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OpenCV 3 Blueprints
OpenCV 3 Blueprints

OpenCV 3 Blueprints: Expand your knowledge of computer vision by building amazing projects with OpenCV 3

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Publication date : Nov 10, 2015
Length 382 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781784399757
Vendor :
Intel
Category :
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OpenCV 3 Blueprints

Chapter 1. Getting the Most out of Your Camera System

Claude Monet, one of the founders of French Impressionist painting, taught his students to paint only what they saw, not what they knew. He even went as far as to say:

"I wish I had been born blind and then suddenly gained my sight so that I could begin to paint without knowing what the objects were that I could see before me."

Monet rejected traditional artistic subjects, which tended to be mystical, heroic, militaristic, or revolutionary. Instead, he relied on his own observations of middle-class life: of social excursions; of sunny gardens, lily ponds, rivers, and the seaside; of foggy boulevards and train stations; and of private loss. With deep sadness, he told his friend, Georges Clemenceau (the future President of France):

"I one day found myself looking at my beloved wife's dead face and just systematically noting the colors according to an automatic reflex!"

Monet painted everything according to his personal impressions. Late in life, he even painted the symptoms of his own deteriorating eyesight. He adopted a reddish palette while he suffered from cataracts and a brilliant bluish palette after cataract surgery left his eyes more sensitive, possibly to the near ultraviolet range.

Like Monet's students, we as scholars of computer vision must confront a distinction between seeing and knowing and likewise between input and processing. Light, a lens, a camera, and a digital imaging pipeline can grant a computer a sense of sight. From the resulting image data, machine-learning (ML) algorithms can extract knowledge or at least a set of meta-senses such as detection, recognition, and reconstruction (scanning). Without proper senses or data, a system's learning potential is limited, perhaps even nil. Thus, when designing any computer vision system, we must consider the foundational requirements in terms of lighting conditions, lenses, cameras, and imaging pipelines.

What do we require in order to clearly see a given subject? This is the central question of our first chapter. Along the way, we will address five subquestions:

  • What do we require to see fast motion or fast changes in light?

  • What do we require to see distant objects?

  • What do we require to see with depth perception?

  • What do we require to see in the dark?

  • How do we obtain good value-for-money when purchasing lenses and cameras?

Tip

For many practical applications of computer vision, the environment is not a well-lit, white room, and the subject is not a human face at a distance of 0.6m (2')!

The choice of hardware is crucial to these problems. Different cameras and lenses are optimized for different imaging scenarios. However, software can also make or break a solution. On the software side, we will focus on the efficient use of OpenCV. Fortunately, OpenCV's videoio module supports many classes of camera systems, including the following:

  • Webcams in Windows, Mac, and Linux via the following frameworks, which come standard with most versions of the operating system:

    • Windows: Microsoft Media Foundation (MSMF), DirectShow, or Video for Windows (VfW)

    • Mac: QuickTime

    • Linux: Video4Linux (V4L), Video4Linux2 (V4L2), or libv4l

  • Built-in cameras in iOS and Android devices

  • OpenNI-compliant depth cameras via OpenNI or OpenNI2, which are open-source under the Apache license

  • Other depth cameras via the proprietary Intel Perceptual Computing SDK

  • Photo cameras via libgphoto2, which is open source under the GPL license. For a list of libgphoto2's supported cameras, see http://gphoto.org/proj/libgphoto2/support.php.

    Note

    Note that the GPL license is not appropriate for use in closed source software.

  • IIDC/DCAM-compliant industrial cameras via libdc1394, which is open-source under the LGPLv2 license

  • For Linux, unicap can be used as an alternative interface for IIDC/DCAM-compliant cameras, but unicap is GPL-licensed and thus not appropriate for use in closed-source software.

  • Other industrial cameras via the following proprietary frameworks:

    • Allied Vision Technologies (AVT) PvAPI for GigE Vision cameras

    • Smartek Vision Giganetix SDK for GigE Vision cameras

    • XIMEA API

Note

The videoio module is new in OpenCV 3. Previously, in OpenCV 2, video capture and recording were part of the highgui module, but in OpenCV 3, the highgui module is only responsible for GUI functionality. For a complete index of OpenCV's modules, see the official documentation at http://docs.opencv.org/3.0.0/.

However, we are not limited to the features of the videoio module; we can use other APIs to configure cameras and capture images. If an API can capture an array of image data, OpenCV can readily use the data, often without any copy operation or conversion. As an example, we will capture and use images from depth cameras via OpenNI2 (without the videoio module) and from industrial cameras via the FlyCapture SDK by Point Grey Research (PGR).

