kenil-shah · harshitpatel96 · Sep 17, 2020 · Sep 17, 2020 · Sep 17, 2020 · Sep 17, 2020
diff --git a/.gitignore b/.gitignore
@@ -139,5 +139,8 @@ dmypy.json
 # Cython debug symbols
 cython_debug/
 
+# Node modules
+node_modules/
+
 # IDE specific files
 .idea
diff --git a/README.md b/README.md
@@ -7,3 +7,12 @@ Streamix is a video conferencing feature that provides you seamless virtual back
 
 # Youtube Link
 [![Watch the video](https://i.ytimg.com/vi/2DVQ2XwhtUI/hqdefault.jpg)](https://www.youtube.com/watch?v=2DVQ2XwhtUI)
+
+# Getting started on the project
+- Install node version 12
+- clone this repo using `git clone https://github.com/kenil-shah/Streamix.git`
+- Go inside repository folder
+- Install node dependencies using `npm install`
+- Install python dependencies using `pip install requirements.txt`
+- To run and test the code `node server.js`
+- In your browser go to `localhost:8888`
diff --git a/code/python_background_pipeline/README.md b/code/python_background_pipeline/README.md
@@ -0,0 +1,8 @@
+# Running onnx converter
+
+    python onnx_converter.py --modelpath "path_to_saved_model/modelname.pth" --opset OP_VERSION --savepath "path_where_to_save_converted_model/modelname.onnx"
+
+### Example 
+    python onnx_converter.py --modelpath '/home/hpatel24/Fall2020/CSC510/project1/Streamix/data/models/segmentation_model.pth' --opset 10 --savepath '/home/hpatel24/Fall2020/CSC510/project1/Streamix/data/models/segmentation_model11.onnx'
+
+
diff --git a/code/python_background_pipeline/onnx_converter.py b/code/python_background_pipeline/onnx_converter.py
@@ -0,0 +1,27 @@
+import torch
+import argparse
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Model conversion (Torch -> ONNX)')
+    parser.add_argument('--modelpath',
+                         default='./data/models/segmentation_model.pth',
+                         help='Location of pytorch model')
+    parser.add_argument('--dummyinputsize',
+                         default=[1, 3, 256, 256],
+                         help='Default input shape for model')
+    parser.add_argument('--opset',
+                        default=int(10),
+                        help='Operator Set version for ONNX conversion')
+    parser.add_argument('--savepath',
+                         default='./data/models/segmentation_model.onnx',
+                         help='Path where you want to save onnx model')
+
+    args = vars(parser.parse_args())
+
+    model = torch.load(args['modelpath'], map_location= lambda storage, loc: storage)
+    shape = args['dummyinputsize']
+    print(type(shape[0]))
+    dummy_input = torch.randn(shape[0], shape[1], shape[2], shape[3])
+    torch.onnx.export(model, dummy_input, args['savepath'], opset_version=int(args['opset'])) 
+
diff --git a/code/src/numjs.min.js b/code/src/numjs.min.js
diff --git a/code/src/onnx.min.js b/code/src/onnx.min.js
diff --git a/code/src/opencv.js b/code/src/opencv.js
diff --git a/data/models/segmentation_model.pb/saved_model.pb b/data/models/segmentation_model.pb/saved_model.pb
diff --git a/data/models/segmentation_model.pb/variables/variables.data-00000-of-00001 b/data/models/segmentation_model.pb/variables/variables.data-00000-of-00001
diff --git a/data/models/segmentation_model.pb/variables/variables.index b/data/models/segmentation_model.pb/variables/variables.index
diff --git a/data/models/segmentation_model11.onnx b/data/models/segmentation_model11.onnx
diff --git a/index.html b/index.html
@@ -0,0 +1,6 @@
+<html>
+    <body>
+        <script src="./code/src/onnx.min.js"></script>
+        <script src="main.js"></script>
+    </body>
+</html>
diff --git a/main.js b/main.js
@@ -0,0 +1,10 @@
+async function test() {
+    const sess = new onnx.InferenceSession()
+    await sess.loadModel('./data/models/segmentation_model.onnx')
+    const input = new onnx.Tensor(new Float32Array(256*256*3), 'float32', [1, 3, 256, 256])
+    const outputMap = await sess.run([input])
+    const outputTensor = outputMap.values().next().value
+    console.log("here")
+    console.log(`Output tensor: ${outputTensor.data}`)
+}
+test()