Update README

README.md

@@ -1,15 +1,17 @@
 
-# YOLO Implementation on Nvidia GPU - for TensorRT 5 and DeepStream SDK 3.0 #
+# Reference Apps for Video Analytics using TensorRT 5 and DeepStream SDK 3.0 #
+
+![DS3 Workflow](.DS3-workflow.png)
 
 ## Installing Pre-requisites: ##
 
 If the target platform is a dGPU, download and install DeepStream 3.0. For Tegra platforms, flash your device with Jetpack 3.3 and install Deepstream 1.5.
 
 Install GStreamer pre-requisites using:   
-   `sudo apt-get install libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev`
+   `$ sudo apt-get install libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev`
 
 Install Google flags using:   
-   `sudo apt-get install libgflags-dev`
+   `$ sudo apt-get install libgflags-dev`
 
 To use just the stand alone trt-yolo-app, Deepstream Installation can be skipped. However CUDA 10.0 and TensorRT 5 should be installed. See [Note](https://github.com/vat-nvidia/deepstream-plugins#note) for additional installation caveats.
 
@@ -22,20 +24,20 @@ Update all the parameters in `Makefile.config` file present in the root director
 1. Go to the `data` directory and add your yolo .cfg and .weights file.
 
     For yolo v2,   
-    Download the config file from the darknet repo located at `https://github.com/pjreddie/darknet/blob/master/cfg/yolov2.cfg`   
-    Download the weights file by running the command `wget https://pjreddie.com/media/files/yolov2.weights`
+    `$ wget https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov2.cfg`   
+    `$ wget https://pjreddie.com/media/files/yolov2.weights`
 
     For yolo v2 tiny,   
-    Download the config file from the darknet repo located at `https://github.com/pjreddie/darknet/blob/master/cfg/yolov2-tiny.cfg`   
-    Download the weights file by running the command `wget https://pjreddie.com/media/files/yolov2-tiny.weights`
+    `$ wget https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov2-tiny.cfg`   
+    `$ wget https://pjreddie.com/media/files/yolov2-tiny.weights`
 
     For yolo v3,    
-    Download the config file from the darknet repo located at `https://github.com/pjreddie/darknet/blob/master/cfg/yolov3.cfg`   
-    Download the weights file by running the command `wget https://pjreddie.com/media/files/yolov3.weights`
+    `$ wget https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3.cfg`   
+    `$ wget https://pjreddie.com/media/files/yolov3.weights`
 
     For yolo v3 tiny,   
-    Download the config file from the darknet repo located at `https://github.com/pjreddie/darknet/blob/master/cfg/yolov3-tiny.cfg`   
-    Download the weights file by running the command `wget https://pjreddie.com/media/files/yolov3-tiny.weights`
+    `$ wget https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3-tiny.cfg`
+    `$ wget https://pjreddie.com/media/files/yolov3-tiny.weights`
 
 4. Set the right macro in the `network_config.h` file to choose a model architecture
 
@@ -46,30 +48,22 @@ Update all the parameters in `Makefile.config` file present in the root director
 7. Run the following command from `sources/plugins/gst-yoloplugin-tesla` for dGPU's or from `sources/plugins/gst-yoloplugin-tegra` for tegra devices to build and install the plugin
     `make && sudo make install` 
 
-## Building and running the trt-yolo-app ##
-
-Go to the `sources/apps/trt-yolo` directory and run the following command to build and install the trt-yolo-app
-    `make && sudo make install`
-
-The TRT Yolo App located at `sources/apps/trt-yolo` is a sample standalone app, which can be used to run inference on test images. This app does not have any deepstream dependencies and can be built independently. Add a list of absolute paths of images to be used for inference in the `test_images.txt` file located at `data` and run `trt-yolo-app` from the root directory of this repo. Additionally, the detections on test images can be saved by setting `kSAVE_DETECTIONS` config param to `true` in `network_config.cpp` file. The images overlayed with detections will be saved in the `data/detections/` directory.
+## Object Detection using DeepStream ##
 
-This app has three command line arguments(optional) that you can pass. One is the batch_size to be used for the TRT engine which is set to 1 by default. The second one is a boolean argument representing if the detections have to be decoded or not which is set to true by default. The last one is an argument to set the seed of random number generators used in the application. It is set to `time(0)` by default. To run the app with the default options run the following command from the root directory of this repo
-    `trt-yolo-app`
+![sample output](.sample_screen.png)
 
-To change the batch_size of the TRT engine use the following command
-    `trt-yolo-app --batch_size=4`
+There are multiple apps that can be used to perform object detection in deepstream.
 