Note

An industrial camera or machine vision camera typically has interchangeable lenses, a high-speed hardware interface (such as FireWire, Gigabit Ethernet, USB 3.0, or Camera Link), and a complete programming interface for all camera settings.

Most industrial cameras have SDKs for Windows and Linux. PGR's FlyCapture SDK supports single-camera and multi-camera setups on Windows as well as single-camera setups on Linux. Some of PGR's competitors, such as Allied Vision Technologies (AVT), offer better support for multi-camera setups on Linux.

We will learn about the differences among categories of cameras, and we will test the capabilities of several specific lenses, cameras, and configurations. By the end of the chapter, you will be better qualified to design either consumer-grade or industrial-grade vision systems for yourself, your lab, your company, or your clients. I hope to surprise you with the results that are possible at each price point!

Coloring the light


The human eye is sensitive to certain wavelengths of electromagnetic radiation. We call these wavelengths "visible light", "colors", or sometimes just "light". However, our definition of "visible light" is anthropocentric as different animals see different wavelengths. For example, bees are blind to red light, but can see ultraviolet light (which is invisible to humans). Moreover, machines can assemble human-viewable images based on almost any stimulus, such as light, radiation, sound, or magnetism. To broaden our horizons, let's consider eight kinds of electromagnetic radiation and their common sources. Here is the list, in order of decreasing wavelength:

  • Radio waves radiate from certain astronomical objects and from lightning. They are also generated by wireless electronics (radio, Wi-Fi, Bluetooth, and so on).

  • Microwaves radiated from the Big Bang and are present throughout the Universe as background radiation. They are also generated by microwave ovens.

  • Far infrared (FIR) light is an invisible glow from warm or hot things such as warm-blooded animals and hot-water pipes.

  • Near infrared (NIR) light radiates brightly from our sun, from flames, and from metal that is red-hot or nearly red-hot. However, it is a relatively weak component in commonplace electric lighting. Leaves and other vegetation brightly reflect NIR light. Skin and certain fabrics are slightly transparent to NIR.

  • Visible light radiates brightly from our sun and from commonplace electric light sources. Visible light includes the colors red, orange, yellow, green, blue, and violet (in order of decreasing wavelength).

  • Ultraviolet (UV) light, too, is abundant in sunlight. On a sunny day, UV light can burn our skin and can become slightly visible to us as a blue-gray haze in the distance. Commonplace, silicate glass is nearly opaque to UV light, so we do not suffer sunburn when we are behind windows (indoors or in a car). For the same reason, UV camera systems rely on lenses made of non-silicate materials such as quartz. Many flowers have UV markings that are visible to insects. Certain bodily fluids such as blood and urine are more opaque to UV than to visible light.

  • X-rays radiate from certain astronomical objects such as black holes. On Earth, radon gas, and certain other radioactive elements are natural X-ray sources.

  • Gamma rays radiate from nuclear explosions, including supernovae. To lesser extents the sources of gamma rays also include radioactive decay and lightning strikes.

NASA provides the following visualization of the wavelength and temperature associated with each kind of light or radiation:

Passive imaging systems rely on ambient (commonplace) light or radiation sources as described in the preceding list. Active imaging systems include sources of their own so that the light or radiation is structured in more predictable ways. For example, an active night vision scope might use a NIR camera plus a NIR light.

For astronomy, passive imaging is feasible across the entire electromagnetic spectrum; the vast expanse of the Universe is flooded with all kinds of light and radiation from sources old and new. However, for terrestrial (Earth-bound) purposes, passive imaging is mostly limited to the FIR, NIR, visible, and UV ranges. Active imaging is feasible across the entire spectrum, but the practicalities of power consumption, safety, and interference (between our use case and others) limit the extent to which we can flood an environment with excess light and radiation.

Whether active or passive, an imaging system typically uses a lens to bring light or radiation into focus on the surface of the camera's sensor. The lens and its coatings transmit some wavelengths while blocking others. Additional filters may be placed in front of the lens or sensor to block more wavelengths. Finally, the sensor itself exhibits a varying spectral response, meaning that for some wavelengths, the sensor registers a strong (bright) signal, but for other wavelengths, it registers a weak (dim) signal or no signal. Typically, a mass-produced digital sensor responds most strongly to green, followed by red, blue, and NIR. Depending on the use case, such a sensor might be deployed with a filter to block a range of light (whether NIR or visible) and/or a filter to superimpose a pattern of varying colors. The latter filter allows for the capture of multichannel images, such as RGB images, whereas the unfiltered sensor would capture monochrome (gray) images.

