version 0.1.7

README.md

@@ -1,7 +1,7 @@
 # HideInfo
 
 Info Hiding Library  
-一些原理简洁的信息隐藏方法  
+一些小而美的信息隐藏方法  
 
 
 

example/example_echo_watermark.py

@@ -1,13 +1,12 @@
 import numpy as np
-from hide_info.echo_watermark import EchoWatermark, get_error_rate, get_snr
-from hide_info import utils
-
+from hide_info.echo_watermark import EchoWatermark
+from hide_info import utils, evaluate
+from scipy.io import wavfile
 
 ori_file = "sounds.wav"  # 载体
 embedded_file = "sounds_with_watermark.wav"  # 嵌入水印后的文件名
 wm_str = "回声水印算法，欢迎 star!"  # 水印
 
-
 wm_bits = utils.bytes2bin(wm_str.encode('utf-8'))
 len_wm_bits = len(wm_bits)
 
@@ -22,7 +21,7 @@
 wm_str_extract = utils.bin2bytes(wm_extract).decode('utf-8', errors='replace')
 print("解出水印：", wm_str_extract)
 # 错误率：
-get_error_rate(wm_extract, wm_bits)
+evaluate.get_error_rate(wm_extract, wm_bits)
 
 # %% There are 3 algorithms：
 wm_bits = np.random.randint(2, size=200).tolist()
@@ -32,6 +31,9 @@
     echo_wm = EchoWatermark(pwd=111001, algo_type=algo_type)
     echo_wm.embed(origin_filename=ori_file, wm_bits=wm_bits, embed_filename=embedded_file)
     wm_extract = echo_wm.extract(embed_filename=embedded_file, len_wm_bits=len_wm_bits)
-    error_rate = get_error_rate(wm_extract, wm_bits)
-    get_snr(embedded_file, ori_file)
+    error_rate = evaluate.get_error_rate(wm_extract, wm_bits)
     assert error_rate <= 0.03
+
+    _, ori_signal = wavfile.read(ori_file)
+    _, wm_signal = wavfile.read(embedded_file)
+    evaluate.get_snr(ori_signal, wm_signal)

hide_info/__init__.py

@@ -1 +1 @@
-__version__ = '0.1.6'
+__version__ = '0.1.7'

hide_info/echo_watermark.py

@@ -167,23 +167,3 @@ def extract(self, embed_filename, len_wm_bits):
 
         return wm_extract
 
-
-def get_error_rate(wm_extract, wm_bits):
-    # 计算错误率
-    len_wm_bits = len(wm_bits)
-    error_num = np.sum(np.abs(wm_extract - wm_bits))
-    error_rate = error_num / len_wm_bits
-    print(f'bit error rate = {error_rate:.2%} ({error_num} / {len_wm_bits})')
-    return error_rate
-
-
-def get_snr(file_with_wm, orig_file):
-    sr, ori_signal = wavfile.read(orig_file)
-    _, wm_signal = wavfile.read(file_with_wm)
-
-    SNR = 10 * np.log10(
-        np.sum(np.square(ori_signal.astype(np.float32)))
-        / np.sum(np.square(ori_signal.astype(np.float32)
-                           - wm_signal.astype(np.float32))))
-    print(f'SNR = {SNR:.2f} dB')
-    return SNR

hide_info/evaluate.py

@@ -0,0 +1,18 @@
+import numpy as np
+
+
+def get_error_rate(wm_extract, wm_bits):
+    len_wm_bits = len(wm_bits)
+    error_num = np.sum(np.abs(wm_extract - wm_bits))
+    error_rate = error_num / len_wm_bits
+    print(f'bit error rate = {error_rate:.2%} ({error_num} / {len_wm_bits})')
+    return error_rate
+
+
+def get_snr(ori_signal, wm_signal):
+    snr = 10 * np.log10(
+        np.sum(np.square(ori_signal.astype(np.float32)))
+        / np.sum(np.square(ori_signal.astype(np.float32)
+                           - wm_signal.astype(np.float32))))
+    print(f'SNR = {snr:.2f} dB')
+    return snr