def match(self, img_arr: np.ndarray) -> int:
cursor = self.sqlite_connection.cursor()
img_arr_resized = image_process.resize(img_arr,
CV_SUPPORT_SERVANT_IMG_SIZE[1],
CV_SUPPORT_SERVANT_IMG_SIZE[0])
servant_part = img_arr_resized[:CV_SUPPORT_SERVANT_SPLIT_Y, :, :3]
hsv_servant_part = image_process.rgb_to_hsv(servant_part)
# querying servant icon database
if self.cached_icon_meta is None:
cursor.execute(
"select id from servant_icon order by id desc limit 1")
newest_svt_id = cursor.fetchone()[0]
cursor.execute("select count(1) from servant_icon")
entries = cursor.fetchone()[0]
cursor.execute("select id, image_key from servant_icon")
self.cached_icon_meta = cursor.fetchall()
logger.info(
'Finished querying support servant database, %d entries with newest servant id: %d'
% (entries, newest_svt_id))
# querying image data
min_servant_id = 0
min_abs_err = 0
for servant_id, image_key in self.cached_icon_meta:
if image_key not in self.cached_icons:
cursor.execute(
"select image_data from image where image_key = ?",
(image_key, ))
binary_data = cursor.fetchone()[0]
# clipping alpha and opacity border
np_image = image_process.imdecode(binary_data)[3:-3, 3:-3, :]
if np_image.shape[:2] != CV_SUPPORT_SERVANT_IMG_SIZE:
if not self.__warn_size_mismatch:
self.__warn_size_mismatch = True
logger.warning(
'The configuration of image size for support servant matching is different from '
'database size, performance will decrease: servant id: %d, key: %s'
% (servant_id, image_key))
np_image = image_process.resize(
np_image, CV_SUPPORT_SERVANT_IMG_SIZE[1],
CV_SUPPORT_SERVANT_IMG_SIZE[0])
np_image, alpha = image_process.split_rgb_alpha(np_image)
hsv_image = image_process.rgb_to_hsv(np_image)
self.cached_icons[image_key] = np.concatenate(
[hsv_image, np.expand_dims(alpha, 2)], axis=2)
anchor_servant_part = self.cached_icons[
image_key][:CV_SUPPORT_SERVANT_SPLIT_Y, ...]
hsv_err = image_process.mean_hsv_diff_err(anchor_servant_part,
hsv_servant_part, 'hsv',
'hsv')
if min_servant_id == 0 or hsv_err < min_abs_err:
min_servant_id = servant_id
min_abs_err = hsv_err
logger.debug('svt_id = %d, key = %s, hsv_err = %f' %
(servant_id, image_key, hsv_err))
cursor.close()
return min_servant_id
def get_screenshot(self, width: Optional[int] = None, height: Optional[int] = None) -> np.ndarray:
"""
Get the current screenshot of simulator.
:param width: the width of reshaped screenshot (in pixels), default: window width
:param height: the height of reshaped screenshot (in pixels), default: window height
:return: returns a numpy array shapes [h, w, c] which c = 3 in RGB order
"""
left, top, right, bottom = win32gui.GetWindowRect(self.handle())
window_height = bottom - top
window_width = right - left
handle_dc = win32gui.GetWindowDC(self.handle())
new_dc = win32ui.CreateDCFromHandle(handle_dc)
compat_dc = new_dc.CreateCompatibleDC()
new_bitmap = win32ui.CreateBitmap()
new_bitmap.CreateCompatibleBitmap(new_dc, window_width, window_height)
compat_dc.SelectObject(new_bitmap)
compat_dc.BitBlt((0, 0), (window_width, window_height), new_dc, (0, 0), win32con.SRCCOPY)
arr = new_bitmap.GetBitmapBits(True)
arr = np.fromstring(arr, dtype='uint8').reshape([window_height, window_width, -1])
new_dc.DeleteDC()
compat_dc.DeleteDC()
win32gui.ReleaseDC(self.handle(), handle_dc)
win32gui.DeleteObject(new_bitmap.GetHandle())
screenshot = np.flip(arr[..., :3], -1)
if width is None:
width = window_width
if height is None:
height = window_height
if width != window_width or height != window_width:
screenshot = image_process.resize(screenshot, width, height)
return screenshot
def get_screenshot(self,
width: Optional[int] = None,
height: Optional[int] = None) -> np.ndarray:
img = self._get_screenshot_internal()
width = width or img.shape[1]
height = height or img.shape[0]
return image_process.resize(img, width, height)
def _craft_essence_empty_check(img1, img2):
img1_h = int(img2.shape[1] / img1.shape[1] * img1.shape[0])
img1 = image_process.resize(img1, img2.shape[1], img1_h)
img1 = img1[-img2.shape[0]:, ...]
