8172b4f7b9
- Introduced `imager_shader` class for rendering images with various scaling modes using GPU shaders. - Implemented `scrollbar_shader` class for customizable scrollbar rendering with support for vertical and horizontal orientations. - Added vertex structure template for automatic generation of vertex input structures based on shader reflection. - Enhanced interaction capabilities for scrollbars, including drag-and-drop functionality and visibility management. - Provided default styles and configuration options for both image and scrollbar widgets.
312 lines
12 KiB
Python
312 lines
12 KiB
Python
#!/usr/bin/env python3
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"""
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类型映射模块
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负责将SPIR-V类型映射到C++类型，以及计算std430内存布局。
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提供两种类型映射策略：
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1. Eigen 类型（用于 Push Constants、Uniform Buffers 等）
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- 向量：Eigen::Vector{N}{type} (例如 Vector3f, Vector4i)
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- 方阵：Eigen::Matrix{N}{type} (例如 Matrix3f, Matrix4f)
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- 非方阵：Eigen::Matrix<float, rows, cols>
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- 对齐：Eigen 自动处理对齐要求
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2. 紧凑类型（用于顶点结构体）
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- 向量：std::array<float, N> 或 float[N]
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- 标量：float, int32_t, uint32_t
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- 无 SIMD 对齐，确保与 GPU 着色器布局一致
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"""
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from __future__ import annotations
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from typing import Dict
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from .types import (
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ArrayTypeInfo,
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BaseType,
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MatrixTypeInfo,
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ScalarTypeInfo,
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StructTypeInfo,
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TypeInfo,
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VectorTypeInfo,
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)
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# ============ Type Conversion ============
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def spirv_type_to_cpp(type_info: TypeInfo, type_map: Dict[int, TypeInfo]) -> str:
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"""
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将SPIR-V类型转换为C++类型字符串（使用Eigen数学库）
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类型映射规则：
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- 标量：float, double, int32_t, uint32_t
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- 向量：Eigen::Vector2f/3f/4f, Vector2i/3i/4i 等
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- 矩阵：Eigen::Matrix2f/3f/4f 或 Matrix<float, rows, cols>
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- 数组：std::array<T, N> 或 T[]（运行时数组）
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"""
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if type_info is None:
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return "uint32_t"
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if isinstance(type_info, ScalarTypeInfo):
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type_map_scalar = {
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(BaseType.FLOAT, 32): "float",
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(BaseType.FLOAT, 64): "double",
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(BaseType.DOUBLE, 64): "double",
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(BaseType.INT, 32): "int32_t",
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(BaseType.UINT, 32): "uint32_t",
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(BaseType.BOOL, 32): "uint32_t",
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}
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return type_map_scalar.get((type_info.base_type, type_info.bit_width), "uint32_t")
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elif isinstance(type_info, VectorTypeInfo):
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component_type = type_info.resolved_component_type
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if not isinstance(component_type, ScalarTypeInfo):
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component_type = type_map.get(type_info.component_type_id)
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if not isinstance(component_type, ScalarTypeInfo):
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return "Eigen::Vector4f"
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count = type_info.component_count
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# 根据组件类型选择合适的Eigen向量类型
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if component_type.base_type == BaseType.FLOAT:
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vector_types = {2: "Eigen::Vector2f", 3: "Eigen::Vector3f", 4: "Eigen::Vector4f"}
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elif component_type.base_type == BaseType.INT:
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vector_types = {2: "Eigen::Vector2i", 3: "Eigen::Vector3i", 4: "Eigen::Vector4i"}
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elif component_type.base_type == BaseType.UINT:
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# Eigen 没有预定义的无符号整数向量，使用模板形式
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vector_types = {2: "Eigen::Vector2<uint32_t>", 3: "Eigen::Vector3<uint32_t>", 4: "Eigen::Vector4<uint32_t>"}
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elif component_type.base_type == BaseType.DOUBLE:
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vector_types = {2: "Eigen::Vector2d", 3: "Eigen::Vector3d", 4: "Eigen::Vector4d"}
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else:
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return "Eigen::Vector4f"
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return vector_types.get(count, "Eigen::Vector4f")
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elif isinstance(type_info, MatrixTypeInfo):
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cols = type_info.column_count
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rows = type_info.row_count
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# Eigen使用行优先存储，但GLSL使用列优先
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# 生成的矩阵类型需要注意这个差异
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if cols == rows:
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# 方阵：使用预定义类型
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matrix_types = {2: "Eigen::Matrix2f", 3: "Eigen::Matrix3f", 4: "Eigen::Matrix4f"}
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return matrix_types.get(cols, "Eigen::Matrix4f")
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else:
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# 非方阵：使用通用模板形式
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# 注意：Eigen::Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime>
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return f"Eigen::Matrix<float, {rows}, {cols}>"
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elif isinstance(type_info, ArrayTypeInfo):
