This commit is contained in:
Alexander Munch-Hansen 2019-04-04 12:59:37 +02:00
parent 8f01dabb66
commit 6d8ffccf7d
9388 changed files with 953 additions and 164 deletions

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main.py Normal file
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from functools import lru_cache
import cv2
import runner
from sklearn.externals import joblib
import numpy as np
import operator
import glob
import os
import heapq
import math
pieces = ['rook', 'knight']
#pieces = ['rook', 'knight']
#piece_to_symbol = {'rook': 1, 'knight': 2, 'empty': 0}
piece_to_symbol = {'rook': 1, 'knight': 2}
colors = ['black', 'white']
def classify(image, sift : cv2.xfeatures2d_SIFT, file, rank, empty_bias=False):
centers = np.load("training_data/centers.npy")
probs = {'rook': {'black': 0, 'white': 0}, 'knight': {'black': 0, 'white': 0}, 'empty': {'black': 0, 'white': 0}}
#probs = {'rook': 0, 'knight': 0, 'empty': 0}
for piece in pieces:
for color in colors:
#color = runner.compute_color(file, rank)
classifier = joblib.load(f"classifiers/classifier_{piece}/{color}.pkl")
features = runner.generate_bag_of_words(image, centers, sift)
prob = classifier.predict_proba(features)
probs[piece][color] = prob[0, 1]
if empty_bias:
probs['empty'] *= 1.2
return probs
def pred_test(file, rank, mystery_image=None, empty_bias=False):
sift = cv2.xfeatures2d.SIFT_create()
if mystery_image is None:
mystery_image = cv2.imread("training_images/rook/white/rook_training_D4_2.png")
probs = classify(mystery_image, sift, file, rank, empty_bias=empty_bias)
return probs
def pre_process_and_train():
runner.do_pre_processing()
runner.train_pieces_svm()
def build_board_from_dict(board_dict : dict):
sift = cv2.xfeatures2d.SIFT_create()
board = [[0]*8 for _ in range(8)]
counter = 0
for idx, value in enumerate(board_dict.values()):
probs = classify(value, sift)
likely_piece = max(probs.items(), key=operator.itemgetter(1))[0]
symbol = piece_to_symbol[likely_piece]
column = idx // 8
row = (idx % 7)
board[row][column] = symbol
print(probs)
if likely_piece != 'empty':
counter += 1
print(counter)
print(64/(counter-1))
return board
def detect_using_nn(spec_image):
probs = {'rook': 0, 'knight': 0}
for piece in pieces:
piece_class = piece_to_symbol[piece]
win_size = (64, 64)
classifier = joblib.load("classifiers/neural_net_" + piece + ".pkl")
spec_image = cv2.resize(spec_image, (64, 128))
features = np.reshape(spec_image, (1, np.product(spec_image.shape)))
prob = classifier.predict_proba(features)
print(piece)
print(prob[0,1])
def test_entire_board():
board = cv2.imread("homo_pls_fuck.jpg")
warped = runner.warp_board(board)
board_dict = runner.get_squares(warped)
board = build_board_from_dict(board_dict)
print(board)
def lel_test():
# img = cv2.imread('training_images/rook/white/rook_training_D4_2.png')
counter = 0
for filename in glob.glob(os.path.join("training_images", "empty", "*", "*.png")):
img = cv2.imread(filename)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 3, 3)
# binarize the image
#ret, bw = cv2.threshold(gray, 100, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
# find connected components
connectivity = 4
nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(ret, connectivity, cv2.CV_32S)
sizes = stats[1:, -1]
nb_components = nb_components - 1
min_size = 250 # threshhold value for objects in scene
img2 = np.zeros((img.shape), np.uint8)
for i in range(0, nb_components + 1):
# use if sizes[i] >= min_size: to identify your objects
color = np.random.randint(255, size=3)
# draw the bounding rectangele around each object
cv2.rectangle(img2, (stats[i][0], stats[i][1]), (stats[i][0] + stats[i][2], stats[i][1] + stats[i][3]),
(0, 255, 0), 2)
img2[output == i + 1] = color
#print(nb_components+1)
if nb_components+1 >= 4:
counter += 1
print(filename)
cv2.imshow("lel", img2)
cv2.waitKey(0)
print(counter)
def selective_search(image, use_fast=False, use_slow=False):
# speed-up using multithreads
cv2.setUseOptimized(True)
cv2.setNumThreads(4)
if type(image) == str:
# read image
im = cv2.imread(image)
else:
im = image
# resize image
#newHeight = 200
#newWidth = int(im.shape[1] * 150 / im.shape[0])
#im = cv2.resize(im, (newWidth, newHeight))
#im = cv2.imread(image)
#lel, im = cv2.threshold(im, 128, 255, cv2.THRESH_BINARY)
# 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)
# Switch to fast but low recall Selective Search method
ss.switchToSingleStrategy()
if (use_fast):
ss.switchToSelectiveSearchFast()
# Switch to high recall but slow Selective Search method
elif (use_slow):
ss.switchToSelectiveSearchQuality()
# run selective search segmentation on input image
rects = ss.process()
#print('Total Number of Region Proposals: {}'.format(len(rects)))
