This document provides an overview of computer vision and how to get started with computer vision using OpenCV. It discusses how computer vision works using matrix math and deep learning models. It then provides step-by-step instructions on loading and processing images using OpenCV in Python. Finally, it briefly introduces several popular computer vision APIs, including TensorFlow, Google Vision API, Microsoft Computer Vision, Amazon Rekognition, and shows examples of tasks like image classification and object detection using these APIs.

1. Computer Vision
A Beginner’s Guide

2. How do computers
actually see?

3. Like this?

4. Okay…maybe like this?

5. Actually it’s more like
this

8. Matrix Math
• From basic to complex
• “Seeing the Matrix”

9. So how do you “do”
computer vision?

10. OpenCV
Step zero is to install OpenCV
and python
• OpenCV no walk in the park
• Resources at the end of my slides

11. OpenCV
Step one is to start with your
image source
import cv2
image =
cv2.imread(“nebraska_small.jpg")

12. OpenCV
Step two is to convert to
greyscale for most libraries /
functions
import cv2
image =
cv2.imread(“nebraska_small.jpg”, 0)

13. OpenCV
Step three is to use OpenCV
to process your image
import cv2
image = cv2.imread(“nebraska_small.jpg”, 0)
ret, thresh1 = cv2.threshold(image, 127, 255,
cv2.THRESH_BINARY)
ret, thresh2 = cv2.threshold(image, 127, 255,
cv2.THRESH_BINARY_INV)
ret, thresh3 = cv2.threshold(image, 127, 255,
cv2.THRESH_TRUNC)
ret, thresh4 = cv2.threshold(image, 127, 255,
cv2.THRESH_TOZERO)
Binary Binary Inverse
Trunc To Zero

14. OpenCV
Step four is to use AI and ML
to create powerful projects!
•TensorFlow + Inception
•Google Vision API
•Microsoft Azure Computer
Vision
•Amazon Rekognition API
•…OpenCV

15. OpenCV
Easier to use in mobile
• iOS - pod ‘OpenCV'
• Android - OpenCV4Android SDK
• Natural fit with built in camera

16. About Me
Unconventional Background = Outsider
Hard work and passion creates amazing opportunities

17. Wide Wide World of
Computer Vision

18. Windows vs. Mac vs.
Linux vs. iOS vs. Android
vs. Raspberry Pi

19. • HOG Detectors (Histogram of Oriented
Gradients)
• SLAM (Simultaneous Localization And Mapping)
• CNN (Convolutional Neural Networks)
• GAN (Generative Adversarial Nets)
• Structure from Motion

20. TensorFlow +
Inception

21. C++ vs. Python

22. TensorFlow + Inception
giant panda, panda, panda bear,
coon bear, Ailuropoda melanoleuca
(score = 0.89107)
indri, indris, Indri indri, Indri
brevicaudatus
(score = 0.00779)
lesser panda, red panda, panda,
bear cat, cat bear, Ailurus
fulgens
(score = 0.00296)
custard apple
(score = 0.00147)
earthstar
(score = 0.00117)

23. TensorFlow + Inception
seashore, coast, seacoast,
sea-coast (score = 0.24771)
monitor
(score = 0.08148)
bell cote, bell cot
(score = 0.06853)
dome
(score = 0.05676)
palace
(score = 0.05457)

24. Google Cloud Vision
API

25. Label Detection
{
"mid": "/m/0j_s4",
"description": "metropolitan area",
"score": 0.9773918
},
{
"mid": "/m/01w5c_",
"description": "aerial photography",
"score": 0.9537865
},
{
"mid": "/m/08863x",
"description": "bird's eye view",
"score": 0.9332659
},

26. Landmark Detection
"mid": "/m/04f57c",
"description": "Union Station",
"score": 0.553656,
"boundingPoly": {
"vertices": […]
},
"locations": [
{
"latLng": {
"latitude": 39.081365,
"longitude": -94.58586931228638
}

27. Text Detection
• Recognized about 1,700
words
• Fairly high success %

28. import io
import os
# Imports the Google Cloud client library
from google.cloud import vision
# Instantiates a client
vision_client = vision.Client()
image = vision_client.image(source_uri='http://u.b5z.net/i/u/10192948/i/
Restaurant_Association_Marker_2014.jpg')
# Performs label detection on the image file
labels = image.detect_text()
print('Text:')
fullText = ""
for label in labels:
fullText += label.description + " "
print fullText

29. Microsoft Azure
Computer Vision

30. Visual Features
{
"categories": [{
"name": "outdoor_",
"score": 0.01171875
}, {
"name": "outdoor_sportsfield",
"score": 0.81640625
}],
"requestId": “e6…df”,
"metadata": {
"width": 838,
"height": 546,
"format": "Jpeg"
}
}

31. Landmarks
{
"categories": [{
"name": "building_",
"score": 0.54296875,
"detail": {
"landmarks": []
}
}, {
"name": "outdoor_",
"score": 0.01171875
}],
"requestId": “0e…f026”,
"metadata": {
"width": 960,
"height": 540,
"format": "Jpeg"

