diff --git a/konabot/plugins/roll_dice/__init__.py b/konabot/plugins/roll_dice/__init__.py index ea6a9c1..9e0e366 100644 --- a/konabot/plugins/roll_dice/__init__.py +++ b/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()) diff --git a/konabot/plugins/roll_dice/assets/stick.png b/konabot/plugins/roll_dice/assets/stick.png new file mode 100644 index 0000000..a518d05 Binary files /dev/null and b/konabot/plugins/roll_dice/assets/stick.png differ diff --git a/konabot/plugins/roll_dice/roll_dice.py b/konabot/plugins/roll_dice/roll_dice.py index ce736ed..b4cc57b 100644 --- a/konabot/plugins/roll_dice/roll_dice.py +++ b/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） - [88, 33], # 右下 + [51 + move_distance[0], 5 + move_distance[1]], # 右上（上移，创建透视） + [88 + move_distance[0], 33 + move_distance[1]], # 右下 [49, 62] # 左下（下移） ], dtype=np.float32) - - # 加载背景图像，保留透明通道 - background = cv2.imread(ASSETS / "template.png", cv2.IMREAD_UNCHANGED) + corners[:, 0] += offset_x + corners[:, 1] += offset_y # 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 \ No newline at end of file diff --git a/konabot/plugins/roll_dice/roll_number.py b/konabot/plugins/roll_dice/roll_number.py index 6568926..75cb645 100644 --- a/konabot/plugins/roll_dice/roll_number.py +++ b/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: