CTF of Z2BNS

#RGB彩色图片jpeg压缩实现
import cv2
import numpy as np
import base64
import math

'''
jpeg压缩函数
data:要压缩的灰度图像数据流
quality_scale控制压缩质量(1-99)，默认为50，值越小图像约清晰
return:得到压缩后的图像数据，为FFD9开头的jpeg格式字符串
'''
def compress(img_data,quality_scale=50):
  #获取图像数据流宽高
  m_height,m_width,_=img_data.shape
  m_YTable=np.zeros(64,dtype=int)
  m_CbCrTable=np.zeros(64,dtype=int)
  #标准亮度量化表
  Luminance_Quantization_Table=np.array([16,  11,  10,  16,  24,  40,  51,  61,
	12,  12,  14,  19,  26,  58,  60,  55,
	14,  13,  16,  24,  40,  57,  69,  56,
	14,  17,  22,  29,  51,  87,  80,  62,
	18,  22,  37,  56,  68, 109, 103,  77,
	24,  35,  55,  64,  81, 104, 113,  92,
	49,  64,  78,  87, 103, 121, 120, 101,
	72,  92,  95,  98, 112, 100, 103,  99],dtype=np.uint8)
  #标准色度量化表
  Chrominance_Quantization_Table=np.array([	17,  18,  24,  47,  99,  99,  99,  99,
	18,  21,  26,  66,  99,  99,  99,  99,
	24,  26,  56,  99,  99,  99,  99,  99,
	47,  66,  99,  99,  99,  99,  99,  99,
	99,  99,  99,  99,  99,  99,  99,  99,
	99,  99,  99,  99,  99,  99,  99,  99,
	99,  99,  99,  99,  99,  99,  99,  99,
	99,  99,  99,  99,  99,  99,  99,  99],dtype=np.uint8)

  ZigZag=np.array([0, 1, 5, 6,14,15,27,28,
	2, 4, 7,13,16,26,29,42,
	3, 8,12,17,25,30,41,43,
	9,11,18,24,31,40,44,53,
	10,19,23,32,39,45,52,54,
	20,22,33,38,46,51,55,60,
	21,34,37,47,50,56,59,61,
	35,36,48,49,57,58,62,63])

  Standard_DC_Luminance_NRCodes= [ 0, 0, 7, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0 ]
  Standard_DC_Luminance_Values= [4, 5, 3, 2, 6, 1, 0, 7, 8, 9, 10, 11]

  Standard_DC_Chrominance_NRCodes=[0, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0]
  Standard_DC_Chrominance_Values=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
  
  Standard_AC_Luminance_NRCodes=[0, 2, 1, 3, 3, 2, 4, 3, 5, 5, 4, 4, 0, 0, 1, 0x7d]
  Standard_AC_Luminance_Values=[0x01, 0x02, 0x03, 0x00, 0x04, 0x11, 0x05, 0x12,
	0x21, 0x31, 0x41, 0x06, 0x13, 0x51, 0x61, 0x07,
	0x22, 0x71, 0x14, 0x32, 0x81, 0x91, 0xa1, 0x08,
	0x23, 0x42, 0xb1, 0xc1, 0x15, 0x52, 0xd1, 0xf0,
	0x24, 0x33, 0x62, 0x72, 0x82, 0x09, 0x0a, 0x16,
	0x17, 0x18, 0x19, 0x1a, 0x25, 0x26, 0x27, 0x28,
	0x29, 0x2a, 0x34, 0x35, 0x36, 0x37, 0x38, 0x39,
	0x3a, 0x43, 0x44, 0x45, 0x46, 0x47, 0x48, 0x49,
	0x4a, 0x53, 0x54, 0x55, 0x56, 0x57, 0x58, 0x59,
	0x5a, 0x63, 0x64, 0x65, 0x66, 0x67, 0x68, 0x69,
	0x6a, 0x73, 0x74, 0x75, 0x76, 0x77, 0x78, 0x79,
	0x7a, 0x83, 0x84, 0x85, 0x86, 0x87, 0x88, 0x89,
	0x8a, 0x92, 0x93, 0x94, 0x95, 0x96, 0x97, 0x98,
	0x99, 0x9a, 0xa2, 0xa3, 0xa4, 0xa5, 0xa6, 0xa7,
	0xa8, 0xa9, 0xaa, 0xb2, 0xb3, 0xb4, 0xb5, 0xb6,
	0xb7, 0xb8, 0xb9, 0xba, 0xc2, 0xc3, 0xc4, 0xc5,
	0xc6, 0xc7, 0xc8, 0xc9, 0xca, 0xd2, 0xd3, 0xd4,
	0xd5, 0xd6, 0xd7, 0xd8, 0xd9, 0xda, 0xe1, 0xe2,
	0xe3, 0xe4, 0xe5, 0xe6, 0xe7, 0xe8, 0xe9, 0xea,
	0xf1, 0xf2, 0xf3, 0xf4, 0xf5, 0xf6, 0xf7, 0xf8,
	0xf9, 0xfa]

