def main():
import sys
classifier = cv2.SVM()
classifier.load(sys.argv[1])
im = cv2.imread(sys.argv[2])
im = cv2.GaussianBlur(im, (3, 3), 0)
imshow_large(__file__, im)
cv2.waitKey()
unrotated = unrotate(im)
#
# Check if image is right way up, correct otherwise
#
imshow_large(__file__, unrotated)
key = cv2.waitKey()
if key & 0xff == ord("r"):
unrotated = cv2.flip(cv2.flip(unrotated, 0), 1)
imshow_large(__file__, unrotated)
cv2.waitKey()
binarized = binarize(unrotated)
rois = get_rois(binarized)
results = {}
grayscale = cv2.cvtColor(unrotated, cv.CV_BGR2GRAY)
for (x, y, width, height) in rois:
roi = grayscale[y:y + height, x:x + width]
vec = extract_features(roi)
label = classifier.predict(vec)
results[(x, y, width, height)] = "01234567890X"[int(label)]
scale = SCREEN_HEIGHT / unrotated.shape[0]
unrotated = cv2.cvtColor(grayscale, cv.CV_GRAY2BGR)
scaled = cv2.resize(unrotated, (0, 0), fx=scale, fy=scale)
for roi in rois:
x = int(roi[0] * scale)
y = int(roi[1] * scale)
width = int(roi[2] * scale)
height = int(roi[3] * scale)
cv2.rectangle(scaled, (x, y), (x + width, y + height), (0, 255, 0, 0),
1)
if results[roi] == "X":
continue
cv2.putText(scaled, results[roi], (x, y), cv.CV_FONT_HERSHEY_SIMPLEX,
1, (0, 255, 0, 0))
cv2.imshow(__file__, scaled)
cv2.waitKey()
def main():
import sys
classifier = cv2.SVM()
classifier.load(sys.argv[1])
im = cv2.imread(sys.argv[2])
im = cv2.GaussianBlur(im, (3,3), 0)
imshow_large(__file__, im)
cv2.waitKey()
unrotated = unrotate(im)
#
# Check if image is right way up, correct otherwise
#
imshow_large(__file__, unrotated)
key = cv2.waitKey()
if key & 0xff == ord("r"):
unrotated = cv2.flip(cv2.flip(unrotated, 0), 1)
imshow_large(__file__, unrotated)
cv2.waitKey()
binarized = binarize(unrotated)
rois = get_rois(binarized)
results = {}
grayscale = cv2.cvtColor(unrotated, cv.CV_BGR2GRAY)
for (x,y,width,height) in rois:
roi = grayscale[y:y+height,x:x+width]
vec = extract_features(roi)
label = classifier.predict(vec)
results[(x,y,width,height)] = "01234567890X"[int(label)]
scale = SCREEN_HEIGHT/unrotated.shape[0]
unrotated = cv2.cvtColor(grayscale, cv.CV_GRAY2BGR)
scaled = cv2.resize(unrotated, (0,0), fx=scale, fy=scale)
for roi in rois:
x = int(roi[0]*scale)
y = int(roi[1]*scale)
width = int(roi[2]*scale)
height = int(roi[3]*scale)
cv2.rectangle(scaled, (x,y), (x+width, y+height), (0,255,0,0), 1)
if results[roi] == "X":
continue
cv2.putText(scaled, results[roi], (x, y), cv.CV_FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0, 0))
cv2.imshow(__file__, scaled)
cv2.waitKey()
def main():
import sys
in_dir = sys.argv[1]
images = map(lambda x: P.join(in_dir, x), os.listdir(in_dir))
out_dir = sys.argv[2]
counters = {}
for subdir in "0123456789X":
path = P.join(out_dir, subdir)
if not P.isdir(path):
os.makedirs(path)
files = os.listdir(path)
indices = map(lambda x: int(P.splitext(x)[0]), files)
try:
counters[subdir] = max(indices) + 1
except ValueError:
counters[subdir] = 0
for fpath in images:
print fpath
im = cv2.imread(fpath)
im = cv2.GaussianBlur(im, (3, 3), 0)
imshow_large(__file__, im)
key = cv2.waitKey()
if key & 0xff == ord("q"):
sys.exit(0)
elif key & 0xff == ord("n"):
