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How to use windowSize method in fMBT

Best Python code snippet using fMBT_python

main.py

Source:main.py

1# python 3.9.52from math import ceil3import cv24import numpy as np5from functions import *6# define quantization tables7QTY = np.array([[16, 11, 10, 16, 24, 40, 51, 61],  # luminance quantization table8                [12, 12, 14, 19, 26, 48, 60, 55],9                [14, 13, 16, 24, 40, 57, 69, 56],10                [14, 17, 22, 29, 51, 87, 80, 62],11                [18, 22, 37, 56, 68, 109, 103, 77],12                [24, 35, 55, 64, 81, 104, 113, 92],13                [49, 64, 78, 87, 103, 121, 120, 101],14                [72, 92, 95, 98, 112, 100, 103, 99]])15QTC = np.array([[17, 18, 24, 47, 99, 99, 99, 99],  # chrominance quantization table16                [18, 21, 26, 66, 99, 99, 99, 99],17                [24, 26, 56, 99, 99, 99, 99, 99],18                [47, 66, 99, 99, 99, 99, 99, 99],19                [99, 99, 99, 99, 99, 99, 99, 99],20                [99, 99, 99, 99, 99, 99, 99, 99],21                [99, 99, 99, 99, 99, 99, 99, 99],22                [99, 99, 99, 99, 99, 99, 99, 99]])23# define window size24windowSize = len(QTY)25# read image26imgOriginal = cv2.imread('marbles.bmp', cv2.IMREAD_COLOR)27# convert BGR to YCrCb28img = cv2.cvtColor(imgOriginal, cv2.COLOR_BGR2YCR_CB)29width = len(img[0])30height = len(img)31y = np.zeros((height, width), np.float32) + img[:, :, 0]32cr = np.zeros((height, width), np.float32) + img[:, :, 1]33cb = np.zeros((height, width), np.float32) + img[:, :, 2]34# size of the image in bits before compression35totalNumberOfBitsWithoutCompression = len(y) * len(y[0]) * 8 + len(cb) * len(cb[0]) * 8 + len(cr) * len(cr[0]) * 836# channel values should be normalized, hence subtract 12837y = y - 12838cr = cr - 12839cb = cb - 12840# 4: 2: 2 subsampling is used # another subsampling scheme can be used41# thus chrominance channels should be sub-sampled42# define subsampling factors in both horizontal and vertical directions43SSH, SSV = 2, 244# filter the chrominance channels using a 2x2 averaging filter # another type of filter can be used45crf = cv2.boxFilter(cr, ddepth=-1, ksize=(2, 2))46cbf = cv2.boxFilter(cb, ddepth=-1, ksize=(2, 2))47crSub = crf[::SSV, ::SSH]48cbSub = cbf[::SSV, ::SSH]49# check if padding is needed,50# if yes define empty arrays to pad each channel DCT with zeros if necessary51yWidth, yLength = ceil(len(y[0]) / windowSize) * windowSize, ceil(len(y) / windowSize) * windowSize52if (len(y[0]) % windowSize == 0) and (len(y) % windowSize == 0):53    yPadded = y.copy()54else:55    yPadded = np.zeros((yLength, yWidth))56    for i in range(len(y)):57        for j in range(len(y[0])):58            yPadded[i, j] += y[i, j]59# chrominance channels have the same dimensions, meaning both can be padded in one loop60cWidth, cLength = ceil(len(cbSub[0]) / windowSize) * windowSize, ceil(len(cbSub) / windowSize) * windowSize61if (len(cbSub[0]) % windowSize == 0) and (len(cbSub) % windowSize == 0):62    crPadded = crSub.copy()63    cbPadded = cbSub.copy()64# since chrominance channels have the same dimensions, one loop is enough65else:66    crPadded = np.zeros((cLength, cWidth))67    cbPadded = np.zeros((cLength, cWidth))68    for i in range(len(crSub)):69        for j in range(len(crSub[0])):70            crPadded[i, j] += crSub[i, j]71            cbPadded[i, j] += cbSub[i, j]72# get DCT of each channel73# define three empty matrices74yDct, crDct, cbDct = np.zeros((yLength, yWidth)), np.zeros((cLength, cWidth)), np.zeros((cLength, cWidth))75# number of iteration on x axis and y axis to calculate the luminance cosine transform values76hBlocksForY = int(len(yDct[0]) / windowSize)  # number of blocks in the horizontal direction for luminance77vBlocksForY = int(len(yDct) / windowSize)  # number of blocks in the vertical direction for luminance78# number of iteration on x axis and y axis to calculate the chrominance channels cosine transforms