  Standard_AC_Chrominance_NRCodes=[0, 2, 1, 2, 4, 4, 3, 4, 7, 5, 4, 4, 0, 1, 2, 0x77]

  Standard_AC_Chrominance_Values=[0x00, 0x01, 0x02, 0x03, 0x11, 0x04, 0x05, 0x21,
	0x31, 0x06, 0x12, 0x41, 0x51, 0x07, 0x61, 0x71,
	0x13, 0x22, 0x32, 0x81, 0x08, 0x14, 0x42, 0x91,
	0xa1, 0xb1, 0xc1, 0x09, 0x23, 0x33, 0x52, 0xf0,
	0x15, 0x62, 0x72, 0xd1, 0x0a, 0x16, 0x24, 0x34,
	0xe1, 0x25, 0xf1, 0x17, 0x18, 0x19, 0x1a, 0x26,
	0x27, 0x28, 0x29, 0x2a, 0x35, 0x36, 0x37, 0x38,
	0x39, 0x3a, 0x43, 0x44, 0x45, 0x46, 0x47, 0x48,
	0x49, 0x4a, 0x53, 0x54, 0x55, 0x56, 0x57, 0x58,
	0x59, 0x5a, 0x63, 0x64, 0x65, 0x66, 0x67, 0x68,
	0x69, 0x6a, 0x73, 0x74, 0x75, 0x76, 0x77, 0x78,
	0x79, 0x7a, 0x82, 0x83, 0x84, 0x85, 0x86, 0x87,
	0x88, 0x89, 0x8a, 0x92, 0x93, 0x94, 0x95, 0x96,
	0x97, 0x98, 0x99, 0x9a, 0xa2, 0xa3, 0xa4, 0xa5,
	0xa6, 0xa7, 0xa8, 0xa9, 0xaa, 0xb2, 0xb3, 0xb4,
	0xb5, 0xb6, 0xb7, 0xb8, 0xb9, 0xba, 0xc2, 0xc3,
	0xc4, 0xc5, 0xc6, 0xc7, 0xc8, 0xc9, 0xca, 0xd2,
	0xd3, 0xd4, 0xd5, 0xd6, 0xd7, 0xd8, 0xd9, 0xda,
	0xe2, 0xe3, 0xe4, 0xe5, 0xe6, 0xe7, 0xe8, 0xe9,
	0xea, 0xf2, 0xf3, 0xf4, 0xf5, 0xf6, 0xf7, 0xf8,
	0xf9, 0xfa]
  #将bgr三维数据 扁平化为一维
  m_rgbBuffer=img_data.flatten(order='C')
  # print(m_rgbBuffer[:10])
  #初始化量化表
  if quality_scale<=0:
  	quality_scale=1
  elif quality_scale>=100:
  	quality_scale=99
  for i in range(64):
  	tmp=int((Luminance_Quantization_Table[i]*quality_scale+50)/100)
  	if tmp<=0:
  		tmp=1
  	elif tmp>255:
  		tmp=255
  	m_YTable[ZigZag[i]]=tmp
  	tmp=int((Chrominance_Quantization_Table[i]*quality_scale+50)/100)
  	if tmp<=0:
  		tmp=1
  	elif tmp>255:
  		tmp=255
  	m_CbCrTable[ZigZag[i]]=tmp

  # print(m_CbCrTable)
  #初始化huffman表
  m_Y_DC_Huffman_Table=np.zeros((12,2),dtype=int)
  m_Y_AC_Huffman_Table=np.zeros((256,2),dtype=int)
  m_CbCr_DC_Huffman_Table=np.zeros((12,2),dtype=int)
  m_CbCr_AC_Huffman_Table=np.zeros((256,2),dtype=int)
  _computeHuffmanTable(Standard_DC_Luminance_NRCodes, Standard_DC_Luminance_Values, m_Y_DC_Huffman_Table);
  _computeHuffmanTable(Standard_AC_Luminance_NRCodes, Standard_AC_Luminance_Values, m_Y_AC_Huffman_Table);
  _computeHuffmanTable(Standard_DC_Chrominance_NRCodes, Standard_DC_Chrominance_Values, m_CbCr_DC_Huffman_Table);
  _computeHuffmanTable(Standard_AC_Chrominance_NRCodes, Standard_AC_Chrominance_Values, m_CbCr_AC_Huffman_Table);
  # print(m_Y_AC_Huffman_Table[:12])
  
  fp=open('out.jpg','wb')
  #添加jpeg文件头
  res=""
  #SOI(文件头),共89个字节
  res+='FFD8'
  #APP0图像识别信息
  res+='FFE000104A46494600010100000100010000'
  #DQT定义量化表
  res+='FFDB008400'
  #64字节的量化表
  for i in m_YTable:
  	res+=hex(i)[2:].rjust(2,'0')
  res+='01'
  for i in m_CbCrTable:
  	res+=hex(i)[2:].rjust(2,'0')