continue
unrotated = unrotate(im)
#
# Check if image is right way up, correct otherwise
#
imshow_large(__file__, unrotated)
key = cv2.waitKey()
if key & 0xff == ord("r"):
unrotated = cv2.flip(cv2.flip(unrotated, 0), 1)
imshow_large(__file__, unrotated)
cv2.waitKey()
elif key & 0xff == ord("n"):
continue
binarized = binarize(unrotated)
scale = SCREEN_HEIGHT / unrotated.shape[0]
colorbin = copy.deepcopy(unrotated)
colorbin = cv2.resize(colorbin, (0, 0), fx=scale, fy=scale)
rois = get_rois(binarized)
for (x, y, width, height) in rois:
x = int(x * scale)
y = int(y * scale)
width = int(width * scale)
height = int(height * scale)
cv2.rectangle(colorbin, (x, y), (x + width, y + height),
(255, 0, 0, 0), 1)
cv2.imshow(__file__, colorbin)
key = cv2.waitKey()
if key & 0xff == ord("q"):
break
elif key & 0xff == ord("n"):
continue
for (x, y, width, height) in rois:
colorbin2 = copy.deepcopy(colorbin)
x_ = int(x * scale)
y_ = int(y * scale)
width_ = int(width * scale)
height_ = int(height * scale)
cv2.rectangle(colorbin2, (x_, y_), (x_ + width_, y_ + height_),
(0, 0, 255, 0), 1)
sub = unrotated[y:y + height, x:x + width]
supers = cv2.resize(sub, (192, 192), interpolation=cv.CV_INTER_NN)
cv2.imshow(__file__, colorbin2)
cv2.imshow("supers", supers)
key = cv2.waitKey()
if key & 0xff == ord("q"):
sys.exit(0)
elif key & 0xff == ord("n"): # move on to the next image
break
elif (key & 0xff) in map(ord, "0123456789"):
digit = chr(key & 0xff)
subdir = P.join(out_dir, digit)
out_file = P.join(subdir, str(counters[digit]) + ".png")
cv2.imwrite(out_file, sub)
counters[digit] += 1
else:
subdir = P.join(out_dir, "X")
out_file = P.join(subdir, str(counters["X"]) + ".png")
cv2.imwrite(out_file, sub)
counters["X"] += 1
def main():
import sys
in_dir = sys.argv[1]
images = map(lambda x: P.join(in_dir, x), os.listdir(in_dir))
out_dir = sys.argv[2]
counters = {}
for subdir in "0123456789X":
path = P.join(out_dir, subdir)
if not P.isdir(path):
os.makedirs(path)
files = os.listdir(path)
indices = map(lambda x: int(P.splitext(x)[0]), files)
try:
counters[subdir] = max(indices) + 1
except ValueError:
counters[subdir] = 0
for fpath in images:
print fpath
im = cv2.imread(fpath)
im = cv2.GaussianBlur(im, (3,3), 0)
imshow_large(__file__, im)
key = cv2.waitKey()
if key & 0xff == ord("q"):
sys.exit(0)
elif key & 0xff == ord("n"):
continue
unrotated = unrotate(im)
#
# Check if image is right way up, correct otherwise
#
imshow_large(__file__, unrotated)
key = cv2.waitKey()
if key & 0xff == ord("r"):
unrotated = cv2.flip(cv2.flip(unrotated, 0), 1)
imshow_large(__file__, unrotated)
cv2.waitKey()
elif key & 0xff == ord("n"):
continue
binarized = binarize(unrotated)
scale = SCREEN_HEIGHT/unrotated.shape[0]
colorbin = copy.deepcopy(unrotated)
colorbin = cv2.resize(colorbin, (0,0), fx=scale, fy=scale)
rois = get_rois(binarized)
for (x,y,width,height) in rois:
x = int(x*scale)
y = int(y*scale)
width = int(width*scale)
height = int(height*scale)
cv2.rectangle(colorbin, (x,y), (x+width, y+height), (255,0,0,0), 1)
cv2.imshow(__file__, colorbin)
key = cv2.waitKey()
if key & 0xff == ord("q"):
break
elif key & 0xff == ord("n"):
continue
