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Java基础复习笔记

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#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()

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