def _test():
tesseract = construct_tesseract()
# debug_images = (
# # 1465, # Yellow
# # 1463, # Blue
# # 1459, # Red
# # 1453, # White
# 1516,
# )
# # for path in ls_debug(explicit_options=debug_images):
# for path in ls(DATA_DIR + "module_classifier/labelled/button", 10):
# path = path.replace("-full-", "-module-").replace("labelled/button", "unlabelled")
# print path
# im = cv2.imread(path)
# print get_button_color_label_and_location(im, tesseract)
# # print get_strip_color(im)-=
# show(im)
# screenshots = (
# ("/Users/danny/Dropbox (Dropbox)/Screenshots/Screenshot 2017-02-14 23.04.00.png", (2, 0)),
# ("/Users/danny/Dropbox (Dropbox)/Screenshots/Screenshot 2017-02-14 23.03.50.png", (2, 1)),
# ("/Users/danny/Dropbox (Dropbox)/Screenshots/Screenshot 2017-02-14 23.03.41.png", (1, 1)),
# ("/Users/danny/Dropbox (Dropbox)/Screenshots/Screenshot 2017-02-14 23.03.31.png", (0, 1)),
# ("/Users/danny/Dropbox (Dropbox)/Screenshots/Screenshot 2017-02-14 23.03.20.png", (0, 0)),
# )
# for path, module_position in screenshots:
for path in ls_debug(1634, 1634):
module_position = (0, 1)
im = cv2.imread(path)
screenshot_helper = ScreenshotHelper(
inflate_classifier(MODULE_CLASSIFIER_DIR))
print get_clock_time_from_full_screenshot(im, module_position,
screenshot_helper)
def __init__(self):
super(WhosOnFirstSolver, self).__init__()
self._button_classifier = inflate_classifier(WHOS_ON_FIRST_BUTTON_CLASSIFIER_DIR)
self._tesseract = _get_tesseract()
self._debug_image = 0
def _test():
tesseract = construct_tesseract()
# debug_images = (
# # 1465, # Yellow
# # 1463, # Blue
# # 1459, # Red
# # 1453, # White
# 1516,
# )
# # for path in ls_debug(explicit_options=debug_images):
# for path in ls(DATA_DIR + "module_classifier/labelled/button", 10):
# path = path.replace("-full-", "-module-").replace("labelled/button", "unlabelled")
# print path
# im = cv2.imread(path)
# print get_button_color_label_and_location(im, tesseract)
# # print get_strip_color(im)-=
# show(im)
# screenshots = (
# ("/Users/danny/Dropbox (Dropbox)/Screenshots/Screenshot 2017-02-14 23.04.00.png", (2, 0)),
# ("/Users/danny/Dropbox (Dropbox)/Screenshots/Screenshot 2017-02-14 23.03.50.png", (2, 1)),
# ("/Users/danny/Dropbox (Dropbox)/Screenshots/Screenshot 2017-02-14 23.03.41.png", (1, 1)),
# ("/Users/danny/Dropbox (Dropbox)/Screenshots/Screenshot 2017-02-14 23.03.31.png", (0, 1)),
# ("/Users/danny/Dropbox (Dropbox)/Screenshots/Screenshot 2017-02-14 23.03.20.png", (0, 0)),
# )
# for path, module_position in screenshots:
for path in ls_debug(1634, 1634):
module_position = (0, 1)
im = cv2.imread(path)
screenshot_helper = ScreenshotHelper(inflate_classifier(MODULE_CLASSIFIER_DIR))
print get_clock_time_from_full_screenshot(im, module_position, screenshot_helper)
def test():
im = cv2.imread(
"/Users/danny/Dropbox (Personal)/Projects/KeepTalkingBot/module_specific_data/debug/0003.png"
)
symbol_classifier = inflate_classifier(SYMBOLS_CLASSIFIER_DIR)
symbols, positions = get_symbols_and_positions(im, symbol_classifier)
print symbols
order = get_symbol_order(symbols)
def test():
# to_test = (275, 280)
# to_test = (1243, 1248)
to_test = (1331, 1336)
letter_classifier = inflate_classifier(PASSWORD_LETTER_CLASSIFIER_DIR)
for f in ls_debug(*to_test):
im = cv2.imread(f)
print get_letters(im, letter_classifier)
show(im)
def test():
# to_test = (275, 280)
# to_test = (1243, 1248)
to_test = (1331, 1336)
letter_classifier = inflate_classifier(PASSWORD_LETTER_CLASSIFIER_DIR)
for f in ls_debug(*to_test):
im = cv2.imread(f)
print get_letters(im, letter_classifier)
show(im)
def _extract_pieces():
