped_board.shape # board is square anyway
|
||||
side = int(width * 0.045)
|
||||
size = width - 2 * side
|
||||
square_size = size // 8
|
||||
|
||||
|
||||
padding = 0
|
||||
|
||||
x1 = side + (square_size * file)
|
||||
x1 = side + (square_size * position.file)
|
||||
x2 = x1 + square_size
|
||||
y1 = max(0, side + (square_size * rank) - 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)
|
||||
|
||||
square = warped_board[y1:y2, x1:x2]
|
||||
return square
|
||||
|
||||
|
||||
def get_squares(warped_board):
|
||||
result = {}
|
||||
for file in "ABCDEFGH":
|
||||
for rank in range(1, 9):
|
||||
square = get_square(warped_board, file, rank)
|
||||
result[f"{file}{rank}"] = square
|
||||
# cv2.imwrite(f"warped_square_{file}{rank}.png", square)
|
||||
return result
|
||||
|
||||
def load_data_nn(spec_piece):
|
||||
X = None
|
||||
Y = None
|
||||
for piece in pieces:
|
||||
piece_class = int(spec_piece == piece)
|
||||
for filename in glob.glob(os.path.join("training_images", piece, "*", "*.png")):
|
||||
image = cv2.imread(filename)
|
||||
image = cv2.resize(image, (64, 128))
|
||||
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:
|
||||
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:
|
||||
X = np.vstack((X, data))
|
||||
Y = np.vstack((Y, [piece_class]))
|
||||
return (X, Y)
|
||||
|
||||
def train_nn():
|
||||
for piece in pieces:
|
||||
X, Y = load_data_nn(piece)
|
||||
classifier = neural_network.MLPClassifier(hidden_layer_sizes=64)
|
||||
classifier.fit(X, Y)
|
||||
joblib.dump(classifier, "classifiers/neural_net_" + piece + ".pkl")
|
||||
def get_squares(warped_board: np.ndarray) -> Squares:
|
||||
# cv2.imwrite(f"warped_square_{square}.png", square)
|
||||
return {position: get_square(warped_board, position)
|
||||
for position in POSITION}
|
||||
|
||||
|
||||
def letter_to_int(letter):
|
||||
alphabet = list('ABCDEFGH')
|
||||
return alphabet.index(letter) + 1
|
||||
|
||||
|
||||
@lru_cache(maxsize=64)
|
||||
def compute_color(file, rank):
|
||||
if ((letter_to_int(file)+rank) % 2):
|
||||
return 'white'
|
||||
else:
|
||||
return 'black'
|
||||
|
||||
|
||||
def save_empty_fields(warped, skip_rank=None):
|
||||
alpha = "ABCDEFGH"
|
||||
ranks = [1, 2, 3, 4, 5, 6, 7, 8]
|
||||
|
||||
if skip_rank is not None:
|
||||
ranks.remove(skip_rank)
|
||||
|
||||
for file in alpha:
|
||||
for rank in ranks:
|
||||
square = get_square(warped, file, rank)
|
||||
color = compute_color(file, rank)
|
||||
|
||||
utils.imwrite(f"training_images/empty/{color}_square/training_{file}{rank}_{datetime.utcnow().timestamp()}.png", square)
|
||||
def save_empty_fields(warped_board: np.ndarray, skip_rank: RANK = None) -> None:
|
||||
for position in POSITION:
|
||||
if position.rank == skip_rank:
|
||||
continue
|
||||
square = get_square(warped_board, position)
|
||||
imwrite(f"training_images/empty/{position.color}_square/training_{position}_{datetime.utcnow().timestamp()}.png", square)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
train_empty_or_piece_var()
|
||||
|
||||
|
||||
|
||||
train_empty_or_piece_hist()
|
||||
|
|
|
|||
92
util.py
Normal file
92
util.py
Normal file
|
|
@ -0,0 +1,92 @@
|
|||
from __future__ import annotations
|
||||
|
||||
import cv2
|
||||
from enum import Enum
|
||||
from functools import lru_cache
|
||||
from pathlib import Path
|
||||
from typing import NewType, NamedTuple, Dict, Tuple
|
||||
|
||||
import numpy as np
|
||||
from sklearn.externals import joblib
|
||||
|
||||
|
||||
class COLOR(Enum):
|
||||
WHITE = "white"
|
||||
BLACK = "black"
|
||||
|
||||
def __str__(self) -> str:
|
||||
return self.value
|
||||
|
||||
|
||||
class PIECE(Enum):
|
||||
PAWN = "pawn"
|
||||
ROOK = "rook"
|
||||
KNIGHT = "knight"
|
||||
BISHOP = "bishop"
|
||||
QUEEN = "queen"
|
||||
KING = "king"
|
||||
EMPTY = "empty"
|
||||
|
||||
def __str__(self) -> str:
|
||||
return self.value
|
||||
|
||||
|
||||
PieceAndColor = Tuple[PIECE, COLOR]
|
||||
|
||||
|
||||
class FILE(int, Enum):
|
||||
A = 1
|
||||
B = 2
|
||||
C = 3
|
||||
D = 4
|
||||
E = 5
|
||||
F = 6
|
||||
G = 7
|
||||
H = 8
|
||||
|
||||
|
||||
class RANK(int, Enum):
|
||||
EIGHT = 8
|
||||
SEVEN = 7
|
||||
SIX = 6
|
||||
FIVE = 5
|
||||
FOUR = 4
|
||||
THREE = 3
|
||||
TWO = 2
|
||||
ONE = 1
|
||||
|
||||
|
||||
class _Position(NamedTuple):
|
||||
file: FILE
|
||||
rank: RANK
|
||||
|
||||
def __str__(self) -> str:
|
||||
return f"{self.file.name}{self.rank}"
|
||||
|
||||
@property
|
||||
def color(self):
|
||||
if (self.file + self.rank) % 2:
|
||||
return COLOR.WHITE
|
||||
return COLOR.BLACK
|
||||
|
||||
|
||||
# POSITION.{A8, A7, ..., H1}
|
||||
POSITION = Enum("POSITION", {str(_Position(f, r)): _Position(f, r) for f in FILE for r in RANK}, type=_Position)
|
||||
|
||||
# Squares is a dict mapping positions to square images, i.e. a board container during image processing
|
||||
Squares = NewType("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"""
|
||||
|
||||
|
||||
def imwrite(*args, **kwargs):
|
||||
Path(args[0]).parent.mkdir(parents=True, exist_ok=True)
|
||||
return cv2.imwrite(*args, **kwargs)
|
||||
|
||||
|
||||
@lru_cache()
|
||||
def load_classifier(filename):
|
||||
print(f"Loading classifier {filename}")
|
||||
return joblib.load(filename)
|
||||
Loading…
Reference in New Issue
Block a user