Transcript Phase unwrapping and surface fitting

GPGPU-based surface inspection
from structured white light
Miguel Bordallo1, Karri Niemelä 2, Olli Silvén1
1
Center for Machine Vision Research - University of Oulu, Finland
2 VTT - Technical Research Center of Finland, Oulu, Finland
Jari Hannuksela, Olli Silvén
Machine Vision Group, Infotech Oulu
Department of Electrical and Information Engineeering
University of Oulu, Finland
MACHINE VISION GROUP
Contents
Introduction
Automatic Surface Inspection
• Phase extraction from white structured light
• Practical problems
Measuring Prototype
• Design and construction
• GPU as a computing engine
• Experimental setup
Description of the system
• Algorithms and Implementation
Experiments
• Qualitative results
• Speed and scalability
Summary
MACHINE VISION GROUP
Motivation
• Automatic surface inspection used in the industry:
– To detect all kinds of surface defects
– To measure the overall quality of a produced piece
• Most convenient inspection method should
provide exact 3D information
• High speed of production lines need:
– Fast imaging methods
– Lots of computational power
• Systems must be cost effective:
– Standard PCs
– Graphics Processing Units (GPUs)
MACHINE VISION GROUP
GPU as a computing engine
•
All computers and many embedded systems include a GPU
•
•
•
•
•
Graphics Processing Units offer important parallelization capabilities
•
•
•
•
•
Standard PCs and components
Cost-effective systems
Highly scalable
GPU can be treated as an independent entity
GPUs offer ”many-core” computation
Thousands of threads can be executed concurrently.
GPU and CPU can be used concurrently
If data transfer is small, CPU load remains low (CPU can be used for other tasks)
CUDA is a highly optimized and attractive accelerator interface
MACHINE VISION GROUP
Surface topography from white
structured light (SLS)
• Phase-shifting methods:
– Based on fringe pattern projections or structured light
– Extensively utilized in topography measurement
– Provide for high resolution height measurements on each pixel.
• The illuminator projects a sine pattern:
– On a moving target
– In a synchronized manner
• The camera system obtains suitable input pictures using:
– Pulse-like illumination
– Synchronized camera subsystem
– Certain known rate
MACHINE VISION GROUP
Phase measurement
The input images are defined by the following:
If δ1, δ2, δ3 are known:
And the height:
In practice δ1, δ2, δ3 are not known in beforehand
MACHINE VISION GROUP
Phase extraction with syntetic images
120dg
Phase
Shifted
patterns
Reconstructed
Images
Phase/Height
Comparison
MACHINE VISION GROUP
Problems and errors
+
Clipping effect:
saturation
+
Wrong phase shift
(δ1, δ2, δ3)
=
Combined effect
MACHINE VISION GROUP
Wrong frequency
Problems and errors
+
Clipping effect:
saturation
+
Wrong phase shift
(δ1, δ2, δ3)
=
+
Input
MACHINE VISION GROUP
Combined effect
Wrong frequency
Problems and errors
+
Clipping effect:
saturation
+
Wrong phase shift
(δ1, δ2, δ3)
Wrong frequency
=
+
Input
MACHINE VISION GROUP
=
Combined effect
Result
Prototype design
MACHINE VISION GROUP
Prototype design
• VTT prototype: Sine period of 250um
–
–
–
–
Camera: Basler Scout scA 1600-14gm. 1628x1236 pixels, Area 4.4*4.4um2
Interface: GiGE, 17 frames per second
Optics: Optosigma Telecentric (TC1236). Pixel size 30 µm
Illuminator: 9 Luxeon K2 Red LEDS + collimating lens. 3 channels
• Laptop: Lenovo W700
– CPU: Intel Core 2 Extreme QX9300 2.53 GHz
– GPU: Nvidia Quadro FX3700 (128 cores)
– IDE: Visual Studio. CUDA & C code environments
• Motor Line Controller: ATMEL microcontroler and PC
– Line speed: 0,3 m/s
• Samples used:
– Offline: 10 cents coin, printed electronics (10 µm thick)
– Online: MDF-fiberboard
MACHINE VISION GROUP
Prototype construction
MACHINE VISION GROUP
Application flow
MACHINE VISION GROUP
Application flow
MACHINE VISION GROUP
Application flow
MACHINE VISION GROUP
Application flow
MACHINE VISION GROUP
Application flow
MACHINE VISION GROUP
Application flow
MACHINE VISION GROUP
Application flow
MACHINE VISION GROUP
Application flow
MACHINE VISION GROUP
Input images
Full frame size: 1628x1236 pixels,
8 or 10 bpp, grayscale, 17 fps
MACHINE VISION GROUP
Input images
Full frame size: 1628x1236 pixels,
8 or 10 bpp, grayscale, 17 fps
64x256 correlation area
MACHINE VISION GROUP
Image registration
• Based on modified phase correlation
–
–
–
–
Tukey window + FFT-based (+ Gaussian filtering)
Robust to blur (even motion blur)
Robust to image intensity changes
Fast to compute
• Easy to parallelize
– CUDA FFT routines already optimized
– Per-pixel operations
• Identifies corresponding pixels
– Subpixel level access as a CUDA texture object
• Predict initial phase shift for phase computation
• Fine tune the motor displacements & camera rate
MACHINE VISION GROUP
600x300
ROIs
Correlation algorithm performance
FFT
64x256
Time /SpeedUp
256x1024
Time /SpeedUp
Correlate 2 images
(3 fft + mul./norm.)
