updated docs

shoaib42 · Jul 15, 2020 · bb95c59 · bb95c59
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diff --git a/docs/segnet-camera-2.md b/docs/segnet-camera-2.md
@@ -1,4 +1,4 @@
-<img src="https://github.com/dusty-nv/jetson-inference/raw/master/docs/images/deep-vision-header.jpg">
+<img src="https://github.com/dusty-nv/jetson-inference/raw/master/docs/images/deep-vision-header.jpg" width="100%">
 <p align="right"><sup><a href="segnet-console-2.md">Back</a> | <a href="pytorch-transfer-learning.md">Next</a> | </sup><a href="../README.md#hello-ai-world"><sup>Contents</sup></a>
 <br/>
 <sup>Semantic Segmentation</sup></s></p>
@@ -24,19 +24,17 @@ Below are some typical scenarios for launching the program - see [this table](se
 #### C++
 
 ``` bash
-# set --network to one of the segmentation models
-$ ./segnet --network=xyz csi://0                    # MIPI CSI camera
-$ ./segnet --network=xyz /dev/video0                # V4L2 camera
-$ ./segnet --network=xyz /dev/video0 output.mp4     # save to video file
+$ ./segnet --network=<model> csi://0                    # MIPI CSI camera
+$ ./segnet --network=<model> /dev/video0                # V4L2 camera
+$ ./segnet --network=<model> /dev/video0 output.mp4     # save to video file
 ```
 
 #### Python
 
 ``` bash
-# set --network to one of the segmentation models
-$ ./segnet.py --network=xyz csi://0                 # MIPI CSI camera
-$ ./segnet.py --network=xyz /dev/video0             # V4L2 camera
-$ ./segnet.py --network=xyz /dev/video0 output.mp4  # save to video file
+$ ./segnet.py --network=<model> csi://0                 # MIPI CSI camera
+$ ./segnet.py --network=<model> /dev/video0             # V4L2 camera
+$ ./segnet.py --network=<model> /dev/video0 output.mp4  # save to video file
 ```
 
 > **note**:  for example cameras to use, see these sections of the Jetson Wiki: <br/>
@@ -78,7 +76,9 @@ $ ./segnet.py --network=fcn-resnet18-deepscene csi://0
 
 <img src="https://github.com/dusty-nv/jetson-inference/raw/pytorch/docs/images/segmentation-deepscene-camera.jpg" width="900">
 
-Feel free to experiment with the different models and resolutions for indoor and outdoor environments.  Next, we're going to introduce the concepts of [Transfer Learning](pytorch-transfer-learning.md) and train some example DNN models on our Jetson using PyTorch.
+Feel free to experiment with the different models and resolutions for indoor and outdoor environments.  
+
+Next, we're going to introduce the concepts of [Transfer Learning](pytorch-transfer-learning.md) and train some example DNN models on our Jetson using PyTorch.
 
 ##
 <p align="right">Next | <b><a href="pytorch-transfer-learning.md">Transfer Learning with PyTorch</a></b>

diff --git a/docs/segnet-console-2.md b/docs/segnet-console-2.md
@@ -15,11 +15,6 @@ As examples of using the `segNet` class, we provide sample programs C++ and Pyth
 - [`segnet.cpp`](../examples/segnet/segnet.cpp) (C++) 
 - [`segnet.py`](../python/examples/segnet.py) (Python) 
 
-Later in the tutorial, we'll also cover segmentation on live camera streams from C++ and Python:
-
-- [`segnet-camera.cpp`](../examples/segnet-camera/segnet-camera.cpp) (C++)
-- [`segnet-camera.py`](../python/examples/segnet-camera.py) (Python) 
-
 These samples are able to segment images, videos, and camera feeds.  For more info about the various types of input/output streams supported, see the [Camera Streaming and Multimedia](aux-streaming.md) page.
 
 See [below](#pretrained-segmentation-models-available) for various pre-trained segmentation models available that use the FCN-ResNet18 network with realtime performance on Jetson.  Models are provided for a variety of environments and subject matter, including urban cities, off-road trails, and indoor office spaces and homes.
@@ -79,7 +74,6 @@ $ ./segnet.py --network=<model> input.jpg output.jpg                  # overlay
 $ ./segnet.py --network=<model> --alpha=200 input.jpg output.jpg      # make the overlay less opaque
 $ ./segnet.py --network=<model> --visualize=mask input.jpg output.jpg # output the segmentation mask
 ```
-<br/>
 
 ### Cityscapes
 
@@ -182,6 +176,9 @@ If you want to process a directory or sequence of images, you can launch the pro
 ``` bash
 # C++
 $ ./segnet --network=fcn-resnet18-sun "images/room_*.jpg" room_output_%i.jpg
+
+# Python
+$ ./segnet.py --network=fcn-resnet18-sun "images/room_*.jpg" room_output_%i.jpg
 ```
 
 > **note:** when using wildcards, always enclose it in quotes (`"*.jpg"`). Otherwise, the OS will auto-expand the sequence and modify the order of arguments on the command-line, which may result in one of the input images being overwritten by the output.