diff --git a/blind_watermark/bwm_core.py b/blind_watermark/bwm_core.py index a6d344c..dadadd2 100644 --- a/blind_watermark/bwm_core.py +++ b/blind_watermark/bwm_core.py @@ -4,7 +4,6 @@ # @Author : github.com/guofei9987 import numpy as np from numpy.linalg import svd -import copy import cv2 from cv2 import dct, idct from pywt import dwt2, idwt2 @@ -17,11 +16,8 @@ def __init__(self, password_img=1, mode='common', processes=None): self.password_img = password_img self.d1, self.d2 = 36, 20 # d1/d2 越大鲁棒性越强,但输出图片的失真越大 - # init data self.img, self.img_YUV = None, None # self.img 是原图，self.img_YUV 对像素做了加白偶数化 - self.ca, self.hvd, = [np.array([])] * 3, [np.array([])] * 3 # 每个通道 dct 的结果 - self.ca_block = [np.array([])] * 3 # 每个 channel 存一个四维 array，代表四维分块后的结果 - self.ca_part = [np.array([])] * 3 # 四维分块后，有时因不整除而少一部分，self.ca_part 是少这一部分的 self.ca + self.ca, self.hvd, = [None] * 3, [None] * 3 # 每个通道 dct 的结果 self.wm_size, self.block_num = 0, 0 # 水印的长度，原图片可插入信息的个数 self.pool = AutoPool(mode=mode, processes=processes) @@ -29,14 +25,6 @@ def __init__(self, password_img=1, mode='common', processes=None): self.fast_mode = False self.alpha = None # 用于处理透明图 - def init_block_index(self): - self.block_num = self.ca_block_shape[0] * self.ca_block_shape[1] - assert self.wm_size < self.block_num, IndexError( - '最多可嵌入{}kb信息，多于水印的{}kb信息，溢出'.format(self.block_num / 1000, self.wm_size / 1000)) - # self.part_shape 是取整后的ca二维大小,用于嵌入时忽略右边和下面对不齐的细条部分。 - self.part_shape = self.ca_block_shape[:2] * self.block_shape - self.block_index = [(i, j) for i in range(self.ca_block_shape[0]) for j in range(self.ca_block_shape[1])] - def read_img_arr(self, img): # 处理透明图 self.alpha = None @@ -47,7 +35,6 @@ def read_img_arr(self, img): # 读入图片->YUV化->加白边使像素变偶数->四维分块 self.img = img.astype(np.float32) - self.img_shape = self.img.shape[:2] self.block_h, self.block_w = self.block_shape self.img_h, self.img_w = self.img.shape[:2] @@ -107,35 +94,6 @@ def embed(self): embed_img = cv2.merge([embed_img.astype(np.uint8), self.alpha]) return embed_img - def read_img_arr2(self, img): - # 处理透明图 - self.alpha = None - if img.shape[2] == 4: - if img[:, :, 3].min() < 255: - self.alpha = img[:, :, 3] - img = img[:, :, :3] - - # 读入图片->YUV化->加白边使像素变偶数->四维分块 - self.img = img.astype(np.float32) - self.img_shape = self.img.shape[:2] - - # 如果不是偶数，那么补上白边，Y（明亮度）UV（颜色） - self.img_YUV = cv2.copyMakeBorder(cv2.cvtColor(self.img, cv2.COLOR_BGR2YUV), - 0, self.img.shape[0] % 2, 0, self.img.shape[1] % 2, - cv2.BORDER_CONSTANT, value=(0, 0, 0)) - - self.ca_shape = [(i + 1) // 2 for i in self.img_shape] - - self.ca_block_shape = (self.ca_shape[0] // self.block_shape[0], self.ca_shape[1] // self.block_shape[1], - self.block_shape[0], self.block_shape[1]) - strides = 4 * np.array([self.ca_shape[1] * self.block_shape[0], self.block_shape[1], self.ca_shape[1], 1]) - - for channel in range(3): - self.ca[channel], self.hvd[channel] = dwt2(self.img_YUV[:, :, channel], 'haar') - # 转为4维度 - self.ca_block[channel] = np.lib.stride_tricks.as_strided(self.ca[channel].astype(np.float32), - self.ca_block_shape, strides) - def read_wm(self, wm_bit): self.wm_bit = wm_bit self.wm_size = wm_bit.size @@ -173,40 +131,6 @@ def block_add_wm_fast(self, arg): return idct(np.dot(u, np.dot(np.diag(s), v))) - def embed2(self): - self.init_block_index() - - embed_ca = copy.deepcopy(self.ca) - embed_YUV = [np.array([])] * 3 - - self.idx_shuffle = random_strategy1(self.password_img, self.block_num, - self.block_shape[0] * self.block_shape[1]) - for channel in range(3): - tmp = self.pool.map(self.block_add_wm, - [(self.ca_block[channel][self.block_index[i]], self.idx_shuffle[i], i) - for i in range(self.block_num)]) - - for i in range(self.block_num): - self.ca_block[channel][self.block_index[i]] = tmp[i] - - # 4维分块变回2维 - self.ca_part[channel] = np.concatenate(np.concatenate(self.ca_block[channel], 1), 1) - # 4维分块时右边和下边不能整除的长条保留，其余是主体部分，换成 embed 之后的频域的数据 - embed_ca[channel][:self.part_shape[0], :self.part_shape[1]] = self.ca_part[channel] - # 逆变换回去 - embed_YUV[channel] = idwt2((embed_ca[channel], self.hvd[channel]), "haar") - - # 合并3通道 - embed_img_YUV = np.stack(embed_YUV, axis=2) - # 之前如果不是2的整数，增加了白边，这里去除掉 - embed_img_YUV = embed_img_YUV[:self.img_shape[0], :self.img_shape[1]] - embed_img = cv2.cvtColor(embed_img_YUV, cv2.COLOR_YUV2BGR) - embed_img = np.clip(embed_img, a_min=0, a_max=255) - - if self.alpha is not None: - embed_img = cv2.merge([embed_img.astype(np.uint8), self.alpha]) - return embed_img - def block_get_wm(self, args): if self.fast_mode: return self.block_get_wm_fast(args) @@ -258,23 +182,6 @@ def extract_raw(self, img): for i in range(self.block_num)]) return wm_block_bit - def extract_raw2(self, img): - # 每个分块提取 1 bit 信息 - self.read_img_arr(img=img) - self.init_block_index() - - wm_block_bit = np.zeros(shape=(3, self.block_num)) # 3个channel，length 个分块提取的水印，全都记录下来 - - self.idx_shuffle = random_strategy1(seed=self.password_img, - size=self.block_num, - block_shape=self.block_shape[0] * self.block_shape[1], # 16 - ) - for channel in range(3): - wm_block_bit[channel, :] = self.pool.map(self.block_get_wm, - [(self.ca_block[channel][self.block_index[i]], self.idx_shuffle[i]) - for i in range(self.block_num)]) - return wm_block_bit - def extract_avg(self, wm_block_bit): # 对循环嵌入+3个 channel 求平均 wm_avg = np.zeros(shape=self.wm_size) @@ -320,6 +227,7 @@ def random_strategy1(seed, size, block_shape): def random_strategy2(seed, size, block_shape): + # same with all blocks one_line = np.random.RandomState(seed) \ .random(size=(1, block_shape)) \ .argsort(axis=1)