735 lines
22 KiB
Python
735 lines
22 KiB
Python
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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import cv2 as cv
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from scipy.spatial import distance_matrix
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def autocorr(x):
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"""Compute autocorrelation function of 1-D array
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Input:
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x: 1-D array
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Output:
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autocorr: autocorrelation function of x
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"""
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# Use FFT method, which has more computing efectiveness for 1-D numpy arrays
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autocorr = signal.correlate(x,x,mode='full', method= 'fft')
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# Fix some shifts due FFT
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half_idx =int(autocorr.size/2)
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max_ind = np.argmax(autocorr[half_idx:])+half_idx
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autocorr = autocorr[max_ind:]
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# Normalise output
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return autocorr/autocorr[0]
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def uint8_to_binarray(integer):
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"""Convert integer into fixed-length 8-bit binary array. LSB in [0].
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Extended and modified code from https://github.com/projf/display_controller/blob/master/model/tmds.py
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"""
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b_array = [int(i) for i in reversed(bin(integer)[2:])]
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b_array += [0]*(8-len(b_array))
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return b_array
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def uint16_to_binarray(integer):
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"""Convert integer into fixed-length 16-bit binary array. LSB in [0].
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Extended and modified code from https://github.com/projf/display_controller/blob/master/model/tmds.py
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"""
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b_array = [int(i) for i in reversed(bin(integer)[2:])]
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b_array += [0]*(16-len(b_array))
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return b_array
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def binarray_to_uint(binarray):
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array = binarray[::-1]
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num = array[0]
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for n in range(1,len(binarray)):
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num = (num << 1) + array[n]
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return num
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def TMDS_pixel (pix,cnt=0):
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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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# Initialize output qout
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qout = qm.copy()
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# Unbalanced condition with previous and current pixels
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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.append(not(qm[8]))
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qout[8] = qm[8]
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qout[:8]=qm[:8] if qm[8] else 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.append(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.append(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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# 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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def TMDS_encoding_original (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 = np.repeat(I[:, :, np.newaxis], 3, axis=2).astype('uint8')
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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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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 = 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 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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return I_c
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def TMDS_pixel_cntdiff (pix,cnt=0):
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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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- cntdiff: balance difference given by the actual coded pixel
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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 pixelo
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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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# Initialize output qout
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qout = qm.copy()
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# Unbalanced condition with previous and current pixels
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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.append(not(qm[8]))
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qout[8]=qm[8]
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qout[:8]=qm[:8] if qm[8] else [not(val) for val in qm[:8]]
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if not(qm[8]):
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cnt_diff = N0_qm - N1_qm
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else:
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cnt_diff = 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.append(1)
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qout[8]=qm[8]
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qout[:8] = [not(val) for val in qm[:8]]
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cnt_diff = 2*qm[8] +N0_qm -N1_qm
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else:
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qout.append(0)
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qout[8]=qm[8]
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qout[:8] = qm[:8]
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cnt_diff = -2*(not(qm[8])) + N1_qm - N0_qm
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# Return the TMDS coded pixel as uint and 0's y 1's balance difference
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uint_out = binarray_to_uint(qout)
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return uint_out, cnt_diff
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### Create TMDS LookUp Tables for fast encoding (3 times faster than the other implementation)
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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_cntdiff_table = np.zeros((256,3),dtype='int8')
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for byte in byte_range:
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p0,p_null, p1 = TMDS_pixel_cntdiff(byte,-1),TMDS_pixel_cntdiff(byte,0),TMDS_pixel_cntdiff(byte,1) # 0's and 1's unbalance respect.
