Merge pull request #1 from LapinCodeur/main

Added an english traduction for the README, updated export_onnx.py and added yolop.onnx directly in the repo

.gitignore

@@ -0,0 +1 @@
+.idea

README.md

@@ -1,18 +1,83 @@
 # YOLOP-opencv-dnn
-使用OpenCV部署全景驾驶感知网络YOLOP，可同时处理交通目标检测、可驾驶区域分割、车道线检测，三项视觉感知任务，依然是包含C++和Python两种版本的程序实现
+This repository contained an OpenCV version of YOLOP, a panoptic driving perception network that can handle simultaneously traffic target detection, drivable area segmentation, and lane line detection.
 
-onnx文件从百度云盘下载，链接：https://pan.baidu.com/s/1A_9cldUHeY9GUle_HO4Crg 
-提取码：mf1x
+You can find joined to the repository, an onnx file created from the provided weight of YOLOP.
 
-C++版本的主程序文件是main.cpp，Python版本的主程序文件是main.py。把onnx文件下载到主程序文件所在目录后，就可以运行程序了。文件夹images
-里含有若干张测试图片，来自于bdd100k自动驾驶数据集。
+You will find in the repository, a C++ version (main.cpp), a Python version (main.py), an onnx file created from the provided weight of YOLOP and images folder that contains several test images from the bdd100k autopilot dataset.
 
-本套程序是在华中科技大学视觉团队在最近发布的项目https://github.com/hustvl/YOLOP
-的基础上做的一个opencv推理部署程序，本套程序只依赖opencv库就可以运行，
-从而彻底摆脱对任何深度学习框架的依赖。如果程序运行出错，那很有可能是您安装的opencv版本低了，这时升级opencv版本就能正常运行的。
+This program is an opencv inference deployment program based on the recently released [project YOLOP](https://github.com/hustvl/YOLOP) by the vision team of Huazhong University of Science and Technology.
+It can be run using only the opencv library, thus completely getting rid of the dependency of any deep learning framework.
+
+This program has been tested with opencv 4.5.3. It doesn't work with opencv 4.2.0 and below.
+
+## Export your own onnx file
+You will find in this repository a file export_onnx.py, which is the program that generates the onnx file. If you want to know how to generate .onnx files, you need to copy the export_onnx.py file to the home directory of [YOLOP](https://github.com/hustvl/YOLOP).
+You will also need to modify the code in YOLOP/lib/models/common.py as follow :
+~~~python
+class Contract(nn.Module):
+    # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
+    def __init__(self, gain = 2):
+        super().__init__()
+        self.gain = gain
+
+    def forward(self, x):
+        N, C, H, W = x.size()  # assert (H / s == 0) and (W / s == 0), 'Indivisible gain'
+        s = self.gain
+        x = x.view(N, C, H // s, s, W // s, s)  # x(1,64,40,2,40,2)
+        x = x.permute(0, 3, 5, 1, 2, 4).contiguous()  # x(1,2,2,64,40,40)
+        return x.view(N, C * s * s, H // s, W // s)  # x(1,256,40,40)
+
+
+class Focus(nn.Module):
+    # Focus wh information into c-space
+    # slice concat conv
+    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
+        super(Focus, self).__init__()
+        self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
+        self.contract = Contract(gain=2)
+
+    def forward(self, x):  # x(b,c,w,h) -> y(b,4c,w/2,h/2)
+        # return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
+        return self.conv(self.contract(x))
+~~~
+We are adding a Contract class and we have modified the content of the Focus class.
+We also need to modify the content of the method forward from the Detect class as follow :
+~~~python
+    def forward(self, x):
+        if not torch.onnx.is_in_onnx_export():
+            z = []  # inference output
+            for i in range(self.nl):
+                x[i] = self.m[i](x[i])  # conv
+                # print(str(i)+str(x[i].shape))
+                bs, _, ny, nx = x[i].shape  # x(bs,255,w,w) to x(bs,3,w,w,85)
+                x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
+                # print(str(i)+str(x[i].shape))
+
+                if not self.training:  # inference
+                    if self.grid[i].shape[2:4] != x[i].shape[2:4]:
+                        self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
+                y = x[i].sigmoid()
+                # print("**")
+                # print(y.shape) #[1, 3, w, h, 85]
+                # print(self.grid[i].shape) #[1, 3, w, h, 2]
+                y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i]  # xy
+                y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
+                """print("**")
+                print(y.shape)  # [1, 3, w, h, 85]
+                print(y.view(bs, -1, self.no).shape)  # [1, 3*w*h, 85]"""
+                z.append(y.view(bs, -1, self.no))
+            return x if self.training else (torch.cat(z, 1), x)
+
+        else:
+            for i in range(self.nl):
+                x[i] = self.m[i](x[i])  # conv
+                # print(str(i)+str(x[i].shape))
+                bs, _, ny, nx = x[i].shape  # x(bs,255,w,w) to x(bs,3,w,w,85)
+                x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
+                x[i] = torch.sigmoid(x[i])
+                x[i] = x[i].view(-1, self.no)
+            return torch.cat(x, dim=0)
+~~~
+After these steps, you can run export_onnx.py to generate the onnx file.
+These steps have been extracted from the following Chinese csdn blog post : https://blog.csdn.net/nihate/article/details/112731327
 
