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 kai-arulkumaran:
   name: Kai Arulkumaran
   web: http://kaixhin.com
+egor-burkov:
+  name: Egor Burkov
+  web: http://vk.com/shrubb
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+---
+layout: post
+title: Deep Fun with OpenCV and Torch
+comments: True
+author: egor-burkov
+excerpt: In this post, we'll have some fun with OpenCV 3.0 + Torch to build live demos of Age & Gender Classification, NeuralStyle, NeuralTalk and live Image classification.
+---
+
+<!---# Deep Fun with OpenCV and Torch-->
+
+The [OpenCV](http://opencv.org/) library implements tons of useful image processing and computer vision algorithms, as well as the high-level GUI API. Written in C++, it has bindings in Python, Java, MATLAB/Octave, C#, Perl and Ruby. We present the Lua bindings that are based on Torch.
+
+By combining OpenCV with Torch's scientific computation abilities, one gets an even more powerful framework capable of handling computer vision routines (e.g. face detection), interfacing video streams (including cameras), easier data visualization, GUI interaction and many more. In addition, most of the computationally intensive algorithms are available on GPU via Cutorch. All these features may be essentially useful for those dealing with deep learning applied to images.
+
+Usage Examples
+===
+
+### Live Image Classification
+
+A basic example may be live CNN-based image classification. In the following demo, we grab a frame from the webcam, then take a central crop from it and use a small ImageNet classification pretrained network to predict what's in the picture. Afterwards, the image itself and the 5 most probable class names are displayed. 
+
+![ImageNet classification demo](https://raw.githubusercontent.com/torch/torch.github.io/master/blog/_posts/images/opencv_imagenet.gif)
+
+The comments should explain the code well. *Note: this sample assumes you already have the trained CNN; see the [original code on GitHub](https://github.com/szagoruyko/torch-opencv-demos/blob/master/imagenet_classification/demo.lua) by Sergey Zagoruyko that automatically downloads it.*
+
+```lua
+local cv = require 'cv'
+require 'cv.highgui' -- GUI
+require 'cv.videoio' -- Video stream
+require 'cv.imgproc' -- Image processing (resize, crop, draw text, ...)
+require 'nn'
+
+local capture = cv.VideoCapture{device=0}
+if not capture:isOpened() then
+   print("Failed to open the default camera")
+   os.exit(-1)
+end
+
+-- Create a new window
+cv.namedWindow{winname="Torch-OpenCV ImageNet classification demo", flags=cv.WINDOW_AUTOSIZE}
+-- Read the first frame
+local _, frame = capture:read{}
+
+-- Using network in network http://openreview.net/document/9b05a3bb-3a5e-49cb-91f7-0f482af65aea
+local net = torch.load('nin_nobn_final.t7'):unpack():float()
+local synset_words = torch.load('synset.t7', 'ascii')
+
+-- NiN input size
+local M = 224
+
+while true do
+   local w = frame:size(2)
+   local h = frame:size(1)
+
+   -- Get central square crop
+   local crop = cv.getRectSubPix{frame, patchSize={h,h}, center={w/2, h/2}}
+   -- Resize it to 256 x 256
+   local im = cv.resize{crop, {256,256}}:float():div(255)
+   -- Subtract channel-wise mean
+   for i=1,3 do 
+      im:select(3,i):add(-net.transform.mean[i]):div(net.transform.std[i])
+   end
+   -- Resize again to CNN input size and swap dimensions
+   -- to CxHxW from HxWxC
+   -- Note that BGR channel order required by ImageNet is already OpenCV's default
+   local I = cv.resize{im, {M,M}}:permute(3,1,2):clone()
+
+   -- Get class predictions
+   local _, classes = net:forward(I):view(-1):float():sort(true)
+
+   -- Caption the image
+   for i=1,5 do
+      cv.putText{
+         crop, 
+         synset_words[classes[i]], 
+         {10, 10 + i * 25},
+         fontFace=cv.FONT_HERSHEY_DUPLEX,
+         fontScale=1,
+         color={200, 200, 50},
+         thickness=2
+      }
+   end
+
+   -- Show it to the user
+   cv.imshow{"Torch-OpenCV ImageNet classification demo", crop}
+   if cv.waitKey{30} >= 0 then break end
+   
+   -- Grab the next frame
+   capture:read{frame}
+end
+```
+
+### Live Age and Gender Prediction
+
+A more interesting demonstration can be made with CNNs [described here](http://www.openu.ac.il/home/hassner/projects/cnn_agegender/) trained to predict age and gender by face. Here, we take a frame from live stream, then use the popular [Viola-Jones cascade object detector](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.10.6807) to extract faces from it. Such detectors ship with OpenCV pretrained to detect faces, eyes, smiles etc. After the faces have been found, we basically crop them and feed to CNNs which yield age and gender predictions data. The faces in the image are marked with rectangles, which are then labeled by predicted age and gender.