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TMDS_pix_table[byte,0] = p0[0]
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TMDS_pix_table[byte,1] = p_null[0]
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TMDS_pix_table[byte,2] = p1[0]
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TMDS_cntdiff_table[byte,0] = p0[1]
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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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def pixel_fastencoding(pix,cnt_prev=0):
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"""8bit pixel TMDS fast 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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balance_idx = int(np.sign(cnt_prev))+1
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pix_out = TMDS_pix_table[pix,balance_idx]
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cnt = cnt_prev + TMDS_cntdiff_table[pix,balance_idx]
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return pix_out, cnt
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def TMDS_blanking (h_total, v_total, h_active, v_active, h_front_porch, v_front_porch, h_back_porch, v_back_porch):
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# Initialize blanking image
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img_blank = np.zeros((v_total,h_total))
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# Get the total blanking on vertical an horizontal axis
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h_blank = h_total - h_active
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v_blank = v_total - v_active
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# (C1,C0)=(0,0) region
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img_blank[:v_front_porch,:h_front_porch] = 0b1101010100
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img_blank[:v_front_porch,h_blank-h_back_porch:] = 0b1101010100
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img_blank[v_blank-v_back_porch:v_blank,:h_front_porch] = 0b1101010100
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img_blank[v_blank-v_back_porch:v_blank,h_blank-h_back_porch:] = 0b1101010100
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img_blank[v_blank:,:h_blank] = 0b1101010100
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# (C1,C0)=(0,1) region
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img_blank[:v_front_porch,h_front_porch:h_blank-h_back_porch] = 0b0010101011
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img_blank[v_blank-v_back_porch:,h_front_porch:h_blank-h_back_porch] = 0b0010101011
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# (C1,C0)=(1,0) region
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img_blank[v_front_porch:v_blank-v_back_porch,:h_front_porch] = 0b0101010100
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img_blank[v_front_porch:v_blank-v_back_porch,h_blank-h_back_porch:] = 0b0101010100
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# (C1,C0)=(1,1) region
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img_blank[v_front_porch:v_blank-v_back_porch,v_front_porch:h_blank-h_back_porch] = 0b1010101011
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return(img_blank)
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def TMDS_encoding (I, blanking = False):
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"""TMDS image coding
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Inputs:
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- I: 2D/3D image array (v_size, h_size, channels)
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- blanking: Boolean that specifies if horizontal and vertical blanking is applied or not
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Output:
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- I_c: 3D TDMS coded 16-bit (only 10 useful) image array
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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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# Gray-scale image
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I = np.repeat(I[:, :, np.newaxis], 3, axis=2).astype('uint8')
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chs = 1
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else:
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# RGB image
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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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if blanking:
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# Get blanking resolution for input image
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v = (v_in==1080)*1125 + (v_in==900)*1000 + (v_in==720)*750 + (v_in==600)*628 + (v_in==480)*525
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h = (h_in==1920)*2200 + (h_in==1600)*1800 + (h_in==1280)*1650 + (h_in==800)*1056 + (h_in==640)*800
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v_diff = v - v_in
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h_diff = h - h_in
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v_front_porch = (v_in==1080)*4 + (v_in==900)*1 + (v_in==720)*5 + (v_in==600)*1 + (v_in==480)*2
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v_back_porch = (v_in==1080)*36 + (v_in==900)*96 + (v_in==720)*20 + (v_in==600)*23 + (v_in==480)*25
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h_front_porch = (h_in==1920)*88 + (h_in==1600)*24 + (h_in==1280)*110 + (h_in==800)*40 + (h_in==640)*8
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h_back_porch = (h_in==1920)*148 + (h_in==1600)*96 + (h_in==1280)*220 + (h_in==800)*88 + (h_in==640)*40
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# Create image with blanking and change type to uint16
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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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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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v_diff = 0
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h_diff = 0
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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 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, j + h_diff, c], cnt[c] = pixel_fastencoding (pix,cnt[c])
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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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Inputs:
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- pix: 16-bit pixel (only 10 first bits useful)
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Output:
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- pix_out: 8-bit TMDS decoded pixel
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"""
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D = uint16_to_binarray(pix)[:10]
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if D[9]:
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D[:8] = np.logical_not(D[:8])
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Q = D.copy()[:8]
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if D[8]:
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for k in range(1,8):
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Q[k] = D[k] ^ D[k-1]
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else:
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for k in range(1,8):
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Q[k] = not(D[k] ^ D[k-1])
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# Return pixel as uint
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return binarray_to_uint(Q)
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def TMDS_decoding (Ic):
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"""Image TMDS decoding
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Inputs:
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- Ic: TMDS coded image
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Output:
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- Idec: 8-bit decoded image
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"""
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# Create "ghost dimension" if gray-scale image (not RGB)
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if len(Ic.shape)!= 3:
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Ic = Ic.reshape(Ic.shape[0],Ic.shape[1],1)
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Idec = Ic.copy()
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# Get image dimensions
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Nx, Ny, Nz = Ic.shape
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# Iterate over channels and pixels
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for c in np.arange(Nz):
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for i in np.arange(Nx):
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for j in np.arange(Ny):
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# Get pixel and use TMDS decoding
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pix = Ic[i,j,c]
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Idec[i,j,c] = DecTMDS_pixel (pix)
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return Idec
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def TMDS_serial(I):
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'''
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Serialize an image as an 1D binary array given a 10bit pixel value.