Correlate 3
full frames
(5 fft + 2 mul./norm.)
Matlab
CUDA
CUDA
Matlab
CUDA
CUDA
Matlab
CUDA
CUDA
Matlab
CUDA
CUDA
Intel
Core2
2.6Ghz
Nvidia
Quadro
FX1700
Nvidia
Quadro
FX3700
Intel
Core2
2.6Ghz
Nvidia
Quadro
FX1700
Nvidia
Quadro
FX3700
Intel
Core2
2.6Ghz
Nvidia
Quadro
FX1700
Nvidia
Quadro
FX3700
Intel
Core2
2.6Ghz
Nvidia
Quadro
FX1700
Nvidia
Quadro
FX3700
18 ms
3.5
ms
0.9
ms
4
ms
0.3
ms
<0.1
ms
62
ms
12
ms
2.9
ms
100
ms
20
ms
4
ms
X
5X
20X
X
12X
>40X
X
5X
22X
X
5X
25X
70
ms
13
ms
3
ms
15
ms
1.1
ms
0.2
ms
230
ms
40
ms
6.1
ms
390
ms
65
ms
15
ms
X
5.5X
25X
X
14X
67X
X
5.8X
37.5X
X
6X
30X
275
ms
42
ms
9
ms
58
ms
3.5
ms
0.7
ms
820
ms
120
ms
24
ms
1500
ms
200
ms
41
ms
X
6.5X
30X
X
17X
80X
X
7X
34X
X
7.5X
36X
Time /SpeedUp
128x512
Multiplication &
Normalization
MACHINE VISION GROUP
Advanced Phase Shifting Algorithm (APSA)
• First introduced by Z. Wang in 2004
• Iterative algorithm
– Initial estimation of phase difference (δ1, δ2, δ3)
• from correlation and previous frames
– Phase of each pixel is computed
• Using a CUDA 2-dimensional kernel
– Average phase of the image is computed
• By adding together the values of all the pixels
• Using CUDPP parallel reductions
– Average phase is the new phase difference
– Iterate until convergent and error < threshold
• Result is a phase wrapped image
– Range between -π and π
MACHINE VISION GROUP
Wrapped image
APSA times
Algorithm
MATLAB time
C/CUDA time
Size
Mpix/s
SpeedUp
APSA1:
130,0 ms/iteration
10,9 ms/iteration
350x826
26,52
11x
470,0 ms/iteration
18,8 ms/iteration
350x826
15,11
24x
6200 ms
300 ms
350x826
0,95
20x
Phase extraction (CUDA)
APSA2:
Average phase
(CUDA)
APSA 10 iterations
MACHINE VISION GROUP
Phase unwrapping and surface
fitting
• Lp Norm algorithm:
–
–
–
–
Developed in CUDA (Mistry, 2009)
Accurate results
Very high computation times (up to 2.5 seconds)
Not suitable for real-time
600x300
Surface map
• Sorting by reliability in noncontinuous path:
–
–
–
–
–
Fast two dimensional unwrapping
Developed in C for a CPU (Arevalillo 2004)
Sufficient accuracy
Very fast (about 125 ms.)
Can be executed concurrently with the GPU phase extraction
• Surface fitting computes the closer average plane
MACHINE VISION GROUP
Display system
MACHINE VISION GROUP
Automatic calibration system
• Phase maps measured continuously in real time
– The information of the phase extraction process can be used to
improve further results and conditions.
• Synchronizes
– Illumination,
– Camera capture
– Motor speed
Phase tuning and system calibration improve the results gradually
• Input parameters:
– Correlation results (adjust motor speed)
– Phase average (adjust illumination and camera capture
MACHINE VISION GROUP
Real-time tests: MDF fibreboard sample
MACHINE VISION GROUP
Real time tests: 3D representation
MACHINE VISION GROUP
Printed electronics sample
MACHINE VISION GROUP
Complete system
CPU
GPU
Get input frames
N=1
Copy Images as
texture
Get Correlation
ROI
Perform correlation
Forward correlation
values
Get Surface
ROI
Get pixel phase
Get average phase
APSA1
Forward phase
average values
Get average phase
APSA2
Get phase map
Phase unwrapping
Surface fitting
MACHINE VISION GROUP
Image size: 3 ROI of
600x300
Computation time: < 150 ms.