for (x,y,width,height) in rois:
colorbin2 = copy.deepcopy(colorbin)
x_ = int(x*scale)
y_ = int(y*scale)
width_ = int(width*scale)
height_ = int(height*scale)
cv2.rectangle(colorbin2, (x_,y_), (x_+width_, y_+height_), (0,0,255,0), 1)
sub = unrotated[y:y+height, x:x+width]
supers = cv2.resize(sub, (192, 192), interpolation=cv.CV_INTER_NN)
cv2.imshow(__file__, colorbin2)
cv2.imshow("supers", supers)
key = cv2.waitKey()
if key & 0xff == ord("q"):
sys.exit(0)
elif key & 0xff == ord("n"): # move on to the next image
break
elif (key & 0xff) in map(ord, "0123456789"):
digit = chr(key & 0xff)
subdir = P.join(out_dir, digit)
out_file = P.join(subdir, str(counters[digit]) + ".png")
cv2.imwrite(out_file, sub)
counters[digit] += 1
else:
subdir = P.join(out_dir, "X")
out_file = P.join(subdir, str(counters["X"]) + ".png")
cv2.imwrite(out_file, sub)
counters["X"] += 1
def main(chess):
ran_stockfish = False
while True:
image = cv2.imread(img_filename)
if image is None:
continue
img_binary, img_r = ur.unrotate(image)
if img_r is None or np.max(img_binary) < 1e-5:
continue
h, w, d = img_r.shape
edges = get_vert_grad(img_r)
row_counts = get_rows(edges)
max_row_indexes = sorted(range(len(row_counts)), key = lambda k: row_counts[k], reverse=True)
rows_changed = get_rows_changed(max_row_indexes)
if rows_changed is None or len(rows_changed) != 9:
continue
edges = np.rot90(img_r)
edges = get_vert_grad(edges)
col_counts = get_rows(edges)
max_col_indexes = sorted(range(len(col_counts)), key = lambda k: col_counts[k], reverse=True)
cols_changed = get_rows_changed(max_col_indexes, True)
if cols_changed is None or len(cols_changed) != 9:
continue
edges = np.rot90(edges, k=3)
low_y, high_y, low_x, high_x = rows_changed[7], rows_changed[8], cols_changed[7], cols_changed[8]
square = img_binary[low_x+10:high_x-10, low_y+10:high_y-10]
if np.average(square) < 100:
print "Could not properly detect bottom right corner: %d with threshold >=100."%np.average(square)
continue
low_y, high_y, low_x, high_x = rows_changed[7], rows_changed[8], cols_changed[0], cols_changed[1]
square = img_r[low_x+10:high_x-10, low_y+10:high_y-10]
if np.average(square) > 80:
print "Could not properly detect top right corner: %d with threshold <=50."%np.average(square)
continue
board = determine_board_configuration(img_r, img_binary, rows_changed, cols_changed)
if GRAPH:
for row_idx in cols_changed:
cv2.line(img_r, (0, row_idx), (w, row_idx), (0, 0, 255), 1)
for row_idx in rows_changed:
cv2.line(img_r, (row_idx, 0), (row_idx, h), (0, 255, 0), 1)
cv2.imwrite("../images/temp.png", img_r)
board = board[:, ::-1].T
if not ran_stockfish:
best_move, score, mate = chess.assisted_human_turn()
if best_move is not None and score is None:
print "\n\nSTOCKFISH RECOMMENDS: %s with mate in %s\n\n"%(best_move, mate)
elif best_move is not None:
print "\n\nSTOCKFISH RECOMMENDS: %s with a score of %f\n\n"%(best_move, score / 100.0)
ran_stockfish = True
move = obtain_moves(board, chess.board)
if move is None:
continue
try:
chess._receive_move(move)
except IllegalMoveException as e:
print "Illegal move detected!"
continue
print "Move %s successful!"%move
ran_stockfish = False
