From b33ea857680e1a8ac4ac4ed46963b254ed2defc2 Mon Sep 17 00:00:00 2001
From: z060142 <z060142@yahoo.com.tw>
Date: Sat, 10 May 2025 01:09:51 +0800
Subject: [PATCH] Added new styles for speech bubbles for detection

---
 bubble_colors.json    |   8 +++
 tools/color_picker.py | 147 ++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 155 insertions(+)
 create mode 100644 tools/color_picker.py

diff --git a/bubble_colors.json b/bubble_colors.json
index 6fdfcf1..c0cb71f 100644
--- a/bubble_colors.json
+++ b/bubble_colors.json
@@ -47,6 +47,14 @@
       "hsv_upper": [107, 255, 255],
       "min_area": 2500,
       "max_area": 300000
+    },
+    {
+      "name": "easter",
+      "is_bot": false,
+      "hsv_lower": [5, 154, 183],
+      "hsv_upper": [29, 255, 255],
+      "min_area": 2500,
+      "max_area": 300000
     }
   ]
 }
diff --git a/tools/color_picker.py b/tools/color_picker.py
new file mode 100644
index 0000000..c88be8d
--- /dev/null
+++ b/tools/color_picker.py
@@ -0,0 +1,147 @@
+import cv2
+import numpy as np
+import pyautogui
+
+def pick_color_fixed():
+    # 截取游戏区域
+    screenshot = pyautogui.screenshot(region=(150, 330, 600, 880))
+    img = np.array(screenshot)
+    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+    
+    # 转为HSV
+    hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+    
+    # 创建窗口和滑块
+    cv2.namedWindow('Color Picker')
+    
+    # 存储采样点
+    sample_points = []
+    
+    # 定义鼠标回调函数
+    def mouse_callback(event, x, y, flags, param):
+        if event == cv2.EVENT_LBUTTONDOWN:
+            # 获取点击位置的HSV值
+            hsv_value = hsv_img[y, x]
+            sample_points.append(hsv_value)
+            print(f"添加采样点 #{len(sample_points)}: HSV = {hsv_value}")
+            
+            # 在图像上显示采样点
+            cv2.circle(img, (x, y), 3, (0, 255, 0), -1)
+            cv2.imshow('Color Picker', img)
+            
+            # 如果有足够多的采样点，计算更精确的范围
+            if len(sample_points) >= 1:
+                calculate_range()
+    
+    def calculate_range():
+        """安全计算HSV范围，避免溢出"""
+        if not sample_points:
+            return
+            
+        # 转换为numpy数组
+        points_array = np.array(sample_points)
+        
+        # 提取各通道的值并安全计算范围
+        h_values = points_array[:, 0].astype(np.int32)  # 转为int32避免溢出
+        s_values = points_array[:, 1].astype(np.int32)
+        v_values = points_array[:, 2].astype(np.int32)
+        
+        # 检查H值是否跨越边界
+        h_range = np.max(h_values) - np.min(h_values)
+        h_crosses_boundary = h_range > 90 and len(h_values) > 2
+        
+        # 计算安全范围值
+        if h_crosses_boundary:
+            print("检测到H值可能跨越红色边界(0/180)!")
+            # 特殊处理跨越边界的H值
+            # 方法1: 简单方式 - 使用宽范围
+            h_min = 0
+            h_max = 179
+            print(f"使用全H范围: [{h_min}, {h_max}]")
+        else:
+            # 正常计算H范围
+            h_min = max(0, np.min(h_values) - 5)
+            h_max = min(179, np.max(h_values) + 5)
+        
+        # 安全计算S和V范围
+        s_min = max(0, np.min(s_values) - 15)
+        s_max = min(255, np.max(s_values) + 15)
+        v_min = max(0, np.min(v_values) - 15)
+        v_max = min(255, np.max(v_values) + 15)
+        
+        print("\n推荐的HSV范围:")
+        print(f"\"hsv_lower\": [{h_min}, {s_min}, {v_min}],")
+        print(f"\"hsv_upper\": [{h_max}, {s_max}, {v_max}],")
+        
+        # 显示掩码预览
+        show_mask_preview(h_min, h_max, s_min, s_max, v_min, v_max)
+    
+    def show_mask_preview(h_min, h_max, s_min, s_max, v_min, v_max):
+        """显示掩码预览，标记检测到的区域"""
+        
+        # 创建掩码
+        if h_min <= h_max:
+            # 标准范围
+            mask = cv2.inRange(hsv_img, 
+                             np.array([h_min, s_min, v_min]), 
+                             np.array([h_max, s_max, v_max]))
+        else:
+            # 处理H值跨越边界情况
+            mask1 = cv2.inRange(hsv_img, 
+                              np.array([h_min, s_min, v_min]), 
+                              np.array([179, s_max, v_max]))
+            mask2 = cv2.inRange(hsv_img, 
+                              np.array([0, s_min, v_min]), 
+                              np.array([h_max, s_max, v_max]))
+            mask = cv2.bitwise_or(mask1, mask2)
+        
+        # 形态学操作 - 闭运算连接临近区域
+        kernel = np.ones((5, 5), np.uint8)
+        mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
+        
+        # 找到连通区域
+        num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(mask)
+        
+        # 创建结果图像
+        result_img = img.copy()
+        detected_count = 0
+        
+        # 处理每个连通区域
+        for i in range(1, num_labels):  # 跳过背景(0)
+            area = stats[i, cv2.CC_STAT_AREA]
+            # 面积筛选 
+            if 3000 <= area <= 100000:
+                detected_count += 1
+                x = stats[i, cv2.CC_STAT_LEFT]
+                y = stats[i, cv2.CC_STAT_TOP]
+                w = stats[i, cv2.CC_STAT_WIDTH]
+                h = stats[i, cv2.CC_STAT_HEIGHT]
+                
+                # 绘制区域边框
+                cv2.rectangle(result_img, (x, y), (x+w, y+h), (0, 255, 0), 2)
+                # 显示区域ID
+                cv2.putText(result_img, f"#{i}", (x+5, y+20), 
+                           cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
+        
+        # 显示结果
+        cv2.imshow('Mask Preview', result_img)
+        print(f"检测到 {detected_count} 个合适大小的区域")
+    
+    # 设置鼠标回调
+    cv2.setMouseCallback('Color Picker', mouse_callback)
+    
+    # 显示操作说明
+    print("使用说明:")
+    print("1. 点击气泡上的多个位置进行采样")
+    print("2. 程序会自动计算合适的HSV范围")
+    print("3. 绿色方框表示检测到的区域")
+    print("4. 按ESC键退出")
+    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")
+    
+    # 显示图像
+    cv2.imshow('Color Picker', img)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+if __name__ == "__main__":
+    pick_color_fixed()
\ No newline at end of file