v = mean_gray_diff_err(img1, img2)
logger.debug('DEBUG value: empty support craft essence check: mean_gray_diff_err = %f' % v)
return v < 10
def match(self, img_arr: np.ndarray) -> int:
cursor = self.sqlite_connection.cursor()
ratio_thresh = 0.7
img_arr_resized = image_process.resize(img_arr,
CV_SUPPORT_SERVANT_IMG_SIZE[1],
CV_SUPPORT_SERVANT_IMG_SIZE[0])
craft_essence_part = img_arr_resized[CV_SUPPORT_SERVANT_SPLIT_Y:-3,
2:-2, :]
# CACHE ACCESS
cache_key = self.image_cacher.get(craft_essence_part, None)
if cache_key is not None:
return cache_key
if craft_essence_part in self.image_cacher:
return self.image_cacher[craft_essence_part]
if self.cached_icon_meta is None:
cursor.execute(
"select id from craft_essence_icon order by id desc limit 1")
newest_craft_essence_id = cursor.fetchone()[0]
cursor.execute("select count(1) from craft_essence_icon")
entries = cursor.fetchone()[0]
cursor.execute("select id, image_key from craft_essence_icon")
self.cached_icon_meta = cursor.fetchall()
logger.info(
'Finished querying craft essence database, %d entries with newest craft essence id: %d'
% (entries, newest_craft_essence_id))
_, target_descriptor = self.sift_detector.detectAndCompute(
craft_essence_part, None)
max_matches = 0
max_craft_essence_id = 0
for craft_essence_id, image_key in self.cached_icon_meta:
if image_key not in self.cached_icons:
cursor.execute(
"select descriptors from image_sift_descriptor where image_key = ?",
(image_key, ))
descriptor_blob = cursor.fetchone()[0]
# keypoint = [deserialize_cv2_keypoint(x) for x in pickle_loads(keypoint_blob)]
descriptors = pickle_loads(descriptor_blob)
self.cached_icons[
image_key] = descriptors # {'key_point': keypoint, 'descriptor': descriptors}
knn_matches = self.matcher.knnMatch(target_descriptor,
self.cached_icons[image_key],
2)
# knn_matches = self.matcher.match(target_descriptor, self.cached_icons[image_key]['descriptor'])
good_matches = []
for m, n in knn_matches:
if m.distance < ratio_thresh * n.distance:
good_matches.append(m)
len_good_matches = len(good_matches)
if len_good_matches > max_matches:
max_matches = len_good_matches
max_craft_essence_id = craft_essence_id
logger.debug('craft_essence_id = %d, sift_matches = %d' %
(craft_essence_id, len_good_matches))
cursor.close()
self.image_cacher[craft_essence_part] = max_craft_essence_id
return max_craft_essence_id
def main():
# =================== 동영상 테스트 ================================
fianl_result_array = []
section_result_array = []
video_path = 'C:\\Users\\Administrator\\Desktop\\2021_ICT\\library_video2.mp4'
# video에서 프레임 추출
frame_images = vp.extract_frame_from_video(video_path)
# 추출된 프레임에서 글자 영역 찾기
for i, frame in enumerate(frame_images):
vp.save_image(
ct.get_gray(frame),
'C:\\Users\\Administrator\\Desktop\\2021_ICT\\test\\frame_{}.jpg'.
