feat: 投影法波谷检测+白底硬切断+帧底部对齐+gifmaker透明GIF
- prompt_agent: 精灵表/瓦片集间隙从2-4px放宽到8-16px纯白 - splitsprite removeWhiteBg: dist<threshold/2直接A=0，消除半透明残留 - splitsprite findCuts: 波峰-波谷检测替代死阈值，归并原阈值回退 - splitsprite alignCenter: 全局画布底部对齐替代独立居中，人物不跳帧 - splitsprite MinFillRatio 调至0.14，MinGapWidth调至2 - 新增 pkg/gifmaker: 统一画布+透明索引0+DisposalBackground - test_prompt_to_gif 改用gifmaker包
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@@ -58,8 +58,9 @@ func DefaultOptions() *Options {
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WhiteThreshold: 40,
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GapThreshold: 0.03,
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MinGapWidth: 2,
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MinFillRatio: 0.3,
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MinFillRatio: 0.14,
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Trim: true,
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CenterAlign: true,
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}
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}
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@@ -70,8 +71,9 @@ func DefaultGreenOptions() *Options {
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GreenTolerance: 0.2,
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GapThreshold: 0.03,
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MinGapWidth: 2,
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MinFillRatio: 0.3,
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MinFillRatio: 0.14,
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Trim: true,
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CenterAlign: true,
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}
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}
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@@ -133,14 +135,15 @@ func CenterFrames(frames []image.Image) []image.Image {
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return alignCenter(frames)
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}
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// alignCenter finds the content bounding box per frame, computes the max
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// dimensions, then pads each frame so content is centered uniformly.
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// alignCenter aligns all frames to a uniform canvas with a fixed reference point.
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// Uses bottom-center alignment so characters share a common ground plane across frames,
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// preventing drift/jitter in animation playback.
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func alignCenter(frames []image.Image) []image.Image {
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type contentBox struct {
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minX, minY, maxX, maxY int
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}
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boxes := make([]contentBox, len(frames))
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maxW, maxH := 0, 0
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maxCW, maxCH := 0, 0
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for i, f := range frames {
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b := f.Bounds()
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@@ -172,29 +175,32 @@ func alignCenter(frames []image.Image) []image.Image {
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} else {
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boxes[i] = contentBox{minX, minY, maxX, maxY}
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}
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w := boxes[i].maxX - boxes[i].minX + 1
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h := boxes[i].maxY - boxes[i].minY + 1
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if w > maxW {
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maxW = w
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cw := boxes[i].maxX - boxes[i].minX + 1
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ch := boxes[i].maxY - boxes[i].minY + 1
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if cw > maxCW {
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maxCW = cw
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}
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if h > maxH {
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maxH = h
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if ch > maxCH {
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maxCH = ch
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}
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}
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// Pad by 10% to avoid edge cropping
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maxW = maxW * 11 / 10
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maxH = maxH * 11 / 10
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// Uniform canvas with 10% padding
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canvasW := maxCW * 11 / 10
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canvasH := maxCH * 11 / 10
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// Fixed X center reference: anchor all frames to the same horizontal center
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fixedCenterX := canvasW / 2
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out := make([]image.Image, len(frames))
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for i, f := range frames {
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cb := boxes[i]
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cw := cb.maxX - cb.minX + 1
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ch := cb.maxY - cb.minY + 1
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ox := (maxW - cw) / 2
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oy := (maxH - ch) / 2
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// All frames share the same center-X and bottom-Y anchor
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ox := fixedCenterX - cw/2 // consistent horizontal center
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oy := canvasH - ch // bottom-align: feet planted at same Y
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canvas := image.NewRGBA(image.Rect(0, 0, maxW, maxH))
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canvas := image.NewRGBA(image.Rect(0, 0, canvasW, canvasH))
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draw.Draw(canvas,
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image.Rect(ox, oy, ox+cw, oy+ch),
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f,
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@@ -229,7 +235,7 @@ type tile struct {
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x, y, w, h int
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}
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// removeWhiteBg removes pixels close to pure white (R,G,B all above threshold).
