v0.1.17; update 7-9

robogauge/utils/visualize/plot_radar_and_bar.py

@@ -0,0 +1,180 @@
+# -*- coding: utf-8 -*-
+'''
+@File    : plot_radar_and_bar.py
+@Time    : 2025/12/27 22:52:24
+@Author  : wty-yy (with Gemini 3)
+@Version : 1.0
+@Blog    : https://wty-yy.github.io/
+@Desc    : 可视化评测结果，生成雷达图和柱状图
+'''
+import matplotlib.pyplot as plt
+from pathlib import Path
+import numpy as np
+import yaml
+import argparse
+import os
+
+# --- 配置 Matplotlib 样式 ---
+config = {
+    "font.family": 'serif',
+    "figure.figsize": (8, 6),
+    "font.size": 12,
+    "mathtext.fontset": 'cm',
+    'axes.unicode_minus': False
+}
+plt.rcParams.update(config)
+plt.rcParams['font.serif'] = ['Times New Roman', 'DejaVu Serif', 'serif']
+
+def parse_value_string(val_str):
+    if isinstance(val_str, (int, float)):
+        return float(val_str)
+    if isinstance(val_str, str):
+        if '±' in val_str:
+            return float(val_str.split('±')[0].strip())
+        return float(val_str)
+    return 0.0
+
+def load_data(file_paths):
+    all_data = []
+    metric_keys = ['lin_vel_err', 'ang_vel_err', 'orientation_stability', 'dof_limits', 'torque_smoothness', 'dof_power']
+    labels_map = {
+        'lin_vel_err': 'Lin Vel\nAccuracy',
+        'ang_vel_err': 'Ang Vel\nAccuracy',
+        'dof_limits': 'Joint Limits\nMargin',
+        'dof_power': 'Energy\nEfficiency',
+        'orientation_stability': 'Orientation\nStability',
+        'torque_smoothness': 'Torque\nSmoothness',
+    }
+
+    for path in file_paths:
+        if not os.path.exists(path):
+            print(f"[ERROR] File not found: {path}")
+            continue
+        with open(path, 'r', encoding='utf-8') as f:
+            content = yaml.safe_load(f)
+        
+        raw_path = content.get('model_path', 'Unknown_Model')
+        model_name = os.path.basename(raw_path).replace('.pt', '')
+
+        values = []
+        summary = content['summary']
+        for k in metric_keys:
+            if k in summary:
+                raw_val = summary[k]['mean@25']
+                values.append(parse_value_string(raw_val))
+            else:
+                values.append(0.0)
+        all_data.append({'name': model_name, 'values': values})
+        
+    return all_data, [labels_map[k] for k in metric_keys]
+
+def plot_radar(data_list, labels, output_file=None, r_range=(0, 1.05)):
+    """绘制雷达图"""
+    if not data_list:
+        return
+
+    num_vars = len(labels)
+    angles = np.linspace(0, 2 * np.pi, num_vars, endpoint=False).tolist()
+    angles += angles[:1]
+    
+    colors = plt.cm.get_cmap("tab10", len(data_list))
+    fig, ax = plt.subplots(figsize=(10, 8), subplot_kw=dict(polar=True))
+    plt.subplots_adjust(left=0.05, right=0.75, top=0.9, bottom=0.1)
+
+    ax.set_theta_offset(np.pi / 2)
+    ax.set_theta_direction(-1)
+    
+    plt.xticks(angles[:-1], labels, color='#444444', size=11)
+    
+    for label, angle in zip(ax.get_xticklabels(), angles[:-1]):
+        if angle in (0, np.pi): label.set_horizontalalignment('center')
+        elif 0 < angle < np.pi: label.set_horizontalalignment('left')
+        else: label.set_horizontalalignment('right')
+
+    r_min, r_max = r_range
+    ax.set_ylim(r_min, r_max)
+    
+    ticks = np.linspace(r_min, r_max, 5)
+    plt.yticks(ticks, [f"{t:.2f}" for t in ticks], color="grey", size=10)
+    
+    ax.set_rlabel_position(180)
+    ax.grid(True, color='gray', linestyle=':', linewidth=1.5, alpha=0.5)
+    ax.spines['polar'].set_visible(False)
+
+    linewidth = 2.5 
+    for idx, item in enumerate(data_list):
+        values_closed = item['values'] + [item['values'][0]]
+        color = colors(idx)
+        ax.plot(angles, values_closed, linewidth=linewidth, label=item['name'], color=color)
+        ax.fill(angles, values_closed, color=color, alpha=0.15)
+
+    plt.legend(loc='lower left', bbox_to_anchor=(0.82, 0.0), title="Models", frameon=False)
+    plt.title('Performance Comparison (Radar)', size=16, y=1.06)
+    
+    if output_file:
+        plt.savefig(output_file, dpi=300, bbox_inches='tight')
+        print(f"Radar plot saved to {output_file}")
+
+def plot_bar(data_list, labels, output_file=None, h_range=(0, 1.05)):
+    """绘制柱状图"""
+    if not data_list:
+        return
+
+    num_models = len(data_list)
+    num_metrics = len(labels)
+    
+    # 设置柱状图参数
+    x = np.arange(num_metrics)
+    width = 0.8 / num_models  # 自动计算柱子宽度
+    
+    fig, ax = plt.subplots(figsize=(12, 6))
+    colors = plt.cm.get_cmap("tab10", num_models)
+    
+    for idx, item in enumerate(data_list):
+        # 计算每个模型柱子的偏移量