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element_type = type_info.resolved_element_type
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if element_type is None:
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element_type = type_map.get(type_info.element_type_id)
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element_cpp = spirv_type_to_cpp(element_type, type_map)
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if type_info.is_runtime:
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return element_cpp # 运行时数组，返回元素类型
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else:
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return f"std::array<{element_cpp}, {type_info.length}>"
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elif isinstance(type_info, StructTypeInfo):
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return type_info.name or f"Struct_{type_info.id}"
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return "uint32_t"
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def spirv_type_to_compact_cpp(type_info: TypeInfo, type_map: Dict[int, TypeInfo]) -> str:
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"""
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将SPIR-V类型转换为紧凑的C++类型字符串（用于顶点结构体）
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此函数生成不带 SIMD 对齐要求的紧凑类型，适用于顶点缓冲区数据结构。
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使用 std::array 替代 Eigen 向量类型，确保内存布局与着色器期望一致。
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类型映射规则：
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- 标量：float, double, int32_t, uint32_t
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- 向量：std::array<float, N>（float 类型）
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std::array<int32_t, N>（int 类型）
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std::array<uint32_t, N>（uint 类型）
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- 矩阵：std::array<float, rows * cols>（展平为数组）
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- 数组：std::array<T, N>
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Args:
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type_info: SPIR-V 类型信息
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type_map: 类型 ID 到类型信息的映射
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Returns:
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紧凑的 C++ 类型字符串
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"""
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if type_info is None:
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return "uint32_t"
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if isinstance(type_info, ScalarTypeInfo):
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type_map_scalar = {
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(BaseType.FLOAT, 32): "float",
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(BaseType.FLOAT, 64): "double",
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(BaseType.DOUBLE, 64): "double",
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(BaseType.INT, 32): "int32_t",
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(BaseType.UINT, 32): "uint32_t",
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(BaseType.BOOL, 32): "uint32_t",
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}
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return type_map_scalar.get((type_info.base_type, type_info.bit_width), "uint32_t")
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elif isinstance(type_info, VectorTypeInfo):
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component_type = type_info.resolved_component_type
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if not isinstance(component_type, ScalarTypeInfo):
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component_type = type_map.get(type_info.component_type_id)
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if not isinstance(component_type, ScalarTypeInfo):
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return f"std::array<float, {type_info.component_count}>"
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count = type_info.component_count
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# 根据组件类型选择基础类型
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if component_type.base_type == BaseType.FLOAT:
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base_cpp = "float"
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elif component_type.base_type == BaseType.DOUBLE:
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base_cpp = "double"
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elif component_type.base_type == BaseType.INT:
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base_cpp = "int32_t"
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elif component_type.base_type == BaseType.UINT:
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base_cpp = "uint32_t"
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else:
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base_cpp = "float"
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return f"std::array<{base_cpp}, {count}>"
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elif isinstance(type_info, MatrixTypeInfo):
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cols = type_info.column_count
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rows = type_info.row_count
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# 矩阵展平为一维数组
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return f"std::array<float, {rows * cols}>"
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elif isinstance(type_info, ArrayTypeInfo):
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element_type = type_info.resolved_element_type
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if element_type is None:
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element_type = type_map.get(type_info.element_type_id)
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element_cpp = spirv_type_to_compact_cpp(element_type, type_map)
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if type_info.is_runtime:
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return element_cpp # 运行时数组，返回元素类型
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else:
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return f"std::array<{element_cpp}, {type_info.length}>"
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elif isinstance(type_info, StructTypeInfo):
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return type_info.name or f"Struct_{type_info.id}"
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return "uint32_t"
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# ============ Layout Calculation ============
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def align_up(value: int, alignment: int) -> int:
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"""向上对齐到指定边界"""
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if alignment == 0:
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return value
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return ((value + alignment - 1) // alignment) * alignment
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def calculate_std430_alignment(type_info: TypeInfo, type_map: Dict[int, TypeInfo]) -> int:
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"""计算std430布局的对齐要求（字节）"""
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if type_info is None:
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return 4
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if isinstance(type_info, ScalarTypeInfo):
