mirror of
https://github.com/LmeSzinc/StarRailCopilot.git
synced 2024-11-27 02:27:12 +00:00
347 lines
9.8 KiB
Python
347 lines
9.8 KiB
Python
import cv2
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import numpy as np
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from module.base.decorator import cached_property, del_cached_property
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from module.base.utils import Points, image_size, load_image
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from module.config.utils import dict_to_kv
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from module.logger import logger
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from tasks.base.ui import UI
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def inrange(image, lower=0, upper=255):
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"""
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Get the coordinates of pixels in range.
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Equivalent to `np.array(np.where(lower <= image <= upper))` but faster.
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Note that this method will change `image`.
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`cv2.findNonZero()` is faster than `np.where`
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points = np.array(np.where(y > 24)).T[:, ::-1]
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points = np.array(cv2.findNonZero((y > 24).astype(np.uint8)))[:, 0, :]
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`cv2.inRange(y, 24)` is faster than `y > 24`
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cv2.inRange(y, 24, 255, dst=y)
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y = y > 24
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Returns:
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np.ndarray: Shape (N, 2)
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E.g. [[x1, y1], [x2, y2], ...]
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"""
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cv2.inRange(image, lower, upper, dst=image)
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try:
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return np.array(cv2.findNonZero(image))[:, 0, :]
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except IndexError:
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# Empty result
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# IndexError: too many indices for array: array is 0-dimensional, but 3 were indexed
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return np.array([])
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def subtract_blur(image, radius=3, negative=False):
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"""
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If you care performance more than quality:
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- radius=3, use medianBlur
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- radius=5,7,9,11, use GaussianBlur
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- radius>11, use stackBlur (requires opencv >= 4.7.0)
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Args:
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image:
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radius:
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negative:
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Returns:
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np.ndarray:
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"""
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if radius <= 3:
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blur = cv2.medianBlur(image, radius)
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elif radius <= 11:
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blur = cv2.GaussianBlur(image, (radius, radius), 0)
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else:
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blur = cv2.stackBlur(image, (radius, radius), 0)
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if negative:
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cv2.subtract(blur, image, dst=blur)
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else:
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cv2.subtract(image, blur, dst=blur)
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return blur
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def remove_border(image, radius):
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"""
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Paint edge pixels black.
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No returns, changes are written to `image`
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Args:
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image:
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radius:
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"""
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width, height = image_size(image)
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image[:, :radius + 1] = 0
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image[:, width - radius:] = 0
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image[:radius + 1, :] = 0
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image[height - radius:, :] = 0
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def create_circle(min_radius, max_radius):
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"""
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Create a circle with min_radius <= R <= max_radius.
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1 represents circle, 0 represents background
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Args:
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min_radius:
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max_radius:
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Returns:
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np.ndarray:
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"""
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circle = np.ones((max_radius * 2 + 1, max_radius * 2 + 1), dtype=np.uint8)
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center = np.array((max_radius, max_radius))
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points = np.array(np.meshgrid(np.arange(circle.shape[0]), np.arange(circle.shape[1]))).T
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distance = np.linalg.norm(points - center, axis=2)
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circle[distance < min_radius] = 0
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circle[distance > max_radius] = 0
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return circle
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def draw_circle(image, circle, points):
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"""
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Add a circle onto image.
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No returns, changes are written to `image`
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Args:
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image:
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circle: Created from create_circle()
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points: (x, y), center of the circle to draw
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"""
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width, height = image_size(circle)
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x1 = -int(width // 2)
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y1 = -int(height // 2)
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x2 = width + x1
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y2 = height + y1
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for point in points:
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x, y = point
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# Fancy index is faster
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index = image[y + y1:y + y2, x + x1:x + x2]
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# print(index.shape)
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cv2.add(index, circle, dst=index)
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class Aim:
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radius_enemy = (24, 25)
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radius_item = (8, 10)
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def __init__(self):
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self.debug = False
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self.draw_item = None
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self.draw_enemy = None
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self.points_item = None
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self.points_enemy = None
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def clear_image_cache(self):
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self.draw_item = None
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self.draw_enemy = None
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self.points_item = None
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self.points_enemy = None
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del_cached_property(self, 'aimed_enemy')
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del_cached_property(self, 'aimed_item')
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@cached_property
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def mask_interact(self):
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return load_image('./assets/mask/MASK_MAP_INTERACT.png')
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@cached_property
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def circle_enemy(self):
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return create_circle(*self.radius_enemy)
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@cached_property
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def circle_item(self):
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return create_circle(*self.radius_item)
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# @timer
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def predict_enemy(self, h, v):
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min_radius, max_radius = self.radius_enemy
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width, height = image_size(v)
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# Get white circle `y`
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y = subtract_blur(h, 3, negative=False)
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cv2.inRange(h, 168, 255, h)
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cv2.bitwise_and(y, h, dst=y)
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# Get red glow `v`
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cv2.inRange(v, 168, 255, dst=v)
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kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
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cv2.dilate(v, kernel, dst=v)
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# Remove noise and leave red circle only
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cv2.bitwise_and(y, v, dst=y)
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# cv2.imshow('predict_enemy', y)
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# Remove game UI
