diff --git a/assets/mask/MASK_MAP_INTERACT.png b/assets/mask/MASK_MAP_INTERACT.png
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Binary files /dev/null and b/assets/mask/MASK_MAP_INTERACT.png differ
diff --git a/tasks/map/interact/aim.py b/tasks/map/interact/aim.py
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+++ b/tasks/map/interact/aim.py
@@ -0,0 +1,347 @@
+import cv2
+import numpy as np
+
+from module.base.decorator import cached_property, del_cached_property
+from module.base.utils import Points, image_size, load_image
+from module.config.utils import dict_to_kv
+from module.logger import logger
+from tasks.base.ui import UI
+
+
+def inrange(image, lower=0, upper=255):
+    """
+    Get the coordinates of pixels in range.
+    Equivalent to `np.array(np.where(lower <= image <= upper))` but faster.
+    Note that this method will change `image`.
+
+    `cv2.findNonZero()` is faster than `np.where`
+    points = np.array(np.where(y > 24)).T[:, ::-1]
+    points = np.array(cv2.findNonZero((y > 24).astype(np.uint8)))[:, 0, :]
+
+    `cv2.inRange(y, 24)` is faster than `y > 24`
+    cv2.inRange(y, 24, 255, dst=y)
+    y = y > 24
+
+    Returns:
+        np.ndarray: Shape (N, 2)
+            E.g. [[x1, y1], [x2, y2], ...]
+    """
+    cv2.inRange(image, lower, upper, dst=image)
+    try:
+        return np.array(cv2.findNonZero(image))[:, 0, :]
+    except IndexError:
+        # Empty result
+        # IndexError: too many indices for array: array is 0-dimensional, but 3 were indexed
+        return np.array([])
+
+
+def subtract_blur(image, radius=3, negative=False):
+    """
+    If you care performance more than quality:
+    - radius=3, use medianBlur
+    - radius=5,7,9,11, use GaussianBlur
+    - radius>11, use stackBlur (requires opencv >= 4.7.0)
+
+    Args:
+        image:
+        radius:
+        negative:
+
+    Returns:
+        np.ndarray:
+    """
+    if radius <= 3:
+        blur = cv2.medianBlur(image, radius)
+    elif radius <= 11:
+        blur = cv2.GaussianBlur(image, (radius, radius), 0)
+    else:
+        blur = cv2.stackBlur(image, (radius, radius), 0)
+
+    if negative:
+        cv2.subtract(blur, image, dst=blur)
+    else:
+        cv2.subtract(image, blur, dst=blur)
+    return blur
+
+
+def remove_border(image, radius):
+    """
+    Paint edge pixels black.
+    No returns, changes are written to `image`
+
+    Args:
+        image:
+        radius:
+    """
+    width, height = image_size(image)
+    image[:, :radius + 1] = 0
+    image[:, width - radius:] = 0
+    image[:radius + 1, :] = 0
+    image[height - radius:, :] = 0
+
+
+def create_circle(min_radius, max_radius):
+    """
+    Create a circle with min_radius <= R <= max_radius.
+    1 represents circle, 0 represents background
+
+    Args:
+        min_radius:
+        max_radius:
+
+    Returns:
+        np.ndarray:
+    """
+    circle = np.ones((max_radius * 2 + 1, max_radius * 2 + 1), dtype=np.uint8)
+    center = np.array((max_radius, max_radius))
+    points = np.array(np.meshgrid(np.arange(circle.shape[0]), np.arange(circle.shape[1]))).T
+    distance = np.linalg.norm(points - center, axis=2)
+    circle[distance < min_radius] = 0
+    circle[distance > max_radius] = 0
+    return circle
+
+
+def draw_circle(image, circle, points):
+    """
+    Add a circle onto image.
+    No returns, changes are written to `image`
+
+    Args:
+        image:
+        circle: Created from create_circle()
+        points: (x, y), center of the circle to draw
+    """
+    width, height = image_size(circle)
+    x1 = -int(width // 2)
+    y1 = -int(height // 2)
+    x2 = width + x1
+    y2 = height + y1
+    for point in points:
+        x, y = point
+        # Fancy index is faster
+        index = image[y + y1:y + y2, x + x1:x + x2]
+        # print(index.shape)
+        cv2.add(index, circle, dst=index)
+
+
+class Aim:
+    radius_enemy = (24, 25)
+    radius_item = (8, 10)
+
+    def __init__(self):
+        self.debug = False
+
+        self.draw_item = None
+        self.draw_enemy = None
+        self.points_item = None
+        self.points_enemy = None
+
+    def clear_image_cache(self):
+        self.draw_item = None
+        self.draw_enemy = None
+        self.points_item = None
+        self.points_enemy = None
+        del_cached_property(self, 'aimed_enemy')
+        del_cached_property(self, 'aimed_item')
+
+    @cached_property
+    def mask_interact(self):
+        return load_image('./assets/mask/MASK_MAP_INTERACT.png')
+
+    @cached_property
+    def circle_enemy(self):
+        return create_circle(*self.radius_enemy)
+
+    @cached_property
+    def circle_item(self):
+        return create_circle(*self.radius_item)
+
+    # @timer
+    def predict_enemy(self, h, v):
+        min_radius, max_radius = self.radius_enemy
+        width, height = image_size(v)
+
+        # Get white circle `y`
+        y = subtract_blur(h, 3, negative=False)
+        cv2.inRange(h, 168, 255, h)
+        cv2.bitwise_and(y, h, dst=y)
+        # Get red glow `v`
+        cv2.inRange(v, 168, 255, dst=v)
+
+        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+        cv2.dilate(v, kernel, dst=v)
+        # Remove noise and leave red circle only
+        cv2.bitwise_and(y, v, dst=y)
+        # cv2.imshow('predict_enemy', y)
+        # Remove game UI
+        cv2.bitwise_and(y, self.mask_interact, dst=y)
