หลักการทำงาน

window = find_client_origin(title)
if window is None:
    raise RuntimeError(active_translate("error_game_window_not_found", title=title))
left, top, width, height = window
if width <= 1 or height <= 1:
    raise RuntimeError(active_translate("error_game_capture_unavailable"))
image = ImageGrab.grab((left, top, left + width, top + height)).convert("RGB")
if image.width <= 1 or image.height <= 1:
    raise RuntimeError(active_translate("error_empty_game_capture_retry"))
return image

if frame_bgr.size == 0 or frame_bgr.ndim != 3:
    return previous or generic_minimap_definition()
search = frame_bgr[:min(frame_bgr.shape[0], 420), :min(frame_bgr.shape[1], 430)]
edges = cv2.Canny(cv2.cvtColor(search, cv2.COLOR_BGR2GRAY), 45, 135)
contours, _ = cv2.findContours(edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)

marker_centers = [
    (x + width / 2.0, y + height / 2.0)
    for x, y, width, height, _ in _marker_components(search, player_pixels)
]
if not marker_centers:
    return previous or generic_minimap_definition()

def match_player_feature(image_bgr: np.ndarray, template_bgr: np.ndarray,
                         mask: np.ndarray | None) -> np.ndarray:
    """Match normal player feature templates with tolerant grayscale correlation."""
    screen_gray = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2GRAY)
    template_gray = cv2.cvtColor(template_bgr, cv2.COLOR_BGR2GRAY)
    return cv2.matchTemplate(
        screen_gray,
        template_gray,
        cv2.TM_CCORR_NORMED,
        mask=mask,
    )

if image_bgr.shape[0] < template.shape[0] or image_bgr.shape[1] < template.shape[1]:
    return []
scores = cv2.matchTemplate(image_bgr, template, cv2.TM_CCOEFF_NORMED)
ys, xs = np.where(scores >= threshold)
targets = [Target(int(x + template.shape[1] / 2),
                  int(y) + top_to_feet if top_to_feet is not None else feature_center_to_feet(int(y), template.shape[0]),
                  float(scores[y, x]))
           for y, x in zip(ys, xs)]
return _non_maximum(targets, 15)

def _non_maximum(points: list[Target], min_distance: int) -> list[Target]:
    kept: list[Target] = []
    for point in sorted(points, key=lambda item: item.confidence, reverse=True):
        if all(abs(point.center_x - other.center_x) > min_distance or abs(point.feet_y - other.feet_y) > min_distance for other in kept):
            kept.append(point)
    return kept

hsv = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2HSV)
hue_low, saturation_low, value_low = player_pixels.activity_hsv_lower
lower = np.asarray(
    (hue_low, max(0, saturation_low - 40), value_low), dtype=np.uint8,
)
upper = np.asarray(player_pixels.activity_hsv_upper, dtype=np.uint8)
tolerant_mask = cv2.inRange(hsv, lower, upper)
return matching_components(tolerant_mask, exact_area=False)

right = min(frame_bgr.shape[1], minimap.end_x + 1)
bottom = min(frame_bgr.shape[0], minimap.end_y + 1)
if minimap.start_x >= right or minimap.start_y >= bottom:
    return None
image = frame_bgr[minimap.start_y:bottom, minimap.start_x:right]
candidates: list[tuple[int, int]] = []
for x, y, _, _, _ in _marker_components(image, player_pixels):
    # Each server profile maps the matched component's origin to the
    # marker's standing point with its own vertical foot offset.
    candidates.append((x, y + player_pixels.foot_offset))
if len(candidates) != 1:
    return None
x, foot_y = candidates[0]
source_width = max(1, right - minimap.start_x)
source_height = max(1, bottom - minimap.start_y)
source_x = min(source_width - 1, x)
source_y = min(source_height - 1, foot_y)
return float(source_x), float(source_y)

valid_columns: list[int] = []
for column in range(bar.width):
    matches = 0
    for row in range(bar.height):
        red, green, blue = pixels[column, row]
        if kind == "hp":
            colored = red >= 130 and red >= green * 1.5 and red >= blue * 1.5
        else:
            colored = blue >= 110 and blue >= red * 1.25 and blue >= green * 1.15
        matches += int(colored)
    if matches >= max(2, int(bar.height * 0.2)):
        valid_columns.append(column)
if not valid_columns:
    return 0.0
percent = 100 * (valid_columns[-1] + 1) / bar.width
return 100.0 if percent >= 95 else round(percent, 1)

def decide(self, observation: Observation) -> Decision:
    targets = self.same_line_targets(observation)
    if targets:
        target = min(targets, key=lambda m: abs(m.center_x - observation.player_x))
        delta = target.center_x - observation.player_x
        if abs(delta) <= self.config.target_distance:
            return Decision(HuntState.ATTACK, "attack_burst", "right" if delta >= 0 else "left", target)
        return Decision(HuntState.APPROACH, "move_pulse", "right" if delta > 0 else "left", target)
    return Decision(HuntState.PATROL, "move_pulse")

def _monsters_for_frame(self, frame: np.ndarray) -> tuple:
    """Run expensive monster matching at a lower rate than route tracking."""
    if self._monster_detection_suppressed():
        self.last_detected_monsters = ()
        self.last_monster_detection_at = 0.0
        return ()
    now = time.monotonic()
    if (self.last_monster_detection_at <= 0.0
            or now - self.last_monster_detection_at >= self.MONSTER_DETECTION_INTERVAL_SECONDS):
        self.last_detected_monsters = tuple(self._detect_monsters(frame))
        self.last_monster_detection_at = time.monotonic()
    return self.last_detected_monsters