说明
在JPEG编码中，CbCr通道的全采样和降采样存储方式是不同的。
在全采样情况下，Cb和Cr的采样率与亮度采样率一样，都是1:1，也就是每个亮度样本点对应一个色度样本点。此时，Cb和Cr的采样值在编码过程中直接存储在压缩数据中。
而在降采样情况下，Cb和Cr的采样率比亮度采样率低，通常是2:1或4:1。此时，为了减小存储空间和编码复杂度，Cb和Cr的采样值需要先进行二次采样，将2x2或4x4的采样块转换为1个采样值。这些二次采样得到的采样值再进行量化和熵编码。在压缩数据中，CbCr的采样值存储为一系列的差分值和直流分量，需要经过解码和反量化才能得到CbCr的采样值。
本文是基于全采样压缩方式实现的JPEG编码解码，兼容官方JPEG（具体表现为本文的编码后的图片可以用win10图片查看器直接解码打开）。
代码
import cv2
import numpy as np
import matplotlib.pyplot as plt
import base64
'''
rgb到ycbcr颜色空间转换
'''
#RGB -> YCbCr
def rgb2ycbcr(rgb):
m = np.array([[0.299, 0.587, 0.114],
[-0.1687, -0.3313, 0.5],
[0.5, -0.4187, -0.0813]])
shape = rgb.shape
if len(shape) == 3:
rgb = rgb.reshape((shape[0] * shape[1], 3))
ycbcr = np.dot(rgb, m.transpose())
return ycbcr.reshape(shape)
# YCbCr -> RGB
def ycbcr2rgb(ycbcr):
m = np.array([[1, 0, 1.402],
[1, -0.344, -0.714],
[1, 1.772, 0]])
shape = ycbcr.shape
if len(shape) == 3:
ycbcr = ycbcr.reshape((shape[0] * shape[1], 3))
rgb = np.dot(ycbcr, m.transpose())
return rgb.reshape(shape)
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]]=bin(code_value)[2:].rjust(k,'0')
pos_in_table+=1
code_value+=1
code_value<<=1
#传入8*8块
def _doHuffmanEncoding(block,ZigZag,m_Table,m_DC_Huffman_Table,m_AC_Huffman_Table,result,prev):
block=block.astype(np.float)
block = cv2.dct(block)
# 数据量化
block[:] = np.round(block / m_Table)
# 把量化后的二维矩阵转成一维数组
arr = [0] * 64
notnull_num = 0
for k in range(64):
tmp = int(block[int(k / 8)][k % 8])
arr[ZigZag[k]] = tmp;
# 统计arr数组中有多少个非0元素
if tmp != 0:
notnull_num += 1
# RLE编码
# 标识连续0的个数
time = 0
# 处理DC
if arr[0] != 0:
notnull_num -= 1
data = arr[0] - prev[0]
prev[0] = arr[0]
data2 = bin(np.abs(data))[2:]
data1 = len(data2)
if data < 0:
data2 = bin(np.abs(data) ^ (2 ** data1 - 1))[2:].rjust(data1, '0')
if data == 0:
data2 = ''
data1 = 0
result += m_DC_Huffman_Table[data1]
result += data2
for k in range(1, 64):
# 有可能AC系数全为0 所以要先进行判断
if notnull_num == 0:
# 添加EOB
result += m_AC_Huffman_Table[0]
break
if arr[k] == 0 and time < 15:
time += 1
else:
# BIT编码
# 处理括号中第二个数
data = arr[k]
data2 = bin(np.abs(data))[2:]
data1 = len(data2)
if data < 0:
data2 = bin(np.abs(data) ^ (2 ** data1 - 1))[2:].rjust(data1, '0')
if data == 0:
data1 = 0
data2 = ''
# 哈夫曼编码，序列化
result += m_AC_Huffman_Table[time * 16 + data1]
result += data2
time = 0
# 判断是否要添加EOB
if int(arr[k]) != 0:
notnull_num -= 1
return result
def _computeReverseHuffmanTable(nr_codes,nr_values,reverse_huffman_table):
pos_in_table = 0;
code_value = 0
for i in range(1, 17):
for j in range(1, nr_codes[i - 1] + 1):
reverse_huffman_table[bin(code_value)[2:].rjust(i, '0')] = nr_values[pos_in_table * 2:pos_in_table * 2 + 2]
pos_in_table += 1
code_value += 1
code_value <<= 1
'''
jpeg压缩函数
data:要压缩的彩色图像数据流
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([8,8], dtype=np.uint8)