  #SOF0图像基本信息，13个字节
  res+='FFC0001108'
  res+=hex(m_height)[2:].rjust(4,'0')
  res+=hex(m_width)[2:].rjust(4,'0')
  res+='03011100021101031101'
  #DHT定义huffman表,33个字节+183
  # res+='FFC4001F0000010501010101010100000000000000'
  res+='FFC401A200'
  for i in Standard_DC_Luminance_NRCodes:
    res+=hex(i)[2:].rjust(2,'0')
  for i in Standard_DC_Luminance_Values:
    res+=hex(i)[2:].rjust(2,'0')
  res+='10'
  for i in Standard_AC_Luminance_NRCodes:
    res+=hex(i)[2:].rjust(2,'0')
  for i in Standard_AC_Luminance_Values:
    res+=hex(i)[2:].rjust(2,'0')
  res+='01'
  for i in Standard_DC_Chrominance_NRCodes:
    res+=hex(i)[2:].rjust(2,'0')
  for i in Standard_DC_Chrominance_Values:
    res+=hex(i)[2:].rjust(2,'0')
  res+='11'
  for i in Standard_AC_Chrominance_NRCodes:
    res+=hex(i)[2:].rjust(2,'0')
  for i in Standard_AC_Chrominance_Values:
    res+=hex(i)[2:].rjust(2,'0')
  # #SOS扫描行开始，10个字节
  res+='FFDA000C03010002110311003f00'
  fp.write(base64.b16decode(res.upper()))
  prev_DC_Y=[0]
  prev_DC_Cb=[0]
  prev_DC_Cr=[0]
  # prev_DC=[0,0,0]
  newByte=[0]
  newBytePos=[7]
  for yPos in range(0,m_height,8):
  	for xPos in range(0,m_width,8):
  		#颜色空间转换后的数据
  		yData=np.zeros((64),dtype=int)
  		cbData=np.zeros((64),dtype=int)
  		crData=np.zeros((64),dtype=int)
  		#dct及量化后的数据
  		yQuant=np.zeros((64),dtype=int)
  		cbQuant=np.zeros((64),dtype=int)
  		crQuant=np.zeros((64),dtype=int)
  		#转换颜色空间 rgb转ycbcr
  		for y in range(8):
  			pos=(y+yPos)*m_width*3+xPos*3
  			for x in range(8):
  				B=m_rgbBuffer[pos]
  				pos+=1
  				G=m_rgbBuffer[pos]
  				pos+=1
  				R=m_rgbBuffer[pos]
  				pos+=1
  				yData[y*8+x] = (int)(0.299 * R + 0.587 * G + 0.114 * B - 128)
  				cbData[y*8+x] = (int)(-0.1687 * R - 0.3313 * G + 0.5 * B );
  				crData[y*8+x] = (int)(0.5 * R - 0.4187 * G - 0.0813 * B);
  		outputBitString=np.zeros((128,2),dtype=int)
  		bitStringCounts=[0];
  		#Y通道压缩
  		_foword_FDC(yData,yQuant,ZigZag,m_YTable)	
  		_doHuffmanEncoding(yQuant,prev_DC_Y,m_Y_DC_Huffman_Table,m_Y_AC_Huffman_Table,outputBitString,bitStringCounts)
  		# print(m_Y_DC_Huffman_Table)
  		_write_bitstring_(outputBitString,bitStringCounts[0],newByte,newBytePos,fp)
  		# print(outputBitString)
  		#Cb通道压缩
  		_foword_FDC(cbData,cbQuant,ZigZag,m_CbCrTable);
  		_doHuffmanEncoding(cbQuant,prev_DC_Cb,m_CbCr_DC_Huffman_Table,m_CbCr_AC_Huffman_Table,outputBitString,bitStringCounts)
  		_write_bitstring_(outputBitString,bitStringCounts[0],newByte,newBytePos,fp)
  		# # print(m_CbCrTable)
  		# # print(cbQuant)
  		# # return
  		#Cr通道压缩
  		_foword_FDC(crData,crQuant,ZigZag,m_CbCrTable);
  		_doHuffmanEncoding(crQuant,prev_DC_Cr,m_CbCr_DC_Huffman_Table,m_CbCr_AC_Huffman_Table,outputBitString,bitStringCounts)
  		_write_bitstring_(outputBitString,bitStringCounts[0],newByte,newBytePos,fp)