The sensor's surface consists of many sensitive points or photosites. These are analogous to pixels in the captured digital image. However, photosites and pixels do not necessarily correspond one-to-one. Depending on the camera system's design and configuration, the signals from several photosites might be blended together to create a neighborhood of multichannel pixels, a brighter pixel, or a less noisy pixel.

Consider the following pair of images. They show a sensor with a Bayer filter, which is a common type of color filter with two green photosites per red or blue photosite. To compute a single RGB pixel, multiple photosite values are blended. The left-hand image is a photo of the filtered sensor under a microscope, while the right-hand image is a cut-away diagram showing the filter and underlying photosites:

Note

The preceding images come from Wikimedia. They are contributed by the users Natural Philo, under the Creative Commons Attribution-Share Alike 3.0 Unported license (left), and Cburnett, under the GNU Free Documentation License (right).

As we see in this example, a simplistic model (an RGB pixel) might hide important details about the way data are captured and stored. To build efficient image pipelines, we need to think about not just pixels, but also channels and macropixels—neighborhoods of pixels that share some channels of data and are captured, stored, and processed in one block. Let's consider three categories of image formats:

  • A raw image is a literal representation of the photosites' signals, scaled to some range such as 8, 12, or 16 bits. For photosites in a given row of the sensor, the data are contiguous but for photosites in a given column, they are not.

  • A packed image stores each pixel or macropixel contiguously in memory. That is to say, data are ordered according to their neighborhood. This is an efficient format if most of our processing pertains to multiple color components at a time. For a typical color camera, a raw image is not packed because each neighborhood's data are split across multiple rows. Packed color images usually use RGB channels, but alternatively, they may use YUV channels, where Y is brightness (grayscale), U is blueness (versus greenness), and V is redness (also versus greenness).

  • A planar image stores each channel contiguously in memory. That is to say, data are ordered according to the color component they represent. This is an efficient format if most of our processing pertains to a single color component at a time. Packed color images usually use YUV channels. Having a Y channel in a planar format is efficient for computer vision because many algorithms are designed to work on grayscale data alone.

An image from a monochrome camera can be efficiently stored and processed in its raw format or (if it must integrate seamlessly into a color imaging pipeline) as the Y plane in a planar YUV format. Later in this chapter, in the sections Supercharging the PlayStation Eye and Supercharging the GS3-U3-23S6M-C and other Point Grey Research cameras, we will discuss code samples that demonstrate efficient handling of various image formats.

Until now, we have covered a brief taxonomy of light, radiation, and color—their sources, their interaction with optics and sensors, and their representation as channels and neighborhoods. Now, let's explore some more dimensions of image capture: time and space.

Capturing the subject in the moment


Robert Capa, a photojournalist who covered five wars and shot images of the first wave of D-Day landings at Omaha Beach, gave this advice:

"If your pictures aren't good enough, you're not close enough."

Like a computer vision program, a photographer is the intelligence behind the lens. (Some would say the photographer is the soul behind the lens.) A good photographer continuously performs detection and tracking tasks—scanning the environment, choosing the subject, predicting actions and expressions that will create the right moment for the photo, and choosing the lens, settings, and viewpoint that will most effectively frame the subject.

By getting "close enough" to the subject and the action, the photographer can observe details quickly with the naked eye and can move to other viewpoints quickly because the distances are short and because the equipment is typically light (compared to a long lens on a tripod for a distant shot). Moreover, a close-up, wide-angle shot pulls the photographer, and viewer, into a first-person perspective of events, as if we become the subject or the subject's comrade for a single moment.

Photographic aesthetics concern us further in Chapter 2, Photographing Nature and Wildlife with an Automated Camera. For now, let's just establish two cardinal rules: don't miss the subject and don't miss the moment! Poor visibility and unfortunate timing are the worst excuses a photographer or a practitioner of computer vision can give. To hold ourselves to account, let us define some measurements that are relevant to these cardinal rules.