m_CbCrTable = np.zeros([8,8], 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)
# Z字型
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]
#初始化量化表，根据压缩质量重新计算量化表
if quality_scale <= 0:
quality_scale = 1
elif quality_scale >= 100:
quality_scale = 99
for i in range(64):
# 亮度量化表
tmp = int((Luminance_Quantization_Table[int(i / 8)][i % 8] * quality_scale + 50) / 100)
if tmp <= 0:
tmp = 1
elif tmp > 255:
tmp = 255
m_YTable[int(i / 8)][i % 8] = tmp
# 色度量化表
tmp = int((Chrominance_Quantization_Table[int(i / 8)][i % 8] * quality_scale + 50) / 100)
if tmp <= 0:
tmp = 1
elif tmp > 255:
tmp = 255
m_CbCrTable[int(i / 8)][i % 8] = tmp
# 初始化哈夫曼编码表
m_Y_DC_Huffman_Table = [0]*12
m_Y_AC_Huffman_Table = [0]*256
m_CbCr_DC_Huffman_Table = [0]*12
m_CbCr_AC_Huffman_Table = [0]*256
_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);
# 转成float类型
img_data = img_data.astype(np.float64)
# 存储最后的哈夫曼编码
result = ''
#色彩空间转换
YCbCr_data=rgb2ycbcr(img_data)
YCbCr_data=YCbCr_data.astype(int)
Y_data, Cb_data, Cr_data = cv2.split(YCbCr_data)
Y_data=Y_data-128
prev_DC_Y = [0]
prev_DC_Cb = [0]
prev_DC_Cr = [0]
#CbCr降采样
# Cb_data=Cb_data[::2,::2]
# Cr_data=Cb_data[1::2,::2]
#三个通道分别编码成独立数据流
h,w=Y_data.shape
# 分成8*8的块
for i in range(h // 8):
for j in range(w // 8):
block = Y_data[i * 8:(i + 1) * 8, j * 8:(j + 1) * 8]
result=_doHuffmanEncoding(block,ZigZag,m_YTable,m_Y_DC_Huffman_Table,m_Y_AC_Huffman_Table,result,prev_DC_Y)
block = Cb_data[i * 8:(i + 1) * 8, j * 8:(j + 1) * 8]
result=_doHuffmanEncoding(block,ZigZag,m_CbCrTable,m_CbCr_DC_Huffman_Table,m_CbCr_AC_Huffman_Table,result,prev_DC_Cb)
block = Cr_data[i * 8:(i + 1) * 8, j * 8:(j + 1) * 8]
result=_doHuffmanEncoding(block,ZigZag,m_CbCrTable,m_CbCr_DC_Huffman_Table,m_CbCr_AC_Huffman_Table,result,prev_DC_Cr)
# 补足为8的整数倍，以便编码成16进制数据
if len(result) % 8 != 0:
result = result.ljust(len(result) + 8 - len(result) % 8, '0')
res_data = ''
for i in range(0, len(result), 8):
temp = int(result[i:i + 8], 2)
res_data += hex(temp)[2:].rjust(2, '0').upper()
#如果16进制对应的字符是FF的话需要添加00辅助字节来区分（FF是JPEG段标识）
if temp == 255:
res_data += '00'
result = res_data
res = ''
# 添加jpeg文件头
# 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 range(64):
res += hex(m_YTable[int(i / 8)][i % 8])[2:].rjust(2, '0')
res+='01'
for i in range(64):
res += hex(m_CbCrTable[int(i / 8)][i % 8])[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')
#降采样4:2:0
# res += '03011100022201032201'
#全采样
res += '03011100021101031101'
# DHT定义huffman表,33个字节+183
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'
# 压缩的图像数据（一个个扫描行），数据存放顺序是从左到右、从上到下
res += result
# EOI文件尾0
res += 'FFD9'
return res
def _doHuffmanDecoding(m_Table,ZigZag,Reverse_DC_Huffman_Table,Reverse_AC_Huffman_Table,result,prev,pos,img_data,j,k):
