Upgraded simulations using numba
This commit is contained in:
Emilio Martinez
parent
aba3257de6
commit
92c30cdf65
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@ -6,7 +6,7 @@ Created on Tue Dec 20 2022
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@author: Emilio Martínez <emilio.martinez@fing.edu.uy>
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Script that reads all images in folder and simulates HDMI tempest capture
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if there is no subfolder with it's name.
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"""
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#%%
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@ -20,7 +20,7 @@ import numpy as np
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from skimage.io import imread
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from scipy import signal
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from PIL import Image
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from utils.DTutils import TMDS_encoding, TMDS_serial
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from utils.DTutils import TMDS_encoding_original, TMDS_serial
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import sys
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#%%
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@ -39,14 +39,14 @@ def get_subfolders_names_from_folder(folder):
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def image_transmition_simulation(I, blanking=False):
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# Encode image for TMDS
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I_TMDS = TMDS_encoding (I, blanking = blanking)
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I_TMDS = TMDS_encoding_original (I, blanking = blanking)
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# Serialize pixel bits and sum channel signals
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I_TMDS_Tx = TMDS_serial(I_TMDS)
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return I_TMDS_Tx, I_TMDS.shape
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def image_capture_simulation(I_Tx, h_total, v_total, N_harmonic, noise_std,
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def image_capture_simulation(I_Tx, h_total, v_total, N_harmonic, noise_std=0,
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fps=60):
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# Compute pixelrate and bitrate
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@ -54,7 +54,7 @@ def image_capture_simulation(I_Tx, h_total, v_total, N_harmonic, noise_std,
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bit_rate = 10*px_rate
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# Continuous samples (interpolate)
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interpolator = 1
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interpolator = int(np.ceil(N_harmonic/5)) # Condition for sampling rate and
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sample_rate = interpolator*bit_rate
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Nsamples = interpolator*(10*h_total*v_total)
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if interpolator > 1:
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@ -115,67 +115,40 @@ def main():
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foldername = sys.argv[-1]
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# Get images and subfolders names
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images_tmp = get_images_names_from_folder(foldername)
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old_subfolders = get_subfolders_names_from_folder(foldername)
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images = get_images_names_from_folder(foldername)
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# Keep images without dedicated folders only
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images = []
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new_subfolders = []
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for image in images_tmp:
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image_name = image.split('.')[0]
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if image_name not in old_subfolders:
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images.append(image)
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new_subfolders.append(image_name)
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simulations_folder = foldername+'/simulations/'
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os.mkdir(simulations_folder)
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# timestamp for simulation starting
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t1_image = time.time()
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for image, subfolder in zip(images,new_subfolders):
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# Create new directory for simulations
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subfolder_path = foldername+'/'+subfolder
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os.mkdir(subfolder_path)
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# timestamp for simulation starting
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t1_image = time.time()
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for image in images:
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# Read image
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image_path = foldername+'/'+image
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imagename = image.split('.')[0]
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I = imread(image_path)
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# TMDS coding and bit serialization
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I_Tx, resolution = image_transmition_simulation(I)
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v_res, h_res, _ = resolution
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# Possible std dev noise simulation values
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noise_stds = np.array([ 0, 5, 10, 15, 20, 25, 40, 50])
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# Make five simulations for each image
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for i in range(5):
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# Choose random pixelrate harmonic number
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N_harmonic = np.random.randint(1,10)
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# Choose random SNR value (SNR=0 for no noise)
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noise_std = np.random.choice(noise_stds)
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I_capture = image_capture_simulation(I_Tx, h_res, v_res, N_harmonic, noise_std)
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path = subfolder_path+'/'+imagename+'_'+str(N_harmonic)+'harm_'+str(noise_std)+"std.png"
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save_simulation_image(I_capture,path)
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if i==0:
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# timestamp for simulation ending
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t2_image = time.time()
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# Choose random pixelrate harmonic number
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N_harmonic = np.random.randint(1,10)
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I_capture = image_capture_simulation(I_Tx, h_res, v_res, N_harmonic)
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t_image = t2_image-t1_image
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path = simulations_folder+image
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print('Tiempo de la primer simulación de '+image+':','{:.2f}'.format(t_image)+'s\n')
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save_simulation_image(I_capture,path)
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# timestamp for simulation ending
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t2_image = time.time()
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t_images = t2_images-t1_images