# number of region proposals to show
numShowRects = 150
# increment to increase/decrease total number
# of reason proposals to be shown
increment = 1
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
# cv2.rectangle(imOut, (x, y), (x + w, y + h), (0, 255, 0), 1, cv2.LINE_AA)
# size = (max(w, x) - min(w, x)) * ((max(h, y) - min(h, y)))
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
#print(f"({x}, {y}), ({w}, {h})\n Of size: { size }")
#cv2.rectangle(imOut, (x, y), (x + w, y + h), (0, 255, 0), 1, cv2.LINE_AA)
#cv2.imshow("lel", imOut)
#cv2.waitKey(0)
best_proposals.append((rect, size))
#if size > biggest_size:
# biggest_rect = (x, y, w, h)
# biggest_size = size
# print(f"New biggest: \n({x}, {y}), ({w}, {h})\nOf size: {biggest_size}")
else:
break
height, width, channels = im.shape
center_x = width // 2
center_y = (height // 2)+5
dists = []
#print(f"Amount of best proposals:\n{len(best_proposals)}")
#print(f"lel: {len(heapq.nlargest(10, best_proposals, key=lambda x: x[1]))}")
for i in heapq.nlargest(10, best_proposals, key=lambda x: x[1]):
width, height, channels = im.shape
#print(width * height)
#print(i[1])
x, y, w, h = i[0]
if i[1] <= (width*height)*0.8 and i[1] > (width*height)*0.25:
imCop = imOut.copy()
#cv2.rectangle(imCop, (x, y), (x + w, y + h), (0, 255, 0), 2, cv2.LINE_AA)
#cv2.imshow("lel", imCop)
#cv2.waitKey(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)
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
#print(f"{box_center_x}, {box_center_y}, {center_x}, {center_y}")
dist = (center_x - box_center_x) ** 2 + (center_y - box_center_y) ** 2
print(dist)
dists.append([i, dist])
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)
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)
#print("-------"*5)
for pls in dists:
imCop = imOut.copy()
x, y, w, h = pls[0][0]
#print(x,y,w,h)
#print(pls[1])
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_y, center_x), color=(0, 0, 255), thickness=3)
cv2.rectangle(imCop, (x, y), (x + w, y + h), (0, 255, 0), 2, cv2.LINE_AA)
#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):
color = runner.compute_color(file, rank)
empty_var_classifier = load_classifier(f"classifiers/classifier_empty_var/{color}.pkl")
magnitude_of_var = np.linalg.norm(cv2.meanStdDev(square)[1])
prob = empty_var_classifier.predict_proba(np.array(magnitude_of_var).reshape(-1, 1))
print(prob[0, 1])
if (prob[0, 1]) > 0.5:
return 'empty'
return None
@lru_cache()
def load_classifier(filename):
return joblib.load(filename)
if __name__ == '__main__':
board = cv2.imread("whole_boards/board_102_1554110461.608167_.png")
warped = runner.warp_board(board)
files = "ABCDEFGH"
ranks = [1,2,3,4,5,6,7,8]
counter = 0
for file in files:
for rank in ranks:
square = runner.get_square(warped, file, rank)
if predict(square, file, rank) == 'empty':
counter += 1
print(counter)
exit()
square = 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)
cv2.waitKey(0)
print(pred_test("C", 2, square))
sift: cv2.xfeatures2d_SIFT = cv2.xfeatures2d.SIFT_create()
gray = cv2.cvtColor(square, cv2.COLOR_BGR2GRAY)
kp, desc = sift.detectAndCompute(gray, None)
cv2.drawKeypoints(square, kp, square)
cv2.imshow("kp", square)
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)

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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
#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
pieces = {
'knight': [("E", rank), ("H", rank)],
'rook': [("A", rank), ("F", rank)],
'bishop': [("C", rank), ("D", rank)],
'king': [("G", rank)],
'queen': [("B", rank)]
}
while(True):
# Capture frame-by-frame
ret, frame = cap.read()
# Display the resulting frame
cv2.imshow('frame', frame)
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)
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])
x, y = position
utils.imwrite(f"training_images/{piece}/{runner.compute_color(x, y)}_square/training_{x}{str(y)}_{datetime.utcnow().timestamp()}.png", square)
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()

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runner.py
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import cv2
import numpy as np
out_height, out_width = 500, 500
dstPoints = np.array([(out_height, 0), (0, 0), (0, out_width), (out_height, out_width)])
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
import utils
img = cv2.imread("IMG_2086.jpeg")
img2 = cv2.imread("new_baseline_board.png")
img_tmp = img.copy()
gray_tmp = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
gray_tmp = np.float32(gray_tmp)
'''
dst = cv2.cornerHarris(gray_tmp, 20, 3, 0.04)
#result is dilated for marking the corners, not important
dst = cv2.dilate(dst,None)