# 逆dc哈夫曼编码
# 正向最大匹配
arr = [0]
# 计算EOB块中0的个数
num = 0
#这里注意CbCr的哈夫曼表的范围
for i in range(11, 1, -1):
tmp = Reverse_DC_Huffman_Table.get(result[pos[0]:pos[0] + i])
# 匹配成功
if (tmp):
pos[0] += i
num += 1
# DC系数为0
if tmp == '00':
# 是差值编码 差点忘了加上prev
arr[0] = 0 + prev[0]
prev[0] = arr[0]
break
time = 0
data1 = int(tmp[1], 16)
data2 = result[pos[0]:pos[0] + data1]
if data2[0] == '0':
# 负数
data = -(int(data2, 2) ^ (2 ** data1 - 1))
else:
data = int(data2, 2)
arr[0] = data + prev[0]
prev[0] = arr[0]
pos[0] += data1
break
# 逆ac哈夫曼编码
while (num < 64):
# AC系数编码长度是从16bits到2bits
for i in range(16, 1, -1):
tmp = Reverse_AC_Huffman_Table.get(result[pos[0]:pos[0] + i])
if (tmp):
pos[0] += i
if (tmp == '00'):
arr += ([0] * (64 - num))
num = 64
break
time = int(tmp[0], 16)
data1 = int(tmp[1], 16)
data2 = result[pos[0]:pos[0] + data1]
pos[0] += data1
# data2为空，赋值为0，应对(15,0)这种情况
data2 = data2 if data2 else '0'
if data2[0] == '0':
# 负数,注意负号和异或运算的优先级
data = -(int(data2, 2) ^ (2 ** data1 - 1))
else:
data = int(data2, 2)
num += time + 1
# time个0
arr += ([0] * time)
# 非零值或最后一个单元0
arr.append(data)
break
# 逆ZigZag扫描,得到block量化块
block = np.zeros((8, 8))
for i in range(64):
block[int(i / 8)][i % 8] = arr[ZigZag[i]]
# 逆量化
block = block * m_Table
# 逆DCT变换
block = cv2.idct(block)
img_data[j * 8:(j + 1) * 8, k * 8:(k + 1) * 8] = block
'''
jpeg解压缩
img:解压缩的jpeg灰度图像文件
return:返回解压缩后的图像原数据，为多维数组形式
'''
def decompress(img):
# jpeg解码的所有参数都是从编码后的jpeg文件中读取的
with open(img, 'rb') as f:
img_data = f.read()
res = ''
for i in img_data:
res += hex(i)[2:].rjust(2, '0').upper()
ZigZag = [
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]
# 获取亮度量化表
m_YTable = np.zeros((8, 8))
for i in range(64):
m_YTable[int(i / 8)][i % 8] = int(res[50 + i * 2:52 + i * 2], 16)
m_CbCrTable = np.zeros((8, 8))
for i in range(64):
m_CbCrTable[int(i / 8)][i % 8] = int(res[180 + i * 2:182 + i * 2], 16)
# 获取SOF0图像基本信息，图像的宽高
h = int(res[318:322], 16)
w = int(res[322:326], 16)
# 获取DHT定义huffman表
Standard_DC_Luminance_NRCodes = [0] * 16
for i in range(16):
Standard_DC_Luminance_NRCodes[i] = int(res[356 + i * 2:358 + i * 2], 16)
Standard_DC_Luminance_Values = res[388:412]
Standard_AC_Luminance_NRCodes = [0] * 16
for i in range(16):
Standard_AC_Luminance_NRCodes[i] = int(res[414 + i * 2:416 + i * 2], 16)
Standard_AC_Luminance_Values = res[446:770]
Standard_DC_Chrominance_NRCodes=[0]*16
for i in range(16):
Standard_DC_Chrominance_NRCodes[i] = int(res[772 + i * 2:774 + i * 2], 16)
Standard_DC_Chrominance_Values=res[804:828]
Standard_AC_Chrominance_NRCodes=[0] * 16
for i in range(16):
Standard_AC_Chrominance_NRCodes[i] = int(res[830 + i * 2:832 + i * 2], 16)
Standard_AC_Chrominance_Values=res[862:1186]
#生成逆huffman编码表
Reverse_Y_DC_Huffman_Table = {}
