// +build linux,arm package main import ( "fmt" "os" "time" "runtime" "os/signal" "strings" "math" "image" "image/draw" _ "image/png" "encoding/base64" "github.com/FEEDFACE-COM/piglet" gl "github.com/FEEDFACE-COM/piglet/gles2" "github.com/go-gl/mathgl/mgl32" ) /* draw ***********************************************************************/ func UpdateScene(shaderProgram uint32, camera mgl32.Mat4, startTime time.Time) { clock := float32( time.Now().Sub( startTime ).Seconds() ) angle := PI/3. * Sin( clock ) cam := camera.Mul4( mgl32.HomogRotate3DY( angle ) ) gl.UseProgram( shaderProgram ) ptr := gl.GetUniformLocation(shaderProgram, gl.Str("camera\x00") ) gl.UniformMatrix4fv(ptr, 1, false, &cam[0]) } func DrawScene(shaderProgram,vertexBuffer,textureName uint32) { gl.Clear( gl.COLOR_BUFFER_BIT | gl.DEPTH_BUFFER_BIT ) gl.UseProgram( shaderProgram ) gl.BindBuffer(gl.ARRAY_BUFFER, vertexBuffer) gl.BindTexture(gl.TEXTURE_2D, textureName) gl.DrawArrays(gl.TRIANGLES, 0, 2*3) err := gl.GetError() if err != gl.NO_ERROR { Error("fail draw: %s", gl.ErrorString(err)) } } /* init ***********************************************************************/ func InitScene(width,height int32) (uint32,uint32,uint32,mgl32.Mat4,time.Time) { Notice("init scene..") cameraMatrix := InitCameraMatrix(float32(width),float32(height)) vertexBuffer := InitVertexBuffer() textureName := InitTextureName() vertexShader := InitShader(VertexSource,gl.VERTEX_SHADER) fragmentShader := InitShader(FragmentSource, gl.FRAGMENT_SHADER) shaderProgram := InitShaderProgram(vertexShader, fragmentShader) gl.Viewport(0, 0, width, height) gl.ClearColor(0.25, 0.25, 0.25, 1.0) if gl.GetError() != gl.NO_ERROR { Error("fail init scene") } startTime := time.Now() return shaderProgram,vertexBuffer,textureName,cameraMatrix,startTime } func InitCameraMatrix(width,height float32) mgl32.Mat4 { fov, ratio := mgl32.DegToRad(45.), width/height near,far := float32(0.01), float32(10.0) projection := mgl32.Perspective(fov, ratio, near, far) pos := mgl32.Vec3{0,0,5} lookat := mgl32.Vec3{0,0,0} up := mgl32.Vec3{0,1,0} view := mgl32.LookAtV( pos, lookat, up ) var cameraMatrix = mgl32.Ident4() cameraMatrix = cameraMatrix.Mul4( projection ) cameraMatrix = cameraMatrix.Mul4( view ) return cameraMatrix } func InitVertexBuffer() uint32 { var vertexBuffer uint32 gl.GenBuffers(1,&vertexBuffer) gl.BindBuffer(gl.ARRAY_BUFFER, vertexBuffer) gl.BufferData(gl.ARRAY_BUFFER, len(VertexData)*4, gl.Ptr(VertexData), gl.STATIC_DRAW) if gl.GetError() != gl.NO_ERROR { Error("fail init buffer") } return vertexBuffer } func InitTextureName() uint32 { var textureName uint32 reader := base64.NewDecoder(base64.StdEncoding, strings.NewReader( TextureData ) ) img,_,_ := image.Decode(reader) textureRGBA := image.NewRGBA( img.Bounds() ) draw.Draw(textureRGBA, textureRGBA.Bounds(), img, image.Point{0,0}, draw.Src) w,h := int32(textureRGBA.Rect.Size().X), int32(textureRGBA.Rect.Size().Y) ptr := gl.Ptr(textureRGBA.Pix) gl.ActiveTexture(gl.TEXTURE0) gl.GenTextures(1, &textureName) gl.BindTexture(gl.TEXTURE_2D, textureName) gl.TexParameteri(gl.TEXTURE_2D, gl.TEXTURE_MIN_FILTER, gl.LINEAR) gl.TexParameteri(gl.TEXTURE_2D, gl.TEXTURE_MAG_FILTER, gl.LINEAR) gl.TexImage2D(gl.TEXTURE_2D, 0, gl.RGBA, w, h, 0, gl.RGBA, gl.UNSIGNED_BYTE, ptr ) if gl.GetError() != gl.NO_ERROR { Error("fail init texture") } return textureName } func InitShader(source string, xtype uint32) uint32 { var status int32 shader := gl.CreateShader( xtype ) src, free := gl.Strs(source+"\x00") defer free() gl.ShaderSource( shader, 1, src, nil ) gl.CompileShader( shader ) gl.GetShaderiv(shader,gl.COMPILE_STATUS,&status) if status == gl.FALSE { Error("fail compile shader") } return shader } func InitShaderProgram(vertexShader,fragmentShader uint32) uint32 { var status int32 shaderProgram := gl.CreateProgram() gl.AttachShader( shaderProgram, vertexShader) gl.AttachShader( shaderProgram, fragmentShader) gl.LinkProgram( shaderProgram ) gl.GetProgramiv(shaderProgram, gl.LINK_STATUS, &status); if status == gl.FALSE { Error("fail link program") } ptr := gl.GetAttribLocation(shaderProgram, gl.Str("position\x00") ) gl.EnableVertexAttribArray( uint32(ptr) ) gl.VertexAttribPointer( uint32(ptr), 3, gl.FLOAT, false, (3+2+4)*4, gl.PtrOffset(0*4) ) ptr = gl.GetAttribLocation(shaderProgram, gl.Str("texCoord\x00") ) gl.EnableVertexAttribArray( uint32(ptr) ) gl.VertexAttribPointer( uint32(ptr), 2, gl.FLOAT, false, (3+2+4)*4, gl.PtrOffset(3*4) ) ptr = gl.GetAttribLocation(shaderProgram, gl.Str("color\x00") ) gl.EnableVertexAttribArray( uint32(ptr) ) gl.VertexAttribPointer( uint32(ptr), 4, gl.FLOAT, false, (3+2+4)*4, gl.PtrOffset((3+2)*4) ) return shaderProgram } /* context ********************************************************************/ func ConfigureContext() (int32,int32) { Notice("create context..") err := piglet.CreateContext() if err != nil { Error("fail create context: %s",err) } width,height := piglet.GetDisplaySize() Notice("display: %dx%d",width,height) piglet.MakeCurrent() gl.InitWithProcAddrFunc( piglet.GetProcAddress ) Notice("renderer: %s %s", gl.GoStr(gl.GetString((gl.VENDOR))),gl.GoStr(gl.GetString((gl.RENDERER)))); Notice("version: %s / %s", gl.GoStr(gl.GetString((gl.VERSION))),gl.GoStr(gl.GetString((gl.SHADING_LANGUAGE_VERSION)))); return width,height } /* main ***********************************************************************/ const FrameRate = 60. func main() { Notice("Hello, Camp!!") // handle user interrupt RegisterSignalHandler() // lock main goroutine to current thread! runtime.LockOSThread() // configure opengl context width,height := ConfigureContext() // init scene program,buffer,texture,camera,startTime := InitScene(width,height) // start ticker tickChannel := make( chan struct{}, 1 ) go Ticker(tickChannel) Notice("start draw..") for { //ever UpdateScene(program,camera,startTime) DrawScene(program,buffer,texture) piglet.SwapBuffers() WaitForTick(tickChannel) } os.Exit(-1) // never reached } /* tick ***********************************************************************/ func Ticker(tickChannel chan struct{}) { for { //ever tickChannel <- struct{}{} // send tick time.Sleep( time.Duration( int64( time.Second/FrameRate) ) ) } } func WaitForTick(tickChannel chan struct{}) { <- tickChannel // read tick for { // clear all ticks select { case <- tickChannel: ; // nop default: return } } } /* math ***********************************************************************/ const PI = 3.1415926535897932384626433832795028841971693993751058209749445920 func Sin(x float32) float32 { return float32( math.Sin( float64(x) ) ) } /* util ***********************************************************************/ func RegisterSignalHandler() { breakChan := make(chan os.Signal, 1) signal.Notify(breakChan, os.Interrupt) go func() { <-breakChan Notice("\rbreak.") os.Exit(0) }() } func Notice(format string, args ...interface{}) { fmt.Fprintf(os.Stderr,format+"\n", args...) } func Error(format string, args ...interface{}) { fmt.Fprintf(os.Stderr,"ERROR: "+format+"\n", args...); os.Exit(-1) } /* data ***********************************************************************/ var VertexData = []float32{ // position texCoord color // x, y, z, s, t, r, g, b, a, -1.0, -1.0, 0., 0., 1., 0., 0., 0., 1., // A D C 1.0, -1.0, 0., 1., 1., 0., 1., 1., 1., // B +---+ 1.0, 1.0, 0., 1., 0., 1., 1., 0., 1., // C | /| // | / | -1.0, -1.0, 0., 0., 1., 0., 0., 0., 1., // A |/ | 1.0, 1.0, 0., 1., 0., 1., 1., 0., 1., // C +---+ -1.0, 1.0, 0., 0., 0., 1., 0., 1., 1., // D A B } const VertexSource = ` uniform mat4 camera; attribute vec3 position; attribute vec2 texCoord; varying vec2 vTexCoord; attribute vec4 color; varying vec4 vColor; void main() { vTexCoord = texCoord; vColor = color; gl_Position = camera * vec4(position, 1); } ` const FragmentSource = ` uniform sampler2D texture; varying vec2 vTexCoord; varying vec4 vColor; void main() { vec4 texColor = texture2D(texture,vTexCoord); gl_FragColor = vec4(vColor.rgb, 1. - texColor.a); } ` // unzip -p cccamp19_design_v.1.0.zip cccamp19_design_v.1.0/logo_icons/3_rockets/screen/cccamp19_signet_colors_RGB.png | convert /dev/stdin -resize 512x512 /dev/stdout | base64 -w 0 const TextureData = 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"