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print('\nTiempo total de las '+str(len(images))+' simulaciones:','{:.2f}'.format(t_images)+'s\n')
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if __name__ == "__main__":
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main()
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@ -1,7 +1,6 @@
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import numpy as np
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from matplotlib import pyplot as plt
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from scipy import signal
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from scipy.fft import fft, ifft, fftshift
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from numba import jit, uint8, int8, prange
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def autocorr(x):
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"""Compute autocorrelation function of 1-D array
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@ -50,6 +49,108 @@ def binarray_to_uint(binarray):
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return num
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def TMDS_pixel_rare (pix):
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"""8bit pixel TMDS coding
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Inputs:
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- pix: 8-bit pixel
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- cnt: 0's and 1's balance. Default in 0 (balanced)
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Outputs:
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- pix_out: TDMS coded 16-bit pixel (only 10 useful)
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- cnt: 0's and 1's balance updated with new pixel coding
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"""
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# Convert 8-bit pixel to binary list D
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D = uint8_to_binarray(pix)
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# Initialize output q
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qm = [D[0]]
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# 1's unbalanced condition at current pixel
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N1_D = np.sum(D)
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if N1_D>4 or (N1_D==4 and not(D[0])):
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# XNOR of consecutive bits
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for k in range(1,8):
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qm.append( not(qm[k-1] ^ D[k]) )
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qm.append(0)
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else:
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# XOR of consecutive bits
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for k in range(1,8):
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qm.append( qm[k-1] ^ D[k] )
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qm.append(1)
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qm.append(np.random.choice([0,1]))
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# Return the TMDS coded pixel as uint and 0's y 1's balance
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return binarray_to_uint(qm)
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@jit(nopython=True)
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def TMDS_pixel_numba(pix:uint8, cnt:int8)->tuple:
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D = np.zeros(8, dtype=np.uint8)
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for i in range(8):
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D[i] = (pix >> i) & 1
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qm = np.zeros(9, dtype=np.uint8)
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qm[0] = D[0]
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N1_D = np.sum(D)
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if N1_D > 4 or (N1_D == 4 and not D[0]):
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for k in range(1, 8):
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qm[k] = not (qm[k-1] ^ D[k])
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qm[8] = 0
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else:
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for k in range(1, 8):
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qm[k] = qm[k-1] ^ D[k]
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qm[8] = 1
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qout = np.zeros(10, dtype=np.uint8)
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N1_qm = np.sum(qm[:8])
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N0_qm = 8 - N1_qm
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if cnt == 0 or N1_qm == 4:
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qout[9] = not(qm[8])
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qout[8] = qm[8]
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if qm[8]:
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qout[:8] = qm[:8]
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else:
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qout[:8] = np.logical_not(qm[:8])
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if not qm[8]:
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cnt += N0_qm - N1_qm
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else:
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cnt += N1_qm - N0_qm
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else:
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if (cnt > 0 and N1_qm > 4) or (cnt < 0 and N1_qm < 4):
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qout[9] = 1
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qout[8] = qm[8]
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qout[:8] = np.logical_not(qm[:8])
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cnt += 2*qm[8] + N0_qm - N1_qm
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else:
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qout[9] = 0
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qout[8] = qm[8]
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qout[:8] = qm[:8]
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cnt += -2*(not(qm[8])) + N1_qm - N0_qm
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# Convert binary array to unsigned int
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pix_tmds = 0
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for bit in qout[::-1]:
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pix_tmds = (pix_tmds << 1) | bit
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# Return the TMDS coded pixel as uint and 0's y 1's balance
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return pix_tmds, cnt
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def TMDS_pixel (pix,cnt=0):
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"""8bit pixel TMDS coding
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@ -118,6 +219,7 @@ def TMDS_pixel (pix,cnt=0):
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# Return the TMDS coded pixel as uint and 0's y 1's balance
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return binarray_to_uint(qout), cnt
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@jit(parallel=True)
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def TMDS_encoding_original (I, blanking = False):
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"""TMDS image coding
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@ -132,9 +234,10 @@ def TMDS_encoding_original (I, blanking = False):
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# Create "ghost dimension" if I is gray-scale image (not RGB)
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if len(I.shape)!= 3:
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I = np.repeat(I[:, :, np.newaxis], 3, axis=2).astype('uint8')
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chs = 3
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I = I[:, :, np.newaxis]
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chs = 1