Reverse_Y_AC_Huffman_Table = {}
Reverse_CbCr_DC_Huffman_Table = {}
Reverse_CbCr_AC_Huffman_Table = {}
_computeReverseHuffmanTable(Standard_DC_Luminance_NRCodes, Standard_DC_Luminance_Values, Reverse_Y_DC_Huffman_Table);
_computeReverseHuffmanTable(Standard_AC_Luminance_NRCodes, Standard_AC_Luminance_Values, Reverse_Y_AC_Huffman_Table);
_computeReverseHuffmanTable(Standard_DC_Chrominance_NRCodes, Standard_DC_Chrominance_Values, Reverse_CbCr_DC_Huffman_Table);
_computeReverseHuffmanTable(Standard_AC_Chrominance_NRCodes, Standard_AC_Chrominance_Values, Reverse_CbCr_AC_Huffman_Table);
# 获取压缩的图像数据
tmp_result = res[1214:-4]
result = ''
i = 0
while i < len(tmp_result):
tmp0 = tmp_result[i:i + 2]
result += tmp0
i += 2
if (tmp0 == 'FF'):
i += 2
# 得到哈夫曼编码后的01字符串
result = bin(int(result, 16))[2:].rjust(len(result) * 4, '0')
prev_DC_Y = [0]
prev_DC_Cb = [0]
prev_DC_Cr = [0]
pos = [0]
#逆huffman编码
Y_data = np.zeros((h, w),dtype=int)
Cb_data = np.zeros((h, w),dtype=int)
Cr_data = np.zeros((h, w),dtype=int)
for j in range(h // 8):
for k in range(w // 8):
_doHuffmanDecoding(m_YTable,ZigZag,Reverse_Y_DC_Huffman_Table,Reverse_Y_AC_Huffman_Table,result,prev_DC_Y,pos,Y_data,j,k)
_doHuffmanDecoding(m_CbCrTable,ZigZag,Reverse_CbCr_DC_Huffman_Table,Reverse_CbCr_AC_Huffman_Table,result,prev_DC_Cb,pos,Cb_data,j,k)
_doHuffmanDecoding(m_CbCrTable,ZigZag,Reverse_CbCr_DC_Huffman_Table,Reverse_CbCr_AC_Huffman_Table,result,prev_DC_Cr,pos,Cr_data,j,k)
Y_data=Y_data+128
YCbCr_data=cv2.merge([Y_data,Cb_data,Cr_data])
img_data=ycbcr2rgb(YCbCr_data)
img_data = img_data.astype(np.uint8)
return img_data
#huffman表需要修改
def main():
# 原始图像路径,彩色图像
img_path = './lena.bmp'
# 读取原始图像
# 得到图像原数据流，注意opencv的颜色通道顺序为[B,G,R]
img_data = cv2.imread(img_path)[:,:,(2,1,0)]
#直接把原始图像存储起来，得到官方压缩的jpeg图像数据img0
cv2.imwrite('./jpeg_compress.jpg', img_data)
img0 = cv2.imread('./jpeg_compress.jpg', -1)
# 本文代码得到压缩后图像数据
img_compress = compress(img_data, 50)
# 存储压缩后的图像
img_compress_path = './img_compress.jpg'
with open(img_compress_path, 'wb') as f:
f.write(base64.b16decode(img_compress.upper()))
# jpeg图像解压缩测试
img_decompress = decompress(img_compress_path)
img1 = cv2.imread(img_compress_path)[:,:,(2,1,0)]
# 结果展示
plt.rcParams['font.sans-serif'] = ['SimHei'] # 中文乱码
# 子图1，原始图像
plt.subplot(221)
# imshow()对图像进行处理，画出图像，show()进行图像显示
plt.imshow(img_data)
plt.title('原始图像')
# 不显示坐标轴
plt.axis('off')
# 子图2，自己写的jpeg压缩后解码图像
plt.subplot(222)
plt.imshow(img_decompress, cmap=plt.cm.gray)
plt.title('自写编码自写解码')
plt.axis('off')
# 子图3，jpeg压缩后解码图像
plt.subplot(223)
plt.imshow(img0, cmap=plt.cm.gray)
plt.title('官方编码官方解码')
plt.axis('off')
# 子图3，jpeg压缩后解码图像
plt.subplot(224)
plt.imshow(img1, cmap=plt.cm.gray)
plt.title('自写编码官方解码)')
plt.axis('off')
plt.show()
if __name__ == '__main__':
main()