+        offset = (idx - (num_models - 1) / 2) * width
+        ax.bar(x + offset, item['values'], width, label=item['name'], color=colors(idx), alpha=0.8)
+
+    ax.set_ylabel('Score / Value')
+    ax.set_title('Performance Comparison (Bar)', size=16)
+    ax.set_xticks(x)
+    # 将标签中的换行符去掉或处理，使其在柱状图中更美观
+    clean_labels = [l.replace('\n', ' ') for l in labels]
+    ax.set_xticklabels(clean_labels, rotation=15, ha='right')
+    ax.legend(title="Models", bbox_to_anchor=(1.05, 1), loc='upper left', frameon=False)
+    ax.grid(axis='y', linestyle='--', alpha=0.7)
+    if h_range:
+        ax.set_ylim(h_range)
+
+    plt.tight_layout()
+    
+    if output_file:
+        plt.savefig(output_file, dpi=300, bbox_inches='tight')
+        print(f"Bar plot saved to {output_file}")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('files_or_dir', metavar='F', type=str, nargs='+', help='YAML files or directory')
+    parser.add_argument('--out', type=str, help='Base name for output files')
+    parser.add_argument('--range', type=float, nargs=2, default=[0.0, 1.0], help='Axis range for radar and bar plots')
+    args = parser.parse_args()
+    
+    # 获取文件列表
+    files = args.files_or_dir
+    if Path(args.files_or_dir[0]).is_dir():
+        dir_path = Path(args.files_or_dir[0])
+        files = [str(p) for p in dir_path.glob('*.yaml')]
+    
+    data, metrics_labels = load_data(files)
+
+    # 处理输出文件名
+    radar_out, bar_out = None, None
+    if args.out:
+        out_path = Path(args.out)
+        radar_out = str(out_path.with_name(f"{out_path.stem}_radar{out_path.suffix}"))
+        bar_out = str(out_path.with_name(f"{out_path.stem}_bar{out_path.suffix}"))
+
+    # 分别调用绘图函数
+    plot_radar(data, metrics_labels, output_file=radar_out, r_range=args.range)
+    plot_bar(data, metrics_labels, output_file=bar_out, h_range=args.range)
+    plt.show()

robogauge/utils/visualize/plot_terrain_levels.py

@@ -0,0 +1,168 @@
+# -*- coding: utf-8 -*-
+'''
+@File    : plot_terrain_levels.py
+@Time    : 2025/12/27 23:04:01
+@Author  : wty-yy (with Gemini 3)
+@Version : 1.0
+@Blog    : https://wty-yy.github.io/
+@Desc    : None
+@Desc    : 可视化不同地形下随着摩擦力变化的关卡通过等级 (Terrain Level vs Friction)
+'''
+import matplotlib.pyplot as plt
+from pathlib import Path
+import numpy as np
+import yaml
+import argparse
+import os
+import re
+
+# --- 配置 Matplotlib 样式 ---
+config = {
+    "font.family": 'serif',
+    "figure.figsize": (18, 10),
+    "font.size": 12,
+    "mathtext.fontset": 'cm',
+    'axes.unicode_minus': False
+}
+plt.rcParams.update(config)
+plt.rcParams['font.serif'] = ['Times New Roman', 'DejaVu Serif', 'serif']
+
+def load_terrain_data(file_paths):
+    """
+    加载数据并按模型、地形、摩擦力组织
+    Returns:
+        model_data: {model_name: {terrain_type: {friction: level}}}
+    """
+    target_terrains = ['wave', 'slope', 'stairs_up', 'stairs_down', 'obstacle']
+    model_data = {}
+    
+    # 用于从键名中提取 friction 的正则 (例如 friction1.25)
+    friction_pattern = re.compile(r'friction([\d\.]+)')
+
+    for path in file_paths:
+        if not os.path.exists(path):
+            print(f"[ERROR] File not found: {path}")
+            continue
+            
+        with open(path, 'r', encoding='utf-8') as f:
+            content = yaml.safe_load(f)
+        
+        # 获取模型名称
+        raw_path = content.get('model_path', 'Unknown_Model')
+        model_name = os.path.basename(raw_path).replace('.pt', '')
+        
+        if model_name not in model_data:
+            model_data[model_name] = {t: {} for t in target_terrains}
+            
+        # 遍历YAML中的每个测试条目
+        for key, value in content.items():
+            if key in ['model_path', 'summary']:
+                continue
+            
+            # 1. 检查地形是否在目标列表中
+            t_name = value.get('terrain_name')
+            if t_name not in target_terrains:
+                continue
+            
+            # 2. 从 key 中提取摩擦力
+            match = friction_pattern.search(key)
+            if not match:
+                continue
+            friction = float(match.group(1))
+            
+            # 3. 获取地形等级 (取该条件下该地形的最大值，防止重复)
+            level = value.get('terrain_level', 0)
+            
+            # 记录数据
+            current_max = model_data[model_name][t_name].get(friction, -1)
+            if level > current_max:
+                model_data[model_name][t_name][friction] = level
+                
+    return model_data, target_terrains
+
+def plot_results(model_data, terrain_labels, output_file=None):
+    """绘制 2x3 的子图"""
+    if not model_data:
+        print("No data to plot.")
