147 lines
5.3 KiB
Python
147 lines
5.3 KiB
Python
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import os
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import re
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import cv2
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import numpy as np
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# 工具类
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class AiUtils(object):
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# 在屏幕中找到对应的图片
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@classmethod
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def findImageInScreen(cls, target):
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# 加载原始图像和模板图像
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image_path = AiUtils.imagePathWithName("bgv") # 替换为你的图像路径
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template_path = AiUtils.imagePathWithName(target) # 替换为你的模板路径
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# 读取图像和模板，确保它们都是单通道灰度图
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image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
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template = cv2.imread(template_path, cv2.IMREAD_GRAYSCALE)
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if image is None:
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raise ValueError("背景图无法加载")
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if template is None:
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raise ValueError("模板图无法加载")
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# 获取模板的宽度和高度
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w, h = template.shape[::-1]
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# 使用模板匹配方法
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res = cv2.matchTemplate(image, template, cv2.TM_CCOEFF_NORMED)
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threshold = 0.7 # 匹配度阈值，可以根据需要调整
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loc = np.where(res >= threshold)
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# 检查是否有匹配结果
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if loc[0].size > 0:
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# 取第一个匹配位置
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pt = zip(*loc[::-1]).__next__() # 获取第一个匹配点的坐标
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center_x = int(pt[0] + w // 2)
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center_y = int(pt[1] + h // 2)
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# print(f"第一个匹配到的小心心中心坐标: ({center_x}, {center_y})")
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return center_x, center_y
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else:
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print("未找到匹配的目标")
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return -1, -1
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# 使用正则查找字符串中的数字
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@classmethod
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def findNumber(cls, str):
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# 使用正则表达式匹配数字
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match = re.search(r'\d+', str)
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if match:
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return int(match.group()) # 将匹配到的数字转换为整数
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return None # 如果没有找到数字，返回 None
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# 选择截图
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@classmethod
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def screenshot(cls):
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image_path = AiUtils.imagePathWithName("bgv") # 替换为你的图像路径
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image = cv2.imread(image_path)
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# 如果图像过大，缩小显示
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scale_percent = 50 # 缩小比例
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width = int(image.shape[1] * scale_percent / 100)
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height = int(image.shape[0] * scale_percent / 100)
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dim = (width, height)
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resized_image = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)
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# 创建一个窗口并显示缩小后的图像
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cv2.namedWindow("Image")
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cv2.imshow("Image", resized_image)
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print("请在图像上选择爱心图标区域，然后按Enter键确认。")
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# 使用selectROI函数手动选择区域
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roi = cv2.selectROI("Image", resized_image, showCrosshair=True, fromCenter=False)
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# 将ROI坐标按原始图像尺寸放大
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x, y, w, h = roi
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x = int(x * image.shape[1] / resized_image.shape[1])
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y = int(y * image.shape[0] / resized_image.shape[0])
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w = int(w * image.shape[1] / resized_image.shape[1])
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h = int(h * image.shape[0] / resized_image.shape[0])
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# 根据选择的ROI提取爱心图标
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if w > 0 and h > 0: # 确保选择的区域有宽度和高度
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heart_icon = image[y:y + h, x:x + w]
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# 转换为HSV颜色空间
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hsv = cv2.cvtColor(heart_icon, cv2.COLOR_BGR2HSV)
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# 定义红色的HSV范围
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lower_red1 = np.array([0, 120, 70])
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upper_red1 = np.array([10, 255, 255])
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lower_red2 = np.array([170, 120, 70])
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upper_red2 = np.array([180, 255, 255])
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# 创建掩模
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mask1 = cv2.inRange(hsv, lower_red1, upper_red1)
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mask2 = cv2.inRange(hsv, lower_red2, upper_red2)
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mask = mask1 + mask2
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# 反转掩模，因为我们想要的是爱心图标，而不是背景
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mask_inv = cv2.bitwise_not(mask)
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# 应用掩模
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heart_icon = cv2.bitwise_and(heart_icon, heart_icon, mask=mask_inv)
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# 创建一个全透明的背景
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height, width, channels = heart_icon.shape
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roi = np.zeros((height, width, channels), dtype=np.uint8)
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# 将爱心图标粘贴到透明背景上
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for c in range(channels):
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roi[:, :, c] = np.where(mask_inv == 255, heart_icon[:, :, c], roi[:, :, c])
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# 图片名称
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imgName = "temp.png"
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# 保存结果
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cv2.imwrite(imgName, roi)
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# 显示结果
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cv2.imshow("Heart Icon with Transparent Background", roi)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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else:
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print("未选择有效区域。")
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# 根据名称获取图片完整地址
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@classmethod
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def imagePathWithName(cls, name):
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current_file_path = os.path.abspath(__file__)
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# 获取当前文件所在的目录（即script目录）
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current_dir = os.path.dirname(current_file_path)
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# 由于script目录位于项目根目录下一级，因此需要向上一级目录移动两次
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project_root = os.path.abspath(os.path.join(current_dir, '..'))
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# 构建资源文件的完整路径，向上两级目录，然后进入 resources 目录
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resource_path = os.path.abspath(os.path.join(project_root, 'resources', name + ".png")).replace('/', '\\')
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return resource_path
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# AiUtils.screenshot() |