| import matplotlib |
| matplotlib.use('Agg') |
| import matplotlib.pyplot as plt |
| import numpy as np |
| from PIL import Image |
| import io |
|
|
| DIMENSIONS = ["Generalization", "Relevance", "Artistry", "Efficiency"] |
|
|
| def compute_sample_scores(results, prompt): |
| """模拟评分 - 实际项目应替换为真实评估逻辑""" |
| return { |
| "sd_v1_5": [4, 4, 4, 3], |
| "openjourney_v4": [3, 4, 5, 3], |
| "ldm_256": [2, 3, 3, 5] |
| } |
|
|
| def plot_radar(scores_dict, out_path="radar.png"): |
| fig = plt.figure(figsize=(8, 8)) |
| ax = fig.add_subplot(111, polar=True) |
| |
| angles = np.linspace(0, 2*np.pi, len(DIMENSIONS), endpoint=False) |
| angles = np.concatenate((angles, [angles[0]])) |
| |
| for model, scores in scores_dict.items(): |
| data = np.concatenate((scores, [scores[0]])) |
| ax.plot(angles, data, linewidth=2, linestyle='solid', label=model) |
| ax.fill(angles, data, alpha=0.1) |
| |
| ax.set_thetagrids(angles[:-1] * 180/np.pi, DIMENSIONS) |
| ax.set_title("GRACE Evaluation Radar", size=14, pad=20) |
| ax.legend(loc='upper right') |
| |
| buf = io.BytesIO() |
| plt.savefig(buf, format='png', bbox_inches='tight') |
| plt.close() |
| buf.seek(0) |
| return Image.open(buf) |