mirror of
https://github.com/LmeSzinc/StarRailCopilot.git
synced 2024-11-27 02:27:12 +00:00
8366d7a258
- 调整了微层混合透视识别的参数 - 增加了在hsv色彩空间下的识别方法 - 增加了对地面闪烁红框的识别 - 增加了已打精英的计数 - 修改精英识别, boss识别, 舰队识别方法为hsv颜色计数 - 修复了自动生成camera_data的报错
388 lines
15 KiB
Python
388 lines
15 KiB
Python
import os
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import time
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import warnings
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import cv2
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import numpy as np
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from PIL import Image, ImageOps, ImageDraw
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from scipy import signal, optimize
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from module.config.config import AzurLaneConfig
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from module.logger import logger
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from module.map.perspective_items import Points, Lines
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warnings.filterwarnings("ignore")
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class Perspective:
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def __init__(self, image, config):
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"""
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Args:
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image: Screenshot
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config (AzurLaneConfig):
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"""
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self.image = image
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self.config = config
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self.correct = True
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start_time = time.time()
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# Image initialisation
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image = self.load_image(image)
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# Lines detection
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inner_h = self.detect_lines(
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image,
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is_horizontal=True,
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param=self.config.INTERNAL_LINES_FIND_PEAKS_PARAMETERS,
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threshold=self.config.INTERNAL_LINES_HOUGHLINES_THRESHOLD,
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theta=self.config.HORIZONTAL_LINES_THETA_THRESHOLD
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).move(*self.config.DETECTING_AREA[:2])
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inner_v = self.detect_lines(
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image,
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is_horizontal=False,
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param=self.config.INTERNAL_LINES_FIND_PEAKS_PARAMETERS,
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threshold=self.config.INTERNAL_LINES_HOUGHLINES_THRESHOLD,
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theta=self.config.VERTICAL_LINES_THETA_THRESHOLD
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).move(*self.config.DETECTING_AREA[:2])
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edge_h = self.detect_lines(
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image,
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is_horizontal=True,
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param=self.config.EDGE_LINES_FIND_PEAKS_PARAMETERS,
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threshold=self.config.EDGE_LINES_HOUGHLINES_THRESHOLD,
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theta=self.config.HORIZONTAL_LINES_THETA_THRESHOLD,
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pad=self.config.DETECTING_AREA[2] - self.config.DETECTING_AREA[0]
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).move(*self.config.DETECTING_AREA[:2])
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edge_v = self.detect_lines(
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image,
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is_horizontal=False,
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param=self.config.EDGE_LINES_FIND_PEAKS_PARAMETERS,
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threshold=self.config.EDGE_LINES_HOUGHLINES_THRESHOLD,
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theta=self.config.VERTICAL_LINES_THETA_THRESHOLD,
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pad=self.config.DETECTING_AREA[3] - self.config.DETECTING_AREA[1]
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).move(*self.config.DETECTING_AREA[:2])
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# Lines pre-cleansing
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# horizontal = inner_h.add(edge_h).group()
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# vertical = inner_v.add(edge_v).group()
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# edge_h = edge_h.group()
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# edge_v = edge_v.group()
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horizontal = inner_h.add(edge_h).group()
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vertical = inner_v.add(edge_v).group()
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edge_h = edge_h.group().delete(inner_h) # Experimental, reduce edge lines.
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edge_v = edge_v.group().delete(inner_v)
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self.horizontal = horizontal
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self.vertical = vertical
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# Calculate perspective
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self.crossings = self.horizontal.cross(self.vertical)
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self.vanish_point = optimize.brute(self._vanish_point_value, self.config.VANISH_POINT_RANGE)
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distance_point_x = optimize.brute(self._distant_point_value, self.config.DISTANCE_POINT_X_RANGE)[0]
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self.distant_point = np.array([distance_point_x, self.vanish_point[1]])
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# Re-generate lines. Useless after mid_cleanse function added.