+
+Detecting faces and drawing the results from Torch for a single image is this easy:
+
+```lua
+require 'cv.objdetect'
+local faceDetector = cv.CascadeClassifier{'haarcascade_frontalface_default.xml'}
+local faces = faceDetector:detectMultiScale{image}
+
+for i=1,faces.size do
+   local f = faces.data[i]
+   cv.rectangle{image, {f.x, f.y}, {f.x+f.w, f.y+f.h}, color={255,0,255,0}}
+end
+```
+
+Of course, this is quite an inefficient way of face detection provided just to become a simple sample. For example, it could involve tracking techniques.
+
+The entire code is [available on GitHub](https://github.com/szagoruyko/torch-opencv-demos/blob/master/age_gender/demo.lua). Here's a sample from IMAGINE Lab by Sergey Zagoruyko:
+
+![Age & Gender Demo](https://cloud.githubusercontent.com/assets/4953728/12299217/fc819f80-ba15-11e5-95de-653c9fda9b83.png)
+
+[And here is is a heavy just-for-fun GIF](https://raw.githubusercontent.com/torch/torch.github.io/master/blog/_posts/images/opencv_age.gif).
+
+### NeuralTalk2
+
+A good image captioning example is [NeuralTalk2](https://github.com/karpathy/neuraltalk2) by Andrej Karpathy. With OpenCV, it's easy to make this model caption live video or camera stream:
+
+![NeuralTalk2 Demo 1](https://raw.githubusercontent.com/torch/torch.github.io/master/blog/_posts/images/opencv_neuraltalk_demo1.gif)
+
+Here are a couple more demos: [one](https://raw.githubusercontent.com/torch/torch.github.io/master/blog/_posts/images/opencv_neuraltalk_demo2.gif) and [another](https://raw.githubusercontent.com/torch/torch.github.io/master/blog/_posts/images/opencv_neuraltalk_demo3.gif).
+
+The script [can be found](https://github.com/karpathy/neuraltalk2/blob/master/videocaptioning.lua) inside the NeuralTalk2 repository itself.
+
+### Interactive Face Recognition with GPU
+
+Another advantage of OpenCV is already mentioned NVIDIA CUDA support. It can help one to significantly speed up image processing and computer vision routines. These include image-processing-specific matrix operations, background segmentation, video [en/de]coding, feature detection and description, image filtering, object detection, computing optical flow, stereo correspondence etc.
+
+Here is another code sample demonstrating some of the above features. This is an interactive face recognition application. After launched, it asks the user to manually classify people appearing in video stream into N (also user-defined) classes. When the number of labeled faces is sufficient for automatic recognition, discriminative descriptors are extracted using the convolutional neural network face descriptor and a SVM for classification is trained on them. Then the program switches to recognition mode and captions detected faces in stream with predicted "person name".
+
+For speed, the face descriptor we use is the most lightweight (~3.7 millions of parameters) of [OpenFace](http://cmusatyalab.github.io/openface/) models, which are based on the CVPR 2015 paper [FaceNet: A Unified Embedding for Face Recognition](http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1A_089.pdf). It was pre-trained with a combination of [FaceScrub](http://vintage.winklerbros.net/facescrub.html) and [CASIA-WebFace](http://arxiv.org/abs/1411.7923) face recognition datasets.
+
+![screenshot](https://cloud.githubusercontent.com/assets/9570420/13470424/2c5d3106-e0bd-11e5-9319-9f1dbf8c86ab.png)  
+![screenshot 1](https://cloud.githubusercontent.com/assets/9570420/13470423/2c5d5064-e0bd-11e5-842c-d99157e22d6c.png)  
+![screenshot 2](https://cloud.githubusercontent.com/assets/9570420/13530688/b1f694ac-e233-11e5-955c-df71688f472b.png)  
+![screenshot 3](https://cloud.githubusercontent.com/assets/9570420/13530687/b1ceebd2-e233-11e5-8947-06684910aeff.png) 
+
+Let us introduce how OpenCV interface for Lua looks like in this case. As usual, there's a single `require` for every separate OpenCV package:
+
+```lua
+local cv = require 'cv'
+require 'cv.highgui'       -- GUI: windows, mouse
+require 'cv.videoio'       -- VideoCapture
+require 'cv.imgproc'       -- resize, rectangle, putText
+require 'cv.cudaobjdetect' -- CascadeClassifier
+require 'cv.cudawarping'   -- resize
+require 'cv.cudaimgproc'   -- cvtColor
+cv.ml = require 'cv.ml'    -- SVM
+```
+
+The GUI commands are very high-level:
+
+```lua
+-- create two windows
+cv.namedWindow{'Stream window'}
+cv.namedWindow{ 'Faces window'}
+cv.setWindowTitle{'Faces window', 'Grabbed faces'}
+cv.moveWindow{'Stream window', 5, 5}
+cv.moveWindow{'Faces window', 700, 100}
+
+local function onMouse(event, x, y, flags)
+   if event == cv.EVENT_LBUTTONDBLCLK then
+      -- do something
+   end
+end
+
+cv.setMouseCallback{'Stream window', onMouse}
+
+cv.imshow{'Stream window', frame}
+cv.imshow{'Faces window', gallery}
+```
+
+Here's how we set up the SVM:
+
+```lua
+-- SVM to classify descriptors in recognition phase
+local svm = cv.ml.SVM{}
+svm:setType         {cv.ml.SVM_C_SVC}
+svm:setKernel       {cv.ml.SVM_LINEAR}
+svm:setDegree       {1}
+svm:setTermCriteria {{cv.TermCriteria_MAX_ITER, 100, 1e-6}}
+```
+
+...and use it:
+
+```lua
+-- svmDataX is a FloatTensor of size (#dataset x #features)
+-- svmDataY is an IntTensor of labels of size (1 x #dataset)
+svm:train{svmDataX, cv.ml.ROW_SAMPLE, svmDataY}
+
+...