+        return
+
+    # 设置 2行3列 的布局
+    fig, axes = plt.subplots(2, 3, figsize=(18, 10), sharex=True, sharey=True)
+    axes = axes.flatten()
+    
+    # 自动获取颜色映射
+    colors = plt.cm.get_cmap("tab10", len(model_data))
+    
+    # 遍历前5个地形进行绘图
+    for i, t_name in enumerate(terrain_labels):
+        ax = axes[i]
+        
+        for idx, (model_name, terrains) in enumerate(model_data.items()):
+            data_points = terrains.get(t_name, {})
+            if not data_points:
+                continue
+            
+            # 排序 (Friction, Level)
+            sorted_points = sorted(data_points.items())
+            xs, ys = zip(*sorted_points)
+            
+            ax.plot(xs, ys, marker='o', linestyle='-', linewidth=2, 
+                    label=model_name, color=colors(idx), alpha=0.8)
+        
+        ax.set_title(t_name.replace('_', ' ').title(), fontsize=14)
+        ax.grid(True, linestyle='--', alpha=0.6)
+
+    # --- 第6张图：绘制平均值 (Average) ---
+    ax_avg = axes[5]
+    for idx, (model_name, terrains) in enumerate(model_data.items()):
+        # 聚合该模型下所有地形的数据用于计算平均值
+        # 结构: friction -> [level_slope, level_wave, ...]
+        friction_agg = {}
+        for t_name in terrain_labels:
+            for f, l in terrains[t_name].items():
+                if f not in friction_agg:
+                    friction_agg[f] = []
+                friction_agg[f].append(l)
+        
+        if not friction_agg:
+            continue
+            
+        # 计算平均值并排序
+        sorted_frictions = sorted(friction_agg.keys())
+        avg_levels = [np.mean(friction_agg[f]) for f in sorted_frictions]
+        
+        ax_avg.plot(sorted_frictions, avg_levels, marker='s', linestyle='-', linewidth=2,
+                    label=model_name, color=colors(idx), alpha=0.8)
+
+    ax_avg.set_title('Average Performance', fontsize=14)
+    ax_avg.grid(True, linestyle='--', alpha=0.6)
+    
+    # 仅在最后一张图显示图例，避免遮挡
+    ax_avg.legend(loc='best', frameon=False, title="Models")
+
+    # 最下面一行设置 X 轴标签
+    for i in [3, 4, 5]:
+        axes[i].set_xlabel('Friction ($\mu$)')
+
+    # 最左边一列设置 Y 轴标签
+    for i in [0, 3]:
+        axes[i].set_ylabel('Terrain Level')
+
+    plt.tight_layout()
+    # 稍微调整间距防止标签重叠
+    # plt.subplots_adjust(wspace=0.1, hspace=0.15)
+    
+    if output_file:
+        plt.savefig(output_file, dpi=300, bbox_inches='tight')
+        print(f"Plot saved to {output_file}")
+    else:
+        plt.show()
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Plot terrain level vs friction curves.")
+    parser.add_argument('files', metavar='F', type=str, nargs='+', help='YAML result files')
+    parser.add_argument('--out', type=str, default='terrain_analysis.jpg', help='Output image filename')
+    args = parser.parse_args()
+    
+    data, labels = load_terrain_data(args.files)
+    plot_results(data, labels, output_file=args.out)