diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..723ef36 --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +.idea \ No newline at end of file diff --git a/README.md b/README.md index 4dfe809..aecc46b 100644 --- a/README.md +++ b/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 diff --git a/README_CH.md b/README_CH.md new file mode 100644 index 0000000..4dfe809 --- /dev/null +++ b/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 diff --git a/export_onnx.py b/export_onnx.py index 53be746..3a4640d 100644 --- a/export_onnx.py +++ b/export_onnx.py @@ -132,5 +132,6 @@ if __name__ == "__main__": try: dnnnet = cv2.dnn.readNet(output_onnx) print('read sucess') - except: - print('read failed') \ No newline at end of file + except cv2.error as err: + print('Your Opencv version : {} may be incompatible, please consider upgrading'.format(cv2.__version__)) + print('Read failed : ', err) \ No newline at end of file diff --git a/yolop.onnx b/yolop.onnx new file mode 100644 index 0000000..67ebc04 Binary files /dev/null and b/yolop.onnx differ