修改分割模块

chose_bands.py

@@ -0,0 +1,160 @@
+import argparse
+import pandas as pd
+import numpy as np
+import itertools
+import re
+from collections import defaultdict
+
+def parse_args():
+    ap = argparse.ArgumentParser(description="Select 3 bands for false-color to maximize inter-class separability")
+    ap.add_argument("--csv", required=True, help="Input CSV: first column is class label, rest are spectra columns")
+    ap.add_argument("--top_k", type=int, default=30, help="How many best single bands to preselect (default: 30)")
+    ap.add_argument("--top_triplets", type=int, default=10, help="How many best triplets to print (default: 10)")
+    ap.add_argument("--map_order", choices=["auto", "wavelength_bgr"], default="auto",
+                    help="RGB mapping order: auto=use scored order; wavelength_bgr=short->B, mid->G, long->R")
+    return ap.parse_args()
+
+def try_parse_wavelength(col_name):
+    # 支持 "wavelength_912.36" / "912.36" / "band_12" 等
+    m = re.search(r"([0-9]+(\.[0-9]+)?)", str(col_name))
+    if m:
+        try:
+            return float(m.group(1))
+        except Exception:
+            return None
+    return None
+
+def read_data(csv_path):
+    df = pd.read_csv(csv_path)
+    if df.shape[1] < 4:
+        raise ValueError("需要至少1个类别列 + 3个光谱列")
+    y = df.iloc[:, 0].to_numpy()
+    X = df.iloc[:, 1:].to_numpy(dtype=np.float64)
+    cols = list(df.columns[1:])
+    waves = []
+    for c in cols:
+        w = try_parse_wavelength(c)
+        waves.append(w if w is not None else c)
+    return X, y, cols, waves
+
+def compute_anova_f_scores(X, y):
+    # 一维ANOVA F-score: F = (SS_between/df_between) / (SS_within/df_within)
+    N, M = X.shape
+    classes = []
+    for cls in np.unique(y):
+        idx = (y == cls)
+        if np.sum(idx) > 0:
+            classes.append(idx)
+    k = len(classes)
+    if k < 2:
+        raise ValueError("类别不足2类，无法计算区分度")
+
+    mu = X.mean(axis=0)
+    between_ss = np.zeros(M, dtype=np.float64)
+    within_ss = np.zeros(M, dtype=np.float64)
+
+    for idx in classes:
+        ng = np.sum(idx)
+        Xg = X[idx]
+        if ng == 0:
+            continue
+        mg = Xg.mean(axis=0)
+        # 无偏方差，若ng==1则设为0
+        vg = Xg.var(axis=0, ddof=1) if ng > 1 else np.zeros(M, dtype=np.float64)
+        between_ss += ng * (mg - mu) ** 2
+        within_ss += (ng - 1) * vg
+
+    dfb = k - 1
+    dfw = N - k if N > k else 1
+    F = (between_ss / max(dfb, 1)) / (within_ss / max(dfw, 1) + 1e-12)
+    return F  # shape (M,)
+
+def score_triplet(X, y, idx_triplet, eps=1e-6):
+    # LDA准则: J = trace( (Sw+epsI)^-1 Sb )，三维空间
+    t = list(idx_triplet)
+    Xt = X[:, t]  # (N,3)
+    classes = []
+    for cls in np.unique(y):
+        idx = (y == cls)
+        if np.sum(idx) > 0:
+            classes.append(idx)
+
+    m = Xt.mean(axis=0)
+    Sb = np.zeros((3, 3), dtype=np.float64)
+    Sw = np.zeros((3, 3), dtype=np.float64)
+
+    for idx in classes:
+        Xg = Xt[idx]
+        ng = Xg.shape[0]
+        if ng == 0:
+            continue
+        mg = Xg.mean(axis=0)
+        diff = (mg - m).reshape(3, 1)
+        Sb += ng * (diff @ diff.T)
+        if ng > 1:
+            Cg = np.cov(Xg, rowvar=False, ddof=1)
+        else:
+            Cg = np.zeros((3, 3), dtype=np.float64)
+        Sw += (ng - 1) * Cg
+
+    # 稳定逆
+    A = Sw + eps * np.eye(3)
+    try:
+        Ainv = np.linalg.pinv(A)
+    except Exception:
+        return -np.inf
+    J = np.trace(Ainv @ Sb)
+    return float(J)
+
+def select_bands(csv_path, top_k=30, top_triplets=10, map_order="auto"):
+    X, y, cols, waves = read_data(csv_path)
+    F = compute_anova_f_scores(X, y)
+    order = np.argsort(-F)
+    top_idx = order[:min(top_k, X.shape[1])]
+
+    best = []
+    for comb in itertools.combinations(top_idx, 3):
+        s = score_triplet(X, y, comb)
+        best.append((s, comb))
+    best.sort(key=lambda x: -x[0])
+
+    results = []
+    for s, comb in best[:top_triplets]:
+        names = [cols[i] for i in comb]
+        wavs = [waves[i] for i in comb]
+        # 建议RGB映射
+        if map_order == "wavelength_bgr" and all(isinstance(w, (int, float)) for w in wavs):
+            order_rgb = np.argsort(wavs)  # 小->大
+            # 小(blue), 中(green), 大(red)
+            b,g,r = [names[order_rgb[0]]], [names[order_rgb[1]]], [names[order_rgb[2]]]
+            rgb = {"R": r[0], "G": g[0], "B": b[0]}
+            rgb_w = {"R": wavs[order_rgb[2]], "G": wavs[order_rgb[1]], "B": wavs[order_rgb[0]]}
+        else:
+            # 直接用得分顺序：第一->R, 第二->G, 第三->B
+            rgb = {"R": names[0], "G": names[1], "B": names[2]}
+            rgb_w = {"R": waves[comb[0]], "G": waves[comb[1]], "B": waves[comb[2]]}
+        results.append({
+            "score": round(s, 6),
+            "bands": names,
+            "wavelengths": wavs,
+            "rgb_mapping": rgb,
+            "rgb_wavelengths": rgb_w
+        })
+    return results, top_idx, F
+
+def main():
+    args = parse_args()
+    results, top_idx, F = select_bands(args.csv, args.top_k, args.top_triplets, args.map_order)
+
+    print(f"Top-{args.top_k} bands by ANOVA F-score (index:col_name):")
+    print(", ".join([f"{i}:{i}" for i in top_idx]))  # 如需列名可自行打印
+
+    print("\nBest triplets:")
+    for r in results:
+        bands = r["bands"]
+        wavs = r["wavelengths"]
+        rgb = r["rgb_mapping"]
+        print(f"- score={r['score']:.4f}  bands={bands}  wavelengths={wavs}  RGB={rgb}")
+
+if __name__ == "__main__":
+    main()