-此外，在本套程序里，还有一个export_onnx.py文件，它是生成onnx文件的程序。不过，export_onnx.py文件不能本套程序目录内运行的，
-假如您想了解如何生成.onnx文件，需要把export_onnx.py文件拷贝到https://github.com/hustvl/YOLOP
-的主目录里之后，并且修改lib/models/common.py里的代码，
-这时运行export_onnx.py就可以生成onnx文件了。在lib/models/common.py里修改哪些代码，可以参见我的csdn博客文章
-https://blog.csdn.net/nihate/article/details/112731327

README_CH.md

@@ -0,0 +1,18 @@
+# YOLOP-opencv-dnn
+使用OpenCV部署全景驾驶感知网络YOLOP，可同时处理交通目标检测、可驾驶区域分割、车道线检测，三项视觉感知任务，依然是包含C++和Python两种版本的程序实现
+
+onnx文件从百度云盘下载，链接：https://pan.baidu.com/s/1A_9cldUHeY9GUle_HO4Crg 
+提取码：mf1x
+
+C++版本的主程序文件是main.cpp，Python版本的主程序文件是main.py。把onnx文件下载到主程序文件所在目录后，就可以运行程序了。文件夹images
+里含有若干张测试图片，来自于bdd100k自动驾驶数据集。
+
+本套程序是在华中科技大学视觉团队在最近发布的项目https://github.com/hustvl/YOLOP
+的基础上做的一个opencv推理部署程序，本套程序只依赖opencv库就可以运行，
+从而彻底摆脱对任何深度学习框架的依赖。如果程序运行出错，那很有可能是您安装的opencv版本低了，这时升级opencv版本就能正常运行的。
+
+此外，在本套程序里，还有一个export_onnx.py文件，它是生成onnx文件的程序。不过，export_onnx.py文件不能本套程序目录内运行的，
+假如您想了解如何生成.onnx文件，需要把export_onnx.py文件拷贝到https://github.com/hustvl/YOLOP
+的主目录里之后，并且修改lib/models/common.py里的代码，
+这时运行export_onnx.py就可以生成onnx文件了。在lib/models/common.py里修改哪些代码，可以参见我的csdn博客文章
+https://blog.csdn.net/nihate/article/details/112731327

export_onnx.py

@@ -132,5 +132,6 @@ if __name__ == "__main__":
     try:
         dnnnet = cv2.dnn.readNet(output_onnx)
         print('read sucess')
-    except:
-        print('read failed')
+    except cv2.error as err:
+        print('Your Opencv version : {} may be incompatible, please consider upgrading'.format(cv2.__version__))
+        print('Read failed : ', err)