def main():
res = open("results.csv", "a")
res.write(
"BlockSize,k,Alpha,ClusteringTime,MiningTime,EncodingTime,CompressionTime,DecompressionTime,ClusterTableSize,CodeTableSize,EncodedImageSize,CompressedSize,ActualSize,JPEG Size,GIF Size,JPEG Cr,GIF Cr,Our Cr,CRP Actual,CRP JPEG,CRP GIF,PSNR,MSE,NormalisedCrossRelation,MaxDiff,AvgDiff,NormalisedAbsoluteError,StructuralContent\n"
)
global blockSize, numberOfClusters, s, rows, columns, numberOfBlocks, minimumSupport
for blockSize in doubling_range(32, 257):
for numberOfClusters in range(8, 33, 8):
for s in range(10, 50, 20):
inputFileName = "4.2.02.tiff"
outputFileName = str(blockSize) + "-" + str(
numberOfClusters) + "-" + str(s)
# res = open("s_results.csv", "a")
image = imread(inputFileName)
(rows, columns) = getImageSize(inputFileName)
print str(blockSize) + "-" + str(numberOfClusters) + "-" + str(
s)
minimumSupport = (s * rows) / 100.0
numberOfBlocks = (rows * columns) / (blockSize * blockSize)
oTS = time.time()
cTS = time.time()
runAllClusteringParallel()
cTE = time.time()
print "Clustering done"
clusteringTime = cTE - cTS
runClusterEncodingParallel()
print "Cluster Encoding Done"
mTS = time.time()
minerParallel()
mTE = time.time()
print "Mining Done"
mineTime = mTE - mTS
huffEncodeParallel()
print "Huffman Encoding Done"
coTS = time.time()
Compressor()
coTE = time.time()
print "Encoding Done"
compressTime = coTE - coTS
oTE = time.time()
totalCompressionTime = oTE - oTS
print "Compression Done"
dTS = time.time()
Decoder()
print "Decoding Done"
runClusterDecodingInParallel()
dTE = time.time()
decompressionTime = dTE - dTS
print "Decompression Done"
reconstruct(rows, columns, outputFileName)
ClTableSize = (3 * numberOfBlocks *
(nob(numberOfClusters) + 8) *
numberOfClusters) / 8000.0
coTableSize = os.stat("redCodetable.txt").st_size + os.stat(
"greenCodetable.txt").st_size + os.stat(
"blueCodetable.txt").st_size
coTableSize = ((coTableSize / (6 * 1.0))) / 1000.0
compressedImageSize = os.stat(
"redCompressed.txt").st_size + os.stat(
"greenCompressed.txt").st_size + os.stat(
"blueCompressed.txt").st_size
compressedImageSize = (
(compressedImageSize / 8 * 1.0)) / 1000.0 - 40
totalSize = ClTableSize + coTableSize + compressedImageSize
# Image Quality Measures
output = cv2.imread(outputFileName + ".bmp", 1).astype(float)
input = cv2.imread(inputFileName).astype(float)
# declare variable for each image quality measurement and assign it to zero
mse = 0
norm_cor = 0
max_diff = 0
avg_diff = 0
nor_abs_error = 0
str_con = 0
psnr = 0
for i in range(0, 3):
psnr += iq.PSNR(input[i], output[i])
mse += iq.mean_square_error(input[i], output[i])
norm_cor += iq.normalised_cross_relation(
input[i], output[i])
max_diff += iq.maximal_difference(input[i], output[i])
avg_diff += iq.average_difference(input[i], output[i])
nor_abs_error += iq.normalised_absolute_error(
input[i], output[i])
str_con += iq.structural_content(input[i], output[i])
mse /= 3
norm_cor /= 3
max_diff /= 3
avg_diff /= 3
nor_abs_error = 3
str_con /= 3
psnr /= 3
ActualSize = 786.6
JPEGSize = 404
GIFSize = 226
OurCr = ActualSize / totalSize
JPEGCr = ActualSize / JPEGSize
GIFCr = ActualSize / GIFSize