# _, _, labelled_src_dir, _, _ = get_classifier_directories(MODULE_CLASSIFIER_DIR)
# src_dir = os.path.join(labelled_src_dir, "memory")
# _, buttons_dir, _, _, _ = get_classifier_directories(BUTTONS_CLASSIFIER_DIR)
# _, screen_dir, _, _, _ = get_classifier_directories(SCREEN_CLASSIFIER_DIR)
# button_template = os.path.join(buttons_dir, "{:04}.png")
# screen_template = os.path.join(screen_dir, "{:04}.png")
#
# next_button = 0
# next_screen = 0
files = (
# "/Users/danny/Dropbox (Personal)/Projects/KeepTalkingBot/module_specific_data/debug/0121.png",
# "/Users/danny/Dropbox (Personal)/Projects/KeepTalkingBot/module_specific_data/debug/0122.png",
# "/Users/danny/Dropbox (Personal)/Projects/KeepTalkingBot/module_specific_data/debug/0123.png",
# "/Users/danny/Dropbox (Personal)/Projects/KeepTalkingBot/module_specific_data/debug/0135.png",
# "/Users/danny/Dropbox (Personal)/Projects/KeepTalkingBot/module_specific_data/debug/0973.png",
# "/Users/danny/Dropbox (Personal)/Projects/KeepTalkingBot/module_specific_data/debug/0974.png",
"/Users/danny/Dropbox (Personal)/Projects/KeepTalkingBot/module_specific_data/debug/0975.png",
# "/Users/danny/Dropbox (Personal)/Projects/KeepTalkingBot/module_specific_data/debug/0976.png",
# "/Users/danny/Dropbox (Personal)/Projects/KeepTalkingBot/module_specific_data/debug/0977.png",
# "/Users/danny/Dropbox (Personal)/Projects/KeepTalkingBot/module_specific_data/debug/0978.png",
)
button_classifier = inflate_classifier(BUTTONS_CLASSIFIER_DIR)
screen_classifier = inflate_classifier(SCREEN_CLASSIFIER_DIR)
for i, f in enumerate(files):
# if i >= 4:
# break
# if i != 3:
# continue
im = cv2.imread(f)
buttons = _get_button_images(im)
print _get_button_locations(im)
screen = _get_screen_image(im)
print "SCREEN:", screen_classifier(screen)
for b in buttons:
print "BUTTON:", button_classifier(b)
show(im)
def _extract_pieces():
# _, _, labelled_src_dir, _, _ = get_classifier_directories(MODULE_CLASSIFIER_DIR)
# src_dir = os.path.join(labelled_src_dir, "memory")
# _, buttons_dir, _, _, _ = get_classifier_directories(BUTTONS_CLASSIFIER_DIR)
# _, screen_dir, _, _, _ = get_classifier_directories(SCREEN_CLASSIFIER_DIR)
# button_template = os.path.join(buttons_dir, "{:04}.png")
# screen_template = os.path.join(screen_dir, "{:04}.png")
#
# next_button = 0
# next_screen = 0
files = (
# "/Users/danny/Dropbox (Personal)/Projects/KeepTalkingBot/module_specific_data/debug/0121.png",
# "/Users/danny/Dropbox (Personal)/Projects/KeepTalkingBot/module_specific_data/debug/0122.png",
# "/Users/danny/Dropbox (Personal)/Projects/KeepTalkingBot/module_specific_data/debug/0123.png",
# "/Users/danny/Dropbox (Personal)/Projects/KeepTalkingBot/module_specific_data/debug/0135.png",
# "/Users/danny/Dropbox (Personal)/Projects/KeepTalkingBot/module_specific_data/debug/0973.png",
# "/Users/danny/Dropbox (Personal)/Projects/KeepTalkingBot/module_specific_data/debug/0974.png",
"/Users/danny/Dropbox (Personal)/Projects/KeepTalkingBot/module_specific_data/debug/0975.png",
# "/Users/danny/Dropbox (Personal)/Projects/KeepTalkingBot/module_specific_data/debug/0976.png",
# "/Users/danny/Dropbox (Personal)/Projects/KeepTalkingBot/module_specific_data/debug/0977.png",
# "/Users/danny/Dropbox (Personal)/Projects/KeepTalkingBot/module_specific_data/debug/0978.png",
)
button_classifier = inflate_classifier(BUTTONS_CLASSIFIER_DIR)
screen_classifier = inflate_classifier(SCREEN_CLASSIFIER_DIR)
for i, f in enumerate(files):
# if i >= 4:
# break
# if i != 3:
# continue
im = cv2.imread(f)
buttons = _get_button_images(im)
print _get_button_locations(im)
screen = _get_screen_image(im)
print "SCREEN:", screen_classifier(screen)
for b in buttons:
