Merged PR 533: Merge review/fixesForV2InitialRelease to master

Fix broken links

INSTALL-Windows.md

@@ -18,7 +18,7 @@ ELL requires a C++ compiler. On Windows, we support *Visual Studio 2015 update 3
 
 ELL uses the [*CMake*](https://cmake.org/) build system, version 3.8 or newer. Download and install it from <https://cmake.org/download/>.
 
-### LLVM 3.9, SWIG 3.0.12, OpenBlas, and Doxygen via NuGet
+### LLVM 3.9, SWIG 3.0.12, OpenBLAS, and Doxygen via NuGet
 
 ELL depends on the [*LLVM*](http://llvm.org/) compiler framework, version 3.9 or newer.
 

docs/index.md

@@ -16,13 +16,13 @@ While the goal of ELL is to deploy software onto resource constrained platforms
 ## Installation and Setup
 Install ELL on a
 [Windows](https://github.com/Microsoft/ELL/blob/master/INSTALL-Windows.md), [Ubuntu Linux](https://github.com/Microsoft/ELL/blob/master/INSTALL-Ubuntu.md), or [Mac](https://github.com/Microsoft/ELL/blob/master/INSTALL-Mac.md)
-laptop or desktop computer. If you intend to deploy models onto a Raspberry Pi, follow our instruction on [setting up the Raspberry Pi](/ELL/tutorials/Setting-Up-your-Raspberry-Pi).
+laptop or desktop computer. If you intend to deploy models onto a Raspberry Pi, follow our instruction on [setting up the Raspberry Pi](/ELL/tutorials/Setting-up-your-Raspberry-Pi).
 
 ## Getting Started
 A great place to start is our [tutorials section](/ELL/tutorials). As we develop and release new functionality in ELL, we publish new tutorials that showcase that functionality. Currently, our tutorials are focused on simple embedded computer vision tasks on Raspberry Pi, but we expect the scope to grow with time. Have fun!
 
 ## The ELL Gallery
-Our [gallery](/ELL/gallery) is a collection of bits and pieces that you can download and use in your projects. Currently, the gallery includes a handful of pre-trained computer vision models and instructions for 3D printing an [active cooling attachment](/ELL/gallery/RPi-cooling) for your Raspberry Pi.
+Our [gallery](/ELL/gallery) is a collection of bits and pieces that you can download and use in your projects. Currently, the gallery includes a handful of pre-trained computer vision models and instructions for 3D printing an [active cooling attachment](/ELL/gallery/Raspberry-Pi-3-Fan-Mount) for your Raspberry Pi.
 
 ## License
 The ELL code and sample code in our tutorials are released under the [MIT Open Source License](https://github.com/Microsoft/ELL/blob/master/LICENSE.txt). Some of the other content on this website, such as the 3D models in our gallery, are released under the [Creative Commons Attribution 4.0 license](https://creativecommons.org/licenses/by/4.0/).

docs/tutorials/Comparing-Image-Classification-models-side-by-side-on-the-Raspberry-Pi/index.md

@@ -16,7 +16,7 @@ In this tutorial, we will download two models from the [ELL gallery](/ELL/galler
 * Laptop or desktop computer (Windows, Linux, or Mac)
 * Raspberry Pi
 * Raspberry Pi camera or USB webcam
-* *optional* - Active cooling attachment (see our [tutorial on cooling your Pi](/ELL/tutorials/Active-Cooling-your-Raspberry-Pi-3/))
+* *optional* - Active cooling attachment (see our [tutorial on cooling your Pi](/ELL/tutorials/Active-cooling-your-Raspberry-Pi-3/))
 
 #### Prerequisites
 
@@ -292,7 +292,7 @@ source activate py34
 python sideBySide.py
 ```
 
-If you have a camera and display connected to your Pi, you should see a window similar to the screenshot at the top of this page. Point your camera at different objects and see how the model classifies them. If you downloaded the full source for [tutorial.py](/ELL/tutorials/shared/tutorial.py), you will also see the average time in milliseconds it takes each model to process a frame. Try to get a sense of the relative accuracy and speed of each model. 
+If you have a camera and display connected to your Pi, you should see a window similar to the screenshot at the top of this page. Point your camera at different objects and see how the model classifies them. If you downloaded the full source for [sideBySide.py](/ELL/tutorials/Comparing-Image-Classification-models-side-by-side-on-the-Raspberry-Pi/sideBySide.py), you will also see the average time in milliseconds it takes each model to process a frame. Try to get a sense of the relative accuracy and speed of each model. 
 
 ## Troubleshooting
 

docs/tutorials/Getting-Started-with-Image-Classification-in-Cpp/index.md

@@ -22,7 +22,7 @@ In this tutorial, we will download a pretrained image classifier from the [ELL g
 
 * Install ELL on your computer ([Windows](https://github.com/Microsoft/ELL/blob/master/INSTALL-Windows.md), [Ubuntu Linux](https://github.com/Microsoft/ELL/blob/master/INSTALL-Ubuntu.md), [Mac](https://github.com/Microsoft/ELL/blob/master/INSTALL-Mac.md)). Specifically, this tutorial requires ELL, CMake, and Python 3.6. Note that Python is required to run a tool named `wrap.py`, which makes compilation easy. If you prefer not to use `wrap.py`, you can perform the compilation steps manually, as described in the [wrap tool documentation](https://github.com/Microsoft/ELL/blob/master/tools/wrap/README.md).
 * Follow the instructions for [setting up your Raspberry Pi](/ELL/tutorials/Setting-up-your-Raspberry-Pi).
-* *optional* - Read through the instructions in [Getting Started with Image Classification on the Raspberry Pi](/ELL/tutorials/Getting-Started-with-Image-Classification-on-the-Raspberry-Pi/).
+* *optional* - Read through the instructions in [Getting started with image classification on the Raspberry Pi](/ELL/tutorials/Getting-started-with-image-classification-on-the-Raspberry-Pi/).
 
 ## Step 1: Create a tutorial directory
 

docs/tutorials/Getting-Started-with-Image-Classification-on-the-Raspberry-Pi/index.md

@@ -118,6 +118,7 @@ First, import a few dependencies and add directories to our path, to allow Pytho
 ```python
 import sys
 import os
+import cv2
 import numpy as np
 
