final changes...

presentation.ipynb

@@ -123,6 +123,19 @@
     "    show_text(\")\", axs[4])\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Three topics in Cryptography\n",
+    "\n",
+    "![logo](files/sqrpnt_logo.png)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -168,7 +181,7 @@
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "subslide"
     }
    },
    "source": [
@@ -202,9 +215,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqwAAAKoCAYAAACyU60mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc41e814780>"
+       "<matplotlib.figure.Figure at 0x7fd8f0242898>"
       ]
      },
      "metadata": {},
@@ -212,7 +225,7 @@
     }
    ],
    "source": [
-    "img = produce_image_layer_from_real_image(\"image_sqrpnt.png\")\n",
+    "img = produce_image_layer_from_real_image(\"cat-512.png\")\n",
     "shares = img.produce_two_shares_from_image()\n",
     "plot_matrix(shares[0], title='first transparent')\n"
    ]
@@ -240,9 +253,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqwAAAKoCAYAAACyU60mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc41e814668>"
+       "<matplotlib.figure.Figure at 0x7fd8f00e4c88>"
       ]
      },
      "metadata": {},
@@ -276,9 +289,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc41e699eb8>"
+       "<matplotlib.figure.Figure at 0x7fd8f02426a0>"
       ]
      },
      "metadata": {},
@@ -331,7 +344,7 @@
     "\n",
     "This image is said as the *message*\n",
     "\n",
-    "![image initiale](files/sqrpnt_logo.png)\n",
+    "![image initiale](files/calvin.png)\n",
     "\n"
    ]
   },
@@ -345,7 +358,7 @@
    "source": [
     "##  We splitted the image into many pixels\n",
     "\n",
-    "![pixel decomposition of the original image](files/image_pixelized.png)"
+    "![pixel decomposition of the original image](files/calvin.gif)"
    ]
   },
   {
@@ -380,9 +393,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc41e78f668>"
+       "<matplotlib.figure.Figure at 0x7fd8f0178898>"
       ]
      },
      "metadata": {},
@@ -392,10 +405,11 @@
    "source": [
     "fig, (ax0, ax1, ax2 ) = plt.subplots(ncols=3)\n",
     "\n",
-    "img=matplotlib.image.imread('sqrpnt_logo.png')\n",
+    "img=matplotlib.image.imread('calvin_pixels.png')\n",
     "do_not_show_axis(ax0)\n",
     "ax0.imshow(img)\n",
     "\n",
+    "\n",
     "show_sub_pixel([[white]*10]*10, ax=ax1, title=\"transparent 1\")\n",
     "show_sub_pixel([[white]*10]*10, ax=ax2, title=\"transparent 2\")\n",
     "\n",
@@ -435,7 +449,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc41e58ba58>"
+       "<matplotlib.figure.Figure at 0x7fd8ecdcda20>"
       ]
      },
      "metadata": {},
@@ -527,7 +541,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc41b1f2f28>"
+       "<matplotlib.figure.Figure at 0x7fd8ecd50550>"
       ]
      },
      "metadata": {},
@@ -591,9 +605,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc41e72d4a8>"
+       "<matplotlib.figure.Figure at 0x7fd8eff739e8>"
       ]
      },
      "metadata": {},
@@ -650,7 +664,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc41b2f7048>"
+       "<matplotlib.figure.Figure at 0x7fd8f005d0b8>"
       ]
      },
      "metadata": {},
@@ -696,7 +710,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc41e412b70>"
+       "<matplotlib.figure.Figure at 0x7fd8ecdc0a58>"
       ]
      },
      "metadata": {},
@@ -814,7 +828,7 @@
      "data": {
       "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc41e725550>"
+       "<matplotlib.figure.Figure at 0x7fd8f01802b0>"
       ]
      },
      "metadata": {},
@@ -851,7 +865,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc41e54d198>"
+       "<matplotlib.figure.Figure at 0x7fd8ecd505f8>"
       ]
      },
      "metadata": {},
@@ -897,7 +911,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc41ae26780>"
+       "<matplotlib.figure.Figure at 0x7fd8ec7468d0>"
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      },
      "metadata": {},
@@ -932,7 +946,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc41e5dd908>"
+       "<matplotlib.figure.Figure at 0x7fd8ecb31c18>"
       ]
      },
      "metadata": {},
@@ -978,7 +992,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc41ad76860>"
+       "<matplotlib.figure.Figure at 0x7fd8ecb544e0>"
       ]
      },
      "metadata": {},
@@ -1013,7 +1027,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc41aebda58>"
+       "<matplotlib.figure.Figure at 0x7fd8eaab4668>"
       ]
      },
      "metadata": {},
@@ -1082,9 +1096,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc41e71ff60>"
+       "<matplotlib.figure.Figure at 0x7fd8ecd6b4a8>"
       ]
      },
      "metadata": {},
@@ -1092,7 +1106,7 @@
     }
    ],
    "source": [
-    "cheated = produce_image_layer_from_real_image(\"cheatedimage_sqrpnt.png\")\n",
+    "cheated = produce_image_layer_from_real_image(\"dog-512.png\")\n",
     "cheated_share = cheated.produce_cheated_image_from_other_share(shares[0])\n",
     "\n",
     "plot_matrix(cheated_share , title='crafted transparent')\n",
@@ -1112,9 +1126,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc41e40b898>"
+       "<matplotlib.figure.Figure at 0x7fd8ecdb4cf8>"
       ]
      },
      "metadata": {},
@@ -1222,7 +1236,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc41b0ebac8>"
+       "<matplotlib.figure.Figure at 0x7fd8ecb228d0>"
       ]
      },
      "metadata": {},
@@ -1263,7 +1277,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc41a9b7470>"
+       "<matplotlib.figure.Figure at 0x7fd8eca65ba8>"
       ]
      },
      "metadata": {},
@@ -1304,7 +1318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 22,
    "metadata": {
     "collapsed": false,
     "slideshow": {
@@ -1316,7 +1330,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc41b270c50>"
+       "<matplotlib.figure.Figure at 0x7fd8ecb4da90>"
       ]
      },
      "metadata": {},
@@ -1702,7 +1716,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc41ae26cf8>"
+       "<matplotlib.figure.Figure at 0x7fd8f001ebe0>"
       ]
      },
      "metadata": {},
@@ -1740,7 +1754,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false,
     "slideshow": {
@@ -1752,7 +1766,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc41e3f26a0>"
+       "<matplotlib.figure.Figure at 0x7fd8eccafa90>"
       ]
      },
      "metadata": {},
@@ -1781,7 +1795,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false,
     "slideshow": {
@@ -1793,7 +1807,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc41ae57198>"
+       "<matplotlib.figure.Figure at 0x7fd8ec8e0ef0>"
       ]
      },
      "metadata": {},
@@ -1899,7 +1913,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false,
     "slideshow": {
@@ -1911,7 +1925,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc41e3cc2b0>"
+       "<matplotlib.figure.Figure at 0x7fd8ecd7f3c8>"
       ]
      },
      "metadata": {},
@@ -2150,14 +2164,42 @@
     "    Proof by intimidation"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# The End :(\n",
+    "\n",
+    ">  Questions?  Comments?  Suggestions?\n",
+    "\n",
+    "\n",
+    "![logo](files/sqrpnt_logo.png)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "collapsed": true
    },
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
   }
  ],
  "metadata": {

