Update solutions for L6

Author: cooper-mj

solutions/lab-6/.DS_Store

@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -166,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -332,17 +332,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
     "\n",
     "def brightness(img, factor):\n",
-    "\tfor row in range(img.shape[0]):\n",
-    "\t\tfor col in range(img.shape[1]):\n",
-    "\t\t\tfor color in range(img.shape[2]):\n",
-    "\t\t\t\timg[row, col, color] = min(img[row, col, color]*factor, 255)\n",
-    "\treturn img\n",
+    "    with np.nditer(img, op_flags=['readwrite']) as it:\n",
+    "        for elem in it:\n",
+    "            elem[...] = min(elem*factor, 255)\n",
+    "    return img\n",
     "\n",
     "img = load_image(\"cs41img.png\").copy()\n",
     "save_image(brightness(img, 1.5), \"cs41img_bright.png\")"
@@ -364,20 +363,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
     "def contrast(img, factor):\n",
-    "\tfor row in range(img.shape[0]):\n",
-    "\t\tfor col in range(img.shape[1]):\n",
-    "\t\t\tfor color in range(img.shape[2]):\n",
-    "\t\t\t\tif img[row, col, color] < 128:\n",
-    "\t\t\t\t\timg[row, col, color] = img[row, col, color]/factor\n",
-    "\t\t\t\telse:\n",
-    "\t\t\t\t\timg[row, col, color] = min(img[row, col, color]*factor, 255)\n",
+    "    with np.nditer(img, op_flags=['readwrite']) as it:\n",
+    "        for elem in it:\n",
+    "            elem[...] = min(elem*factor, 255)\n",
+    "            if elem < 128:\n",
+    "                elem[...] = elem/factor\n",
+    "            else:\n",
+    "                elem[...] = min(elem*factor, 255)\n",
     "\n",
-    "\treturn img\n",
+    "    return img\n",
     "\n",
     "img = load_image(\"cs41img.png\").copy()\n",
     "save_image(contrast(img, 1.5), \"cs41img_contrast.png\")"
@@ -413,19 +412,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
     "def reconstruct(img1, img2, img3):\n",
-    "    reconstructed_img = np.zeros(img1.shape)\n",
-    "    for row in range(img1.shape[0]):\n",
-    "        for col in range(img1.shape[1]):\n",
-    "            for color in range(img1.shape[2]):\n",
-    "                pixel_choices = np.asarray([img1[row, col, color], img2[row, col, color], img3[row, col, color]])\n",
-    "                reconstructed_img[row, col, color] = np.argmax(np.bincount(pixel_choices))\n",
-    "\n",
-    "    return reconstructed_img\n",
+    "    with np.nditer(img1, op_flags=['readwrite'], flags=['multi_index']) as it:\n",
+    "        for elem in it:\n",
+    "            pixel_choices = np.asarray([img1[it.multi_index], img2[it.multi_index], img3[it.multi_index]])\n",
+    "\n",
+    "            elem[...] = np.argmax(np.bincount(pixel_choices))\n",
+    "\n",
+    "    return img1\n",
     "\n",
     "img1 = load_image(\"SOLcs41img_unicorn1.png\").copy()\n",
     "img2 = load_image(\"SOLcs41img_unicorn2.png\").copy()\n",