  		# print(bitStringCounts[0])
  		# print(outputBitString)
  		# print(yQuant)
  		# return
  fp.write(base64.b16decode('FFD9'))
  fp.close()


# #转换颜色空间
# def _convertColorSpace(xPos,yPos,yData,cbData,crData):
# 	for y in range(8):
#把huffman编码后的结果写入文件
def _write_bitstring_(bs,counts,newByte,newBytePos,fp):
	mask=[1,2,4,8,16,32,64,128,256,512,1024,2048,4096,8192,16384,32768]
	for i in range(counts):
		value=bs[i][1]
		posval=bs[i][0]-1
		# print(posval,value)
		while(posval>=0):
			if value & mask[posval] !=0:
				newByte[0]=newByte[0] | mask[newBytePos[0]]
			posval-=1
			newBytePos[0]-=1
			if newBytePos[0]<0:
				#write to steam
				fp.write(base64.b16decode(hex(newByte[0]).upper()[2:].rjust(2,'0')))
				if newByte[0]==0xFF:
					#Handle special case
					fp.write(base64.b16decode('00'))
				#Reinitialize
				newBytePos[0]=7
				newByte[0]=0

#dct离散余弦变换加量化
def _foword_FDC(channel_data,fdc_data,ZigZag,m_YTable):
	PI=3.1415926
	for v in range(8):
		for u in range(8):
			alpha_u = 1/math.sqrt(8.) if u==0 else 0.5
			alpha_v = 1/math.sqrt(8.) if v==0 else 0.5
			temp=0.
			for x in range(8):
				for y in range(8):
					data=channel_data[y*8+x]
					data*=np.cos((2*x+1)*u*PI/16.)
					data*=np.cos((2*y+1)*v*PI/16.)
					temp+=data
			temp*=alpha_u*alpha_v/m_YTable[ZigZag[v*8+u]]
			fdc_data[ZigZag[v*8+u]]=int((int)(temp+16384.5)-16384)

def _doHuffmanEncoding(DU,prevDC,HTDC,HTAC,outputBitString,bitStringCounts):
	EOB=HTAC[0x00]
	SIXTEEN_ZEROS=HTAC[0xF0]
	index=0
	#encode DC
	dcDiff=(int)(DU[0]-prevDC[0])
	prevDC[0]=DU[0]
	if dcDiff==0:
		outputBitString[index]=HTDC[0]
		index+=1
	else:
		bs=_getBitCode(dcDiff)
		outputBitString[index]=HTDC[bs[0]]
		index+=1
		outputBitString[index]=bs
		index+=1
	#encode ACs
	endPos=63
	#最后一个不为0的索引
	while((endPos>0) and (DU[endPos]==0)):
		endPos-=1
	i=1
	while(i<=endPos):

		startPos=i
		while((DU[i]==0) and (i<=endPos)):
			i+=1
		zeroCounts=i-startPos
		if zeroCounts>=16:
			for j in range(1,(int)(zeroCounts/16)+1):
				outputBitString[index]=SIXTEEN_ZEROS
				index+=1
			zeroCounts=zeroCounts%16
		bs=_getBitCode(DU[i])
		outputBitString[index]=HTAC[(zeroCounts<<4)|bs[0]]
		index+=1
		outputBitString[index]=bs
		index+=1
		i+=1
	if endPos!=63:
		outputBitString[index]=EOB
		index+=1
	bitStringCounts[0]=index

def _getBitCode(value):
	ret=np.zeros((2),dtype=int)
	v=value if value>0 else -value
	#bit的长度
	length=0
	while(v!=0):
		v>>=1
		length+=1
	ret[1]=value if value>0 else ((1<<length)+value-1)
	ret[0]=length
	return ret



def _computeHuffmanTable(nr_codes,std_table,huffman_table):
	pos_in_table=0;
	code_value=0
	for k in range(1,17):
		for j in range(1,nr_codes[k-1]+1):
			huffman_table[std_table[pos_in_table]][1]=code_value
			huffman_table[std_table[pos_in_table]][0]=k;
			pos_in_table+=1
			code_value+=1
		code_value<<=1

def main():
	# img_path=sys.argv[1]
	img_path='./lena.bmp'
	# img_data=cv2.imread(img_path)[:,:,(2,1,0)]
	#bgr顺序
	img_data=cv2.imread(img_path)
	compress(img_data)
	# print(img_data)
	# plt.imshow(img_data)
	# plt.show()

if __name__=="__main__":
	main()