Resolution is the finest level of detail that the lens and camera can see. For many computer vision applications, recognizable details are the subject of the work, and if the system's resolution is poor, we might miss this subject completely. Resolution is often expressed in terms of the sensor's photosite counts or the captured image's pixel counts, but at best these measurements only tell us about one limiting factor. A better, empirical measurement, which reflects all characteristics of the lens, sensor, and setup, is called line pairs per millimeter (lp/mm). This means the maximum density of black-on-white lines that the lens and camera can resolve, in a given setup. At any higher density than this, the lines in the captured image blur together. Note that lp/mm varies with the subject's distance and the lens's settings, including the focal length (optical zoom) of a zoom lens. When you approach the subject, zoom in, or swap out a short lens for a long lens, the system should of course capture more detail! However, lp/mm does not vary when you crop (digitally zoom) a captured image.

Lighting conditions and the camera's ISO speed setting also have an effect on lp/mm. High ISO speeds are used in low light and they boost both the signal (which is weak in low light) and the noise (which is as strong as ever). Thus, at high ISO speeds, some details are blotted out by the boosted noise.

To achieve anything near its potential resolution, the lens must be properly focused. Dante Stella, a contemporary photographer, describes a problem with modern camera technology:

"For starters, it lacks … thought-controlled predictive autofocus."

That is to say, autofocus can fail miserably when its algorithm is mismatched to a particular, intelligent use or a particular pattern of evolving conditions in the scene. Long lenses are especially unforgiving with respect to improper focus. The depth of field (the distance between the nearest and farthest points in focus) is shallower in longer lenses. For some computer vision setups—for example, a camera hanging over an assembly line—the distance to the subject is highly predictable and in such cases manual focus is an acceptable solution.

Field of view (FOV) is the extent of the lens's vision. Typically, FOV is measured as an angle, but it can be measured as the distance between two peripherally observable points at a given depth from the lens. For example, a FOV of 90 degrees may also be expressed as a FOV of 2m at a depth of 1m or a FOV of 4m at a depth of 2m. Where not otherwise specified, FOV usually means diagonal FOV (the diagonal of the lens's vision), as opposed to horizontal FOV or vertical FOV. A longer lens has a narrower FOV. Typically, a longer lens also has higher resolution and less distortion. If our subject falls outside the FOV, we miss the subject completely! Toward the edges of the FOV, resolution tends to decrease and distortion tends to increase, so preferably the FOV should be wide enough to leave a margin around the subject.

The camera's throughput is the rate at which it captures image data. For many computer vision applications, a visual event might start and end in a fleeting moment and if the throughput is low, we might miss the moment completely or our image of it might suffer from motion blur. Typically, throughput is measured in frames per second (FPS), though measuring it as a bitrate can be useful, too. Throughput is limited by the following factors:

  • Shutter speed (exposure time): For a well-exposed image, the shutter speed is limited by lighting conditions, the lens's aperture setting, and the camera's ISO speed setting. (Conversely, a slower shutter speed allows for a narrower aperture setting or slower ISO speed.) We will discuss aperture settings after this list.

  • The type of shutter: A global shutter synchronizes the capture across all photosites. A rolling shutter does not; rather, the capture is sequential such that photosites at the bottom of the sensor register their signals later than photosites at the top. A rolling shutter is inferior because it can make an object appear skewed when the object or the camera moves rapidly. (This is sometimes called the "Jell-O effect" because of the video's resemblance to a wobbling mound of gelatin.) Also, under rapidly flickering lighting, a rolling shutter creates light and dark bands in the image. If the start of the capture is synchronized but the end is not, the shutter is referred to as a rolling shutter with global reset.

  • The camera's onboard image processing routines, such as conversion of raw photosite signals to a given number of pixels in a given format. As the number of pixels and bytes per pixel increase, the throughput decreases.

  • The interface between the camera and host computer: Common camera interfaces, in order of decreasing bit rates, include CoaXPress full, Camera Link full, USB 3.0, CoaXPress base, Camera Link base, Gigabit Ethernet, IEEE 1394b (FireWire full), USB 2.0, and IEEE 1394 (FireWire base).

A wide aperture setting lets in more light to allow for a faster exposure, a lower ISO speed, or a brighter image. However, a narrower aperture has the advantage of offering a greater depth of field. A lens supports a limited range of aperture settings. Depending on the lens, some aperture settings exhibit higher resolution than others. Long lenses tend to exhibit more stable resolution across aperture settings.