# Threshold for an optimal value, it may vary depending on the image.
img_tmp[dst>0.01*dst.max()]=[0,0,255]
cv2.imwrite('fuck.jpg',img_tmp)
pieces = ["rook", "knight"]
colors = ['black', 'white']
img = cv2.GaussianBlur(img,(5,5),0)
kernel = np.ones((3,3),np.float32)
kernel[0,1] = 0
kernel[0,2] = -1
kernel[1,0] = 3
kernel[1,1] = 0
kernel[1,2] = -3
kernel[2,1] = 0
kernel[2,2] = -1
img = cv2.filter2D(img,-1,kernel)
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))
img_tmp_tmp = img.copy()
gray_2 = cv2.cvtColor(img_tmp_tmp, cv2.COLOR_BGR2GRAY)
gray_3 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
else:
break
MAX_FEATURES = 500
GOOD_MATCH_PERCENT = 0.0005
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:
cv2.imwrite('pls_lasse.jpg', img)
top_left = (x,y)
bottom_left = (x, y+h)
top_right = (x+w, y)
bottom_right = (x+w, y+h)
img_tmp = img.copy()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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
sift = cv2.xfeatures2d.SIFT_create()
#sift = cv2.ORB_create(MAX_FEATURES)
#sift = cv2.xfeatures2d.SURF_create()
kp = sift.detect(gray_2, None)
kp2 = sift.detect(gray_3, None)
kp, des = sift.compute(gray_2, kp)
kp2, des2 = sift.compute(gray_3, kp2)
cv2.drawKeypoints(img_tmp_tmp, keypoints=kp, outImage=img_tmp_tmp)
cv2.imwrite('keypoints_img.jpg', img_tmp_tmp)
# FLANN parameters
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 8)
search_params = dict(checks=100) # or pass empty dictionary
dist = (center_x - box_center_x) ** 2 + (center_y - box_center_y) ** 2
dists.append([i, dist])
#matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING)
#matches = matcher.match(des, des2, None)
imCop = imOut.copy()
flann = cv2.FlannBasedMatcher(index_params,search_params)
matches = flann.knnMatch(des, des2, k=2)
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)
# Need to draw only good matches, so create a mask
matchesMask = [[0,0] for i in range(len(matches))]
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:
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))
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):
num_centers = centers.shape[0]
histogram = np.zeros((1, num_centers))
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
kp, desc = sift.detectAndCompute(gray_image, None)
if not kp:
return histogram
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)
return histogram
def do_pre_processing():
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 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)
def load_training_data(spec_piece, color):
X = None
Y = None
for piece in pieces:
piece_class = int(spec_piece == 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
def train_empty_or_piece_var():
pieces = ['empty', 'knight', 'rook']
for color in colors:
X = None
Y = None
total_weight = 0
for piece in pieces:
total_weight += len(glob.glob(os.path.join("training_images", f"{piece}", f"{color}_square", "*.png")))
current_weight = len(glob.glob(os.path.join("training_images", 'empty', f"{color}_square", "*.png")))
for piece in pieces:
piece_class = int('empty' == piece)
for filename in glob.glob(os.path.join("training_images", piece, f"{color}_square", "*.png")):
img = cv2.imread(filename)
magnitude_of_var = np.linalg.norm(cv2.meanStdDev(img)[1])
if X is None:
X = np.array(magnitude_of_var)
Y = np.array([piece_class])
else:
X = np.vstack((X, magnitude_of_var))
Y = np.vstack((Y, [piece_class]))
classifier = svm.SVC(class_weight={0: current_weight, 1: total_weight - current_weight}, probability=True)
classifier.fit(X, Y)
joblib.dump(classifier, f"classifiers/classifier_empty_var/{color}.pkl")
good_matches = []
# ratio test as per Lowe's paper
for i,(m,n) in enumerate(matches):
if m.distance < 0.5*n.distance:
def train_pieces_svm():
for piece in pieces:
for color in colors:
# 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")))
print(f"Trainig for piece: {piece}")
X, Y = load_training_data(piece, color)
classifier = svm.SVC(class_weight={0: current_weight, 1: total_weights - current_weight}, probability=True)
classifier.fit(X, Y)
