First raw image characterization

Lets study the two raw images we have : one of a plate and one of a hand



In [1]:

    
import numpy as np
import scipy.signal as sp
import matplotlib.pyplot as plt
plt.close('all')

# Constants
Nsample = 1689 # number of samples by line
Nline = 64 # number of lines

# Load data
plate = np.loadtxt('plate.txt')
hand = np.loadtxt('hand.txt')

The plate

Let mesure the SNR and have a look at the data. for computing the SNR, I compute the variance one the empty half side of the image. This will be my estimate of the noise power. To compute the power of the signal, i mesure the highest possible amplitude and square it.



In [67]:

    
# image plotting

plt.figure(1)
plt.title('plate envelope')
plt.imshow(np.abs(sp.hilbert(plate)), cmap='gray', aspect='auto')
plt.colorbar()
plt.xlabel('samples')
plt.ylabel('lines')

Nempty = 32 # separe the empty from the not empty side
Nplot = 46 # one line chosen for graph display

# repere plotting

plt.plot([0,Nsample-1],[Nempty-0.5,Nempty-0.5],'b--',label='empty border')
plt.plot([0,Nsample-1],[Nplot,Nplot],'r--',label='plot line')
plt.legend()
plt.show()

# graph plotting

plt.figure(2)
plt.title('one raw line plotting')
plt.plot(plate[Nplot,:],'r')
plt.xlabel('samples')
plt.ylabel('amplitude')
plt.show() 

# SNR computation

Pnoise = np.var(plate[:Nempty,:])
Psignal = ((np.max(plate) - np.min(plate))/2)**2
print('SNR :', '{:.1f}'.format(10*np.log10(Psignal/Pnoise)),'dB',"/",Pnoise," noise var")









    












    












    



('SNR :', '40.4', 'dB', '/', 9.6582750947335683, ' noise var')

Result : actually the data is coded on 14 bits. But the reference of the red pitaya is 20V and the input is only 1V So on the +/-8192 possible values only 410 are useful. Some noise is visible so i computed the SNR. For that i took the plate image and computed the variance on the half top. It give me the power of the noise. I Then took max and min of the whole signal to compute max amplitude and thus the max signal power. This give 40.4 db of SNR. This agreed s with the value stated by jerome.

Conclusion : If the noise we can see is analog noise, an ADC of 10bit should be enough (60 dB of dynamic range for 40db of SNR). Moreover, if we don't find a way of fully use the 14bits of the red pytaia, this will still be a improvement since we pass from +/-410 values to +/-512.

The hand



In [3]:

    
# plot it for fun

plt.figure(3)
plt.title('hand envelope')
plt.imshow(abs(sp.hilbert(hand)), cmap='gray', aspect='auto')
plt.colorbar()
plt.xlabel('samples')
plt.ylabel('lines')
plt.show()

What does the noise look like?

Basing ourselves on the fact that the Acquisition rate is 125MHz and Decimation 8.



In [32]:

    
ACQ = 125000000
DECIMATION = 8
SizeFFT = len(plate[Nempty])

XScale = range(SizeFFT)

print len(XScale),SizeFFT
for i in range(SizeFFT):
    XScale[i] = (1.0*ACQ/DECIMATION)*float(XScale[i])/(1.0*SizeFFT)
    
plt.figure(3)
FFT = np.fft.fft(plate[Nempty])
# We clean the low frequencies
for i in range(10):
    FFT[i]=0
    FFT[-i]=0
    
plt.plot(XScale,np.real(FFT),"r")
plt.plot(XScale,np.imag(FFT),"b")
plt.plot(XScale,np.abs(FFT),"g")
plt.xlabel('Frequency')
plt.ylabel("FFT'ed")
plt.show()

Let's average the noise over the first Nempty lines



In [114]:

    
FFT = np.zeros(SizeFFT)
for k in range(Nempty):
    FFT = FFT + np.abs(np.fft.fft(plate[k]))
FFT = FFT/Nempty

for i in range(10):
    FFT[i] = 0
    FFT[-i] = 0

plt.plot(XScale,np.abs(FFT),"g")
plt.xlabel('Frequency')
plt.ylabel("FFT'ed")
plt.show()



In [115]:

    
# Let's save the noise pattern 
np.savez_compressed("plate.noise",XScale=XScale,FFT=FFT)



In [116]:

    
# We observe an interesting pattern
plt.plot(XScale[20:400],np.abs(FFT[20:400]),"g")
plt.xlabel('Frequency')
plt.ylabel("FFT'ed")
plt.show()



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