"""
分析 diff 图的像素差异分布，按纵向区域统计差异密度。
输出每 50px 逻辑高度段的差异百分比，帮助定位需要调整的区域。
"""
from pathlib import Path
from PIL import Image
import struct

SCREENSHOTS_DIR = Path(__file__).resolve().parents[2] / "docs" / "h5_ui" / "screenshots"

def analyze_diff_image(diff_path: Path, band_height: int = 150):
    """按纵向条带分析 diff 图的红色像素密度"""
    img = Image.open(diff_path).convert("RGB")
    w, h = img.size
    pixels = img.load()
    
    print(f"Diff 图尺寸: {w}×{h}")
    print(f"条带高度: {band_height}px (逻辑 {band_height/3:.0f}px)")
    print(f"{'区域':>8} {'逻辑Y':>8} {'差异像素':>10} {'总像素':>10} {'差异%':>8} {'条形图'}")
    print("-" * 80)
    
    bands = []
    for y_start in range(0, h, band_height):
        y_end = min(y_start + band_height, h)
        diff_count = 0
        total = 0
        for y in range(y_start, y_end):
            for x in range(w):
                r, g, b = pixels[x, y]
                total += 1
                # diff 图中红色/品红色像素表示差异
                if r > 200 and g < 100:
                    diff_count += 1
        pct = (diff_count / total * 100) if total > 0 else 0
        bar = "█" * int(pct / 2)
        logical_y = y_start / 3
        print(f"{y_start:>6}-{y_end:<6} {logical_y:>6.0f}px {diff_count:>10,} {total:>10,} {pct:>7.1f}% {bar}")
        bands.append({"y_start": y_start, "y_end": y_end, "diff_pct": pct})
    
    # 找出差异最大的区域
    top_bands = sorted(bands, key=lambda b: b["diff_pct"], reverse=True)[:5]
    print(f"\n差异最大的 5 个区域:")
    for b in top_bands:
        logical_y = b["y_start"] / 3
        print(f"  逻辑 Y={logical_y:.0f}px: {b['diff_pct']:.1f}%")

def main():
    diff_path = SCREENSHOTS_DIR / "diff-board-finance-v2.png"
    if not diff_path.exists():
        print(f"❌ diff 图不存在: {diff_path}")
        return
    analyze_diff_image(diff_path, band_height=150)

if __name__ == "__main__":
    main()