全新骰子

konabot/plugins/roll_dice/__init__.py

@@ -1,11 +1,11 @@
-from typing import Optional
+from typing import Optional, Union
 from nonebot.adapters import Event as BaseEvent
 from nonebot.adapters.console.event import MessageEvent as ConsoleMessageEvent
 from nonebot.adapters.discord.event import MessageEvent as DiscordMessageEvent
 from nonebot_plugin_alconna import Alconna, Args, UniMessage, on_alconna
 
 from konabot.plugins.roll_dice.roll_dice import generate_dice_image
-from konabot.plugins.roll_dice.roll_number import get_random_number, roll_number
+from konabot.plugins.roll_dice.roll_number import get_random_number, get_random_number_string, roll_number
 
 evt = on_alconna(Alconna(
     "摇数字"
@@ -22,21 +22,26 @@ async def _(event: BaseEvent):
 
 evt = on_alconna(Alconna(
     "摇骰子",
-    Args["f1?", int]["f2?", int]
+    Args["f1?", str]["f2?", str]
 ), use_cmd_start=True, use_cmd_sep=False, skip_for_unmatch=True)
 
 @evt.handle()
-async def _(event: BaseEvent, f1: Optional[int] = None, f2: Optional[int] = None):
+async def _(event: BaseEvent, f1: Optional[str] = None, f2: Optional[str] = None):
     # if isinstance(event, DiscordMessageEvent):
     #     await evt.send(await UniMessage().text("```\n" + roll_dice() + "\n```").export())
     # elif isinstance(event, ConsoleMessageEvent):
-    number = 0
+    number = ""
     if(f1 is not None and f2 is not None):
-        number = get_random_number(f1, f2)
+        number = get_random_number_string(f1, f2)
     elif f1 is not None:
-        number = get_random_number(1, f1)
+        if(float(f1) > 1):
+            number = get_random_number_string("1", f1)
+        elif (float(f1) > 0):
+            number = get_random_number_string("0", f1)
+        else:
+            number = get_random_number_string(f1, "0")
     else:
-        number = get_random_number()
+        number = get_random_number_string()
     await evt.send(await UniMessage().image(raw=await generate_dice_image(number)).export())
     # else:
     #     await evt.send(await UniMessage().text(roll_dice(wide=True)).export())

konabot/plugins/roll_dice/roll_dice.py

@@ -158,26 +158,206 @@ def precise_blend_with_perspective(background, foreground, corners):
     
