package vision

import (
	"image"
	"math"
)

// MatchResult 模板匹配结果
type MatchResult struct {
	Point image.Point
	Score float64 // 相似度分数 (0.0 - 1.0)
}

// FindTemplate 在当前画布中查找子图 (模板匹配)
// 使用简单的平方差和 (Sum of Squared Differences) 算法
func (c *Canvas) FindTemplate(template *Canvas) MatchResult {
	src := c.dc.Image()
	tpl := template.dc.Image()
	srcBounds := src.Bounds()
	tplBounds := tpl.Bounds()
	
	sw, sh := srcBounds.Dx(), srcBounds.Dy()
	tw, th := tplBounds.Dx(), tplBounds.Dy()
	
	if tw > sw || th > sh {
		return MatchResult{Score: 0}
	}

	bestPoint := image.Point{}
	minDiff := math.MaxFloat64

	// 为了性能，在大图中进行步长采样
	step := 1
	if sw > 500 || sh > 500 { step = 2 }

	for y := 0; y <= sh-th; y += step {
		for x := 0; x <= sw-tw; x += step {
			diff := 0.0
			// 简单的像素比较
			for ty := 0; ty < th; ty += 2 {
				for tx := 0; tx < tw; tx += 2 {
					sr, sg, sb, _ := src.At(x+tx, y+ty).RGBA()
					tr, tg, tb, _ := tpl.At(tx, ty).RGBA()
					
					dr := float64(sr>>8) - float64(tr>>8)
					dg := float64(sg>>8) - float64(tg>>8)
					db := float64(sb>>8) - float64(tb>>8)
					diff += dr*dr + dg*dg + db*db
				}
			}
			
			if diff < minDiff {
				minDiff = diff
				bestPoint = image.Point{X: x, Y: y}
			}
		}
	}

	// 归一化分数 (1.0 为完美匹配)
	// 最大可能差异：(255*255 * 3) * (tw/2 * th/2)
	maxPossibleDiff := (255.0 * 255.0 * 3.0) * (float64(tw) / 2.0 * float64(th) / 2.0)
	score := 1.0 - (minDiff / maxPossibleDiff)
	if score < 0 { score = 0 }

	return MatchResult{
		Point: bestPoint,
		Score: score,
	}
}