print "BUTTON:", button_classifier(b)
show(im)
def play_game():
time.sleep(2)
classifier = inflate_classifier(MODULE_CLASSIFIER_DIR)
solvers = create_solvers()
screenshot_helper = ScreenshotHelper(classifier)
while True:
start_game()
open_bomb()
screenshot_helper.initialize_while_open()
sides_info = get_sides_info_while_open(screenshot_helper)
solve_modules_on_this_side(solvers, sides_info, screenshot_helper)
flip_side()
# Ideally we could remove this close/open cycle
close_once()
open_bomb()
solve_modules_on_this_side(solvers, sides_info, screenshot_helper)
close_once()
quit_game()
def play_game():
time.sleep(2)
classifier = inflate_classifier(MODULE_CLASSIFIER_DIR)
solvers = create_solvers()
screenshot_helper = ScreenshotHelper(classifier)
while True:
start_game()
open_bomb()
screenshot_helper.initialize_while_open()
sides_info = get_sides_info_while_open(screenshot_helper)
solve_modules_on_this_side(solvers, sides_info, screenshot_helper)
flip_side()
# Ideally we could remove this close/open cycle
close_once()
open_bomb()
solve_modules_on_this_side(solvers, sides_info, screenshot_helper)
close_once()
quit_game()
def test():
vocab_path, unlabelled_dir, labelled_dir, features_dir, svm_data_dir = \
get_classifier_directories(MODULE_CLASSIFIER_DIR)
for path in ls(os.path.join(labelled_dir, "password")):
im = cv2.imread(path)
letter_classifier = inflate_classifier(PASSWORD_LETTER_CLASSIFIER_DIR)
top_buttons, bottom_buttons = find_column_buttons(im)
submit_button = find_submit_button(im)
print get_letters(im, letter_classifier)
for i, b in enumerate(top_buttons):
cv2.circle(im, b, 5, (0, 0, 255))
cv2.putText(im, str(i), b, cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 0))
for i, b in enumerate(bottom_buttons):
cv2.circle(im, b, 5, (0, 255, 0))
cv2.putText(im, str(i), b, cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 0))
cv2.circle(im, submit_button, 5, (255, 0, 0))
show(im)
def test():
vocab_path, unlabelled_dir, labelled_dir, features_dir, svm_data_dir = \
get_classifier_directories(MODULE_CLASSIFIER_DIR)
for path in ls(os.path.join(labelled_dir, "password")):
im = cv2.imread(path)
letter_classifier = inflate_classifier(PASSWORD_LETTER_CLASSIFIER_DIR)
top_buttons, bottom_buttons = find_column_buttons(im)
submit_button = find_submit_button(im)
print get_letters(im, letter_classifier)
for i, b in enumerate(top_buttons):
cv2.circle(im, b, 5, (0, 0, 255))
cv2.putText(im, str(i), b, cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 0))
for i, b in enumerate(bottom_buttons):
cv2.circle(im, b, 5, (0, 255, 0))
cv2.putText(im, str(i), b, cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 0))
cv2.circle(im, submit_button, 5, (255, 0, 0))
show(im)
def test():
tesseract = _get_tesseract()
classifier = inflate_classifier(WHOS_ON_FIRST_BUTTON_CLASSIFIER_DIR)
vocab_path, unlabelled_dir, labelled_dir, features_dir, svm_data_dir = \
get_classifier_directories(MODULE_CLASSIFIER_DIR)
i = 0
# for path in ["/Users/danny/Dropbox (Personal)/Projects/KeepTalkingBot/module_specific_data/whos_on_first/in_game_6.png"]:
# for path in ls(os.path.join(labelled_dir, "whos_on_first")):
for path in ls_debug(1486, 1486):
i += 1
# if i < 50:
# continue
# if i >= 50:
# break
# name = "-module-".join(os.path.basename(path).split("-full-"))
# path = os.path.join(unlabelled_dir, name)
im = cv2.imread(path)
# show(im)
screen_text = get_screen_content(im, tesseract, 9999)
def _get_text_from_letters(letters):
# type: (List[np.array]) -> List[str]
is_zero_classifier = inflate_classifier(SERIAL_IS_ZERO_CLASSIFIER_DIR)
text = []
with PyTessBaseAPI() as api:
api.SetVariable("load_system_dawg", "F")
