Merge remote-tracking branch 'origin/master'

# Conflicts:
#	README.md

Datasets_Processing/SuperfluousFilesSearch.py

@@ -5,8 +5,8 @@
 
 import os
 
-path1 = "D:/python_project/object_detection/yolov5-u/signal/images/train2017/"      # path1为存储图片的文件夹JPEGImages
-path2 = "D:/python_project/object_detection/yolov5-u/signal/labels/train2017/"     # path2为存储标签文件的文件夹Annotations
+path1 = "C:/Users/17262/Desktop/datasets/images/"      # path1为存储图片的文件夹JPEGImages
+path2 = "C:/Users/17262/Desktop/datasets/labels/"     # path2为存储标签文件的文件夹Annotations
 
 filelist1 = os.listdir(path1)   # 该文件夹下所有的文件（包括文件夹）
 filelist2 = os.listdir(path2)   # 该文件夹下所有的文件（包括文件夹）
@@ -24,12 +24,15 @@
     if count == 0:
         Illegalfiles.append(filename1)
 
-## 输出结果
+## 输出结果，并删除没有对应标签文件的多余图片
 print('----------------------------------------')
 if len(Illegalfiles) != 0:
-    print('以下图片不存在与之对应的标签文件:')
+    print('以下%d张图片不存在与之对应的标签文件:'%(len(Illegalfiles)))
     for Illegalfile in Illegalfiles:
-        print(Illegalfile)
+        # print(Illegalfile)
+        os.remove(path1+Illegalfile+'.jpg')
+        print('Image %s.jpg has been removed.'%(Illegalfile))
+    print("%d images has been removed."%(len(Illegalfiles)))
 else:
     print('所有图片均存在与之对应的标签文件')
 print('----------------------------------------')

Datasets_Processing/rename.py

@@ -4,10 +4,10 @@
 
 import os
 
-path = "D:/python_project/object_detection/yolov5-u/signal/images/train2017/"     # 存储图片的文件夹路径
+path = "C:/Users/17262/Desktop/datasets/red&green/"     # 存储图片的文件夹路径
 filelist = os.listdir(path)             # 该文件夹下所有的文件（包括文件夹）
 
-count = 1  # 确定重命名后起始图片名
+count = 10496  # 确定重命名后起始图片名
 
 for file in filelist:   # 遍历所有文件
     Olddir = os.path.join(path , file)   # 原来的文件路径
@@ -16,7 +16,7 @@
     filename = os.path.splitext(file)[0]   # 文件名
     filetype = os.path.splitext(file)[1]   # 文件扩展名
 
-    Newdir = os.path.join(path,str(count).zfill(4)+filetype)    # 用字符串函数zfill,以0补全所需位数,zfill()中的'6'表示图片名称为6位数字
+    Newdir = os.path.join(path,str(count).zfill(5)+filetype)    # 用字符串函数zfill,以0补全所需位数,zfill()中的'6'表示图片名称为6位数字
     os.rename(Olddir,Newdir)    # 重命名
     count+=1
 print('重命名完成')

README.md

@@ -43,7 +43,7 @@ python mainwindow.py
 本项目包含图片检测、视频检测和摄像头实时检测三个功能模块，下面为项目演示视频：
 [道路信号机视频自动识别与仿真系统演示视频](http://wang-typora.oss-cn-beijing.aliyuncs.com/img/铁路信号机视频自动识别仿真系统演示视频（终）.mp4)
 
-Update：对铁路信号机进行检测的演示视频：[铁路信号机视频检测演示视频](http://wang-typora.oss-cn-beijing.aliyuncs.com/img/presentation(train)_23_02_20.mp4)
+->Update：对铁路信号机进行检测的演示视频：[铁路信号机视频检测演示视频](http://wang-typora.oss-cn-beijing.aliyuncs.com/img/presentation(train)_23_02_20.mp4)
 
 ## Datasets
 

mainwindow.py

@@ -585,6 +585,7 @@ def run(self):
                     time_log.append(t3 - t0)
 
             print(f'平均每帧用时({sum(time_log) / len(time_log):.3f}s)')
+
             root.mainloop()
 
         except FileNotFoundError:

models/common.py

@@ -1,6 +1,7 @@
 # YOLOv5 common modules
 
 import math
+import warnings
 from copy import copy
 from pathlib import Path
 
@@ -176,6 +177,23 @@ def forward(self, x):
         return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
 
 
+class SPPF(nn.Module):
+    # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
+    def __init__(self, c1, c2, k=5):  # equivalent to SPP(k=(5, 9, 13))
+        super().__init__()
+        c_ = c1 // 2  # hidden channels
+        self.cv1 = Conv(c1, c_, 1, 1)
+        self.cv2 = Conv(c_ * 4, c2, 1, 1)
+        self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)
+
+    def forward(self, x):
+        x = self.cv1(x)
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore')  # suppress torch 1.9.0 max_pool2d() warning
+            y1 = self.m(x)
+            y2 = self.m(y1)
+            return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))
+
 class Focus(nn.Module):
     # Focus wh information into c-space
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups

utils/datasets.py

@@ -545,11 +545,13 @@ def __getitem__(self, index):
         if mosaic:
             # Load mosaic
             img, labels = load_mosaic(self, index)
+            # img, labels = load_mosaic9(self, index)
             shapes = None
 
             # MixUp https://arxiv.org/pdf/1710.09412.pdf
             if random.random() < hyp['mixup']:
                 img2, labels2 = load_mosaic(self, random.randint(0, self.n - 1))
+                # img2, labels2 = load_mosaic9(self, random.randint(0, self.n - 1))
                 r = np.random.beta(8.0, 8.0)  # mixup ratio, alpha=beta=8.0
                 img = (img * r + img2 * (1 - r)).astype(np.uint8)
                 labels = np.concatenate((labels, labels2), 0)