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)
baseline = cv2.imread("new_baseline_board.png")
camera_image_gray = cv2.cvtColor(camera_image, cv2.COLOR_BGR2GRAY)
baseline_gray = cv2.cvtColor(baseline, cv2.COLOR_BGR2GRAY)
sift = cv2.xfeatures2d.SIFT_create()
camera_image_keypoints = sift.detect(camera_image_gray, None)
baseline_keypoints = sift.detect(baseline_gray, None)
camera_image_keypoints, des = sift.compute(camera_image_gray, camera_image_keypoints)
baseline_keypoints, des2 = sift.compute(baseline_gray, baseline_keypoints)
if debug_image is not None:
cv2.drawKeypoints(camera_image, keypoints=camera_image_keypoints, outImage=debug_image)
cv2.imwrite('keypoints_img.jpg', camera_image)
# FLANN parameters
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=8)
search_params = dict(checks=100) # or pass empty dictionary
flann = cv2.FlannBasedMatcher(index_params,search_params)
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))]
good_matches = []
# ratio test as per Lowe's paper
for i,(m,n) in enumerate(matches):
if m.distance < 0.55*n.distance:
matchesMask[i]=[1,0]
good_matches.append([m,n])
draw_params = dict(matchColor = (0,255,0),
singlePointColor = (255,0,0),
matchesMask = matchesMask,
flags = 0)
draw_params = dict(matchColor=(0,255,0),
singlePointColor=(255,0,0),
matchesMask=matchesMask,
flags=0)
img3 = cv2.drawMatchesKnn(img_tmp_tmp, kp, img2, kp2, matches, None, **draw_params)
cv2.imwrite("matches.jpg", img3)
img3 = cv2.drawMatchesKnn(camera_image,
camera_image_keypoints,
baseline,
baseline_keypoints,
matches,
None,
**draw_params)
cv2.imwrite("matches.jpg", img3)
# Extract location of good matches
points1 = np.zeros((len(good_matches), 2), dtype=np.float32)
points2 = np.zeros((len(good_matches), 2), dtype=np.float32)
for i, (m, n) in enumerate(good_matches):
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
matches.sort(key=lambda x: x[0].distance, reverse=False)
# Remove poor matches
numGoodMatches = int(len(matches) * GOOD_MATCH_PERCENT)
#good_matches = matches[:numGoodMatches]
def get_square(warped_board, file, rank):
files = "ABCDEFGH"
file = files.index(file)
rank = 8 - rank
width, _, _ = warped_board.shape # board is square anyway
# Extract location of good matches
points1 = np.zeros((len(good_matches), 2), dtype=np.float32)
points2 = np.zeros((len(good_matches), 2), dtype=np.float32)
side = int(width * 0.04)
size = width - 2 * side
square_size = size // 8
for i, (m, n) in enumerate(good_matches):
points1[i, :] = kp[m.queryIdx].pt
points2[i, :] = kp2[m.trainIdx].pt
padding = 0
print(points1)
print(len(points2))
x1 = side + (square_size * file)
x2 = x1 + square_size
y1 = max(0, side + (square_size * rank) - padding)
y2 = min(width, y1 + square_size + padding)
h, mask = cv2.findHomography(points1, points2, cv2.RANSAC)
height, width, channels = img2.shape
im1Reg = cv2.warpPerspective(img_tmp_tmp, h, (width, height))
cv2.imwrite('homo_pls_fuck.jpg', im1Reg)
'''
# Sort matches by score
matches.sort(key=lambda x: x[0].distance, reverse=False)
# Remove poor matches
numGoodMatches = int(len(matches) * GOOD_MATCH_PERCENT)
matches = matches[:numGoodMatches]
# Draw the top matches
imMatches = cv2.drawMatches(img_tmp_tmp, kp, img2, kp2, matches, None)
cv2.imwrite("matches.jpg", imMatches)
# Extract location of good matches
points1 = np.zeros((len(matches), 2), dtype=np.float32)
points2 = np.zeros((len(matches), 2), dtype=np.float32)
for i, match in enumerate(matches):
points1[i, :] = kp[match.queryIdx].pt
points2[i, :] = kp2[match.trainIdx].pt
print(len(points1))
print(len(points2))
'''
'''
h, mask = cv2.findHomography(points1, points2, cv2.RANSAC)
height, width, channels = img2.shape
im1Reg = cv2.warpPerspective(img_tmp_tmp, h, (width, height))
cv2.imwrite('homo_pls_fuck.jpg', im1Reg)
'''
square = warped_board[y1:y2, x1:x2]
return square
'''
gray_tmp = gray.copy()
gray_tmp = np.float32(gray_tmp)
dst = cv2.cornerHarris(gray_tmp,10,17,0.1)
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
#result is dilated for marking the corners, not important
dst = cv2.dilate(dst,None)
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)