CRPActual = ((ActualSize - totalSize) * 100) / ActualSize
CRPJPEG = ((JPEGSize - totalSize) * 100) / JPEGSize
CRPGIF = ((GIFSize - totalSize) * 100) / GIFSize
res.write(
str(blockSize) + "," + str(numberOfClusters) + "," +
str(s) + "," + str(clusteringTime) + "," + str(mineTime) +
"," + str(compressTime) + "," + str(totalCompressionTime) +
"," + str(decompressionTime) + "," + str(ClTableSize) +
"," + str(coTableSize) + "," + str(compressedImageSize) +
"," + str(totalSize) + "," + str(ActualSize) + "," +
str(JPEGSize) + "," + str(GIFSize) + "," + str(JPEGCr) +
"," + str(GIFCr) + "," + str(OurCr) + "," +
str(CRPActual) + "," + str(CRPJPEG) + "," + str(CRPGIF) +
"," + str(psnr) + "," + str(mse) + "," + str(norm_cor) +
"," + str(max_diff) + "," + str(avg_diff) + "," +
str(nor_abs_error) + "," + str(str_con) + "\n")
filelist = [f for f in os.listdir(".") if f.endswith(".txt")]
for f in filelist:
os.remove(f)
res.close()
def ss():
res = open("image_quality.csv", "w")
tiff_ex = ["airplane", "zelda", "scene"]
tif_ex = ["baboon", "lena"]
jpg_ex = ["barbara", "high_parrot", "high_tiger", "medical", "medical2"]
for inputFileName in tiff_ex:
conv = inputFileName
res.write(conv + "\n")
orig = "../../data/" + conv + ".tiff"
for blockSize in doubling_range(32, 257):
for numberOfClusters in range(8, 33, 4):
for s in range(10, 51, 20):
inputFileName = "../../results/new/" + conv + "/" + conv + ".tiff_" + str(
blockSize) + "-" + str(numberOfClusters) + "-" + str(
s) + ".bmp"
input = cv2.imread(orig)
output = cv2.imread(inputFileName)
input = input.astype(float)
print inputFileName
output = output.astype(float)
result = 0
for i in range(0, 3):
result += iq.normalised_absolute_error(
input[:, :, i], output[:, :, i])
result /= 3
res.write(str(result))
res.write("\n")
res.write("\n\n\n")
for inputFileName in tif_ex:
conv = inputFileName
res.write(conv + "\n")
orig = "../../data/" + conv + ".tif"
for blockSize in doubling_range(32, 257):
for numberOfClusters in range(8, 33, 4):
for s in range(10, 51, 20):
inputFileName = "../../results/new/" + conv + "/" + conv + ".tif_" + str(
blockSize) + "-" + str(numberOfClusters) + "-" + str(
s) + ".bmp"
input = cv2.imread(orig)
output = cv2.imread(inputFileName)
input = input.astype(float)
print inputFileName
output = output.astype(float)
result = 0
for i in range(0, 3):
result += iq.normalised_absolute_error(
input[:, :, i], output[:, :, i])
result /= 3
res.write(str(result))
res.write("\n")
res.write("\n\n\n")
for inputFileName in jpg_ex:
conv = inputFileName
res.write(conv + "\n")
orig = "../../data/" + conv + ".jpg"
for blockSize in doubling_range(32, 257):
for numberOfClusters in range(8, 33, 4):
for s in range(10, 51, 20):
inputFileName = "../../results/new/" + conv + "/" + conv + ".jpg_" + str(
blockSize) + "-" + str(numberOfClusters) + "-" + str(
s) + ".bmp"
input = cv2.imread(orig)
output = cv2.imread(inputFileName)
input = input.astype(float)
print inputFileName
output = output.astype(float)
result = 0
for i in range(0, 3):
result += iq.normalised_absolute_error(
input[:, :, i], output[:, :, i])
result /= 3
res.write(str(result))
res.write("\n")
res.write("\n\n\n")
res.close()