api.SetVariable("load_freq_dawg", "F")
api.SetVariable("load_punc_dawg", "F")
api.SetVariable("load_number_dawg", "F")
api.SetVariable("load_unambig_dawg", "F")
api.SetVariable("load_bigram_dawg", "F")
api.SetVariable("load_fixed_length_dawgs", "F")
api.SetVariable("classify_enable_learning", "F")
api.SetVariable("classify_enable_adaptive_matcher", "F")
api.SetVariable("segment_penalty_garbage", "F")
api.SetVariable("segment_penalty_dict_nonword", "F")
api.SetVariable("segment_penalty_dict_frequent_word", "F")
api.SetVariable("segment_penalty_dict_case_ok", "F")
api.SetVariable("segment_penalty_dict_case_bad", "F")
api.SetVariable("edges_use_new_outline_complexity", "T")
api.SetVariable("tessedit_char_whitelist",
"ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789")
api.SetPageSegMode(PSM.SINGLE_CHAR)
for letter in letters:
if LABEL_TO_IS_ZERO[is_zero_classifier(letter)]:
text.append("0")
continue
pil_image = Image.fromarray(letter)
api.SetImage(pil_image)
# show(np.array(api.GetThresholdedImage()))
text.append(api.GetUTF8Text().replace("\n\n", ""))
return text
def _get_text_from_letters(letters):
# type: (List[np.array]) -> List[str]
is_zero_classifier = inflate_classifier(SERIAL_IS_ZERO_CLASSIFIER_DIR)
text = []
with PyTessBaseAPI() as api:
api.SetVariable("load_system_dawg", "F")
api.SetVariable("load_freq_dawg", "F")
api.SetVariable("load_punc_dawg", "F")
api.SetVariable("load_number_dawg", "F")
api.SetVariable("load_unambig_dawg", "F")
api.SetVariable("load_bigram_dawg", "F")
api.SetVariable("load_fixed_length_dawgs", "F")
api.SetVariable("classify_enable_learning", "F")
api.SetVariable("classify_enable_adaptive_matcher", "F")
api.SetVariable("segment_penalty_garbage", "F")
api.SetVariable("segment_penalty_dict_nonword", "F")
api.SetVariable("segment_penalty_dict_frequent_word", "F")
api.SetVariable("segment_penalty_dict_case_ok", "F")
api.SetVariable("segment_penalty_dict_case_bad", "F")
api.SetVariable("edges_use_new_outline_complexity", "T")
api.SetVariable("tessedit_char_whitelist", "ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789")
api.SetPageSegMode(PSM.SINGLE_CHAR)
for letter in letters:
if LABEL_TO_IS_ZERO[is_zero_classifier(letter)]:
text.append("0")
continue
pil_image = Image.fromarray(letter)
api.SetImage(pil_image)
# show(np.array(api.GetThresholdedImage()))
text.append(api.GetUTF8Text().replace("\n\n", ""))
return text
def __init__(self):
super(MemorySolver, self).__init__()
self.button_classifier = inflate_classifier(BUTTONS_CLASSIFIER_DIR)
self.screen_classifier = inflate_classifier(SCREEN_CLASSIFIER_DIR)
def __init__(self):
super(MemorySolver, self).__init__()
self.button_classifier = inflate_classifier(BUTTONS_CLASSIFIER_DIR)
self.screen_classifier = inflate_classifier(SCREEN_CLASSIFIER_DIR)
def __init__(self):
super(PasswordSolver, self).__init__()
self._letter_classifier = inflate_classifier(
PASSWORD_LETTER_CLASSIFIER_DIR)
def test():
im = cv2.imread("/Users/danny/Dropbox (Personal)/Projects/KeepTalkingBot/module_specific_data/debug/0003.png")
symbol_classifier = inflate_classifier(SYMBOLS_CLASSIFIER_DIR)
symbols, positions = get_symbols_and_positions(im, symbol_classifier)
print symbols
order = get_symbol_order(symbols)
def __init__(self):
super(SymbolsSolver, self).__init__()
self._symbol_classifier = inflate_classifier(SYMBOLS_CLASSIFIER_DIR)
def __init__(self):
super(SymbolsSolver, self).__init__()
self._symbol_classifier = inflate_classifier(SYMBOLS_CLASSIFIER_DIR)
def __init__(self):
super(PasswordSolver, self).__init__()
self._letter_classifier = inflate_classifier(PASSWORD_LETTER_CLASSIFIER_DIR)