# Threshold for an optimal value, it may vary depending on the image.
img_tmp[dst>0.07*dst.max()]=[0,0,255]
cv2.imwrite('fuck.jpg',img_tmp)
'''
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")
'''
ret, corners = cv2.findChessboardCorners(gray, (3,3), None)
def letter_to_int(letter):
alphabet = list('ABCDEFGH')
return alphabet.index(letter) + 1
imgpoints = []
def compute_color(file, rank):
if ((letter_to_int(file)+rank) % 2):
return 'white'
else:
return 'black'
print(ret)
if ret == True:
corners2 = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)
imgpoints.append(corners)
# Draw and display the corners
cv2.drawChessboardCorners(img, (3,3), corners2, ret)
cv2.imwrite('corners_chess.jpg', img)
'''
def save_empty_fields(warped, skip_rank=None):
alpha = "ABCDEFGH"
ranks = [1, 2, 3, 4, 5, 6, 7, 8]
'''
# Detect edges using Canny
canny_output = cv2.Canny(gray, 140, 160)
if skip_rank is not None:
ranks.remove(skip_rank)
cv2.imwrite('canny_out.jpg', canny_output)
'''
'''
ret, thresholded = cv2.threshold(gray, 80, 255, cv2.THRESH_BINARY)
cv2.imwrite('threshold_out.jpg', thresholded)
for file in alpha:
for rank in ranks:
square = get_square(warped, file, rank)
color = compute_color(file, rank)
lines = cv2.HoughLinesP(canny_output, 0.1, np.pi/60, 1, 30, 20)
for line in lines:
print(line)
cv2.line(img, (line[0][0], line[0][1]), (line[0][2], line[0][3]), (0, 0, 255), 2, 8)
cv2.imwrite('lined_chess.jpg', img)
utils.imwrite(f"training_images/empty/{color}_square/training_{file}{rank}_{datetime.utcnow().timestamp()}.png", square)
_, contours, hierarchy = cv2.findContours(canny_output, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
'''
#cv2.drawContours(img, contours, -1, (255, 0, 0), 2)
'''
pls_square = []
prev_max = -1
for contour in contours:
approx = cv2.approxPolyDP(contour, 0.01 * cv2.arcLength(contour, True), True)
if len(approx) == 4:
point_set = [(x[0,0], x[0,1]) for x in approx]
max_x = max([x[0] for x in point_set])
min_x = min([x[0] for x in point_set])
max_y = max([x[1] for x in point_set])
min_y = min([x[1] for x in point_set])
print(((max_x - min_x) * (max_y - min_y)))
pls_square.append(approx)
#if ((max_x - min_x) * (max_y - min_y)) > prev_max:
# prev_max = ((max_x - min_x) * (max_y - min_y))
# pls_square = approx
print(pls_square)
#h, mask = cv2.findHomography(pls_square, dstPoints, cv2.RANSAC)
#height, width, channels = img.shape
#warped = cv2.warpPerspective(img, h, (out_height, out_width))
cv2.drawContours(img, contours, -1, (255, 0, 0), 2)
cv2.imwrite('contours_chess.jpg', img)
#cv2.imwrite('homo_img_chess.jpg', warped)'''

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