+
+-- recognition phase
+-- :predict() input is a FloatTensor of size (1 x #features)
+local person = svm:predict{descriptor}
+```
+
+GPU calls and their CPU analogues look very much alike, with a couple of differences: first, they are placed in a separate table, and second, they handle `torch.CudaTensor`s.
+
+```lua
+-- convert to grayscale and store result in original image's blue (first) channel
+cv.cuda.cvtColor{frameCUDA, frameCUDA:select(3,1), cv.COLOR_BGR2GRAY}
+
+...
+
+cv.cuda.resize{smallFaceCUDA, {netInputSize, netInputSize}, dst=netInputHWC}
+```
+
+Don't forget that these functions adapt to Cutorch stream and device settings, so calling `cutorch.setStream()`, `cutorch.streamWaitFor()`, `cutorch.setDevice()` etc. matters.
+
+The whole runnable script is [available here](https://github.com/shrubb/torch-opencv-demos/blob/face-recognition/face_recognition/demo.lua).
+
+### Live Image Stylization
+
+The [Texture Networks: Feed-forward Synthesis of Textures and Stylized Images](http://arxiv.org/abs/1603.03417) paper proposes an architecture to stylize images straightforwardly, shipping with an [open source implementation in Torch](https://github.com/DmitryUlyanov/texture_nets/). It takes ~20 ms to process a single image with Tesla K40 GPU, and ~1000 ms with CPU. Having this, a tiny modification allows us to render any scene in a particular style in real time:
+
+![Demo 1](https://raw.githubusercontent.com/torch/torch.github.io/master/blog/_posts/images/opencv_stylization_demo1.gif)
+
+![Demo 2](https://raw.githubusercontent.com/torch/torch.github.io/master/blog/_posts/images/opencv_stylization_demo2.gif)
+
+By the way, these very GIFs (originally in form of encoded videos) were rendered using OpenCV as well. There is a `VideoWriter` class that serves as a simple interface to video codecs. Here is a sketch of a program that encodes similar sequence of frames as a video file and saves it to disk:
+
+```lua
+local cv = require 'cv'
+require 'cv.videoio' -- VideoWriter
+require 'cv.imgproc' -- resize
+
+local size = 256
+local frameToSaveSize = {sz*2, sz}
+
+local videoWriter = cv.VideoWriter{
+   "sample.mp4",
+   cv.VideoWriter.fourcc{'D', 'I', 'V', 'X'}, -- or any other codec
+   fps = 25,
+   frameSize = frameToSaveSize
+}
+
+if not videoWriter:isOpened() then
+   print('Failed to initialize video writer. Possibly wrong codec or file name/path trouble')
+   os.exit(-1)
+end
+
+for i = 1,numFrames do
+   -- get next image; for example, read it from camera
+   local frame = cv.resize{retrieveNextFrame(), {sz, sz}}
+   
+   -- the next frame in the resulting video
+   local frameToSave = torch.Tensor(frameToSaveSize[2], frameToSaveSize[1], 3)
+   
+   -- first, copy the original frame into the left half of frameToSave:
+   frameToSave:narrow(2, 1, sz):copy(frame)
+   
+   -- second, copy the processed (for example, rendered in painter style)
+   -- frame into the other half:
+   frameToSave:narrow(2, sz+1, sz):copy(someCoolProcessingFunction(frame))
+   
+   -- finally, tell videoWriter to push frameToSave into the video
+   videoWriter:write{frameToSave}
+end
+```
+
+[Here](https://github.com/szagoruyko/torch-opencv-demos/tree/master/texture_nets) goes the full code, including a model trained on *The Starry Night*.
+
+Acknowledgements
+===
+
+Sergey Zagoruyko for putting up most of the demo code and creating sample screenshots.  
+Soumith Chintala for the support from Torch side.  
+Everyone who contributed and contributes to the project by making PRs and helping catch bugs.  
+
+This project was created and is maintained in [VisionLabs](http://visionlabs.ru/) by [Egor Burkov](https://github.com/shrubb).
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