32. OCR
THE GREATER KANSAS CITY RESTAURANT ASSOCIAT!ON The Greater
Cit' Restaurant • in 1916. when three Visionary My row, C e
orge Fowler. and Guy Taylor began meeting the baseme#i Of
Myron Green" Cafeteria on this at Walnut. 'g17ö . 'the;
asuocIat20n organized a boycott to combat the rtslnq-
co$t„ eggs from Suppliers Charging unfair prices. effect* -
attention and 1919 the group launched ASSOC i •tiOn CNR A).
The NRA headquarters remained tn Kansas City until 1927 yhen
moved to Chicago. and eventually to Washington. D.C. Today.
the NRA supports the foodservice Industry, which consists of
13.1 employees (10% of the U.S. workforce) generates $660.5
billion in annual sales of the U.S. gross domestic product),
The GKCRA remains One of the strongest chapters of the NRA.
serving the needs of the area's hospitality industry tn parts
of Missouri and Kansas. Focused education and community
support. each year the GKCRA invests thoussnds of dollars into
hospitality scholarships for area youth, Wen as continuing the
education gf alt hospitality employees. Also dedicated to the
financial wen- being or Keans City end the promotion Of
businesses in our reglon, the CKCRA joined the city's effort
to initiate convention and tourism funding in 1974. Original
construction and continued maintenance of Bartle Hall end
other taurtsm and neighborhood development project' ere fUnded
by patrons Of the restaurant and hotel Industries. 2016 marks
the centennial anniversary of the Greater Kansas City
Restaurant Association. 1214 T KE SONS of KANSAS
• Not quiet as good as Google
• JSON structure is a PAIN

33. using System;
using System.IO;
using System.Net.Http;
using System.Net.Http.Headers;
namespace CSHttpClientSample
{
static class Program
{
static void Main()
{
MakeAnalysisRequest("/Users/me/Desktop/nebraska_small.jpg");
Console.WriteLine("nnnHit ENTER to exit...");
Console.ReadLine();
}
static byte[] GetImageAsByteArray(string imageFilePath)
{
FileStream fileStream = new FileStream(imageFilePath, FileMode.Open, FileAccess.Read);
BinaryReader binaryReader = new BinaryReader(fileStream);
return binaryReader.ReadBytes((int)fileStream.Length);
}
static async void MakeAnalysisRequest(string imageFilePath)
{
var client = new HttpClient();
// Request headers - replace this example key with your valid subscription key.
client.DefaultRequestHeaders.Add("Ocp-Apim-Subscription-Key", "key");
// Request parameters. A third optional parameter is "details".
string requestParameters = "visualFeatures=Categories&language=en&details=Landmarks";
string uri = "https://westus.api.cognitive.microsoft.com/vision/v1.0/analyze?" + requestParameters;
Console.WriteLine(uri);
HttpResponseMessage response;
// Request body. Try this sample with a locally stored JPEG image.
byte[] byteData = GetImageAsByteArray(imageFilePath);
using (var content = new ByteArrayContent(byteData))
{
// This example uses content type "application/octet-stream".
// The other content types you can use are "application/json" and "multipart/form-data".
content.Headers.ContentType = new MediaTypeHeaderValue("application/octet-stream");
response = await client.PostAsync(uri, content);
string responseBody = await response.Content.ReadAsStringAsync();
Console.Write(responseBody);
}
}
}
}

34. Amazon Rekognition

35. Object and Scene Detection
Aerial View 99%
City 95.9%
Downtown 95.9%
Neighborhood 74.3%
Town 74.3%
Metropolis 73.7%
Urban 73.7%
Building 67.7%

36. Computer Vision
• Standing on the shoulders of giants
• Projects over tools
• Explore, Use, Learn
• Taking the leap

37. Computer Vision at VML

38. Computer Vision at VML

39. Special Thanks

40. • David Mitchell VML
• Heather Downing VML
• u/davis685
• u/run7b
• u/dcc194
• u/slippy0
• u/vraGG_

41. Resources

42. • Install OpenCV http://www.pyimagesearch.com/
2016/12/19/install-opencv-3-on-macos-with-
homebrew-the-easy-way/
• Master OpenCV in Python Udemy https://
www.udemy.com/master-computer-vision-with-
opencv-in-python/

43. • http://www.learnopencv.com/histogram-of-oriented-gradients/
• https://ocw.mit.edu/courses/aeronautics-and-astronautics/
16-412j-cognitive-robotics-spring-2005/projects/
1aslam_blas_repo.pdf
• http://eric-yuan.me/cnn/
• http://bamos.github.io/2016/08/09/deep-completion/
• http://www.morethantechnical.com/2012/02/07/structure-from-
motion-and-3d-reconstruction-on-the-easy-in-opencv-2-3-w-
code/
• http://docs.opencv.org/trunk/d7/d8b/
tutorial_py_face_detection.html