values79hBlocksForC = int(len(crDct[0]) / windowSize)  # number of blocks in the horizontal direction for chrominance80vBlocksForC = int(len(crDct) / windowSize)  # number of blocks in the vertical direction for chrominance81# define 3 empty matrices to store the quantized values82yq, crq, cbq = np.zeros((yLength, yWidth)), np.zeros((cLength, cWidth)), np.zeros((cLength, cWidth))83# and another 3 for the zigzags84yZigzag = np.zeros(((vBlocksForY * hBlocksForY), windowSize * windowSize))85crZigzag = np.zeros(((vBlocksForC * hBlocksForC), windowSize * windowSize))86cbZigzag = np.zeros(((vBlocksForC * hBlocksForC), windowSize * windowSize))87for i in range(vBlocksForY):88    for j in range(hBlocksForY):89        yDct[i * windowSize: i * windowSize + windowSize, j * windowSize: j * windowSize + windowSize] = cv2.dct(90            yPadded[i * windowSize: i * windowSize + windowSize, j * windowSize: j * windowSize + windowSize])91        yq[i * windowSize: i * windowSize + windowSize, j * windowSize: j * windowSize + windowSize] = np.ceil(92            yDct[i * windowSize: i * windowSize + windowSize, j * windowSize: j * windowSize + windowSize] / QTY)93        yZigzag[i * j] += zigzag(94            yq[i * windowSize: i * windowSize + windowSize, j * windowSize: j * windowSize + windowSize])95yZigzag = yZigzag.astype(np.int16)96# either crq or cbq can be used to compute the number of blocks97for i in range(vBlocksForC):98    for j in range(hBlocksForC):99        crDct[i * windowSize: i * windowSize + windowSize, j * windowSize: j * windowSize + windowSize] = cv2.dct(100            crPadded[i * windowSize: i * windowSize + windowSize, j * windowSize: j * windowSize + windowSize])101        crq[i * windowSize: i * windowSize + windowSize, j * windowSize: j * windowSize + windowSize] = np.ceil(102            crDct[i * windowSize: i * windowSize + windowSize, j * windowSize: j * windowSize + windowSize] / QTC)103        crZigzag[i * j] += zigzag(104            crq[i * windowSize: i * windowSize + windowSize, j * windowSize: j * windowSize + windowSize])105        cbDct[i * windowSize: i * windowSize + windowSize, j * windowSize: j * windowSize + windowSize] = cv2.dct(106            cbPadded[i * windowSize: i * windowSize + windowSize, j * windowSize: j * windowSize + windowSize])107        cbq[i * windowSize: i * windowSize + windowSize, j * windowSize: j * windowSize + windowSize] = np.ceil(108            cbDct[i * windowSize: i * windowSize + windowSize, j * windowSize: j * windowSize + windowSize] / QTC)109        cbZigzag[i * j] += zigzag(110            cbq[i * windowSize: i * windowSize + windowSize, j * windowSize: j * windowSize + windowSize])111crZigzag = crZigzag.astype(np.int16)112cbZigzag = cbZigzag.astype(np.int16)113# find the run length encoding for each channel114# then get the frequency of each component in order to form a Huffman dictionary115yEncoded = run_length_encoding(yZigzag)116yFrequencyTable = get_freq_dict(yEncoded)117yHuffman = find_huffman(yFrequencyTable)118crEncoded = run_length_encoding(crZigzag)119crFrequencyTable = get_freq_dict(crEncoded)120crHuffman = find_huffman(crFrequencyTable)121cbEncoded = run_length_encoding(cbZigzag)122cbFrequencyTable = get_freq_dict(cbEncoded)123cbHuffman = find_huffman(cbFrequencyTable)124# calculate the number of bits to transmit for each channel125# and write them to an output file126file = open("CompressedImage.asfh", "w")127yBitsToTransmit = str()128for value in yEncoded:129    yBitsToTransmit += yHuffman[value]130crBitsToTransmit = str()131for value in crEncoded:132    crBitsToTransmit += crHuffman[value]133cbBitsToTransmit = str()134for value in cbEncoded:135    cbBitsToTransmit += cbHuffman[value]136if file.writable():137    file.write(yBitsToTransmit + "\n" + crBitsToTransmit + "\n" + cbBitsToTransmit)138file.close()139totalNumberOfBitsAfterCompression = len(yBitsToTransmit) + len(crBitsToTransmit) + len(cbBitsToTransmit)140print(141    "Compression Ratio is " + str(...