+        # Remove points on the edge, or draw_circle() will overflow
+        remove_border(y, max_radius)
+
+        # Get all pixels
+        points = inrange(y, lower=18)
+        if points.shape[0] > 1000:
+            logger.warning(f'AimDetector.predict_enemy() too many points to draw: {points.shape}')
+        # Draw circles
+        draw = np.zeros((height, width), dtype=np.uint8)
+        draw_circle(draw, self.circle_enemy, points)
+        if self.debug:
+            self.draw_enemy = cv2.multiply(draw, 4)
+        subtract_blur(draw, 3)
+
+        # Find peaks
+        points = inrange(draw, lower=36)
+        points = Points(points).group(threshold=10)
+        if points.shape[0] > 3:
+            logger.warning(f'AimDetector.predict_enemy() too many peaks: {points.shape}')
+        self.points_enemy = points
+        # print(points)
+        return points
+
+    # @timer
+    def predict_item(self, v):
+        min_radius, max_radius = self.radius_item
+        width, height = image_size(v)
+
+        # Get white circle `y`
+        y = subtract_blur(v, 9)
+        white = cv2.inRange(v, 112, 144)
+        cv2.bitwise_and(y, white, dst=y)
+        # Get cyan glow `v`
+        cv2.inRange(v, 0, 84, dst=v)
+
+        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
+        cv2.dilate(v, kernel, dst=v)
+
+        # Remove noise and leave cyan circle only
+        cv2.bitwise_and(y, v, dst=y)
+        # Remove game UI
+        cv2.bitwise_and(y, self.mask_interact, dst=y)
+        # Remove points on the edge, or draw_circle() will overflow
+        remove_border(y, max_radius)
+
+        # Get all pixels
+        points = inrange(y, lower=18)
+        # print(points.shape)
+        if points.shape[0] > 1000:
+            logger.warning(f'AimDetector.predict_item() too many points to draw: {points.shape}')
+        # Draw circles
+        draw = np.zeros((height, width), dtype=np.uint8)
+        draw_circle(draw, self.circle_item, points)
+        if self.debug:
+            self.draw_item = cv2.multiply(draw, 2)
+        subtract_blur(draw, 7)
+
+        # Find peaks
+        points = inrange(draw, lower=64)
+        points = Points(points).group(threshold=10)
+        if points.shape[0] > 3:
+            logger.warning(f'AimDetector.predict_item() too many peaks: {points.shape}')
+        self.points_item = points
+        # print(points)
+        return points
+
+    # @timer
+    def predict(self, image, enemy=True, item=True, show_log=True, debug=False):
+        """
+        Predict `aim` on image, costs about 10.0~10.5ms.
+
+        Args:
+            image:
+            enemy: True to predict enemy
+            item: True to predict item
+            show_log:
+            debug: True to show AimDetector image
+        """
+        self.debug = debug
+        self.clear_image_cache()
+        if isinstance(image, str):
+            image = load_image(image)
+
+        # 1.5~2.0ms
+        yuv = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
+        v = yuv[:, :, 2]
+        h = yuv[:, :, 0]
+        # 4.0~4.5ms
+        if enemy:
+            self.predict_enemy(h.copy(), v.copy())
+        # 3.0~3.5ms
+        if item:
+            self.predict_item(v.copy())
+
+        if show_log:
+            kv = {}
+            if self.aimed_enemy:
+                kv['enemy'] = self.aimed_enemy
+            if self.aimed_item:
+                kv['item'] = self.aimed_item
+            if kv:
+                logger.info(f'Aimed: {dict_to_kv(kv)}')
+        if debug:
+            self.show_aim()
+
+    def show_aim(self):
+        if self.draw_enemy is None:
+            if self.draw_item is None:
+                return
+            else:
+                r = g = b = self.draw_item
+        else:
+            if self.draw_item is None:
+                r = g = b = self.draw_enemy
+            else:
+                r = self.draw_enemy
+                g = b = self.draw_item
+
+        image = cv2.merge([b, g, r])
+
+        cv2.imshow('AimDetector', image)
+        cv2.waitKey(1)
+
+    @cached_property
+    def aimed_enemy(self) -> tuple[int, int] | None:
+        if self.points_enemy is None:
+            return None
+        try:
+            _ = self.points_enemy[1]
+            logger.warning(f'Multiple aimed enemy found, using first point of {self.points_enemy}')
+        except IndexError:
+            pass
+        try:
+            point = self.points_enemy[0]
+            return tuple(point)
+        except IndexError:
+            return None
+
+    @cached_property
+    def aimed_item(self) -> tuple[int, int] | None:
+        if self.points_item is None:
+            return None
+        try:
+            _ = self.points_item[1]
+            logger.warning(f'Multiple aimed item found, using first point of {self.points_item}')
+        except IndexError:
+            pass
+        try:
+            point = self.points_item[0]
+            return tuple(point)
+        except IndexError:
+            return None
+
+
+class AimDetectorMixin(UI):
+    @cached_property
+    def aim(self):
+        return Aim()
+
+
+if __name__ == '__main__':
+    """
+    Test
+    """
+    self = AimDetectorMixin('src')
+    self.device.disable_stuck_detection()
+
+    while 1:
+        self.device.screenshot()
+        self.aim.predict(self.device.image, debug=True)