# 导入必要的库import osfrom PIL import Imageimport matplotlib.pyplot as pltfrom matplotlib.colors import ListedColormap, Normalizeimport numpy as npimport pandas as pdimport seaborn as snsfrom scipy.ndimage import gaussian_filterdef smooth_data(data, sigma=3):# 使用 Gaussian 滤波进行数据平滑处理smoothed_data = gaussian_filter(data, sigma=sigma)return smoothed_data# 设置全局字体plt.rcParams['font.family'] = 'Arial' # 请替换 ' Arial ' 为你希望使用的字体plt.rcParams['font.size'] = 10 # 设置字体大小# 获取当前路径下的所有图像文件image_files = [file for file in os.listdir() if file.lower().endswith(('.png', '.jpg', '.jpeg', '.tif'))]for image_file in image_files:# 读取图像image_path = os.path.join(os.getcwd(), image_file)image = Image.open(image_path)# 将图像转换为灰度图gray_image = image.convert("L")# 将灰度图转换为NumPy数组data = np.array(gray_image)# 对数据进行平滑处理smoothed_data = smooth_data(data)# 将平滑后的数据转换为长格式df = pd.DataFrame(smoothed_data, columns=np.arange(data.shape[1])).stack().reset_index()df.columns = ["X", "Y", "Z"]# 将旧的列名转换为数值df['X'] = pd.Categorical(df['X'])df['X'] = df['X'].cat.codes# 创建图形fig = plt.figure()ax = fig.add_subplot(111, projection='3d')#自定义颜色映射colors = sns.color_palette("icefire", 256) # 请替换 ' icefire ' 为你希望使用的颜色映射fire_cmap = ListedColormap(colors)# 设置颜色映射范围为 0 到 100norm = Normalize(vmin=0, vmax=100)surf = ax.plot_trisurf(df['Y'], df['X'], df['Z'], cmap=fire_cmap, norm=norm, linewidth=0.6)# 将结果保存为图像文件result_image_path = f"result_{os.path.splitext(image_file)[0]}.png"plt.savefig(result_image_path, bbox_inches='tight', dpi=300)
https://www.practicalpythonfordatascience.com/ap_seaborn_palettepip install pillow matplotlib seaborn numpy scipy如果您喜欢我们的文章，欢迎关注