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// removeWhiteBg removes pixels close to pure white (R,G,B all within threshold of 255).
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func removeWhiteBg(rgba *image.RGBA, threshold uint8) *image.RGBA {
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if threshold == 0 {
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threshold = 40
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@@ -245,11 +251,14 @@ func removeWhiteBg(rgba *image.RGBA, threshold uint8) *image.RGBA {
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continue
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}
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r8, g8, b8 := uint8(r>>8), uint8(g>>8), uint8(bl>>8)
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// Pixel is "white" when all channels are near 255
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if int(255-r8) < int(threshold) && int(255-g8) < int(threshold) && int(255-b8) < int(threshold) {
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// Calculate alpha: closer to white = more transparent
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dist := max(int(255-r8), max(int(255-g8), int(255-b8)))
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alpha := float64(dist) / float64(threshold)
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// Distance from pure white
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dist := max(int(255-r8), max(int(255-g8), int(255-b8)))
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if dist < int(threshold)/2 {
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// Very close to white — fully transparent
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dst.SetRGBA(x, y, color.RGBA{R: r8, G: g8, B: b8, A: 0})
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} else if dist < int(threshold) {
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// Semi-white — fade alpha
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alpha := float64(dist-int(threshold)/2) / float64(int(threshold)/2)
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dst.SetRGBA(x, y, color.RGBA{R: r8, G: g8, B: b8, A: uint8(alpha * 255)})
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}
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}
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@@ -378,6 +387,90 @@ func tileFillRatio(rgba *image.RGBA, x0, y0, w, h int) float64 {
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}
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func findCuts(ratios []float64, threshold float64, minGap int) []int {
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n := len(ratios)
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if n == 0 {
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return nil
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}
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// Smooth the ratio curve with a moving average (kernel size = minGap)
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smoothed := make([]float64, n)
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kernel := max(minGap, 3)
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for i := 0; i < n; i++ {
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sum := 0.0
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count := 0
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for j := max(0, i-kernel/2); j < min(n, i+kernel/2+1); j++ {
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sum += ratios[j]
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count++
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}
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if count > 0 {
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smoothed[i] = sum / float64(count)
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}
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}
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// Compute mean to use as reference
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mean := 0.0
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for _, r := range smoothed {
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mean += r
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}
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mean /= float64(n)
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// Find peaks: contiguous regions where smoothed ratio > mean*1.2
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type segment struct{ start, end int }
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var peaks []segment
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i := 0
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for i < n {
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if smoothed[i] > mean*1.2 {
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start := i
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for i < n && smoothed[i] > mean*0.8 {
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i++
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}
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peaks = append(peaks, segment{start, i})
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} else {
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i++
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}
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}
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if len(peaks) < 2 {
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// Fallback: use threshold-based gap detection
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return findCutsByGap(ratios, threshold, minGap)
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}
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// Find valleys between adjacent peaks (minimum smoothed ratio between them)
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cuts := []int{0}
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for p := 0; p < len(peaks)-1; p++ {
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valleyStart := peaks[p].end
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valleyEnd := peaks[p+1].start
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if valleyStart >= valleyEnd {
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// Peaks adjacent — cut at midpoint
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cuts = append(cuts, (peaks[p].end+peaks[p+1].start)/2)
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continue
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}
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// Find minimum in the valley region
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minIdx := valleyStart
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minVal := smoothed[valleyStart]
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for j := valleyStart + 1; j < valleyEnd; j++ {
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if smoothed[j] < minVal {
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minVal = smoothed[j]
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minIdx = j
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}
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}
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cuts = append(cuts, minIdx)
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}
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cuts = append(cuts, n)
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sort.Ints(cuts)
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// Deduplicate
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dedup := cuts[:1]
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for j := 1; j < len(cuts); j++ {
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if cuts[j] != dedup[len(dedup)-1] {
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dedup = append(dedup, cuts[j])
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}
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}
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return dedup
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}
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// findCutsByGap is the original threshold-based fallback.
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func findCutsByGap(ratios []float64, threshold float64, minGap int) []int {
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n := len(ratios)
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isGap := make([]bool, n)
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for i, r := range ratios {
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