A lens's aperture size is expressed as an f-number or f-stop, which is the ratio of the lens's focal length to the diameter of its aperture. Roughly speaking, focal length is related to the length of the lens. More precisely, it is the distance between the camera's sensor and the lens system's optical center when the lens is focused at an infinitely distant target. The focal length should not be confused with the focus distance—the distance to objects that are in focus. The following diagram illustrates the meanings of focal length and focal distance as well as FOV:

With a higher f-number (a proportionally narrower aperture), a lens transmits a smaller proportion of incoming light. Specifically, the intensity of the transmitted light is inversely proportional to the square of the f-number. For example, when comparing the maximum apertures of two lenses, a photographer might write, "The f/2 lens is twice as fast as the f/2.8 lens." This means that the former lens can transmit twice the intensity of light, allowing an equivalent exposure to be taken in half the time.

A lens's efficiency or transmittance (the proportion of light transmitted) depends on not only the f-number but also non-ideal factors. For example, some light is reflected off the lens elements instead of being transmitted. The T-number or T-stop is an adjustment to the f-number based on empirical findings about a given lens's transmittance. For example, regardless of its f-number, a T/2.4 lens has the same transmittance as an ideal f/2.4 lens. For cinema lenses, manufacturers often provide T-number specifications but for other lenses, it is much more common to see only f-number specifications.

The sensor's efficiency is the proportion of the lens-transmitted light that reaches photosites and gets converted to a signal. If the efficiency is poor, the sensor misses much of the light! A more efficient sensor will tend to take well-exposed images for a broader range of camera settings, lens settings, and lighting conditions. Thus, efficiency gives the system more freedom to auto-select settings that are optimal for resolution and throughput. For the common type of sensor described in the previous section, Coloring the light, the choice of color filters has a big effect on efficiency. A camera designed to capture visible light in grayscale has high efficiency because it can receive all visible wavelengths at each photosite. A camera designed to capture visible light in multiple color channels typically has much lower efficiency because some wavelengths are filtered out at each photosite. A camera designed to capture NIR alone, by filtering out all visible light, typically has even lower efficiency.

Efficiency is a good indication of the system's ability to form some kind of image under diverse lighting (or radiation) conditions. However, depending on the subject and the real lighting, a relatively inefficient system could have higher contrast and better resolution. The advantages of selectively filtering wavelengths are not necessarily reflected in lp/mm, which measures black-on-white resolution.

By now, we have seen many quantifiable tradeoffs that complicate our efforts to capture a subject in a moment. As Robert Capa's advice implies, getting close with a short lens is a relatively robust recipe. It allows for good resolution with minimal risk of completely missing the framing or the focus. On the other hand, such a setup suffers from high distortion and, by definition, a short working distance. Moving beyond the capabilities of cameras in Capa's day, we have also considered the features and configurations that allow for high-throughput and high-efficiency video capture.

Having primed ourselves on wavelengths, image formats, cameras, lenses, capture settings, and photographers' common sense, let us now select several systems to study.

Left arrow icon Right arrow icon

Key benefits

What you will learn

Select and configure camera systems to see invisible light, fast motion, and distant objects Build a -camera trap, as used by nature photographers, and process photos to create beautiful effects Develop a facial expression recognition system with various feature extraction techniques and machine learning methods Build a panorama Android application using the OpenCV stitching module in C++ with NDK support Optimize your object detection model, make it rotation invariant, and apply scene-specific constraints to make it faster and more robust Create a person identification and registration system based on biometric properties of that person, such as their fingerprint, iris, and face Fuse data from videos and gyroscopes to stabilize videos shot from your mobile phone and create hyperlapse style videos

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Product Details


Publication date : Nov 10, 2015
Length 382 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781784399757
Vendor :
Intel
Category :

Table of Contents

14 Chapters
OpenCV 3 Blueprints Chevron down icon Chevron up icon
Credits Chevron down icon Chevron up icon
About the Authors Chevron down icon Chevron up icon
About the Reviewers Chevron down icon Chevron up icon
www.PacktPub.com Chevron down icon Chevron up icon
Preface Chevron down icon Chevron up icon
1. Getting the Most out of Your Camera System Chevron down icon Chevron up icon
2. Photographing Nature and Wildlife with an Automated Camera Chevron down icon Chevron up icon
3. Recognizing Facial Expressions with Machine Learning Chevron down icon Chevron up icon
4. Panoramic Image Stitching Application Using Android Studio and NDK Chevron down icon Chevron up icon
5. Generic Object Detection for Industrial Applications Chevron down icon Chevron up icon
6. Efficient Person Identification Using Biometric Properties Chevron down icon Chevron up icon
7. Gyroscopic Video Stabilization Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

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