update

datasets_preprocess/split_train_val_.py

@@ -0,0 +1,69 @@
+# 加入负样本，根据图片数量划分数据集
+import os
+import random
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--img_path', default='/home/lc/data_center/gift/ori_image/images', type=str,
+                    help='input xml label path')  # 图片存放地址
+# 数据集的划分，地址选择自己数据下的ImageSets/Main
+parser.add_argument('--txt_path', default='/home/lc/data_center/gift/yolov10_data/Main', type=str,
+                    help='output txt label path')
+opt = parser.parse_args()
+
+trainval_percent = 1.0
+train_percent = 0.8
+val_percent = 1.0
+# train_percent = 1.0
+# val_percent = 0.0
+
+imgfilepath = opt.img_path
+txtsavepath = opt.txt_path
+total_img = os.listdir(imgfilepath)
+if not os.path.exists(txtsavepath):
+    os.makedirs(txtsavepath)
+
+num = len(total_img)
+print("all num:", num)
+list_index = range(num)
+tv = int(num * trainval_percent)
+tr = int(tv * train_percent)
+trainval = random.sample(list_index, tv)
+train = random.sample(trainval, tr)
+
+val_test = []
+for i in trainval:
+    if i not in train:
+        val_test.append(i)
+num_ = len(val_test)
+print("val-test num:", num_)
+list_index_ = range(num_)
+va = int(num_ * val_percent)
+val = random.sample(val_test, va)
+
+file_trainval = open(txtsavepath + '/trainval.txt', 'w')
+file_test = open(txtsavepath + '/test.txt', 'w')
+file_train = open(txtsavepath + '/train.txt', 'w')
+file_val = open(txtsavepath + '/val.txt', 'w')
+
+addtrain_path = ""
+for i in list_index:
+    name = total_img[i][:-4] + '\n'
+    addimg_name = name.strip() + ".jpg"
+    # print(addimg_name,type(addimg_name),len(addimg_name))
+    if i in trainval:
+        file_trainval.write(name)
+        # if addimg_name in os.listdir(addtrain_path):#把某些数据加入训练集中
+        #     print("addimg_name:",addimg_name)
+        #     file_train.write(name)
+        if i in train:
+            file_train.write(name)
+        if i in val:
+            file_val.write(name)
+        if (i not in train) and (i not in val):
+            file_test.write(name)
+
+file_trainval.close()
+file_train.close()
+file_val.close()
+file_test.close()

datasets_preprocess/voc_label_.py

@@ -0,0 +1,101 @@
+# 标签xml转txt以及无xml的负样本空txt
+import xml.etree.ElementTree as ET
+import os, cv2
+from os import getcwd
+
+sets = ['train', 'val', 'test']
+
+classes = ['tag', 'bandage']
+
+
+def convert(size, box):
+    dw = 1. / (size[0])
+    dh = 1. / (size[1])
+    x = (box[0] + box[1]) / 2.0 - 1
+    y = (box[2] + box[3]) / 2.0 - 1
+    w = box[1] - box[0]
+    h = box[3] - box[2]
+    x = x * dw
+    y = y * dh
+    w = w * dw
+    h = h * dh
+    return x, y, w, h
+
+
+def convert_annotation(image_id, imgname_list, label_path, Annotation_path, image_path):
+    out_file = open(label_path + '%s.txt' % (image_id), 'w')
+    if os.path.exists(Annotation_path + '%s.xml' % (image_id)):
+        try:
+            in_file = open(Annotation_path + '%s.xml' % (image_id), encoding='UTF-8')
+            tree = ET.parse(in_file)
+            root = tree.getroot()
+            size = root.find('size')
+            # if size!=None:
+            #    w = int(size.find('width').text)
+            #    h = int(size.find('height').text)
+            # else:
+            img_name = image_id + ".jpg"
+            img_path = image_path + f"{img_name}"
+            img_data_ = cv2.imread(img_path)
+            h, w = img_data_.shape[0], img_data_.shape[1]
+
+            for obj in root.iter('object'):
+                # difficult = obj.find('difficult').text
+                # difficult = obj.find('Difficult').text
+                cls = obj.find('name').text
+                # cls = cls[:13]
+                # print("cls:", cls, len(cls))
+                # if cls not in classes or int(difficult) == 1:
+                #     continue
+                try:
+                    cls_id = classes.index(cls)
+                    xmlbox = obj.find('bndbox')
+                    b = (
+                    float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),
+                    float(xmlbox.find('ymax').text))
+
+                    b1, b2, b3, b4 = b
+                    # 标注越界修正
+                    if b2 > w:
+                        b2 = w
+                    if b4 > h:
+                        b4 = h
+                    b = (b1, b2, b3, b4)
+                    bb = convert((w, h), b)
+                    out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
+
+                except Exception as e:
+                    print(e, image_id)
+                    imgname_list.append(image_id)
+        except Exception as e:
+            print('error:', e, image_id)
+    else:
+        out_file.write('\n')
+
+
+# abs_path = os.getcwd()
+
+
+image_path = '/home/lc/data_center/gift/ori_image/images/'  # img实际存放地址
+Annotation_path = '/home/lc/data_center/gift/ori_image/xmls/'  # xml实际存放地址
+label_path = '/home/lc/data_center/gift/ori_image/labels/'  # 保存路径
+
+# wd = getcwd()
+imgname_list = []
+for image_set in sets:
+    if not os.path.exists(label_path):
+        os.makedirs(label_path)
+    # image_ids = open('data/ImageSets/Main/%s.txt' % (image_set), encoding='gbk').read().strip().split()
+    # image_ids = open('data/ImageSets/Main_0820/%s.txt' % (image_set)).read().strip().split()
+    image_ids = open('/home/lc/data_center/gift/yolov10_data/Main/%s.txt' % (image_set)).read().split('\n')
+    list_file = open('/home/lc/data_center/gift/yolov10_data/%s.txt' % (image_set), 'w')
+    for image_id in image_ids:
+        list_file.write(image_path + '%s.jpg\n' % (image_id))
+        # print(image_id, "Converting...")
+        convert_annotation(image_id, imgname_list, label_path, Annotation_path, image_path)
+
+    list_file.close()
+with open('error_img.txt', 'w+') as f:
+    for i_name in imgname_list:
+        f.write(i_name + '\n')
+print("error imgname_list:", imgname_list)