groundtruth的多个标签在一个文件夹,预测的多个标签在另一个文件夹,如何计算该目标检测任务的F1分数(直接给出Python代码及注释)
时间: 2023-05-13 09:07:14 浏览: 58
可以使用sklearn.metrics中的f1_score函数来计算目标检测任务的F1分数。具体实现步骤如下:
1. 读取groundtruth标签和预测标签,将它们分别存储在两个列表中。
```python
import os
# 读取groundtruth标签和预测标签
gt_labels = []
pred_labels = []
gt_folder = 'path/to/groundtruth/folder'
pred_folder = 'path/to/prediction/folder'
for filename in os.listdir(gt_folder):
with open(os.path.join(gt_folder, filename), 'r') as f:
gt_labels.append(f.read().splitlines())
for filename in os.listdir(pred_folder):
with open(os.path.join(pred_folder, filename), 'r') as f:
pred_labels.append(f.read().splitlines())
```
2. 计算每个标签的TP、FP、FN数量。
```python
# 计算每个标签的TP、FP、FN数量
tp = 0
fp = 0
fn = 0
for i in range(len(gt_labels)):
gt_set = set(gt_labels[i])
pred_set = set(pred_labels[i])
tp += len(gt_set & pred_set)
fp += len(pred_set - gt_set)
fn += len(gt_set - pred_set)
```
3. 根据TP、FP、FN数量计算F1分数。
```python
# 根据TP、FP、FN数量计算F1分数
precision = tp / (tp + fp)
recall = tp / (tp + fn)
f1_score = 2 * precision * recall / (precision + recall)
print('F1 score:', f1_score)
```
完整代码如下:
```python
import os
from sklearn.metrics import f1_score
# 读取groundtruth标签和预测标签
gt_labels = []
pred_labels = []
gt_folder = 'path/to/groundtruth/folder'
pred_folder = 'path/to/prediction/folder'
for filename in os.listdir(gt_folder):
with open(os.path.join(gt_folder, filename), 'r') as f:
gt_labels.append(f.read().splitlines())
for filename in os.listdir(pred_folder):
with open(os.path.join(pred_folder, filename), 'r') as f:
pred_labels.append(f.read().splitlines())
# 计算每个标签的TP、FP、FN数量
tp = 0
fp = 0
fn = 0
for i in range(len(gt_labels)):
gt_set = set(gt_labels[i])
pred_set = set(pred_labels[i])
tp += len(gt_set & pred_set)
fp += len(pred_set - gt_set)
fn += len(gt_set - pred_set)
# 根据TP、FP、FN数量计算F1分数
precision = tp / (tp + fp)
recall = tp / (tp + fn)
f1_score = 2 * precision * recall / (precision + recall)
print('F1 score:', f1_score)
```
注意:这段代码假设每个文件中的标签都是按行分隔的,如果不是,需要根据实际情况进行修改。