Frame rate: > 5 fps.
Resolution: 30µm per pixel.
Complete system
CPU
GPU
Get input frames
N=1
Copy Images as
texture
Get Correlation
ROI
Perform correlation
Forward correlation
values
Get Surface
ROI
Get pixel phase
Get average phase
APSA1
Forward phase
average values
Get average phase
APSA2
Get phase map
Phase unwrapping
Surface fitting
MACHINE VISION GROUP
Image size: 3 ROI of
600x300
Computation time: < 150 ms.
Frame rate: > 5 fps.
Resolution: 30µm per pixel.
Complete system
CPU
GPU
Get input frames
N=1
Calculate
wrapped phase
Image
N=1
Image size: 3 ROI of
600x300
Computation time: < 150 ms.
Frame rate: > 5 fps.
Resolution: 30µm per pixel.
Phase unwrapping
Surface ftting
MACHINE VISION GROUP
Complete system
CPU
GPU
Get input frames
N=1
Calculate
wrapped phase
Image
N=1
Phase unwrapping
Surface fitting
N=1
MACHINE VISION GROUP
Image size: 3 ROI of
600x300
Computation time: < 150 ms.
Frame rate: > 5 fps.
Resolution: 30µm per pixel.
Complete system
CPU
GPU
Get input frames
N=1
Calculate
wrapped phase
Image
N=1
Get input frames
N=2
Phase unwrapping
Surface fitting
N=1
MACHINE VISION GROUP
Image size: 3 ROI of
600x300
Computation time: < 150 ms.
Frame rate: > 5 fps.
Resolution: 30µm per pixel.
Complete system
CPU
GPU
Get input frames
N=1
Calculate
wrapped phase
Image
N=1
Get input frames
N=2
Phase unwrapping
Surface fitting
N=1
Calculate
wrapped phase
Image
N=2
MACHINE VISION GROUP
Image size: 3 ROI of
600x300
Computation time: < 150 ms.
Frame rate: > 5 fps.
Resolution: 30µm per pixel.
Complete system
CPU
GPU
Get input frames
N=1
Calculate
wrapped phase
Image
N=1
Get input frames
N=2
Phase unwrapping
Surface fitting
N=1
Calculate
wrapped phase
Image
N=2
Get input frames
N=3
Phase unwrapping
Surface fitting
N=2
Calculate
wrapped phase
Image
N=3
MACHINE VISION GROUP
Image size: 3 ROI of
600x300
Computation time: < 150 ms.
Frame rate: > 5 fps.
Resolution: 30µm per pixel.
Complete system
CPU
GPU
Get input frames
N=1
Calculate
wrapped phase
Image
N=1
Get input frames
N=2
Phase unwrapping
Surface fitting
N=1
Calculate
wrapped phase
Image
N=2
Get input frames
N=3
Phase unwrapping
Surface fitting
N=2
Calculate
wrapped phase
Image
N=3
MACHINE VISION GROUP
Image size: 3 ROI of
600x300
Computation time: < 150 ms.
Frame rate: > 5 fps.
Resolution: 30µm per pixel.
Complete system
CPU
GPU
Get input frames
N=1
Calculate
wrapped phase
Image
N=1
Get input frames
N=2
Phase unwrapping
Surface fitting
N=1
Calculate
wrapped phase
Image
N=2
Get input frames
N=3
Phase unwrapping
Surface fitting
N=2
Calculate
wrapped phase
Image
N=3
Get input frames
N=n
Phase unwrapping
Surface fitting
N = n-1
Calculate
wrapped phase
Image
N=n
MACHINE VISION GROUP
Image size: 3 ROI of
600x300
Computation time: < 150 ms.
Frame rate: > 5 fps.
Resolution: 30µm per pixel.
Complete system
CPU
GPU
Get input frames
N=1
Calculate
wrapped phase
Image
N=1
Get input frames
N=2
Phase unwrapping
Surface fitting
N=1
Calculate
wrapped phase
Image
N=2
Get input frames
N=3
Phase unwrapping
Surface fitting
N=2
Calculate
wrapped phase
Image
N=3
Get input frames
N=n
Phase unwrapping
Surface fitting
N = n-1
Calculate
wrapped phase
Image
N=n
MACHINE VISION GROUP
Image size: 3 ROI of
600x300
Computation time: < 150 ms.
Frame rate: > 5 fps.
Resolution: 30µm per pixel.
Summary
• A sine projection technique is a suitable method to
optically measure a layer-like surface topography
• The system could be used in rapid motor lines with proper
synchronization
• An integrated automatic calibration system helps
synchronization and increases quality and robustness
•High accuracy can be achieved with fast imaging methods
and intensive computation
• Time critical algorithms can be executed with GPU-based
parallel computing
MACHINE VISION GROUP
Thank you!
• Any questions ???
MACHINE VISION GROUP