147 lines
5.3 KiB
Python
147 lines
5.3 KiB
Python
import cv2
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import numpy as np
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import pyautogui
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def pick_color_fixed():
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# 截取游戏区域
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screenshot = pyautogui.screenshot(region=(150, 330, 600, 880))
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img = np.array(screenshot)
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img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
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# 转为HSV
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hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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# 创建窗口和滑块
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cv2.namedWindow('Color Picker')
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# 存储采样点
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sample_points = []
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# 定义鼠标回调函数
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def mouse_callback(event, x, y, flags, param):
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if event == cv2.EVENT_LBUTTONDOWN:
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# 获取点击位置的HSV值
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hsv_value = hsv_img[y, x]
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sample_points.append(hsv_value)
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print(f"添加采样点 #{len(sample_points)}: HSV = {hsv_value}")
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# 在图像上显示采样点
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cv2.circle(img, (x, y), 3, (0, 255, 0), -1)
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cv2.imshow('Color Picker', img)
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# 如果有足够多的采样点，计算更精确的范围
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if len(sample_points) >= 1:
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calculate_range()
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def calculate_range():
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"""安全计算HSV范围，避免溢出"""
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if not sample_points:
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return
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# 转换为numpy数组
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points_array = np.array(sample_points)
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# 提取各通道的值并安全计算范围
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h_values = points_array[:, 0].astype(np.int32) # 转为int32避免溢出
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s_values = points_array[:, 1].astype(np.int32)
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v_values = points_array[:, 2].astype(np.int32)
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# 检查H值是否跨越边界
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h_range = np.max(h_values) - np.min(h_values)
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h_crosses_boundary = h_range > 90 and len(h_values) > 2
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# 计算安全范围值
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if h_crosses_boundary:
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print("检测到H值可能跨越红色边界(0/180)!")
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# 特殊处理跨越边界的H值
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# 方法1: 简单方式 - 使用宽范围
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h_min = 0
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h_max = 179
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print(f"使用全H范围: [{h_min}, {h_max}]")
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else:
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# 正常计算H范围
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h_min = max(0, np.min(h_values) - 5)
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h_max = min(179, np.max(h_values) + 5)
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# 安全计算S和V范围
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s_min = max(0, np.min(s_values) - 15)
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s_max = min(255, np.max(s_values) + 15)
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v_min = max(0, np.min(v_values) - 15)
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v_max = min(255, np.max(v_values) + 15)
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print("\n推荐的HSV范围:")
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print(f"\"hsv_lower\": [{h_min}, {s_min}, {v_min}],")
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print(f"\"hsv_upper\": [{h_max}, {s_max}, {v_max}],")
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# 显示掩码预览
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show_mask_preview(h_min, h_max, s_min, s_max, v_min, v_max)
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def show_mask_preview(h_min, h_max, s_min, s_max, v_min, v_max):
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"""显示掩码预览，标记检测到的区域"""
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# 创建掩码
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if h_min <= h_max:
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# 标准范围
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mask = cv2.inRange(hsv_img,
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np.array([h_min, s_min, v_min]),
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np.array([h_max, s_max, v_max]))
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else:
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# 处理H值跨越边界情况
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mask1 = cv2.inRange(hsv_img,
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np.array([h_min, s_min, v_min]),
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np.array([179, s_max, v_max]))
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mask2 = cv2.inRange(hsv_img,
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np.array([0, s_min, v_min]),
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np.array([h_max, s_max, v_max]))
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mask = cv2.bitwise_or(mask1, mask2)
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# 形态学操作 - 闭运算连接临近区域
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kernel = np.ones((5, 5), np.uint8)
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mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
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# 找到连通区域
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num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(mask)
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# 创建结果图像
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result_img = img.copy()
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detected_count = 0
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# 处理每个连通区域
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for i in range(1, num_labels): # 跳过背景(0)
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area = stats[i, cv2.CC_STAT_AREA]
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# 面积筛选
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if 3000 <= area <= 100000:
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detected_count += 1
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x = stats[i, cv2.CC_STAT_LEFT]
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y = stats[i, cv2.CC_STAT_TOP]
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w = stats[i, cv2.CC_STAT_WIDTH]
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h = stats[i, cv2.CC_STAT_HEIGHT]
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# 绘制区域边框
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cv2.rectangle(result_img, (x, y), (x+w, y+h), (0, 255, 0), 2)
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# 显示区域ID
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cv2.putText(result_img, f"#{i}", (x+5, y+20),
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cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
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# 显示结果
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cv2.imshow('Mask Preview', result_img)
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print(f"检测到 {detected_count} 个合适大小的区域")
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# 设置鼠标回调
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cv2.setMouseCallback('Color Picker', mouse_callback)
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# 显示操作说明
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print("使用说明:")
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print("1. 点击气泡上的多个位置进行采样")
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print("2. 程序会自动计算合适的HSV范围")
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print("3. 绿色方框表示检测到的区域")
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print("4. 按ESC键退出")
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print("\n【特别提示】如果气泡混合了红色和紫色，可能需要创建两个配置以处理H通道的边界问题")
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# 显示图像
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cv2.imshow('Color Picker', img)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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if __name__ == "__main__":
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pick_color_fixed() |