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Inputs:
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- I: TMDS image to serialize. Pixel values must be between 0 and 1023
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Output:
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- Iserials: 1D binary array per image channel which represents
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the voltage value to be transmitted
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'''
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assert np.min(I)>=0 and np.max(I)<= 1023, "Pixel values must be between 0 and 1023"
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# Initialize lists per channel
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Iserials = []
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n_rows,n_columns, n_channels = I.shape
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# Iterate over pixel
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for c in range(n_channels):
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channel_list = []
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for i in range(n_rows):
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for j in range(n_columns):
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# Get pixel value and cast it as binary string
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binstring = bin(I[i,j,c])[2:]
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# Fill string with 0's to get length 10
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binstring = '0'*(10-len(binstring))+binstring
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# Re-order string for LSB first
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binstring = binstring[::-1]
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binarray = list(binstring)
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# Extend the bit stream
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channel_list.extend(binarray)
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Iserials.append(channel_list)
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# Digital to analog value mapping: [0,1]-->[-A,A] (A=1)
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Iserials = np.sum(2*np.array(Iserials,dtype='int32') - 1, axis=0)
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del(channel_list)
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return Iserials
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def remove_outliers(I, radius=3, threshold=20):
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"""
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Replaces a pixel by the median of the pixels in the surrounding if it deviates from the median by more than a certain value (the threshold).
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"""
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# Copy input
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I_output = I.copy()
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# Apply median filter
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I_median = cv.medianBlur(I,radius)
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# Replace with median value where difference with median exceedes threshold
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where_replace = np.abs(I-I_median) > threshold
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I_output[where_replace] = I_median[where_replace]
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|
|
|
return I_output
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|
|
|
def adjust_dynamic_range(I):
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|
|
|
I_output = I.astype('float32')
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I_output = (I_output - I_output.min()) / (I_output.max() - I_output.min())
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|
I_output = 255 * I_output
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|
return I_output.astype('uint8')
|
|
|
|
def find_intersection(line1, line2):
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"""Finds the intersection of two lines given in Hesse normal form.
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|
|
|
Returns closest integer pixel locations.
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|
See https://stackoverflow.com/a/383527/5087436
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"""
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|
rho1, theta1 = line1
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|
rho2, theta2 = line2
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A = np.array([
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|
[np.cos(theta1), np.sin(theta1)],
|
|
[np.cos(theta2), np.sin(theta2)]
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|
])
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|
b = np.array([[rho1], [rho2]])
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x0, y0 = np.linalg.solve(A, b)
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|
x0, y0 = int(np.round(x0)), int(np.round(y0))
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|
|
|
# Return as row-column coordinates
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|
return [y0, x0]
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|
def apply_blanking_shift(I, h_active=1600, v_active=900,
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|
h_blanking=200,v_blanking=100,
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|
pad_len=300, debug=False):
|
|
"""
|
|
Correct capture shift to center image using VESA blanking information.