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# self.horizontal = self.crossings.link(None, is_horizontal=True).group()
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# self.vertical = self.crossings.link(self.vanish_point).group()
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# self.draw(self.crossings.link(self.distant_point))
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# print(edge_h)
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# print(inner_h.group())
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# Lines cleansing
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# self.draw(edge_h)
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self.horizontal, self.lower_edge, self.upper_edge = self.line_cleanse(
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self.horizontal, inner=inner_h.group(), edge=edge_h)
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self.vertical, self.left_edge, self.right_edge = self.line_cleanse(
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self.vertical, inner=inner_v.group(), edge=edge_v)
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# self.draw()
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# print(self.horizontal)
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# print(self.lower_edge, self.upper_edge)
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# print(self.vertical)
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# print(self.left_edge, self.right_edge)
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# Log
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time_cost = round(time.time() - start_time, 3)
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logger.info('%ss %s Horizontal: %s (%s inner, %s edge)' % (
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str(time_cost).ljust(5, '0'), '_' if self.lower_edge else ' ',
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len(self.horizontal), len(horizontal), len(edge_h))
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)
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logger.info('Edges: %s%s%s Vertical: %s (%s inner, %s edge)' % (
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'/' if self.left_edge else ' ', '_' if self.upper_edge else ' ',
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'\\' if self.right_edge else ' ', len(self.vertical), len(vertical), len(edge_v))
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)
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if len(horizontal) - len(self.horizontal) >= 3 or len(vertical) - len(self.vertical) >= 3:
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logger.warning('Too many deleted lines')
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self.save_error_image()
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def load_image(self, image):
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"""Method that turns image to monochrome and hide UI.
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Args:
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image: Screenshot.
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Returns:
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np.ndarray
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"""
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image = np.array(image.crop(self.config.DETECTING_AREA))
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image = 255 - ((np.max(image, axis=2) // 2 + np.min(image, axis=2) // 2) & self.config.UI_MASK)
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return image
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def find_peaks(self, image, is_horizontal, param, pad=0):
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"""
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Args:
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image(np.ndarray): Processed screenshot.
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is_horizontal(bool): True if detects horizontal lines.
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param(dict): Parameters use in scipy.signal.find_peaks.
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pad(int):
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Returns:
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np.ndarray:
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"""
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if is_horizontal:
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image = image.T
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if pad:
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image = np.pad(image, ((0, 0), (0, pad)), mode='constant', constant_values=255)
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origin_shape = image.shape
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out = np.zeros(origin_shape[0] * origin_shape[1], dtype='uint8')
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peaks, _ = signal.find_peaks(image.ravel(), **param)
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out[peaks] = 255
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out = out.reshape(origin_shape)
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if pad:
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out = out[:, :-pad]
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if is_horizontal:
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out = out.T
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out &= self.config.UI_MASK
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return out
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def hough_lines(self, image, is_horizontal, threshold, theta):
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"""
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Args:
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image (np.ndarray): Peaks image.
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is_horizontal (bool): True if detects horizontal lines.
