perf(util): 移除不相关函数

py/utils/util.py

@@ -8,8 +8,6 @@
 """
 
 import os
-import numpy as np
-import xmltodict
 import torch
 import random
 import matplotlib.pyplot as plt
@@ -29,71 +27,6 @@ def num_model(model):
     return sum(param.numel() for param in model.parameters())
 
 
-def parse_car_csv(csv_dir):
-    csv_path = os.path.join(csv_dir, 'car.csv')
-    samples = np.loadtxt(csv_path, dtype=np.str)
-    return samples
-
-
-def parse_xml(xml_path):
-    """
-    解析xml文件，返回标注边界框坐标
-    """
-    # print(xml_path)
-    with open(xml_path, 'rb') as f:
-        xml_dict = xmltodict.parse(f)
-        # print(xml_dict)
-
-        bndboxs = list()
-        objects = xml_dict['annotation']['object']
-        if isinstance(objects, list):
-            for obj in objects:
-                obj_name = obj['name']
-                difficult = int(obj['difficult'])
-                if 'car'.__eq__(obj_name) and difficult != 1:
-                    bndbox = obj['bndbox']
-                    bndboxs.append((int(bndbox['xmin']), int(bndbox['ymin']), int(bndbox['xmax']), int(bndbox['ymax'])))
-        elif isinstance(objects, dict):
-            obj_name = objects['name']
-            difficult = int(objects['difficult'])
-            if 'car'.__eq__(obj_name) and difficult != 1:
-                bndbox = objects['bndbox']
-                bndboxs.append((int(bndbox['xmin']), int(bndbox['ymin']), int(bndbox['xmax']), int(bndbox['ymax'])))
-        else:
-            pass
-
-        return np.array(bndboxs)
-
-
-def iou(pred_box, target_box):
-    """
-    计算候选建议和标注边界框的IoU
-    :param pred_box: 大小为[4]
-    :param target_box: 大小为[N, 4]
-    :return: [N]
-    """
-    xA = np.maximum(pred_box[0], target_box[:, 0])
-    yA = np.maximum(pred_box[1], target_box[:, 1])
-    xB = np.minimum(pred_box[2], target_box[:, 2])
-    yB = np.minimum(pred_box[3], target_box[:, 3])
-    # 计算交集面积
-    intersection = np.maximum(0.0, xB - xA) * np.maximum(0.0, yB - yA)
-    # 计算两个边界框面积
-    boxAArea = (pred_box[2] - pred_box[0]) * (pred_box[3] - pred_box[1])
-    boxBArea = (target_box[:, 2] - target_box[:, 0]) * (target_box[:, 3] - target_box[:, 1])
-
-    scores = intersection / (boxAArea + boxBArea - intersection)
-    return scores
-
-
-def compute_ious(rects, bndboxs):
-    iou_list = list()
-    for rect in rects:
-        scores = iou(rect, bndboxs)
-        iou_list.append(max(scores))
-    return iou_list
-
-
 def save_model(model, model_save_path):
     # 保存最好的模型参数
     check_dir('./models')