visual_cryptography/visual_cryptography.py

@@ -22,8 +22,8 @@ class ImageLayer(object):
         [[0,1,1,0], [1,0,0,1]],
         [[1,0,0,1], [0,1,1,0]],
     )
-    
-    pixel_width = 2 
+
+    pixel_width = 2
 
     def __init__(self, height, width):
         self.data = self.get_empty_data(height, width)
@@ -41,10 +41,10 @@ class ImageLayer(object):
         sub_pixel_matrix = self.c_0[0]
         number_of_share_to_produce = len(sub_pixel_matrix)
         number_of_sub_pixels = len(sub_pixel_matrix[0])
-        
+
         current_width = len(self.data[0])
         current_height = len(self.data)
-        
+
         ret = [self.get_empty_data(current_height * self.pixel_width, current_width * self.pixel_width) for i in range(len(sub_pixel_matrix))]
         return ret
 
@@ -93,29 +93,35 @@ class ImageLayer(object):
         return cheated_share
 
 
-def cmyk_to_luminance(r, g, b):
+def cmyk_to_luminance(r, g, b, a):
     """
     takes a RGB color and returns it grayscale value.  See
-    http://www.johndcook.com/blog/2009/08/24/algorithms-convert-color-grayscale/ 
+    http://www.johndcook.com/blog/2009/08/24/algorithms-convert-color-grayscale/
     for more information
     """
-    return 0.299 * r + 0.587 * g + 0.114 * b
+    if (r, g, b) == (0, 0, 0):
+        return a
+    return (0.299 * r + 0.587 * g + 0.114 * b) * a / 255
 
 
-def cmyk_to_black_and_white(r, g, b):
-    black_threshold = 255 / 3
-    if cmyk_to_luminance(r, g, b) > black_threshold:
+def cmyk_to_black_and_white(r, g, b, a):
+    black_threshold = 255 / 2
+    if cmyk_to_luminance(r, g, b, a) < black_threshold:
         return 0
     return 1
 
 
 def produce_image_layer_from_real_image(image_path):
-    im = Image.open(image_path).convert('RGB')
+    im = Image.open(image_path).convert('RGBA')
     ret = ImageLayer(*im.size)
+    d = {}
     for i in range(im.size[0]):
         for j in range(im.size[1]):
-            r, g, b = im.im.getpixel((i,j))
-            ret.data[j][i] = cmyk_to_black_and_white(r, g, b)
+            r, g, b, a = im.im.getpixel((i,j))
+            d[(r, g, b, a)] = cmyk_to_black_and_white(r, g, b, a)
+            ret.data[j][i] = cmyk_to_black_and_white(r, g, b, a)
     return ret
-    
-    
+
+
+if __name__ == '__main__':
+    print(produce_image_layer_from_real_image('dog-512.png'))