evalution.py

import numpy as np
import cv2
import pandas as pd
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score, jaccard_score
import os

np.set_printoptions(threshold=np.inf)
# 定义标签颜色和类别
label_colors = [
    (254, 246, 201),   # 耕地
    (16, 118, 75),     # 森林
    (172, 212, 90),    # 草地
    (57, 179, 115),    # 灌木地
    (124, 209, 245),   # 湿地
    (0, 87, 155),      # 水体
    (96, 102, 48),     # 苔原
    (147, 45, 16),     # 人造地表
    (206, 203, 206),   # 裸土
    (214, 242, 255)    # 冰川和永久积雪
]
print(label_colors)

num_classes = len(label_colors)

model_name="manet"
average_mode="micro"
# 读取标签图和预测结果图
label_path = "dataset/label/"
result_path = f"test_results_{model_name}/"

def read_images(image_path):
    image = cv2.imread(image_path)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    return image

def load_data(path,image_names):
    images = []
    for name in image_names:
        image = read_images(path+name)
        images.append(image)
    return np.array(images)

label_images = load_data(label_path,os.listdir(label_path))
result_images = load_data(result_path,os.listdir(result_path))

# 将RGB图像转换为类别索引图像
def rgb_to_index(image, label_colors):
    index_image = np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8)
    for i, color in enumerate(label_colors):
        index_image[np.all(image == color, axis=-1)] = i
    return index_image

label_index_images = [rgb_to_index(image, label_colors) for image in label_images]
result_index_images = [rgb_to_index(image, label_colors) for image in result_images]

# 计算混淆矩阵
conf_matrices = [confusion_matrix(label.flatten(), result.flatten(), labels=list(range(num_classes))) for label, result in zip(label_index_images, result_index_images)]

# 计算性能指标
accuracy = [accuracy_score(label.flatten(), result.flatten()) for label, result in zip(label_index_images, result_index_images)]
precision = [precision_score(label.flatten(), result.flatten(), average=average_mode, zero_division=0) for label, result in zip(label_index_images, result_index_images)]
recall = [recall_score(label.flatten(), result.flatten(), average=average_mode, zero_division=0) for label, result in zip(label_index_images, result_index_images)]
f1 = [f1_score(label.flatten(), result.flatten(), average=average_mode, zero_division=0) for label, result in zip(label_index_images, result_index_images)]
iou = [jaccard_score(label.flatten(), result.flatten(), average=average_mode) for label, result in zip(label_index_images, result_index_images)]

# 获取文件名
label_files = os.listdir(label_path)
result_files = os.listdir(result_path)

# 创建DataFrame保存结果
results_df = pd.DataFrame({
    "Label File": label_files,
    "Result File": result_files,
    "Accuracy": accuracy,
    "Precision": precision,
    "Recall": recall,
    "F1 Score": f1,
    "IoU (Jaccard Score)": iou
})

# 将DataFrame写入Excel文件
results_df.to_excel(f"results_{model_name}_{average_mode}.xlsx", index=False)

print(f"Results saved to results_{model_name}_{average_mode}.xlsx")