def embed(self,mess):
# вычислить блоки коэффициентов DCT из изображения
DCT_blocks = [np.round(cv2.dct(block)) for block in self.blocks]
# разделить каждый коэффициент на величину квантования
quantizedDCT = [np.round(DCT_block/self.quant) for DCT_block in DCT_blocks]
# quantizedDCT = [np.round(DCT_block/1) for DCT_block in DCT_blocks]
# перевести сообщение в двоичную форму
mess = self.binary_text(mess)
messIndex = 0
# вставлять каждый бит сообщения в 1-й коэффициент каждого блока
for block in quantizedDCT:
DC = int(block[0,0])
if DC % 2 == 0:
DC ^= int(mess[messIndex])
else:
DC ^= int(mess[messIndex]) ^ 1
block[0,0] = np.float32(DC)
messIndex += 1
if messIndex == len(mess): break
if messIndex < len(mess)-1: raise StegaException("not enough spaces to embed")
# умножить каждый коэффициент на значение квантования
DCT_blocks = [block * self.quant for block in quantizedDCT]
# DCT_blocks = [block * 1 for block in quantizedDCT]
# рассчитать обратное DCT, чтобы получить значения изображения
Img_blocks = [np.round(cv2.idct(block)) for block in DCT_blocks]
# изменить форму блоков в форму изображения
img = self.reshape(Img_blocks)
# img = cv2.resize(img,(self.oricol,self.orirow))
return img
def jpeg_gray(x):
output = np.zeros(x.shape, dtype=int)
w = x.shape[0]
h = x.shape[1]
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, 36, 55, 64, 81, 104, 113, 92],
[49, 64, 78, 87, 103, 121, 120, 101],
[72, 92, 95, 98, 112, 100, 103, 99]
])
for i in range(0, h, 8):
for j in range(0, w, 8):
sample = x[j:j+8, i:i+8]
dct = np.around(cv2.dct(sample.astype(float)))
dct_quant = np.around(np.divide(dct, quantization_table))
scaled_quant = np.multiply(dct_quant, quantization_table)
out_block = np.around(cv2.idct(scaled_quant))
output[j:j+8, i:i+8] = out_block
output = output/np.amax(output)*255
print(np.amax(output))
return output.astype(np.uint8)
def embed(self, mess):
# calculate DCT coefficients blocks from image
DCT_blocks = [np.round(cv2.dct(block)) for block in self.blocks]
# divide each coefficient by quantization value
quantizedDCT = [
np.round(DCT_block / self.quant) for DCT_block in DCT_blocks
]
# quantizedDCT = [np.round(DCT_block/1) for DCT_block in DCT_blocks]
# translate message to binary form
mess = self.binary_text(mess)
messIndex = 0
# embed each bit of message to the 1st coefficient of each block
for block in quantizedDCT:
DC = int(block[0, 0])
if DC % 2 == 0:
DC ^= int(mess[messIndex])
else:
DC ^= int(mess[messIndex]) ^ 1
block[0, 0] = np.float32(DC)
messIndex += 1
if messIndex == len(mess): break
if messIndex < len(mess) - 1:
raise StegaException("not enough spaces to embed")
# multiply each coefficient with quantization value
DCT_blocks = [block * self.quant for block in quantizedDCT]
# DCT_blocks = [block * 1 for block in quantizedDCT]
# calculate inverse DCT to get image's values
Img_blocks = [np.round(cv2.idct(block)) for block in DCT_blocks]
# reshape blocks to image form
img = self.reshape(Img_blocks)
# img = cv2.resize(img,(self.oricol,self.orirow))
return img
def DCT_IDCT_OpenCv(image):
dct = cv2.dct(np.float32(image), flags=cv2.DFT_COMPLEX_OUTPUT)
cosine = np.log(np.abs(dct))
iimage = cv2.idct(dct)
Plt_Contrast(image, cosine, iimage)
def idctBlock(dctBlockList):
'''
对每一个块进行逆dct变换
'''
return [cv.idct(block) for block in dctBlockList]
def blocks_idct(dct_blocks):
idct_blocks = []
for blocks_no in range(len(dct_blocks)):
idct_block = cv2.idct(dct_blocks[blocks_no])
idct_blocks.append(idct_block)
return idct_blocks