-## Building and running deepStream-yolo-app ##
-
-Go to the `sources/apps/deepstream-yolo` directory and run the following command to build and install the deepstream-yolo-app
-    `make && sudo make install`
+### deepstream-yolo-app ###
 
 The deepStream-yolo-app located at `sources/apps/deepstream_yolo` is a sample app similar to the Test-1 & Test-2 apps available in the DeepStream SDK. Using the yolo app we build a sample gstreamer pipeline using various components like H264 parser, Decoder, Video Converter, OSD and Yolo plugin to run inference on an elementary h264 video stream.
 
-Once you have built the deepstream-yolo-app as described above, go to the root directory of this repo and run the command
-`deepstream-yolo-app /path/to/sample_video.h264`
+`$ cd sources/apps/deepstream-yolo`   
+`$ make && sudo make install`   
+`$ cd ../../../`   
+`$ deepstream-yolo-app /path/to/sample_video.h264`
 
-## Running the deepStream-app [TESLA platform only] ##
+### deepstream-app [TESLA platform only] ###
 
 Following steps describe how to run the YOLO plugin in the deepstream-app
 
@@ -92,9 +86,9 @@ Following steps describe how to run the YOLO plugin in the deepstream-app
     `uri=file://relative/path/to/source/video`
 
 3.  Go to the root folder of this repo and run
-    `deepstream-app -c config/deepstream-app_yolo_config.txt`
+    `$ deepstream-app -c config/deepstream-app_yolo_config.txt`
 
-## Running the nvgstiva-app [TEGRA platform only] ##
+### nvgstiva-app [TEGRA platform only] ###
 
 1.  The section below in the config file corresponds to ds-example(yolo) plugin in deepstream.
     The config file is located at `config/nvgstiva-app_yolo_config.txt`. Make any changes
@@ -114,13 +108,34 @@ Following steps describe how to run the YOLO plugin in the deepstream-app
     `uri=file://path/to/source/video`
 
 3.  Go to the root folder of this repo and run
-    `nvgstiva-app -c config/nvgstiva-app_yolo_config.txt`
+    `$ nvgstiva-app -c config/nvgstiva-app_yolo_config.txt`
+
+## Object Detection using trt-yolo-app ##
+
+The trt-yolo-app located at `sources/apps/trt-yolo` is a sample standalone app, which can be used to run inference on test images. This app does not have any deepstream dependencies and can be built independently. Add a list of absolute paths of images to be used for inference in the `test_images.txt` file located at `data` and run `trt-yolo-app` from the root directory of this repo. Additionally, the detections on test images can be saved by setting `kSAVE_DETECTIONS` config param to `true` in `network_config.cpp` file. The images overlayed with detections will be saved in the `data/detections/` directory.
+
+This app has three command line arguments(optional). 
+
+    1. batch_size - Integer value to be used for batch size of TRT inference. Default value is 1.   
+    2. decode - Boolean value representing if the detections have to be decoded. Default value is true.   
+    3. seed - Integer value to set the seed of random number generators. Default value is `time(0)`.
+
+`$ cd sources/apps/trt-yolo`    
+`$ make && sudo make install`   
+`$ cd ../../../`
+
+To run the app with default arguments   
+`$ trt-yolo-app`
+
+To change the batch_size of the TRT engine   
+`$ trt-yolo-app --batch_size=4`
+
 
 ### Note ###
 
 1. If you are using the plugin with deepstream-app (located at `/usr/bin/deepstream-app`), register the yolo plugin as dsexample. To do so, replace line 671 in `gstyoloplugin.cpp` with `return gst_element_register(plugin, "dsexample", GST_RANK_PRIMARY, GST_TYPE_YOLOPLUGIN);`
 
-This registers the plugin with the name `dsexample` so that the deepstream-app can pick it up and add to it's pipeline. Now go to `sources/gst-yoloplugin/` and run `make && sudo make install` to build and install the plugin.
+This registers the plugin with the name `dsexample` so that the deepstream-app can pick it up and add to it's pipeline. Now go to `sources/gst-yoloplugin/` and run `$ make && sudo make install` to build and install the plugin.
 
 2. Tegra users working with Deepstream 1.5 and Jetpack 3.3 will have to regenerate the `.cache` files to use the standard caffe models available in the SDK. This can be done by deleting all the `.cache` files in `/home/nvidia/Model` directory and all its subdirectories and then running the nvgstiva-app using the default config file.