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else:
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chs = 3
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# Get image resolution
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v_in, h_in = I.shape[:2]
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@ -158,13 +261,13 @@ def TMDS_encoding_original (I, blanking = False):
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I_c = np.zeros((v_in,h_in,chs)).astype('uint16')
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# Iterate over channels and pixels
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for c in range(chs):
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for c in prange(chs):
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for i in range(v_in):
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cnt=[0,0,0]
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for j in range(h_in):
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# Get pixel and code it TMDS between blanking
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pix = I[i,j,c]
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I_c[i + v_diff//2 , j + h_diff//2, c], cnt[c] = TMDS_pixel (pix,cnt[c])
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I_c[i + v_diff//2 , j + h_diff//2, c], cnt[c] = TMDS_pixel_numba (pix,cnt[c])
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return I_c
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@ -242,6 +345,7 @@ def TMDS_pixel_cntdiff (pix,cnt=0):
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byte_range = np.arange(256)
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# Initialize pixel coding and cnt-difference arrays
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TMDS_pix_table = np.zeros((256,3),dtype='uint16')
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TMDS_rare_pix_table = np.zeros((256),dtype='uint16')
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TMDS_cntdiff_table = np.zeros((256,3),dtype='int8')
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for byte in byte_range:
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@ -253,6 +357,8 @@ for byte in byte_range:
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TMDS_cntdiff_table[byte,1] = p_null[1]
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TMDS_cntdiff_table[byte,2] = p1[1]
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TMDS_rare_pix_table[byte] = TMDS_pixel_rare(byte)
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def pixel_fastencoding(pix,cnt_prev=0):
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"""8bit pixel TMDS fast coding
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@ -314,11 +420,9 @@ def TMDS_encoding (I, blanking = False):
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# Create "ghost dimension" if I is gray-scale image (not RGB)
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if len(I.shape)!= 3:
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# Gray-scale image
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I = np.repeat(I[:, :, np.newaxis], 3, axis=2).astype('uint8')
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I = I[:, :, np.newaxis]
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chs = 1
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else:
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# RGB image
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else:
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chs = 3
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# Get image resolution
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@ -343,7 +447,8 @@ def TMDS_encoding (I, blanking = False):
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# Assuming the blanking corresponds to 10bit number
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# [0, 0, 1, 0, 1, 0, 1, 0, 1, 1] (LSB first) for channels R and G
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I_c = 852*np.ones((v,h,chs)).astype('uint16')
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I_c[:,:,2] = TMDS_blanking(h_total=h, v_total=v, h_active=h_in, v_active=v_in,
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if chs==3:
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I_c[:,:,2] = TMDS_blanking(h_total=h, v_total=v, h_active=h_in, v_active=v_in,
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h_front_porch=h_front_porch, v_front_porch=v_front_porch, h_back_porch=h_back_porch, v_back_porch=v_back_porch)
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else:
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@ -362,6 +467,61 @@ def TMDS_encoding (I, blanking = False):
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return I_c
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def TMDS_encoding_rare (I, blanking = False):
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"""TMDS image coding
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Inputs:
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- I: 2-D image array
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- blanking: Boolean that specifies if horizontal and vertical blanking is applied
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Output:
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- I_c: TDMS coded 16-bit image (only 10 useful)
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"""
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# Create "ghost dimension" if I is gray-scale image (not RGB)
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if len(I.shape)!= 3:
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I = I[:, :, np.newaxis]
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chs = 1
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else:
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chs = 3
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# Get image resolution
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v_in, h_in = I.shape[:2]
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# Get image resolution
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v_in, h_in = I.shape[:2]
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if blanking:
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# Get blanking resolution for input image
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v = (v_in==1080)*1125 + (v_in==720)*750 + (v_in==600)*628 + (v_in==480)*525
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h = (h_in==1920)*2200 + (h_in==1280)*1650 + (h_in==800)*1056 + (h_in==640)*800
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vdiff = v - v_in
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hdiff = h - h_in
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# Create image with blanking and change type to uint16
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# Assuming the blanking corresponds to 10bit number [0, 0, 1, 0, 1, 0, 1, 0, 1, 1] (LSB first)
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I_c = 852*np.ones((v,h,chs)).astype('uint16')
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else:
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v_diff = 0
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h_diff = 0
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I_c = I.copy()
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I_c = I_c.astype('uint16')
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# Iterate over channels and pixels
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for c in range(chs):
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for i in range(v_in):
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for j in range(h_in):
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# Get pixel and code it TMDS between blanking
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pix = I[i,j,c]
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I_c[i + v_diff//2 , j + h_diff//2, c] = TMDS_rare_pix_table[pix]
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return I_c
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def DecTMDS_pixel (pix):
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"""10-bit pixel TMDS decoding
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