format(i))
final_result = []
copy = ct.resize(frame)
cropped_images = ct.image_all_process(copy)[1]
num = 0
# 한 프레임의 글자 영역들의 텍스트 추출
# 자막들
for con in cropped_images["contours"]:
# 프레임에서 추출한 영역들 저장
ct.save_crooped_contours(
con,
'C:\\Users\\Administrator\\Desktop\\2021_ICT\\test\\fcontours_{}'
.format(num))
# 영역들에서 글자 추출
result = reco.extract_text(con)
# # 추출된 글자 전처리 후 임시 보관
tmp = txt.text_pre_process(result)
if (tmp is not None):
final_result.append(tmp)
# final_result.append(result)
num += 1
# 한 프레임 씩 배열에 저장
fianl_result_array.append(final_result)
# 섹션
# 섹션 영역 저장
ct.save_crooped_contours(
cropped_images["section"],
'C:\\Users\\Administrator\\Desktop\\2021_ICT\\test\\fsection_{}'.
format(i))
# 섹션에서 글자 추출
section = reco.extract_text(cropped_images["section"])
# 추출된 글자 전처리 후 저장
section_result_array.append(txt.text_pre_process(section))
# section_result_array.append(section)
# 모든 결과 값들 저장 하기
txt.text_save(
fianl_result_array, section_result_array,
'C:\\Users\\Administrator\\Desktop\\2021_ICT\\test\\fnew3_jjan.csv')
def __getitem__(self, idex):
batch_size = self.batch_size
image_size = self.image_size
images_num = self.images_num
inputs = np.zeros((batch_size, image_size, image_size, 3),
dtype=np.uint8)
if self.inplace:
image_size *= 4
outputs = np.zeros((batch_size, image_size, image_size, 3),
dtype=np.uint8)
path_index = idex * batch_size
num = 0
while True:
if path_index >= images_num:
path_index = path_index % images_num
path = str(self.image_paths[path_index])
img = imread(path)
h, w, _ = img.shape
mini = min(h, w)
if mini < image_size:
img = resize(img, image_size // mini + 1)
h, w, _ = img.shape
x = random.randint(0, w - image_size)
y = random.randint(0, h - image_size)
img = img[y:y + image_size, x:x + image_size]
img_LR = LR_image(img, self.de_num, self.inplace)
inputs[num] = img_LR
outputs[num] = img
num += 1
path_index += 1
if num >= batch_size:
break
return inputs, outputs
def _imread_to_screen_size(path):
return image_process.resize(image_process.imread(path),
CV_SCREENSHOT_RESOLUTION_X,
CV_SCREENSHOT_RESOLUTION_Y)
def main():
# =================== 동영상 테스트 ================================
fianl_result_array = []
section_result_array = []
video_path = 'C:\\Users\\Administrator\\Desktop\\2021_ICT\\library_video2.mp4'
# video에서 프레임 추출
frame_images = vp.extract_frame_from_video(video_path)
f_len = len(frame_images)
# 추출된 프레임에서 글자 영역 찾기
num = 0
num2 = 0
for i, frame in enumerate(frame_images):
vp.save_image(
frame,
'C:\\Users\\Administrator\\Desktop\\2021_ICT\\test2\\frame_{}.jpg'.
format(i))
final_result = []
copy = ct.resize(frame)
cropped_images = ct.image_all_process(copy)
# 섹션 추출
ct.save_crooped_contours(
cropped_images[0]["section"],
'C:\\Users\\Administrator\\Desktop\\2021_ICT\\test2\\section_{}'.