|
|
The image must not have geometric distortion (generaly caused by sampling error)
|
|
"""
|
|
|
|
if debug:
|
|
# Show original image
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|
plt.figure(figsize=(12,10))
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|
plt.title(f'Original image ')
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|
plt.imshow(I)
|
|
plt.axis('off')
|
|
plt.show()
|
|
|
|
# Color to RGB
|
|
I_gray = cv.cvtColor(I,cv.COLOR_BGR2GRAY)
|
|
# I_gray = cv.medianBlur(I_gray,3)
|
|
I_gray = cv.GaussianBlur(I_gray,ksize=(5,5),sigmaX=3,sigmaY=3)
|
|
# I_gray = cv.blur(I_gray,(3,3))
|
|
|
|
# Wrap-padding image to get whole blanking pattern
|
|
pad_size_gray = ((pad_len,0),(pad_len,0))
|
|
pad_size = ((pad_len,0),(pad_len,0),(0,0))
|
|
I_gray = np.pad(I_gray,pad_size_gray,'wrap')
|
|
I_pad = np.pad(I,pad_size,'wrap')
|
|
|
|
# Edge-detector with Canny filter with empirical parameters
|
|
I_edge = cv.Canny(I_gray,40,50,apertureSize = 3) # sin blur
|
|
|
|
|
|
if debug:
|
|
# Show padded image and edges
|
|
plt.figure()
|
|
plt.title(f'Original image wrap-padded {pad_len} pixels up and left sided')
|
|
plt.imshow(I_pad)
|
|
plt.axis('off')
|
|
plt.show()
|
|
plt.figure()
|
|
plt.title('Canny edged image')
|
|
plt.imshow(I_edge,cmap='gray')
|
|
plt.axis('off')
|
|
plt.show()
|
|
|
|
# Hough Transform resolution and minimum votes threshold
|
|
theta_step = np.pi/2
|
|
rho_step = 1
|
|
# votes_thrs = 255 # sin blur
|
|
votes_thrs = 80
|
|
rho_max = np.sqrt(I_gray.shape[0]**2 + I_gray.shape[1]**2)
|
|
|
|
# Find Hough Transform
|
|
lines = cv.HoughLines(I_edge, rho_step, theta_step, votes_thrs, None, 0, 0)
|
|
|
|
if debug:
|
|
# Show detected lines
|
|
I_lines = I_gray.copy()
|
|
for line in lines:
|
|
rho = line[0][0]
|
|
theta = line[0][1]
|
|
a = np.cos(theta)
|
|
b = np.sin(theta)
|
|
x0 = a * rho
|
|
y0 = b * rho
|
|
pt1 = (int(x0 + rho_max*(-b)), int(y0 + rho_max*(a)))
|
|
pt2 = (int(x0 - rho_max*(-b)), int(y0 - rho_max*(a)))
|
|
|
|
cv.line(I_lines, pt1, pt2, (255,0,0), 1, cv.LINE_AA)
|
|
|
|
plt.figure()
|
|
plt.title('All detected lines')
|
|
plt.imshow(I_lines)
|
|
plt.axis('off')
|
|
plt.show()
|
|
|
|
# Angle and rho arrays
|
|
try:
|
|
lines_angles = lines[:,0,1]
|
|
except:
|
|
print("No lines detected. Returning original image")
|
|
return I
|
|
|
|
|
|
# Find unique lines angles detected
|
|
unique_angles = np.unique(lines_angles)
|
|
blankings = [h_blanking, v_blanking]
|
|
|
|
# Initiate blanking lines variable
|
|
blanking_lines = []
|
|
|
|
for angle, blanking in zip(unique_angles, blankings):
|
|
|
|
# Keep lines with certain angle
|
|
angle_lines = lines[lines[:,0,1]==angle]
|
|
angle_lines = angle_lines[:,0,:]
|
|
|
|
# Keep the pair with rho distance that equals blanking
|
|
# First, compute the distance matrix over all rhos:
|
|
rho_angle_lines = np.array(angle_lines[:,0]).reshape(-1,1)
|
|
|
|
rho_distances = np.abs(np.abs(distance_matrix(rho_angle_lines, rho_angle_lines)) - blanking)
|
|
|
|
# Find minimum index
|
|
pair_lines_idx = np.unravel_index(rho_distances.argmin(), rho_distances.shape)
|
|
|
|
# Add blanking limit line
|
|
for line in pair_lines_idx:
|
|
blanking_lines.append(angle_lines[line])
|
|
|
|
# List to array
|
|
blanking_lines = np.array(blanking_lines)
|
|
|
|
# Show blanking limit lines
|
|
if debug:
|
|
I_lines = I_gray.copy()
|
|
|
|
for line in blanking_lines:
