单目3D初始代码

eval_tools/model_comparison/generate_eval_report.py

@@ -0,0 +1,207 @@
+#!/usr/bin/env python3
+"""
+自动将单模型 evaluation_report.json 转换为中文 Markdown 评测报告。
+
+用法:
+    python generate_eval_report.py <evaluation_report.json 路径>
+    python generate_eval_report.py <evaluation_report.json 路径> --output <输出路径>
+    python generate_eval_report.py <evaluation_report.json 路径> --model "模型名称" --date 2026-03-01
+
+示例:
+    python eval_tools/model_comparison/generate_eval_report.py \
+        evaluation_results/.../evaluation_report.json \
+        --model yolov5s-300w-newdata-cncap
+"""
+
+import json
+import argparse
+import sys
+from datetime import date
+from pathlib import Path
+
+# Allow importing class_config from the eval_tools root
+sys.path.insert(0, str(Path(__file__).parent.parent))
+from class_config import REPORT_3D_CLASS_LABELS
+
+
+def fmt(v: float, decimals: int = 4) -> str:
+    return f"{v:.{decimals}f}"
+
+
+def fmt2(v: float) -> str:
+    return f"{v:.2f}"
+
+
+def pct(v: float) -> str:
+    return f"{v * 100:.1f}%"
+
+
+def build_report(data: dict, model_name: str, report_date: str) -> str:
+    lines = []
+
+    # ── 标题 ─────────────────────────────────────────────────────────────────
+    lines.append(f"# 模型评测报告: {model_name}")
+    lines.append("")
+    lines.append(f"**模型**: {model_name}  ")
+    lines.append(f"**评测日期**: {report_date}  ")
+
+    eval_cfg = data.get("evaluation_config", {})
+    if eval_cfg:
+        lines.append(f"**置信度阈值 (P/R/F1)**: {eval_cfg.get('conf_threshold', '-')}  ")
+        lines.append(f"**IoU 阈值**: {eval_cfg.get('iou_threshold', '-')}  ")
+        lines.append(f"**AP 计算方法**: {eval_cfg.get('ap_method', '-')}  ")
+
+    lines.append("")
+    lines.append("---")
+    lines.append("")
+
+    # ── 2D Overall ────────────────────────────────────────────────────────────
+    ov2d = data["2d_evaluation"]["overall"]
+    lines.append("## 📊 2D检测指标 (Overall)")
+    lines.append("")
+    lines.append("| 指标 | 数值 |")
+    lines.append("|------|------|")
+    lines.append(f"| **Precision** | {fmt(ov2d['precision'])} |")
+    lines.append(f"| **Recall** | {fmt(ov2d['recall'])} |")
+    lines.append(f"| **F1-Score** | {fmt(ov2d['f1_score'])} |")
+    lines.append(f"| **mAP** | {fmt(ov2d['map'])} |")
+    lines.append(f"| **TP** | {ov2d['tp']:,} |")
+    lines.append(f"| **FP** | {ov2d['fp']:,} |")
+    lines.append(f"| **FN** | {ov2d['fn']:,} |")
+    lines.append("")
+
+    # ── 2D Per-Class ──────────────────────────────────────────────────────────
+    lines.append("## 📋 2D检测指标 (Per Class)")
+    lines.append("")
+    lines.append("| 类别 | Precision | Recall | F1 | AP | GT | TP | FP | FN |")
+    lines.append("|------|-----------|--------|----|----|-----|-----|-----|-----|")
+
+    pc2d = data["2d_evaluation"]["per_class"]
+    for cls, cd in pc2d.items():
+        lines.append(
+            f"| **{cls}** | {fmt(cd['precision'])} | {fmt(cd['recall'])} "
+            f"| {fmt(cd['f1_score'])} | {fmt(cd['ap'])} "
+            f"| {cd['num_gt']:,} | {cd['tp']:,} | {cd['fp']:,} | {cd['fn']:,} |"
+        )
+    lines.append("")
+    lines.append("---")
+    lines.append("")
+
+    # ── 3D Overall per class ──────────────────────────────────────────────────
+    m3d = data.get("3d_evaluation", {})
+    if not m3d:
+        return "\n".join(lines)
+
+    lines.append("## 🎯 3D检测指标")
+    lines.append("")
+
+    CLS_LABELS = REPORT_3D_CLASS_LABELS
+
+    LONG_RANGES = ["long_0-10m", "long_10-20m", "long_20-30m", "long_30-40m",
+                   "long_40-50m", "long_50-60m", "long_60-70m", "long_70-80m",
+                   "long_80-90m", "long_90-100m", "long_100-999m"]
+
+    for cls_key, cls_label in CLS_LABELS.items():
+        if cls_key not in m3d:
+            continue
+        cd = m3d[cls_key]
+        ov = cd["overall"]
+        n = ov["num_samples"]