format(i))
for con in cropped_images[0]["contours"]:
# 프레임에서 추출한 영역들 저장 (origin 로)
ct.save_crooped_contours(
con,
'C:\\Users\\Administrator\\Desktop\\2021_ICT\\test2\\contours_{}_frame_{}_'
.format(num2, i))
num2 += 1
# 한 프레임의 글자 영역들에서 텍스트 추출 과정 시작
for con in cropped_images[1]["contours"]:
# 프레임에서 추출한 영역들 저장 (gray-scale로)
ct.save_crooped_contours(
ct.get_gradient(con),
'C:\\Users\\Administrator\\Desktop\\2021_ICT\\test2\\contours_{}_frame_{}_'
.format(num, i))
num += 1
judge_result = judge.get_text_image(num, 'final_new/')
path_dir = 'C:\\Users\\Administrator\\Desktop\\2021_ICT\\test2\\contours'
num_text = 0
num_not_text = 0
tmp_result = {}
section = {}
flie_list = os.listdir(path_dir)
flie_list.sort()
for j in range(1, num):
print(flie_list[j])
second = flie_list[j].split('_')[-2]
# 섹션 처리하기
if (second in section.keys()):
pass
else:
section[second] = vision.image_ocr(
'C:\\Users\\Administrator\\Desktop\\2021_ICT\\test2\\section_{}.jpg'
.format(second))
# 텍스트인 contour 찾아내기
if judge_result[j] == 'text':
val = vision.image_ocr(
'C:\\Users\\Administrator\\Desktop\\2021_ICT\\test2\\contours/'
+ flie_list[j])
if second in tmp_result:
tmp_result[second] = [tmp_result[second], val]
else:
tmp_result[second] = val
# print('num: %d, text: %s' %(num_text, val))
num_text += 1
# 모든 결과 값들 저장 하기
txt.directory_save(
tmp_result, section,
'C:\\Users\\Administrator\\Desktop\\2021_ICT\\test2\\jjan.csv')
def match(self, img_arr: np.ndarray) -> int:
blur_radius = 2
cursor = self.sqlite_connection.cursor()
target_size = CV_COMMAND_CARD_IMG_SIZE
# import matplotlib.pyplot as plt
# plt.figure()
# plt.imshow(img_arr)
# plt.show()
img_arr_resized = image_process.resize(img_arr, target_size[1],
target_size[0])
img_arr_resized, img_alpha = image_process.split_rgb_alpha(
img_arr_resized)
# use blur to remove high frequency noise introduced by interpolation, but blurring with alpha channel will
# produce some weird artifacts on the edge of alpha, same ops to db images
img_arr_resized = image_process.gauss_blur(img_arr_resized,
blur_radius)
hsv_img = np.concatenate([
image_process.rgb_to_hsv(img_arr_resized),
np.expand_dims(img_alpha, 2)
], 2)
# querying servant icon database
if self.cached_icon_meta is None:
cursor.execute(
"select id from servant_command_card_icon order by id desc limit 1"
)
newest_svt_id = cursor.fetchone()[0]
cursor.execute("select count(1) from servant_command_card_icon")
entries = cursor.fetchone()[0]
cursor.execute(
"select id, image_key from servant_command_card_icon")
self.cached_icon_meta = cursor.fetchall()
logger.info(
'Finished querying servant command card database, %d entries with newest servant id: %d'
% (entries, newest_svt_id))
# querying image data
min_servant_id = 0
min_err = 0
for servant_id, image_key in self.cached_icon_meta:
if image_key not in self.cached_icons:
cursor.execute(
"select image_data, name from image where image_key = ?",
(image_key, ))
binary_data, name = cursor.fetchone()
# All icon are PNG file with extra alpha channel
np_image = image_process.imdecode(binary_data)
# split alpha channel
assert np_image.shape[
-1] == 4, 'Servant Icon should be RGBA channel'
if np_image.shape[:2] != target_size:
if not self.__warn_size_mismatch:
self.__warn_size_mismatch = True
logger.warning(
