Update README.md

README.md

@@ -10,20 +10,91 @@ This application is designed to count the number of people and their duration in
 
 ### Extracting the model
 First, download the [ssd_mobilenet_v2_coco model](http:https://download.tensorflow.org/models/object_detection/ssd_mobilenet_v2_coco_2018_03_29.tar.gz)
+
 From command line/terminal, navigate to the directory where the model was downloaded and extract it using
+
 '''
 tar -xvf ssd_mobilenet_v2_coco_2018_03_29.tar.gz
 '''
+
 Go to the just extracted directory (ssd_mobilenet_v2_coco_2018_03_29) and generate the intermediate represention (IR) files i.e.(.xml and .bin) using the model optimizer by running the following
+
 '''
 python /opt/intel/openvino/deployment_tools/model_optimizer/mo_tf.py --input_model frozen_inference_graph.pb --tensorflow_object_detection_api_pipeline_config pipeline.config --tensorflow_use_custom_operations_config /opt/intel/openvino/deployment_tools/model_optimizer/extensions/front/tf/ssd_v2_support.json --reverse_input_channel
 '''
+
 Clone this repo and create a "model" directory
 
 Copy the generated .xml and .bin file into the "model" directory
 
+From terminal, navigate to the cloned root directory and give permission to the script to install the required modules (i.e. nodejs, ffserver) for Mac and/or Linux with the following command 
+
+'''
+chmod +x setup.sh
+'''
+
+Run the script using
+
+'''
+./setup.sh
+'''
+
 ## Running the App
+Four terminal windows will be needed to run the app.
+
+### Start the Mosca Server on the first terminal window
+'''
+cd <app_dir>/webservice/server/node-server
+node ./server.js
+'''
 
+If successful, the following message should appear
+'''
+connected to ./db/data.db
+Mosca server started.
+'''
 
+### Start the UI on the second terminal Window
+Open new terminal (i.e. second terminal) and run the commands below
 
+'''
+cd <app_dir>/webservice/ui
+npm run dev
+'''
+
+If successful, the following message should appear
+'''
+webpack: Compiled successfully
+'''
+
+### Start the FFmpeg Server on the third terminal Window
+Open the third terminal and run the commands below
+
+'''
+cd <app_dir>
+sudo ffserver -f ./ffmpeg/server.conf
+'''
+
+### Running the program on the fourth terminal 
+First, initialize the Openvino environment by running the command below
+
+'''
+source /opt/intel/openvino/bin/setupvars.sh -pyver 3.5
+'''
+
+#### To capture from video, 
+On the same terminal (i.e. the fourth terminal), run: 
+
+'''
+python3 main.py -i <location of video> -m model/frozen_inference_graph.xml -d GPU -pt 0.6 | ffmpeg -v warning -f rawvideo -pixel_format bgr24 -video_size 768x432 -framerate 24 -i - http:https://0.0.0.0:3004/fac.ffm
+'''
+
+#### To capture from camera,
+On the same terminal (i.e. the fourth terminal), run: 
+
+'''
+python3 main.py -i 0 -m model/frozen_inference_graph.xml -d GPU -pt 0.6 | ffmpeg -v warning -f rawvideo -pixel_format bgr24 -video_size 768x432 -framerate 24 -i - http:https://0.0.0.0:3004/fac.ffm
+'''
 
+## View Output
+To see the output on a web based interface, open the link http:https://0.0.0.0:3004 in a browser.