 scriptPath = os.path.dirname(os.path.abspath(__file__))

docs/tutorials/Importing-models/index.md

@@ -148,7 +148,7 @@ curl --location -o categories.txt https://raw.githubusercontent.com/pjreddie/dar
 
 ## Using the  model
 
-Once the model is in the ELL format, it no longer matters whether it came from CNTK or Darknet, and the only difference is the categories file. To test the model from Python, follow the steps in [Getting started with image classification on the Raspberry Pi](/ELL/tutorials/Getting-Started-with-Image-Classification-on-the-Raspberry-Pi/), but replace the model suggested in that tutorial with the model you just imported. Alternatively, to test the model from C++, follow the steps in [Getting started with image classification in C++](/ELL/tutorials/Getting-Started-with-Image-Classification-in-Cpp/). 
+Once the model is in the ELL format, it no longer matters whether it came from CNTK or Darknet, and the only difference is the categories file. To test the model from Python, follow the steps in [Getting started with image classification on the Raspberry Pi](/ELL/tutorials/Getting-started-with-image-classification-on-the-Raspberry-Pi/), but replace the model suggested in that tutorial with the model you just imported. Alternatively, to test the model from C++, follow the steps in [Getting started with image classification on the Raspberry Pi in C++](/ELL/tutorials/Getting-started-with-image-classification-in-cpp/). 
 
 ## Troubleshooting
 

docs/tutorials/Setting-Up-your-Raspberry-Pi.md

@@ -19,7 +19,7 @@ Most of our tutorials follow a common workflow. The first steps involve authorin
 AI workloads guzzle power, so we recommend using a high quality USB power adapter and micro USB cable. An adapter rated for 12 Watts (2.4 Amps) per USB port works best. We've had a good experience with 12W-per-port USB power adapters from Apple, Anker, and Amazon Basics. Long and thin cables will often result in a noticeable voltage drop and fail to provide sufficient power to your Raspberry Pi. Generic unbranded cables are hit-and-miss. For a few extra dollars you can get a nice name-brand cable, like the Anker PowerLine, and save yourself a lot of frustration.
 
 ### Operating System
-Our tutorials assume that the operating system running on your Pi is [*Raspbian Jessie*](https://downloads.raspberrypi.org/raspbian/images/raspbian-2017-07-05/2017-07-05-raspbian-jessie.zip), not the more recent *Raspian Stretch*.
+Our tutorials assume that the operating system running on your Pi is *Raspbian Jessie* ([NOOBS](https://downloads.raspberrypi.org/NOOBS/images/NOOBS-2017-07-05/) or [image](https://downloads.raspberrypi.org/raspbian/images/raspbian-2017-07-05/)), not the more recent *Raspbian Stretch*.
 
 ### CMake
 We use `CMake` on the Raspberry Pi to create Python modules that can be called from our tutorial code. To install `CMake` on your Pi, connect to the network, open a terminal window, and type

tools/utilities/pythonlibs/demoHelper.py

@@ -256,12 +256,12 @@ def report_times(self, node_level=True):
             if hasattr(self.compiled_module, self.model_name + "_PrintNodeProfilingInfo"):
                 getattr(self.compiled_module, self.model_name + "_PrintNodeProfilingInfo")()
 
-    def get_top_n_predictions(self, predictions, N = 5, threshold = 0.20):
+    def get_top_n_predictions(self, predictions, N = 5):
         """Return at most the top N predictions as a list of tuples that meet the threshold.
         The first of element of each tuple represents the index or class of the prediction and the second 
         element represents that probability or confidence value.
         """
-        map = [(i,predictions[i]) for i in range(len(predictions)) if predictions[i] >= threshold]
+        map = [(i,predictions[i]) for i in range(len(predictions)) if predictions[i] >= self.threshold]
         map.sort(key=lambda tup: tup[1], reverse=True)
         result = map[:N]
         return result