'The configuration of image size for command card matching is different from '
'database size, performance will decrease: servant id: %d, key: %s'
% (servant_id, image_key))
np_image = image_process.resize(np_image, target_size[1],
target_size[0])
np_image = image_process.gauss_blur(np_image, blur_radius)
np_image, alpha = image_process.split_rgb_alpha(np_image)
hsv_image = image_process.rgb_to_hsv(np_image)
# weighted by alpha channel size
self.cached_icons[image_key] = np.concatenate(
[hsv_image, np.expand_dims(alpha, 2)], 2)
anchor = self.cached_icons[image_key]
# err_map = image_process.mean_hsv_diff_err_dbg(anchor, hsv_img, 'hsv', 'hsv')
hsv_err = image_process.mean_hsv_diff_err(anchor, hsv_img, 'hsv',
'hsv')
if min_servant_id == 0 or hsv_err < min_err:
min_servant_id = servant_id
min_err = hsv_err
logger.debug('svt_id = %d, key = %s, hsv_err = %f' %
(servant_id, image_key, hsv_err))
cursor.close()
return min_servant_id
def callback(ch, method, properties, body):
job_id = str(body, 'utf-8')
logger.info(" [x] Received %s" % job_id)
resize(job_id + '.png')
logger.info(" [x] Done")
ch.basic_ack(delivery_tag=method.delivery_tag)
def _is_in_requesting_friend_ui(self) -> bool:
img = self.attacher.get_screenshot(CV_SCREENSHOT_RESOLUTION_X, CV_SCREENSHOT_RESOLUTION_Y)
val = mean_gray_diff_err(image_process.resize(img, self._support_anchor.shape[1],
self._support_anchor.shape[0]), self._support_anchor)
logger.debug('DEBUG value friend_ui mean_gray_diff_err = %f' % val)
return val < 10
def detect_command_cards(img: np.ndarray) -> List[DispatchedCommandCard]:
"""
Detect in-battle command card for current turn (attack button must be pressed before calling this method!)
:param img: In-game screenshot, with shape (h, w, 3) in RGB format or (h, w, 4) in RGBA format (A channel will
be ignored)
:return: A list containing command card info
"""
assert len(img.shape) == 3, 'Invalid image shape, expected RGB format'
if img.shape[-1] == 4:
img = img.shape[..., :3]
ret_list = []
# 从者头像与指令卡的padding: Top 25px, Left 42px, Right 42px, Bottom 12px
t = time()
for card_idx, (x1, x2) in enumerate(
zip(CV_COMMAND_CARD_X1S, CV_COMMAND_CARD_X2S)):
command_card = img[CV_COMMAND_CARD_Y:, x1:x2, :3].copy()
card_type = 0
target_err = float('inf')
y_offset = 0
for idx, (command_card_type, offset) in enumerate(
zip(CommandCardDetector._command_card_type_anchor,
CV_COMMAND_CARD_TYPE_OFFSET)):
score = np.empty(CV_COMMAND_CARD_Y_DETECTION_LENGTH, np.float)
h = command_card_type.shape[0]
for i in range(CV_COMMAND_CARD_Y_DETECTION_LENGTH):
y = CV_COMMAND_CARD_Y_DETECTION_OFFSET + i
score[i] = image_process.mean_gray_diff_err(
command_card[y:y + h, ...], command_card_type)
min_score = np.min(score)
if min_score < target_err:
card_type = idx
target_err = min_score
y_offset = np.argmin(
score
) + CV_COMMAND_CARD_Y_DETECTION_OFFSET - offset + CV_COMMAND_CARD_Y
# Extend pixels
command_card = img[y_offset - CV_COMMAND_CARD_EXTEND_TOP:y_offset +
CV_COMMAND_CARD_EXTEND_BOTTOM +
CV_COMMAND_CARD_HEIGHT,
x1 - CV_COMMAND_CARD_EXTEND_LEFT:x2 +
CV_COMMAND_CARD_EXTEND_RIGHT]
# support detection
support_part = command_card[
CV_COMMAND_CARD_SUPPORT_Y1:CV_COMMAND_CARD_SUPPORT_Y2,
CV_COMMAND_CARD_SUPPORT_X1:CV_COMMAND_CARD_SUPPORT_X2, :]