|
|
rho = line[0]
|
|
theta = line[1]
|
|
a = np.cos(theta)
|
|
b = np.sin(theta)
|
|
x0 = a * rho
|
|
y0 = b * rho
|
|
pt1 = (int(x0 + rho_max*(-b)), int(y0 + rho_max*(a)))
|
|
pt2 = (int(x0 - rho_max*(-b)), int(y0 - rho_max*(a)))
|
|
|
|
cv.line(I_lines, pt1, pt2, (255,0,0), 3, cv.LINE_AA)
|
|
|
|
plt.figure(figsize=(12,10))
|
|
plt.title('Blanking limit lines')
|
|
plt.imshow(I_lines)
|
|
plt.axis('off')
|
|
plt.show()
|
|
|
|
# Initialize top-left corner blanking lines to find intersection
|
|
# These lines statisfies to be the ones with bigger rho value
|
|
blanking_start = []
|
|
unique_angles = np.unique(blanking_lines[:,1])
|
|
for angulo in unique_angles:
|
|
angle_lines = blanking_lines[blanking_lines[:,1]==angulo]
|
|
max_rho_line = angle_lines[np.argmax(angle_lines[:,0])]
|
|
blanking_start.append(max_rho_line)
|
|
|
|
if len(blanking_start)!=2:
|
|
print("Not enough lines detected. Returning original image")
|
|
return I
|
|
|
|
if debug:
|
|
I_lines = I_gray.copy()
|
|
for line in blanking_start:
|
|
rho = line[0]
|
|
theta = line[1]
|
|
a = np.cos(theta)
|
|
b = np.sin(theta)
|
|
x0 = a * rho
|
|
y0 = b * rho
|
|
pt1 = (int(x0 + rho_max*(-b)), int(y0 + rho_max*(a)))
|
|
pt2 = (int(x0 - rho_max*(-b)), int(y0 - rho_max*(a)))
|
|
|
|
cv.line(I_lines, pt1, pt2, (255,0,0), 3, cv.LINE_AA)
|
|
|
|
plt.figure(figsize=(12,10))
|
|
plt.title('Blanking start lines')
|
|
plt.imshow(I_lines)
|
|
plt.axis('off')
|
|
plt.show()
|
|
|
|
# Find blanking start lines intersection coordinates
|
|
x_shift, y_shift = find_intersection(blanking_start[0],blanking_start[1])
|
|
|
|
# Adjust to active image only (remove all blanking)
|
|
I_shift = I_pad[pad_len:,pad_len:]
|
|
I_shift = np.roll(I_shift, -x_shift+pad_len, axis=0)
|
|
I_shift = np.roll(I_shift, -y_shift+pad_len, axis=1)
|
|
I_shift = I_shift[:v_active,:h_active]
|
|
|
|
if debug:
|
|
plt.figure(figsize=(12,10))
|
|
plt.title('Blanking removed image')
|
|
plt.imshow(I_shift)
|
|
plt.axis('off')
|
|
plt.show()
|
|
|
|
return I_shift
|
|
|
|
def preprocess_raw_capture(I, h_active, v_active,
|
|
h_blanking, v_blanking, debug=False):
|
|
"""
|
|
Center raw captured image, filter noise and adjust the contrast
|
|
"""
|
|
|
|
# Center image. Returns input image if no sufficient lines where detected
|
|
# In the latter case, use image as is without centering
|
|
is_centered = True
|
|
I_shift_fix = apply_blanking_shift(I,
|
|
h_active=h_active, v_active=v_active,
|
|
h_blanking=h_blanking, v_blanking=v_blanking,
|
|
debug=debug
|
|
)
|
|
|
|
# Remove outliers with median thresholding heuristic
|
|
# Default: radius=3, threshold=20
|
|
I_no_outliers = remove_outliers(I_shift_fix)
|
|
|
|
# Stretch dynamic range to [0,255]
|
|
I_out = adjust_dynamic_range(I_no_outliers)
|
|
|
|
if debug:
|
|
is_centered = (I == I_shift_fix)
|
|
|
|
plt.figure(figsize=(12,10))
|
|
ax0 = plt.subplot(3,1,1)
|
|
ax0.imshow(I_shift_fix, interpolation='none')
|
|
ax0.set_title('Centered image'*is_centered + 'Image'*(~is_centered))
|
|
ax0.axis('off')
|
|
ax1 = plt.subplot(3,1,2, sharex=ax0, sharey=ax0)
|
|
ax1.imshow(I_no_outliers, interpolation='none')
|
|
ax1.set_title('Outliers removed')
|
|
ax1.axis('off')
|
|
ax1 = plt.subplot(3,1,3, sharex=ax0, sharey=ax0)
|
|
ax1.imshow(I_out, interpolation='none')
|
|
ax1.set_title('Contrast adjusted')
|
|
ax1.axis('off')
|
|
plt.show()
|
|
|
|
return I_out |