+
+        lines.append(f"### {cls_label} ({n:,} 样本)")
+        lines.append("")
+
+        # Overall stats
+        lines.append("#### 总体指标")
+        lines.append("")
+        lines.append("| 指标 | Mean | Median | Std | P90 |")
+        lines.append("|------|------|--------|-----|-----|")
+
+        def stat_row(label, key):
+            s = ov[key]
+            return (f"| **{label}** | {fmt2(s['mean'])} | {fmt2(s['median'])} "
+                    f"| {fmt2(s['std'])} | {fmt2(s['percentile_90'])} |")
+
+        lines.append(stat_row("Lateral Error (m)",         "lateral_error"))
+        lines.append(stat_row("Longitudinal Error (m)",    "longitudinal_error"))
+        lines.append(stat_row("Long. Relative Error",      "longitudinal_relative_error"))
+        lines.append(stat_row("Heading Error (rad)",       "heading_error"))
+        lines.append(stat_row("Heading Error Relaxed",     "heading_error_relaxed"))
+        rev_pct = ov['reversal_percentage']
+        lines.append(f"| **Reversal** | {ov['reversal_count']:,} ({rev_pct:.2f}%) | - | - | - |")
+        lines.append("")
+
+        # Distance range breakdown
+        avail_ranges = [r for r in LONG_RANGES if r in cd]
+        if avail_ranges:
+            lines.append("#### 按距离分段 (纵向误差 Mean / Lateral Mean / Samples)")
+            lines.append("")
+            lines.append("| 距离段 | 样本数 | Lateral (m) | Longitudinal (m) | Long.Rel | Heading | Reversal% |")
+            lines.append("|--------|--------|-------------|------------------|----------|---------|-----------|")
+            for rng in avail_ranges:
+                r = cd[rng]
+                rov = r
+                rn = rov["num_samples"]
+                if rn == 0:
+                    continue
+                lat  = rov["lateral_error"]["mean"]
+                lon  = rov["longitudinal_error"]["mean"]
+                lrel = rov["longitudinal_relative_error"]["mean"]
+                hd   = rov["heading_error"]["mean"]
+                rev  = rov["reversal_percentage"]
+                lines.append(
+                    f"| {rng.replace('long_', '')} | {rn:,} | {fmt2(lat)} | {fmt2(lon)} "
+                    f"| {lrel:.3f} | {fmt2(hd)} | {rev:.1f}% |"
+                )
+            lines.append("")
+
+    lines.append("---")
+    lines.append("")
+
+    return "\n".join(lines)
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="将单模型 evaluation_report.json 转换为中文 Markdown 评测报告"
+    )
+    parser.add_argument("json_path", help="evaluation_report.json 的路径")
+    parser.add_argument("--output", "-o", default=None,
+                        help="输出 Markdown 文件路径（默认与 JSON 同目录，文件名 EVALUATION_REPORT.md）")
+    parser.add_argument("--model", default=None,
+                        help="模型名称（默认从目录名推断）")
+    parser.add_argument("--date", default=str(date.today()),
+                        help="评测日期 (默认今天，格式 YYYY-MM-DD)")
+    args = parser.parse_args()
+
+    json_path = Path(args.json_path).resolve()
+    if not json_path.exists():
+        print(f"错误: 文件不存在: {json_path}", file=sys.stderr)
+        sys.exit(1)
+
+    with open(json_path, "r", encoding="utf-8") as f:
+        data = json.load(f)
+
+    # 从路径推断模型名称：取 json 所在目录的上级目录名
+    if args.model:
+        model_name = args.model
+    else:
+        # e.g. .../yolov5s-300w-newdata-cncap/20260228_102849/evaluation_report.json
+        model_name = json_path.parent.parent.name
+        if not model_name or model_name == ".":
+            model_name = json_path.parent.name
+    print(f"模型: {model_name}")
+
+    report = build_report(data, model_name, args.date)
+
+    if args.output:
+        out_path = Path(args.output)
+    else:
+        out_path = json_path.parent / "EVALUATION_REPORT.md"
+
+    out_path.write_text(report, encoding="utf-8")
+    print(f"报告已生成: {out_path}")
+
+
+if __name__ == "__main__":
+    main()