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cv2.bitwise_and(y, self.mask_interact, dst=y)
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# Remove points on the edge, or draw_circle() will overflow
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remove_border(y, max_radius)
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# Get all pixels
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points = inrange(y, lower=18)
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if points.shape[0] > 1000:
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logger.warning(f'AimDetector.predict_enemy() too many points to draw: {points.shape}')
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# Draw circles
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draw = np.zeros((height, width), dtype=np.uint8)
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draw_circle(draw, self.circle_enemy, points)
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if self.debug:
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self.draw_enemy = cv2.multiply(draw, 4)
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draw = subtract_blur(draw, 3)
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# Find peaks
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points = inrange(draw, lower=36)
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points = Points(points).group(threshold=10)
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if points.shape[0] > 3:
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logger.warning(f'AimDetector.predict_enemy() too many peaks: {points.shape}')
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self.points_enemy = points
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# print(points)
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return points
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# @timer
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def predict_item(self, v):
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min_radius, max_radius = self.radius_item
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width, height = image_size(v)
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# Get white circle `y`
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y = subtract_blur(v, 9)
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white = cv2.inRange(v, 112, 144)
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cv2.bitwise_and(y, white, dst=y)
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# Get cyan glow `v`
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cv2.inRange(v, 0, 84, dst=v)
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kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
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cv2.dilate(v, kernel, dst=v)
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# Remove noise and leave cyan circle only
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cv2.bitwise_and(y, v, dst=y)
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# Remove game UI
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cv2.bitwise_and(y, self.mask_interact, dst=y)
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# Remove points on the edge, or draw_circle() will overflow
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remove_border(y, max_radius)
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# Get all pixels
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points = inrange(y, lower=18)
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# print(points.shape)
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if points.shape[0] > 1000:
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logger.warning(f'AimDetector.predict_item() too many points to draw: {points.shape}')
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# Draw circles
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draw = np.zeros((height, width), dtype=np.uint8)
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draw_circle(draw, self.circle_item, points)
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if self.debug:
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self.draw_item = cv2.multiply(draw, 2)
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# Find peaks
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points = inrange(draw, lower=64)
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points = Points(points).group(threshold=10)
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if points.shape[0] > 3:
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logger.warning(f'AimDetector.predict_item() too many peaks: {points.shape}')
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self.points_item = points
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# print(points)
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return points
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# @timer
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def predict(self, image, enemy=True, item=True, show_log=True, debug=False):
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"""
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Predict `aim` on image, costs about 10.0~10.5ms.
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Args:
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image:
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enemy: True to predict enemy
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item: True to predict item
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show_log:
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debug: True to show AimDetector image
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"""
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self.debug = debug
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self.clear_image_cache()
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if isinstance(image, str):
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image = load_image(image)
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# 1.5~2.0ms
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yuv = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
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v = yuv[:, :, 2]
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h = yuv[:, :, 0]
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# 4.0~4.5ms
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if enemy:
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self.predict_enemy(h.copy(), v.copy())
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# 3.0~3.5ms
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if item:
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self.predict_item(v.copy())
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if show_log:
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kv = {}
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if self.aimed_enemy:
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kv['enemy'] = self.aimed_enemy
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if self.aimed_item:
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kv['item'] = self.aimed_item
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if kv:
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logger.info(f'Aimed: {dict_to_kv(kv)}')
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if debug:
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self.show_aim()
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def show_aim(self):
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if self.draw_enemy is None:
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if self.draw_item is None:
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return
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else:
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r = g = b = self.draw_item
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else:
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if self.draw_item is None:
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r = g = b = self.draw_enemy
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else:
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r = self.draw_enemy
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g = b = self.draw_item
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image = cv2.merge([b, g, r])
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cv2.imshow('AimDetector', image)
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cv2.waitKey(1)
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@cached_property
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def aimed_enemy(self) -> tuple[int, int] | None:
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if self.points_enemy is None:
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return None
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try:
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_ = self.points_enemy[1]
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logger.warning(f'Multiple aimed enemy found, using first point of {self.points_enemy}')
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except IndexError:
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pass
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try:
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point = self.points_enemy[0]
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return tuple(point)
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except IndexError:
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return None
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@cached_property
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def aimed_item(self) -> tuple[int, int] | None:
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if self.points_item is None:
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return None
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try:
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_ = self.points_item[1]
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logger.warning(f'Multiple aimed item found, using first point of {self.points_item}')
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except IndexError:
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pass
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try:
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point = self.points_item[0]
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return tuple(point)
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except IndexError:
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return None
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class AimDetectorMixin(UI):
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@cached_property
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def aim(self):
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return Aim()
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if __name__ == '__main__':
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"""
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Test
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"""
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self = AimDetectorMixin('src')
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self.device.disable_stuck_detection()
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while 1:
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self.device.screenshot()
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self.aim.predict(self.device.image, debug=True)
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