Deep OpenCV. Intel Delta 8 course

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1 Deep OpenCV Intel Delta 8 course

2 Deep learning in computer vision Image classification Object detection Car Semantic segmentation Car Internet of Things Group 2

3 Deep learning in computer vision Researchers use different frameworks to train their models No converters between models No conventional format Different environment: training scripts, samples Deployment hardware limitations Number of target platforms is growing up Internet of Things Group 3

4 Deep learning in OpenCV Caffe CPU: C++ GPU: OpenCL Internet of Things Group 4

5 Deep learning in OpenCV Classification models Detection models DenseNet Face detector AlexNet TextBoxes text detection & recognition GoogLeNet vx MobileNet-SSD 1000 classes ResNet-xxx VGG-SSD 20 classes SqueezeNet v1.x MobileNet-SSD 90 classes MobileNet YOLO 20 classes Emotion recognition 7 classes tiny-yolo 20 or 80 classes FCN segmentation OpenFace face recognition Image colorization ENet road segmentation (19 classes) BEGAN face generation Fast-neural-style style transfer Caffe TensorFlow Torch Darknet Internet of Things Group 5

6 Deep learning in OpenCV Text detection and recognition space shuttle: airliner: Image classification Face detection and emotions recognition Colorization Face generation Style transfer Object detection Internet of Things Group 6

7 Deep learning in OpenCV Model Estimated efficiency, CPU 2 Estimated efficiency, GPU 3 AlexNet 68 FPS 1 53 FPS SqueezeNet v FPS 72.5 FPS 512x FPS 24 FPS 300x300 (90 classes) 47.6 FPS 11 FPS (Halide, mixed devices) Face 300x FPS 13 FPS (Halide, mixed devices) TextBoxes 5 FPS 1 The higher FPS (number of frames per second) the better 2 Intel Core i7-6700k 4.00GHz x 8 3 Intel HD Graphics 530 (Skylake GT2) Internet of Things Group 7

8 Deep learning using OpenCV OpenCV as an out-of-box solution for computer vision pipelines with DL Deep learning layers optimized for different architectures especially Intel s Image preprocessing Deep learning model invocation Output postprocessing Internet of Things Group 8

9 Preprocessing: resize cv::mat img = cv::imread("toucan.jpg"); cv::resize(img, img, cv::size(224, 224)); Internet of Things Group 9

10 Preprocessing: cast to floats img.convertto(img, CV_32F); values in [0, 255] values in [0.0, 255.0] Internet of Things Group 10

11 Preprocessing: subtract mean cv::subtract(img, cv::scalar(104.0f, 117.0f, 123.0f), img); B in [-104.0, 151.0] G in [-117.0, 138.0] R in [-123.0, 132.0] Internet of Things Group 11

12 Preprocessing: scale cv::divide(img, cv::scalar(127.5f, 127.5f, 127.5f), img); B in (-0.82, 0.19) G in (-0.92, 1.09) R in (-0.97, 1.04) Internet of Things Group 12

13 Preprocessing: planar blob from interleaved image 1 std::vector<cv::mat> channels(3); 2 cv::split(img, channels); 3 cv::mat blob({1, 3, 224, 224}, CV_32F); 4 for (int i = 0; i < 3; ++i) { 5 cv::mat dst(224, 224, CV_32F, blob.ptr<float>(0, i)); 6 channels[i].copyto(dst); 7 } Internet of Things Group 13

14 Preprocessing: all at once cv::mat blob = cv::dnn::blobfromimage( cv::imread("toucan.jpg"), 1.0/127.5, cv::size(224, 224), cv::scalar(104, 117, 123), false, // Swap red and blue channels. false); // Keep aspect ratio, crop. image resize cast to floats mean subtraction scale deinterleaving Internet of Things Group 14

15 Load and run the model import numpy as np import cv2 as cv import sys # читаем метки классов with open('synset_words.txt') as f: classes = [x[x.find(' ') + 1:] for x in f] # загружаем модель Caffe net = cv.dnn.readnetfromcaffe('bvlc_googlenet.prototxt', 'bvlc_googlenet.caffemodel') # и входное изображение image = cv.imread(sys.argv[1]) # преобразуем изображение в тензор так же как и при тренировке данной сети input = cv.dnn.blobfromimage(image, 1, (224, 224), (104, 117, 123)) # устанавливаем тензор как вход сети net.setinput(input) # запускаем сеть, получаем выход out = net.forward() # печатаем наиболее вероятные метки и их вероятности indices = np.argsort(out[0])[-5:] for i in reversed(indices): print classes[i].strip() + ":", out[0][i] space shuttle: airliner: bullet train, bullet: submarine, pigboat, sub, U-boat: airship, dirigible: Internet of Things Group 15

16 Practice Run an OpenCV sample on Intel Galileo Arduino board Build a deep learning pipeline Internet of Things Group 16

17 Let s collaborate! Look for a good first issue label: Or contact with OpenCV community members by (OpenCV Q&A forum) dmitry.kurtaev@intel.com (Dmitry Kurtaev) Internet of Things Group 17

18 Internet of Things Group 18

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