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threshold (int): Threshold use in cv2.HoughLines
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theta:
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Returns:
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Lines:
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"""
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lines = cv2.HoughLines(image, 1, np.pi / 180, threshold)
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if lines is None:
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return Lines(None, is_horizontal=is_horizontal, config=self.config)
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else:
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lines = lines[:, 0, :]
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if is_horizontal:
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lines = lines[(np.deg2rad(90 - theta) < lines[:, 1]) & (lines[:, 1] < np.deg2rad(90 + theta))]
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else:
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lines = lines[(lines[:, 1] < np.deg2rad(theta)) | (np.deg2rad(180 - theta) < lines[:, 1])]
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lines = [[-rho, theta - np.pi] if rho < 0 else [rho, theta] for rho, theta in lines]
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# if len(lines) > 0:
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# return Lines(lines, is_horizontal=is_horizontal, config=self.config)
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return Lines(lines, is_horizontal=is_horizontal, config=self.config)
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def detect_lines(self, image, is_horizontal, param, threshold, theta, pad=0):
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"""
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Method that wraps find_peaks and hough_lines
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"""
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peaks = self.find_peaks(image, is_horizontal=is_horizontal, param=param, pad=pad)
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# self.show_array(peaks)
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lines = self.hough_lines(peaks, is_horizontal=is_horizontal, threshold=threshold, theta=theta)
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# self.draw(lines, Image.fromarray(peaks.astype(np.uint8), mode='L'))
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return lines
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@staticmethod
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def show_array(arr):
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image = Image.fromarray(arr.astype(np.uint8), mode='L')
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image.show()
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def draw(self, lines=None, bg=None, expend=0):
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if bg is None:
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image = self.image.copy()
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else:
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image = bg.copy()
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if expend:
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image = ImageOps.expand(image, border=expend, fill=0)
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draw = ImageDraw.Draw(image)
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if lines is None:
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lines = self.horizontal.add(self.vertical)
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for rho, theta in zip(lines.rho, lines.theta):
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a = np.cos(theta)
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b = np.sin(theta)
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x0 = a * rho
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y0 = b * rho
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x1 = int(x0 + 10000 * (-b)) + expend
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y1 = int(y0 + 10000 * a) + expend
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x2 = int(x0 - 10000 * (-b)) + expend
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y2 = int(y0 - 10000 * a) + expend
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draw.line([x1, y1, x2, y2], 'white')
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image.show()
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# image.save('123.png')
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def _vanish_point_value(self, point):
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"""Value that measures how close a point to the perspective vanish point. The smaller the better.
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Use log10 to encourage a group of coincident lines and discourage wrong lines.
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Args:
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point(np.ndarray): np.array([x, y])
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Returns:
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float: value.
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"""
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# Add 0.001 to avoid log10(0).
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distance = np.sum(np.log10(np.abs(self.vertical.distance_to_point(point)) + 0.001))
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return distance
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def _distant_point_value(self, x):
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"""Value that measures how close a point to the perspective distant point. The smaller the better.
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Use log10 to encourage a group of coincident lines and discourage wrong lines.
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Args:
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x(np.ndarray): np.array([x])
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Returns:
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float: value
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"""
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links = self.crossings.link((x[0], self.vanish_point[1]))
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mid = np.sort(links.mid)
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distance = np.sum(np.log10(np.diff(mid) + 0.001)) # Add 0.001 to avoid log10(0).
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return distance
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def mid_cleanse(self, mids, is_horizontal, threshold=3):
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"""
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Args:
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mids(np.ndarray): Lines.mid
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is_horizontal(bool): True if detects horizontal lines.
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threshold(int):
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Returns:
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np.ndarray
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"""
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right_distant_point = (self.vanish_point[0] * 2 - self.distant_point[0], self.distant_point[1])
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def convert_to_x(ys):
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return Points([[self.config.SCREEN_CENTER[0], y] for y in ys], config=self.config) \
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.link(right_distant_point) \
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.mid
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def convert_to_y(xs):
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return Points([[x, self.config.SCREEN_CENTER[1]] for x in xs], config=self.config) \
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.link(right_distant_point) \
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.get_y(x=self.config.SCREEN_CENTER[0])
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def coincident_point_value(point):
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"""Value that measures how close a point to the coincident point. The smaller the better.
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Coincident point may be many.
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Use an activation function to encourage a group of coincident lines and ignore wrong lines.
