[Model.py 02] 地图按比例放大的实现

按比例放大的实现"/>

[Model.py 02] 地图按比例放大的实现

要求：实现地图按比例放大

分析：考虑到地图放大过程中需要保留河流道路这些物体的相对位置关系，这里选择将河流和道路这些物体的坐标矩阵合并成terrain_matrix并对这个合并后的矩阵进行缩放处理。放大后的矩阵，根据矩阵中标记的物体位置，更新各自物体的放大矩阵。

思路：对于地图按比例放大，借用图片放大的思路，使用最近邻插值的方式，填充放大后产生的空隙。

实现：

补充大小调整参数self.zoomScale

在 _init_的self.grid初始化之前，补充大小调整参数：self.zoomScale。_init_函数修改部分的代码如下：

    def __init__(self, N=10, K=0, width=50, height=102,civil_info_exchange=True, model_layer=-1, is_fired=False, is_flood=True, count=0):self.warning_UI = ""  # 警示信息self.resizeScale=3 # parameter using to control the scale of the map

在_init_函数中补充地图放大、更新代码

        # 将安全通道坐标加载到地图中self.draw_environment(self.pos_exits)# 创建坐标空间self.graph = path_finding.create_graph(self)selfbine_matrices()# To rescale the terrain map size after combination.self.resize_matrices(self.resizeScale)self.separate_matrix() # update different object matrix from the resized matrix.

地图放大函数resize_matrices的定义

    def resize_matrices(self, degree):"""Resize the terrain and constituent matrices while maintaining aspect ratio.:param degree: New rescaling degree."""scaling_factor = (degree, degree)# Resize the main terrain matrixself.terrain_matrix = zoom(self.terrain_matrix, scaling_factor, order=0)# order=1 for bilinear interpolation and 0 for nearest-neighbor interpolation.# This change will ensure that your matrices' integer values are preserved during the resizing process.

放大后更新其他物体的坐标矩阵函数separate_matrix

    def separate_matrix(self):"""Separate the combined terrain matrix into individual feature matrices.Each matrix should represent one feature (e.g., river, road) with 1s where the feature existsand 0s where it doesn't."""# Identify all unique features in the terrain matrix, excluding 0 (empty)unique_features = np.unique(self.terrain_matrix)# Define a mapping from feature codes to the corresponding class attributesfeature_mapping = {1: 'river_matrix',2: 'road_matrix',3: 'wall_matrix',4: 'indoor_matrix',5: 'exits_matrix',6: 'pillar_matrix',7: 'ditch_matrix'}# Initialize each matrix as an array of zerosfor matrix_name in feature_mapping.values():setattr(self, matrix_name, np.zeros_like(self.terrain_matrix))# For each feature, populate the corresponding matrixfor feature in unique_features:if feature == 0:continue  # Skip the 'empty' featurematrix_name = feature_mapping.get(feature)if not matrix_name:continue  # Skip if the feature is not recognized# Update the corresponding matrix directlyfeature_matrix = np.where(self.terrain_matrix == feature, 1, 0)setattr(self, matrix_name, feature_matrix)# At this point, each feature matrix (e.g., self.river_matrix) has been updated directly# No need to return anything since we're modifying the class attributes directly

补充：放大后地图的可视化脚本

请与单独的.py文件中运行。

# It's assumed you have executed the following installation command in your local environment:
# !pip install datashaderimport datashader as ds
import datashader.transfer_functions as tf
import pandas as pd# Load and prepare the data
from matplotlib import pyplot as pltfile_path = 'G:\\terrain_matrix3.csv'
data = pd.read_csv(file_path)# Calculate the aspect ratio of the original data
num_rows, num_cols = data.shape  # Assuming 'data' is your DataFrame
aspect_ratio = num_cols / num_rows# Ensure column headers are consistent and represent coordinates or indexing
# If headers are numeric: good; if not, you might want to set headers as a range of numbers representing columns
data.columns = range(len(data.columns))# Resetting the index to turn it into a column for melting
data = data.reset_index()# Melting the data (now 'X' should be consistently numeric)
melted_data = data.melt(id_vars=['index'], var_name='X', value_name='Value')
melted_data['Y'] = melted_data['index']
melted_data.drop(columns=['index'], inplace=True)# Convert 'X' and 'Y' to numeric values, coercing errors (i.e., non-numeric values are set as NaN)
melted_data['X'] = pd.to_numeric(melted_data['X'], errors='coerce')
melted_data['Y'] = pd.to_numeric(melted_data['Y'], errors='coerce')# Handle or remove any rows with NaN if necessary (created by 'coerce')
melted_data.dropna(subset=['X', 'Y'], inplace=True)# Define the dimensions for the canvas
plot_width = 800
plot_height = int(plot_width / aspect_ratio)  # Maintain the aspect ratio of the original data# Set up the canvas with the correct aspect ratio
canvas = ds.Canvas(plot_width=plot_width, plot_height=plot_height)# Aggregating data into a grid
agg = canvas.points(melted_data, 'X', 'Y', ds.mean('Value'))# Creating an image by coloring the aggregated data
img = tf.shade(agg, cmap=['lightblue', 'darkblue'], how='linear')# Convert the Datashader image to a format that can be displayed by matplotlib
img_to_plot = tf.shade(agg, cmap=['lightblue', 'darkblue'], how='linear')
img_plt = tf.set_background(img_to_plot, 'white')# Display the image using matplotlib
plt.imshow(img_plt.to_pil())
plt.axis('off')  # Optional: this removes the axes for a cleaner look
plt.title('Presentation of the rescaling map data(3X).')plt.show()

以下是可视化脚本的输出案例。其中，2X和3X分别表示设置的地图放大倍数。