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return type_info.bit_width // 8
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elif isinstance(type_info, VectorTypeInfo):
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count = type_info.component_count
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component_type = type_info.resolved_component_type
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if component_type is None:
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component_type = type_map.get(type_info.component_type_id)
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if component_type:
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component_align = calculate_std430_alignment(component_type, type_map)
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if count == 2:
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return 2 * component_align
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elif count in (3, 4):
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return 4 * component_align
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return 16
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elif isinstance(type_info, MatrixTypeInfo):
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col_type = type_info.resolved_column_type
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if col_type is None:
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col_type = type_map.get(type_info.column_type_id)
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if col_type:
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return calculate_std430_alignment(col_type, type_map)
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return 16
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elif isinstance(type_info, ArrayTypeInfo):
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element_type = type_info.resolved_element_type
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if element_type is None:
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element_type = type_map.get(type_info.element_type_id)
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if element_type:
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return calculate_std430_alignment(element_type, type_map)
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return 16
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elif isinstance(type_info, StructTypeInfo):
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max_align = 4
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for member in type_info.members:
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member_type = member.resolved_type
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if member_type is None:
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member_type = type_map.get(member.type_id)
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if member_type:
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member_align = calculate_std430_alignment(member_type, type_map)
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max_align = max(max_align, member_align)
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return max_align
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return 4
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def calculate_std430_size(type_info: TypeInfo, type_map: Dict[int, TypeInfo]) -> int:
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"""计算std430布局的大小（字节）"""
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if type_info is None:
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return 4
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if isinstance(type_info, ScalarTypeInfo):
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return type_info.bit_width // 8
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elif isinstance(type_info, VectorTypeInfo):
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count = type_info.component_count
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component_type = type_info.resolved_component_type
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if component_type is None:
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component_type = type_map.get(type_info.component_type_id)
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if component_type:
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component_size = calculate_std430_size(component_type, type_map)
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return count * component_size
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return 16
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elif isinstance(type_info, MatrixTypeInfo):
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col_type = type_info.resolved_column_type
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if col_type is None:
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col_type = type_map.get(type_info.column_type_id)
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if col_type:
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col_size = calculate_std430_size(col_type, type_map)
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col_align = calculate_std430_alignment(col_type, type_map)
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aligned_col_size = align_up(col_size, col_align)
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return type_info.column_count * aligned_col_size
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return 64
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elif isinstance(type_info, ArrayTypeInfo):
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if type_info.is_runtime:
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return 0 # 运行时数组大小未知
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element_type = type_info.resolved_element_type
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if element_type is None:
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element_type = type_map.get(type_info.element_type_id)
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if element_type and type_info.length:
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element_size = calculate_std430_size(element_type, type_map)
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element_align = calculate_std430_alignment(element_type, type_map)
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stride = align_up(element_size, element_align)
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return stride * type_info.length
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return 0
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elif isinstance(type_info, StructTypeInfo):
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if not type_info.members:
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return 0
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last_member = type_info.members[-1]
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last_type = last_member.resolved_type
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if last_type is None:
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last_type = type_map.get(last_member.type_id)
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last_size = calculate_std430_size(last_type, type_map) if last_type else 4
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total_size = last_member.offset + last_size
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struct_align = calculate_std430_alignment(type_info, type_map)
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return align_up(total_size, struct_align)
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return 4 |