LBPfuncs.py

Source:LBPfuncs.py

1import cv22import numpy as np34import get_LBP_from_Image as LBP567def region_flag(Sub, yt, xt, windowSize, region_thresh):8    return np.sum(Sub[yt:yt + windowSize, xt:xt + windowSize]) > (windowSize ** 2) / region_thresh910def region_flag_out(out, yt, xt, windowSize, region_thresh_out):11    return np.sum(out[yt:yt + windowSize, xt:xt + windowSize]) <= region_thresh_out12131415def MyResize(I, factor):16    w = I.shape[0]17    h = I.shape[1]18    w0 = int(w / factor)19    h0 = int(h / factor)20    out = cv2.resize(I, (w0, h0), interpolation=cv2.INTER_LINEAR)21    return out222324def calc_regional_LBP(target, background, lbp, out, yt, xt, windowSize):25    t_windowed = target[yt:yt + windowSize, xt:xt + windowSize]26    b_windowed = background[yt:yt + windowSize, xt:xt + windowSize]27    t_LBP_map = lbp.lbp_uniform(t_windowed) # LBPçä»·æ¨¡å¼ç¹å¾28    b_LBP_map = lbp.lbp_uniform(b_windowed)29    t_hist = lbp.get_uniform_hist(t_LBP_map) # è·åLBPç¹å¾å½ä¸åç»è®¡ç´æ¹å¾30    b_hist = lbp.get_uniform_hist(b_LBP_map)31    d = LBP.chi2_distance(t_hist, b_hist)32    # print((j, i))33    out[yt:yt + windowSize, xt:xt + windowSize] += np.ones((windowSize, windowSize)) * d343536def CompareLBP(target, background, Sub, windowSize=32, step=4, region_thresh=3, decay=0.05):37    [h, w] = [target.shape[0], target.shape[1]]38    x_iter = int((w - windowSize) / step) + 139    y_iter = int((h - windowSize) / step) + 140    x_left = w - windowSize - step * (x_iter - 1)41    y_left = h - windowSize - step * (y_iter - 1)42    out = np.zeros((h, w))43    lbp = LBP.LBP()44    flag = 045    deviation = decay46    deviation1 = deviation * 0.7547    deviation2 = deviation * 0.7548    deviation3 = deviation * 0.7549    deviation4 = deviation * 0.7550    for j in range(y_iter):51        for i in range(x_iter):52            xt = i * step53            yt = j * step54            CalcFlag = (np.sum(Sub[yt:yt + windowSize, xt:xt + windowSize]) > (windowSize ** 2) / region_thresh)55            if j < y_iter - 2 and j > 1 and i < x_iter - 2 and i > 1:56                CalcFlag = CalcFlag or region_flag(Sub, yt + windowSize, xt - windowSize, windowSize, region_thresh)57                CalcFlag = CalcFlag or region_flag(Sub, yt + windowSize, xt, windowSize, region_thresh)58                CalcFlag = CalcFlag or region_flag(Sub, yt + windowSize, xt + windowSize, windowSize, region_thresh)59                CalcFlag = CalcFlag or region_flag(Sub, yt, xt + windowSize, windowSize, region_thresh)60                CalcFlag = CalcFlag and region_flag_out(out, yt + windowSize, xt - windowSize, windowSize, 1)61                CalcFlag = CalcFlag and region_flag_out(out, yt + windowSize, xt, windowSize, 1)62                CalcFlag = CalcFlag and region_flag_out(out, yt + windowSize, xt + windowSize, windowSize, 1)63                CalcFlag = CalcFlag and region_flag_out(out, yt, xt + windowSize, windowSize, 1)64            if CalcFlag:65                flag += 366                if flag > 0:67                    calc_regional_LBP(target, background, lbp, out, yt, xt, windowSize)68                    69                    if j < y_iter - 2 and j > 1 and i < x_iter - 2 and i > 1:70                        # if not region_flag(Sub, yt - windowSize, xt - windowSize, windowSize, region_thresh):71                        if region_flag(out, yt - windowSize, xt - windowSize, windowSize, region_thresh * deviation1):72                            calc_regional_LBP(target, background, lbp, out, yt - windowSize, xt - windowSize,73                                              