err = image_process.mean_hsv_diff_err(
support_part, CommandCardDetector._command_card_support_anchor)
logger.debug(
'DEBUG value: command card support detection, hsv_err = %f' %
err)
is_support = err < CV_COMMAND_CARD_SUPPORT_HSV_THRESHOLD
# mask command card: concat RGB with extra alpha channel
alpha = CommandCardDetector._command_card_rev_alpha[card_type]
if command_card.shape[:2] != alpha.shape[:2]:
logger.warning(
'Width or height of command card rect does not match the alpha mask, alpha mask must be'
' updated to obtain best matching accuracy')
alpha = image_process.resize(alpha, command_card.shape[1],
command_card.shape[0])
CommandCardDetector._command_card_rev_alpha[card_type] = alpha
# composite support mask
if is_support:
alpha = alpha.copy()
alpha[CV_COMMAND_CARD_SUPPORT_Y1:CV_COMMAND_CARD_SUPPORT_Y2,
CV_COMMAND_CARD_SUPPORT_X1:CV_COMMAND_CARD_SUPPORT_X2] = \
CommandCardDetector._command_card_support_rev_alpha
command_card = np.concatenate(
[command_card, np.expand_dims(alpha, 2)], 2)
servant_id = CommandCardDetector._servant_matcher.match(
command_card)
ret_list.append(
DispatchedCommandCard(servant_id,
CommandCardType(card_type + 1), card_idx,
is_support, 0))
logger.info('Detected command card data: %s (used %f sec(s))' %
(str(ret_list), time() - t))
return ret_list
def _servant_empty_check(img1, img2):
v = mean_gray_diff_err(image_process.resize(img1, img2.shape[1], img2.shape[0]), img2)
logger.debug('DEBUG value: empty support servant check: mean_gray_diff_err = %f' % v)
return v < 10
def match_support_servant(self, img: np.ndarray, range_list: List[Tuple[int, int]]) -> List[SupportServant]:
# match servant
def _servant_empty_check(img1, img2):
v = mean_gray_diff_err(image_process.resize(img1, img2.shape[1], img2.shape[0]), img2)
logger.debug('DEBUG value: empty support servant check: mean_gray_diff_err = %f' % v)
return v < 10
svt_id, t = self._wrap_call_matcher(self.servant_matcher.match, _servant_empty_check, img,
self._support_empty_img, range_list)
logger.debug('Detected support servant ID: %s (used %f sec(s))' % (str(svt_id), t))
# match craft essence
def _craft_essence_empty_check(img1, img2):
img1_h = int(img2.shape[1] / img1.shape[1] * img1.shape[0])
img1 = image_process.resize(img1, img2.shape[1], img1_h)
img1 = img1[-img2.shape[0]:, ...]
v = mean_gray_diff_err(img1, img2)
logger.debug('DEBUG value: empty support craft essence check: mean_gray_diff_err = %f' % v)
return v < 10
# todo fix bug when empty servant and non-empty craft essence
ce_id, t = self._wrap_call_matcher(self.craft_essence_matcher.match, _craft_essence_empty_check, img,
self._support_craft_essence_img, range_list)
logger.debug('Detected support craft essence ID: %s (used %f sec(s))' % (str(ce_id), t))
ret_list = [SupportServant(x, y) for x, y in zip(svt_id, ce_id)]
for i, (y1, y2) in enumerate(range_list):
# detect craft essence max break state
if ce_id[i] == 0:
ret_list[i].craft_essence_max_break = False
else:
icon = img[y1:y2, CV_SUPPORT_SERVANT_X1:CV_SUPPORT_SERVANT_X2, :]
icon = icon[CV_SUPPORT_CRAFT_ESSENCE_MAX_BREAK_Y1:CV_SUPPORT_CRAFT_ESSENCE_MAX_BREAK_Y2,
CV_SUPPORT_CRAFT_ESSENCE_MAX_BREAK_X1:CV_SUPPORT_CRAFT_ESSENCE_MAX_BREAK_X2, :]
if self._support_craft_essence_img_resized is None:
