tool extends MMGenBase class_name MMGenConvolution var convolution_params : Dictionary = {} func get_type() -> String: return "convolution" func get_type_name() -> String: if convolution_params.has("name"): return convolution_params.name return .get_type_name() func get_parameter_defs() -> Array: var rv : Array = [ { name="size", type="size", first=4, last=11, default=7 } ] if convolution_params.has("parameters"): for p in convolution_params.parameters: rv.push_back(p) return rv func get_input_defs() -> Array: return [ { name="in", type=convolution_params.input_type } ] func get_output_defs() -> Array: return [ { type=convolution_params.output_type } ] func set_convolution_params(data: Dictionary) -> void: convolution_params = data func _get_shader_code(uv : String, output_index : int, context : MMGenContext) -> Dictionary: var genname = "o"+str(get_instance_id()) var epsilon = 1.0/pow(2, parameters.size) var types = { "rgba": { type="vec4", init="vec4(0.0)" }, "rgb": { type="vec3", init="vec3(0.0)" }, "f": { type="float", init="0.0" } } var rv = { globals=[], defs="", code="", textures={} } var source = get_source(0) if source == null: return rv var variant_index = context.get_variant(self, uv) if variant_index == -1: variant_index = context.get_variant(self, uv) # Calculate matrix var errors = 0 var sum = [ 0.0, 0.0, 0.0, 0.0 ] var matrix = [] var expr : Expression = null var expr_variables : PoolStringArray var expr_values : Array var expr_variables_x_index : int if convolution_params.has("matrix_function"): expr = Expression.new() expr_variables = PoolStringArray(["size"]) expr_values = [ pow(2, parameters.size) ] if convolution_params.has("parameters"): for p in convolution_params.parameters: expr_variables.push_back(p.name) if parameters.has(p.name): expr_values.push_back(parameters[p.name]) elif p.has("default"): expr_values.push_back(p.default) else: expr_values.push_back(0) errors += 1 print("No value for "+p.name) expr_variables_x_index = expr_values.size() expr_variables.push_back("x") expr_values.push_back(0) expr_variables.push_back("y") expr_values.push_back(0) var error = expr.parse(convolution_params.matrix_function, expr_variables) if error != OK: print("Error in expression: "+expr.get_error_text()) return rv for dy in range(-convolution_params.y, convolution_params.y+1): var line = [] for dx in range(-convolution_params.x, convolution_params.x+1): var coef = 0.0 if convolution_params.has("matrix") and dy+convolution_params.y < convolution_params.matrix.size() and dx+convolution_params.x < convolution_params.matrix[dy+convolution_params.y].size() and convolution_params.matrix[dy+convolution_params.y][dx+convolution_params.x] != null: coef = convolution_params.matrix[dy+convolution_params.y][dx+convolution_params.x] elif convolution_params.has("matrix_sparse") and convolution_params.matrix_sparse.has(str(dy)) and convolution_params.matrix_sparse[str(dy)].has(str(dx)): coef = convolution_params.matrix_sparse[str(dy)][str(dx)] elif expr != null: expr_values[expr_variables_x_index] = dx expr_values[expr_variables_x_index+1] = dy coef = expr.execute(expr_values) if typeof(coef) == TYPE_INT: coef = float(coef) match convolution_params.output_type: "f": if typeof(coef) == TYPE_REAL or convolution_params.input_type == "f": sum[0] += coef else: errors += 1 "rgb": if typeof(coef) == TYPE_REAL: sum[0] += coef sum[1] += coef sum[2] += coef coef = [ coef, coef, coef ] if convolution_params.input_type != "f" and convolution_params.input_type != "rgb": errors += 1 elif typeof(coef) == TYPE_ARRAY and coef.size() == 3: if convolution_params.input_type == "f" or convolution_params.input_type == "rgb": sum[0] += coef[0] sum[1] += coef[1] sum[2] += coef[2] else: errors += 1 else: errors += 1 "rgba": if typeof(coef) == TYPE_REAL: sum[0] += coef sum[1] += coef sum[2] += coef sum[3] += coef coef = [ coef, coef, coef, coef ] if convolution_params.input_type != "f" and convolution_params.input_type != "rgba": errors += 1 elif typeof(coef) == TYPE_ARRAY and coef.size() == 4: if convolution_params.input_type == "f" or convolution_params.input_type == "rgba": sum[0] += coef[0] sum[1] += coef[1] sum[2] += coef[2] sum[3] += coef[3] else: errors += 1 else: errors += 1 line.push_back(coef) matrix.push_back(line) # Generate code rv.code += "%s %s_%d = %s;\n" % [ types[convolution_params.output_type].type, genname, variant_index, types[convolution_params.output_type].init ] if errors > 0: pass else: if convolution_params.has("normalized") and convolution_params.normalized: for i in range(sum.size()): if sum[i] != 0: sum[i] = 1.0/sum[i] else: sum[i] = 1.0 else: sum = [ 1.0, 1.0, 1.0, 1.0 ] for dy in range(-convolution_params.y, convolution_params.y+1): var line = matrix[dy+convolution_params.y] for dx in range(-convolution_params.x, convolution_params.x+1): var coef = line[dx+convolution_params.x] var uv_str = "(%s)+vec2(%.9f,%.9f)" % [ uv, dx*epsilon, dy*epsilon ] var src_code = source.generator.get_shader_code(uv_str, source.output_index, context) while src_code is GDScriptFunctionState: src_code = yield(src_code, "completed") # Add global definitions if src_code.has("globals"): for d in src_code.globals: if rv.globals.find(d) == -1: rv.globals.push_back(d) # Add generated definitions if src_code.has("defs"): rv.defs += src_code.defs # Add generated code if src_code.has("code"): rv.code += src_code.code var coef_str : String match convolution_params.output_type: "f": coef_str = "%.9f" % [ coef * sum[0] ] "rgb": coef_str = "vec3(%.9f, %.9f, %.9f)" % [ coef[0] * sum[0], coef[1] * sum[1], coef[2] * sum[2] ] "rgba": coef_str = "vec4(%.9f, %.9f, %.9f, %.9f)" % [ coef[0] * sum[0], coef[1] * sum[1], coef[2] * sum[2], coef[3] * sum[3] ] rv.code += "%s_%d += %s*%s;\n" % [ genname, variant_index, coef_str, src_code[convolution_params.input_type] ] for t in src_code.textures.keys(): rv.textures[t] = src_code.textures[t] rv[convolution_params.output_type] = "%s_%d" % [ genname, variant_index ] return rv func _serialize(data: Dictionary) -> Dictionary: data.convolution_params = convolution_params return data func _deserialize(data : Dictionary) -> void: set_convolution_params(data.convolution_params)