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"""
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x, y = point
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# Do not use:
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# distance = coincident.distance_to_point(point)
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distance = np.abs(x - coincident.get_x(y))
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# print((distance * 1).astype(int).reshape(len(mids), np.diff(self.config.ERROR_LINES_TOLERANCE)[0]+1))
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# Activation function
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# distance = 1 / (1 + np.exp(16 / distance - distance))
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distance = 1 / (1 + np.exp(9 / distance) / distance)
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distance = np.sum(distance)
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return distance
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if is_horizontal:
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mids = convert_to_x(mids)
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# Drawing lines
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lines = []
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for index, mid in enumerate(mids):
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for n in range(self.config.ERROR_LINES_TOLERANCE[0], self.config.ERROR_LINES_TOLERANCE[1] + 1):
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theta = np.arctan(index + n)
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rho = mid * np.cos(theta)
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lines.append([rho, theta])
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# Fitting mid
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coincident = Lines(np.vstack(lines), is_horizontal=False, config=self.config)
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# print(np.round(np.sort(coincident.get_x(128))).astype(int))
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coincident_point = optimize.brute(coincident_point_value, self.config.COINCIDENT_POINT_RANGE)
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# print(coincident_point, is_horizontal)
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diff = abs(coincident_point[1] - 129)
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if diff > 3:
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self.correct = False
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logger.warning('%s coincident point unexpected: %s' % (
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'Horizontal' if is_horizontal else 'Vertical',
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str(coincident_point)))
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if diff > 6:
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self.save_error_image()
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# The limits of detecting area
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if is_horizontal:
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border = Points(
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[[self.config.SCREEN_CENTER[0], self.config.DETECTING_AREA[1]],
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[self.config.SCREEN_CENTER[0], self.config.DETECTING_AREA[3]]], config=self.config) \
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.link(right_distant_point) \
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.mid
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else:
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border = Points(
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[self.config.DETECTING_AREA[0:2], self.config.DETECTING_AREA[1:3][::-1]], config=self.config) \
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.link(self.vanish_point) \
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.mid
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left, right = border
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# print(mids)
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# Filling mid
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mids = np.arange(-25, 25) * coincident_point[1] + coincident_point[0]
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mids = mids[(mids > left - threshold) & (mids < right + threshold)]
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# print(mids)
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if is_horizontal:
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mids = convert_to_y(mids)
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return mids
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def line_cleanse(self, lines, inner, edge, threshold=3):
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origin = lines.mid
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clean = self.mid_cleanse(origin, is_horizontal=lines.is_horizontal, threshold=threshold)
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# Cleansing edge
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edge = edge.mid
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inner = inner.mid
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edge = edge[(edge > np.max(inner) - threshold) | (edge < np.min(inner) + threshold)]
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edge = [c for c in clean if np.any(np.abs(c - edge) < 5)]
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# Separate edges
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if len(edge) == 0:
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lower, upper = None, None
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elif len(edge) == 1:
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edge = edge[0]
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if lines.is_horizontal:
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lower, upper = (None, edge) if edge > self.config.SCREEN_CENTER[1] else (edge, None)
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else:
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lower, upper = (None, edge) if edge > self.config.SCREEN_CENTER[0] else (edge, None)
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else:
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lower, upper = edge[0], edge[-1]
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# crop mid
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if lower:
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clean = clean[clean > lower - threshold]
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if upper:
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clean = clean[clean < upper + threshold]
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# mid to lines
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if lines.is_horizontal:
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lines = Points([[self.config.SCREEN_CENTER[0], y] for y in clean], config=self.config) \
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.link(None, is_horizontal=True)
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lower = Points([self.config.SCREEN_CENTER[0], lower], config=self.config).link(None, is_horizontal=True) \
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if lower else Lines(None, config=self.config, is_horizontal=True)
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upper = Points([self.config.SCREEN_CENTER[0], upper], config=self.config).link(None, is_horizontal=True) \
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if upper else Lines(None, config=self.config, is_horizontal=True)
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else:
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lines = Points([[x, self.config.SCREEN_CENTER[1]] for x in clean], config=self.config) \
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.link(self.vanish_point)
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lower = Points([lower, self.config.SCREEN_CENTER[1]], config=self.config).link(self.vanish_point) \
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if lower else Lines(None, config=self.config, is_horizontal=False)
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upper = Points([upper, self.config.SCREEN_CENTER[1]], config=self.config).link(self.vanish_point) \
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if upper else Lines(None, config=self.config, is_horizontal=False)
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return lines, lower, upper
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def save_error_image(self):
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file = '%s.%s' % (int(time.time() * 1000), 'png')
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file = os.path.join(self.config.ERROR_LOG_FOLDER, file)
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self.image.save(file)
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