# reduce unnecessary resize ops
anchor = image_process.resize(self._support_max_break_img, icon.shape[1], icon.shape[0])
self._support_craft_essence_img_resized = anchor
else:
anchor = self._support_craft_essence_img_resized
# err = mean_gray_diff_err(icon, anchor)
hsv_err = image_process.mean_hsv_diff_err(icon, anchor)
# logger.debug('DEBUG value: support craft essence max break check: gray_diff_err = %f, hsv_err = %f' %
# (err, hsv_err))
ret_list[i].craft_essence_max_break = hsv_err < CV_SUPPORT_CRAFT_ESSENCE_MAX_BREAK_THRESHOLD
# skip when support servant is empty
if svt_id[i] == 0:
continue
# detect friend state
friend_img = img[y1+CV_SUPPORT_FRIEND_DETECT_Y1:y1+CV_SUPPORT_FRIEND_DETECT_Y2,
CV_SUPPORT_FRIEND_DETECT_X1:CV_SUPPORT_FRIEND_DETECT_X2, :]
# omit B channel here
friend_part_binary = np.greater_equal(np.mean(friend_img[..., :2], 2),
CV_SUPPORT_FRIEND_DISCRETE_THRESHOLD)
is_friend = np.mean(friend_part_binary) > CV_SUPPORT_FRIEND_DETECT_THRESHOLD
ret_list[i].is_friend = is_friend
# skill level detection
skill_img = img[y1+CV_SUPPORT_SKILL_BOX_OFFSET_Y:y1+CV_SUPPORT_SKILL_BOX_OFFSET_Y+CV_SUPPORT_SKILL_BOX_SIZE,
CV_SUPPORT_SKILL_BOX_OFFSET_X1:CV_SUPPORT_SKILL_BOX_OFFSET_X2, :3].copy()
gray = np.mean(skill_img, -1)
vertical_diff = np.zeros_like(gray, dtype=np.float32)
step_size = CV_SUPPORT_SKILL_V_DIFF_STEP_SIZE # pixel offset for computing abs difference
vertical_diff[:-step_size, :] = np.abs(gray[:-step_size, :] - gray[step_size:, :])
# just use the first several pixels and last several pixels to determine
edge_size = CV_SUPPORT_SKILL_V_DIFF_EDGE_SIZE
skills = []
for j in range(3):
begin_x = j * (CV_SUPPORT_SKILL_BOX_MARGIN_X + CV_SUPPORT_SKILL_BOX_SIZE)
v_diff_current_skill = np.mean(vertical_diff[:, begin_x:begin_x+CV_SUPPORT_SKILL_BOX_SIZE], -1)
max_v_diff = np.maximum(np.max(v_diff_current_skill[:edge_size]),
np.max(v_diff_current_skill[-edge_size:]))
if max_v_diff > CV_SUPPORT_SKILL_V_DIFF_THRESHOLD:
# digit recognition, using SSIM metric, split by S (-> 0) and V (-> 255)
current_skill_img = skill_img[:, begin_x:begin_x + CV_SUPPORT_SKILL_BOX_SIZE, :]
hsv = image_process.rgb_to_hsv(current_skill_img).astype(np.float32)
img_digit_part = (1. - hsv[..., 1] / 255.) * (hsv[..., 2] / 255.)
img_digit_part = img_digit_part[30:, 3:30]
bin_digits = np.greater_equal(img_digit_part, CV_SUPPORT_SKILL_BINARIZATION_THRESHOLD)
digit_segments = image_process.split_image(bin_digits)
digits = []
for segment in sorted(digit_segments, key=lambda x: (x.max_x + x.min_x)):
if 50 < segment.associated_pixels.shape[0] < 150 \
and abs(segment.min_y + segment.max_y - 26) <= 3 \
and segment.max_x - segment.min_x < 14 <= segment.max_y - segment.min_y:
digits.append(self._digit_recognizer.recognize(segment.get_image_segment()))
if len(digits) == 2:
skill_lvl = digits[0] * 10 + digits[1]
if skill_lvl != 10:
logger.warning(f'Invalid 2 digits skill level: expected 10, but got {skill_lvl}, set to 10')
skill_lvl = 10
else:
skill_lvl = digits[0]
if skill_lvl == 0:
logger.warning(f'Invalid 1 digit skill level: expected 1~9, but got {skill_lvl}')
skills.append(skill_lvl)
else:
# skill unavailable
skills.append(None)
ret_list[i].skill_level = skills
logger.info('Detected support servant info: %s' % str(ret_list))
return ret_list