     return result
 
-async def generate_dice_image(number: int) -> BytesIO:
+def draw_line_bresenham(image, x0, y0, x1, y1, color):
+    """使用Bresenham算法画线，避免间隙"""
+    dx = abs(x1 - x0)
+    dy = abs(y1 - y0)
+    sx = 1 if x0 < x1 else -1
+    sy = 1 if y0 < y1 else -1
+    err = dx - dy
+    
+    while True:
+        if 0 <= x0 < image.shape[1] and 0 <= y0 < image.shape[0]:
+            image[y0, x0] = color
+        
+        if x0 == x1 and y0 == y1:
+            break
+            
+        e2 = 2 * err
+        if e2 > -dy:
+            err -= dy
+            x0 += sx
+        if e2 < dx:
+            err += dx
+            y0 += sy
+
+def slice_and_stretch(image, slice_lines, direction):
+    '''
+    image: 图像
+    slice_lines: 切割线（两个点的列表），一般是倾斜45度的直线
+    direction: 移动方向向量（二元数组）
+    '''
+    # 获取图片的尺寸
+    height, width = image.shape[:2]
+    # 创建一个由移动方向扩充后，更大的图片
+    new_width = int(width + abs(direction[0]))
+    new_height = int(height + abs(direction[1]))
+    new_image = np.zeros((new_height, new_width, 4), dtype=image.dtype)
+    # 先把图片放在新图的和方向相反的一侧
+    offset_x = int(abs(min(0, direction[0])))
+    offset_y = int(abs(min(0, direction[1])))
+    new_image[offset_y:offset_y+height, offset_x:offset_x+width] = image
+    # 切割线也跟着偏移
+    slice_lines = [(x + offset_x, y + offset_y) for (x, y) in slice_lines]
+    # 复制切割线经过的像素，沿着方向移动，实现类似拖尾的效果
+    apply_trail_effect_vectorized(new_image, slice_lines, direction)
+    apply_stroke_vectorized(new_image, slice_lines, direction)
+
+
+    return new_image, offset_x, offset_y
+
+def apply_trail_effect_vectorized(new_image, slice_lines, direction):
+    """向量化实现拖尾效果"""
+    height, width = new_image.shape[:2]
+    
+    # 创建坐标网格
+    y_coords, x_coords = np.mgrid[0:height, 0:width]
+    
+    # 向量化计算点到直线的距离
+    line_vec = np.array([slice_lines[1][0] - slice_lines[0][0], 
+                        slice_lines[1][1] - slice_lines[0][1]])
+    point_vecs = np.stack([x_coords - slice_lines[0][0], 
+                          y_coords - slice_lines[0][1]], axis=-1)
+    
+    # 计算叉积（有向距离）
+    cross_products = (line_vec[0] * point_vecs[:, :, 1] - 
+                     line_vec[1] * point_vecs[:, :, 0])
+    
+    # 选择直线右侧的像素 (d1 > 0)
+    mask = cross_products > 0
+    
+    # 计算目标位置
+    target_x = (x_coords + direction[0]).astype(int)
+    target_y = (y_coords + direction[1]).astype(int)
+    
+    # 创建有效位置掩码
+    valid_mask = mask & (target_x >= 0) & (target_x < width) & \
+                (target_y >= 0) & (target_y < height)
+    
+    # 批量复制像素
+    new_image[target_y[valid_mask], target_x[valid_mask]] = \
+        new_image[y_coords[valid_mask], x_coords[valid_mask]]
+
+def apply_stroke_vectorized(new_image, slice_lines, direction):
+    """使用向量化操作优化笔画效果"""
+    height, width = new_image.shape[:2]
+    
+    # 1. 找到所有非透明像素
+    non_transparent = np.where(new_image[:, :, 3] > 0)
+    if len(non_transparent[0]) == 0:
+        return
+    
+    y_coords, x_coords = non_transparent
+    
+    # 2. 向量化计算点到直线的距离
+    line_vec = np.array([slice_lines[1][0] - slice_lines[0][0], 
+                        slice_lines[1][1] - slice_lines[0][1]])
+    point_vecs = np.column_stack([x_coords - slice_lines[0][0], 
+                                 y_coords - slice_lines[0][1]])
+    
+    # 计算叉积（距离）
+    cross_products = (line_vec[0] * point_vecs[:, 1] - 
+                     line_vec[1] * point_vecs[:, 0])
+    
+    # 3. 选择靠近直线的像素
+    mask = np.abs(cross_products) < 1.0
+    selected_y = y_coords[mask]
+    selected_x = x_coords[mask]
+    selected_pixels = new_image[selected_y, selected_x]
+    
+    if len(selected_x) == 0:
+        return
+    
+    # 4. 预计算采样点
+    length = np.sqrt(direction[0]**2 + direction[1]**2)
+    if length == 0:
+        return
+    
+    # 创建采样偏移
+    dx_dy = np.array([(dx, dy) for dx in [-0.5, 0, 0.5] 
+                     for dy in [-0.5, 0, 0.5]])
+    
+    # 5. 批量计算目标位置
+    steps = max(1, int(length * 2))
+    alpha = 0.7
+    
+    for k in range(1, steps + 1):
+        # 对所有选中的像素批量计算新位置
+        scale = k / steps
+        
+        # 为每个像素和每个采样点计算目标位置
+        for dx, dy in dx_dy:
+            target_x = np.round(selected_x + dx + direction[0] * scale).astype(int)
+            target_y = np.round(selected_y + dy + direction[1] * scale).astype(int)
+            
+            # 创建有效位置掩码
+            valid_mask = (target_x >= 0) & (target_x < width) & \
+                        (target_y >= 0) & (target_y < height)
+            
+            if np.any(valid_mask):
+                valid_target_x = target_x[valid_mask]
+                valid_target_y = target_y[valid_mask]
+                valid_source_idx = np.where(valid_mask)[0]
+                
+                # 批量混合像素
+                source_pixels = selected_pixels[valid_source_idx]
+                target_pixels = new_image[valid_target_y, valid_target_x]
+                
+                new_image[valid_target_y, valid_target_x] = (
+                    alpha * source_pixels + (1 - alpha) * target_pixels
+                )
+
+async def generate_dice_image(number: str) -> BytesIO:
     # 将文本转换为带透明背景的图像
-    text = str(number)
+    text = number
+
+    # 如果文本太长，直接返回金箍棒
+    if(len(text) > 50):
+        output = BytesIO()
+        push_image = Image.open(ASSETS / "stick.png")
+        push_image.save(output,format='PNG')
+        output.seek(0)
+        return output
+
     text_image = text_to_transparent_image(
         text, 
         font_size=60, 
         text_color=(0, 0, 0)  # 黑色文字
     )
+
+    # 获取长宽比
+    height, width = text_image.shape[:2]
+    aspect_ratio = width / height
+
+    # 根据长宽比设置拉伸系数
+    stretch_k = 1
+    if aspect_ratio > 1:
+        stretch_k = aspect_ratio
+
+    # 骰子的方向
+    up_direction = (51 - 16, 5 - 30)  # 右上角点 - 左上角点
+
+    move_distance = (up_direction[0] * (stretch_k - 1), up_direction[1] * (stretch_k - 1))
     
