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