windowSize)74                        if region_flag(out, yt - windowSize, xt, windowSize, region_thresh * deviation2):75                            calc_regional_LBP(target, background, lbp, out, yt - windowSize, xt, windowSize)76                        if region_flag(out, yt - windowSize, xt + windowSize, windowSize, region_thresh * deviation3):77                            calc_regional_LBP(target, background, lbp, out, yt - windowSize, xt + windowSize,78                                              windowSize)79                        if region_flag(out, yt, xt - windowSize, windowSize, region_thresh * deviation4):80                            calc_regional_LBP(target, background, lbp, out, yt, xt - windowSize, windowSize)81                        if region_flag(out, yt, xt + windowSize, windowSize, region_thresh * deviation):82                            calc_regional_LBP(target, background, lbp, out, yt, xt + windowSize, windowSize)83                        if region_flag(out, yt + windowSize, xt - windowSize, windowSize, region_thresh * deviation):84                            calc_regional_LBP(target, background, lbp, out, yt + windowSize, xt - windowSize,85                                              windowSize)86                        if region_flag(out, yt + windowSize, xt, windowSize, region_thresh * deviation):87                            calc_regional_LBP(target, background, lbp, out, yt + windowSize, xt, windowSize)88                        if region_flag(out, yt + windowSize, xt + windowSize, windowSize, region_thresh * deviation):89                            calc_regional_LBP(target, background, lbp, out, yt + windowSize, xt + windowSize,90                                              windowSize)9192                    flag -= 193                # LBP.ShowSubIm("out", out, out[yt:yt + windowSize, xt:xt + windowSize])949596    if x_left:97        for j in range(y_iter):98            yt = j * step99            t_windowed = target[yt:yt + windowSize, w - x_left:w]100            b_windowed = background[yt:yt + windowSize, w - x_left:w]101            t_LBP_map = lbp.lbp_revolve(t_windowed) # LBPæè½¬ä¸åæ¨¡å¼ç¹å¾102            b_LBP_map = lbp.lbp_revolve(b_windowed)103            t_hist = lbp.get_revolve_hist(t_LBP_map) # LBPæè½¬ä¸åå½ä¸åç»è®¡ç´æ¹å¾104            b_hist = lbp.get_revolve_hist(b_LBP_map)105            d = LBP.chi2_distance(t_hist, b_hist)106            out[yt:yt + windowSize, w - x_left:w] += np.ones((windowSize, x_left)) * d107    if y_left:108        for i in range(x_iter):109            xt = i * step110            t_windowed = target[h - y_left:h, xt:xt + windowSize]111            b_windowed = background[h - y_left:h, xt:xt + windowSize]112            t_LBP_map = lbp.lbp_revolve(t_windowed)113            b_LBP_map = lbp.lbp_revolve(b_windowed)114            t_hist = lbp.get_revolve_hist(t_LBP_map)115            b_hist = lbp.get_revolve_hist(b_LBP_map)116            d = LBP.chi2_distance(t_hist, b_hist)117            out[h - y_left:h, xt:xt + windowSize] += np.ones((y_left, windowSize)) * d118    if y_left and x_left:119        t_windowed = target[h - y_left:h, w - x_left:w]120        b_windowed = background[h - y_left:h, w - x_left:w]121        t_LBP_map = lbp.lbp_revolve(t_windowed)122        b_LBP_map = lbp.lbp_revolve(b_windowed)123        t_hist = lbp.get_revolve_hist(t_LBP_map)124        b_hist = lbp.get_revolve_hist(b_LBP_map)125        d = LBP.chi2_distance(t_hist, b_hist)126        out[h - y_left:h, w - x_left:w] += np.ones((y_left, x_left)) * d127    # out = out / np.max(out)
...

match_features.py

Source:match_features.py

1import cv22import numpy as np3def match_features(feature_coords1, feature_coords2, image1, image2, windowSize = None):4    if windowSize == None :5        windowSize = 206        7        dictFeature2To1 = {}8        dictFeature1To2 = {}9        image1 = cv2.cvtColor(image1, cv2.COLOR_RGB2GRAY)10        image2 = cv2.cvtColor(image2, cv2.COLOR_RGB2GRAY)11        for i in feature_coords1:12            windowIn1 = image1[i[0] - windowSize:i[0] + windowSize, i[1] - windowSize:i[1] + windowSize]13            if (windowIn1.shape != (windowSize * 2, windowSize * 2)): continue14            maxVal = -115            index = [0, 0]16            for j in feature_coords2:17                windowIn2 = image2[j[0] - windowSize:j[0] + windowSize, j[1] - windowSize:j[1] + windowSize]18                if (windowIn2.shape != (windowSize * 2, windowSize * 2)): continue19                product = np.mean((windowIn1 - windowIn1.mean()) * (windowIn2 - windowIn2.mean()))20                stds = windowIn1.std() * windowIn2.std()21                product /= stds22                if maxVal < product:23                    maxVal = product24                    index = j25            dictFeature2To1[i] = index26        for i in feature_coords2:27            windowIn2 = image2[i[0] - windowSize:i[0] + windowSize, i[1] - windowSize:i[1] + windowSize]28            if (windowIn2.shape != (windowSize * 2, windowSize * 2)): continue29            maxVal = -130            index = [0, 0]31            for j in feature_coords1:32                windowIn1 = image1[j[0] - windowSize:j[0] + windowSize, j[1] - windowSize:j[1] + windowSize]33                if (windowIn1.shape != (windowSize * 2, windowSize * 2)): continue34                product = np.mean((windowIn2 - windowIn2.mean()) * (windowIn1 - windowIn1.mean()))35                stds = windowIn2.std() * windowIn1.std()36                product /= stds37                if maxVal < product:38                    maxVal = product39                    index = j40            dictFeature1To2[i] = index41    else:42        windowSize = windowSize43        dictFeature2To1 = {}44        dictFeature1To2 = {}45        image1 = cv2.cvtColor(image1, cv2.COLOR_RGB2GRAY)46        image2 = cv2.cvtColor(image2, cv2.COLOR_RGB2GRAY)47        for i in feature_coords1:48            windowIn1 = image1[i[0] - windowSize:i[0] + windowSize, i[1] - windowSize:i[1] + windowSize]49            if (windowIn1.shape != (windowSize * 2, windowSize * 2)): continue50            maxVal = -151            index = [0,0]52            for j in feature_coords2:53                windowIn2 = image2[j[0] - windowSize:j[0] + windowSize, j[1] - windowSize:j[1] + windowSize]54                if (windowIn2.shape != (windowSize * 2, windowSize * 2)): continue55                product = np.mean((windowIn1 - windowIn1.mean()) * (windowIn2 - windowIn2.mean()))56                stds = windowIn1.std() * windowIn2.std()57                product /= stds58                if maxVal < product:59                    maxVal = product60                    index = j61            dictFeature2To1[i] = index62        for i in feature_coords2:63            windowIn2 = image2[i[0] - windowSize:i[0] + windowSize, i[1] - windowSize:i[1] + windowSize]64            if (windowIn2.shape != (windowSize * 2, windowSize * 2)):continue65            maxVal = -166            index = [0,0]67            for j in feature_coords1:68                windowIn1 = image1[j[0] - windowSize:j[0] + windowSize, j[1] - windowSize:j[1] + windowSize]69                if (windowIn1.shape != (windowSize * 2, windowSize * 2)):continue70                product = np.mean((windowIn2 - windowIn2.mean()) * (windowIn1 - windowIn1.mean()))71                stds = windowIn2.std() * windowIn1.std()72                product /= stds73                if maxVal < product:74                    maxVal = product75                    index = j76            dictFeature1To2[i] = index77    matches = list()78    listInPairs = list()79    for i in dictFeature2To1.keys():80        temp = dictFeature2To1[i]81        if dictFeature1To2[temp] == i:82            index1 = feature_coords1.index(i)83            index2 = feature_coords2.index(temp)84            listInPairs.append([index1,index2])85            matches.append(i)86            matches.append(temp)87    print len(matches)...

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