+    # 加载背景图像，保留透明通道
+    background = cv2.imread(ASSETS / "template.png", cv2.IMREAD_UNCHANGED)
+
+    height, width = background.shape[:2]
+
+    background, offset_x, offset_y = slice_and_stretch(background, 
+                                                       [(10,10),(0,0)], 
+                                                        move_distance)
+
     # 定义3D变换的四个角点（透视效果）
     # 顺序: [左上, 右上, 右下, 左下]
     corners = np.array([
         [16, 30],    # 左上
-        [51, 5],     # 右上（上移，创建透视）
+        [51 + move_distance[0], 5 + move_distance[1]],     # 右上（上移，创建透视）
-        [88, 33],    # 右下
+        [88 + move_distance[0], 33 + move_distance[1]],    # 右下
         [49, 62]     # 左下（下移）
     ], dtype=np.float32)
-    
+    corners[:, 0] += offset_x
-    # 加载背景图像，保留透明通道
+    corners[:, 1] += offset_y
-    background = cv2.imread(ASSETS / "template.png", cv2.IMREAD_UNCHANGED)
 
 
     # 对文本图像进行3D变换（保持透明通道）
@@ -192,6 +372,26 @@ async def generate_dice_image(number: int) -> BytesIO:
     images = [Image.open(ASSETS / f"{i}.png") for i in range(1, 12)]
     images.append(pil_final)
     frame_durations = [100] * (len(images) - 1) + [100000]
+    # 将导入的图像尺寸扩展为和 pil_final 相同的大小，随帧数进行扩展，然后不放大的情况下放在最中间
+    if(aspect_ratio > 1):
+        target_size = pil_final.size
+        for i in range(len(images) - 1):
+            k = i / (len(images) - 1)
+            now_distance = (move_distance[0] * k, move_distance[1] * k)
+            img = np.array(images[i])
+            img, _, _ = slice_and_stretch(img,
+                            [(10,10),(0,0)], 
+                            now_distance)
+            # 只扩展边界，图像本身不放大
+            img_width, img_height = img.shape[1], img.shape[0]
+            new_img = Image.new("RGBA", target_size, (0, 0, 0, 0))
+            this_offset_x = (target_size[0] - img_width) // 2
+            this_offset_y = (target_size[1] - img_height) // 2
+            # new_img.paste(img, (this_offset_x, this_offset_y))
+            new_img.paste(Image.fromarray(img), (this_offset_x, this_offset_y))
+            images[i] = new_img
+        
+
     # 保存为BytesIO对象
     output = BytesIO()
     images[0].save(output,
@@ -200,4 +400,6 @@ async def generate_dice_image(number: int) -> BytesIO:
                duration=frame_durations,
                format='GIF',
                loop=1)
+    output.seek(0)
+    # pil_final.save(output, format='PNG')
     return output

konabot/plugins/roll_dice/roll_number.py

@@ -42,6 +42,24 @@ def get_random_number(min: int = 1, max: int = 6) -> int:
     import random
     return random.randint(min, max)
 
+def get_random_number_string(min_value: str = "1", max_value: str = "6") -> str:
+    import random
+
+    # 先判断二者是不是整数
+    if (float(min_value).is_integer()
+        and float(max_value).is_integer()
+        and "." not in min_value
+        and "." not in max_value):
+        return str(random.randint(int(float(min_value)), int(float(max_value))))
+    
+    # 根据传入小数的位数，决定保留几位小数
+    if "." in str(min_value) or "." in str(max_value):
+        decimal_places = max(len(str(min_value).split(".")[1]) if "." in str(min_value) else 0,
+                             len(str(max_value).split(".")[1]) if "." in str(max_value) else 0)
+        return str(round(random.uniform(float(min_value), float(max_value)), decimal_places))
+    
+    # 如果没有小数点，很可能二者都是指数表示或均为 inf，直接返回随机小数
+    return str(random.uniform(float(min_value), float(max_value)))
 def roll_number(wide: bool = False) -> str:
     raw = number_arts[get_random_number()]
     if wide: