Merge pull request #2 from Tobias-Fechner/002-advanced-filters

002 advanced filters
tbsfchnr · Nov 25, 2020 · 6210391 · 6210391
2 parents 365d973 + c407ff3
commit 6210391
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Showing 11 changed files with 1,542 additions and 456 deletions.
diff --git a/TF00-initial-exploration.ipynb b/TF00-initial-exploration.ipynb
@@ -62,26 +62,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "meanie = filters.Mean(maskSize=5)\n",
-    "print(meanie)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "imgFiltered = meanie.convolve(img)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "handler.plotFigs([img, imgFiltered])"
+    "for maskSize in [3,5,7,9,11,21,51]:\n",
+    "    meanie = filters.Mean(maskSize=maskSize)\n",
+    "    imgFiltered = meanie.convolve(img)\n",
+    "    handler.plotFigs([img, imgFiltered], meanie)"
    ]
   },
   {
@@ -275,6 +259,86 @@
     "handler.saveAll(imgFiltered, lowP)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create and Apply Adaptive Weighted Median Filter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "awMed = filters.AdaptiveWeightedMedian(maskSize=7, constant=20, centralWeight=100)\n",
+    "print(awMed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "imgFiltered = awMed.convolve(img)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "handler.plotFigs([img, imgFiltered])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "handler.saveAll(imgFiltered, awMed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create and Apply Trimmed Mean Filter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for maskSize in [3,5,7,9,11,21,51]:\n",
+    "    for trim in [maskSize, maskSize*2, maskSize*3]:\n",
+    "        trimmedMean = filters.TrimmedMean(maskSize=5, trimStart=4, trimEnd=4)\n",
+    "        imgFiltered = trimmedMean.convolve(img)\n",
+    "        filteredTitle = trimmedMean.name + \"_maskSize\" + str(trimmedMean.maskSize) + \"_trim\" + str(trim)\n",
+    "        handler.plotFigs([img, imgFiltered], trimmedMean, filteredTitle)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "handler.saveAll(imgFiltered, trimmedMean)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

diff --git a/TF02-histogram-exploration.ipynb b/TF02-histogram-exploration.ipynb
@@ -65,7 +65,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "filtr = filters.Equalise()"
+    "eq = filters.Equalise()"
    ]
   },
   {
@@ -74,7 +74,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "imgNew = filtr.filter(img)"
+    "imgNew = eq.filter(img)"
    ]
   },
   {
@@ -83,7 +83,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "handler.plotFigs([img, imgNew])"
+    "handler.plotFigs([img, imgNew], eq, filteredTitle=eq.name)"
    ]
   },
   {
@@ -99,7 +99,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "filtr = filters.AHE(maskSize=126)"
+    "ahe = filters.AHE(maskSize=21)"
    ]
   },
   {
@@ -108,7 +108,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "imgNew = filtr.filter(img)"
+    "imgNew = ahe.filter(img)"
    ]
   },
   {
@@ -117,60 +117,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "handler.plotFigs([img, imgNew])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Save Results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pathlib import Path\n",
-    "import os.path\n",
-    "from PIL import Image"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "currentDir = Path().absolute()\n",
-    "root = str(currentDir) + '\\\\..\\outputs\\\\hist_Adaptive_Equalise\\\\'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if not os.path.exists(root):\n",
-    "    os.makedirs(root)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create pillow image object from filtered image array\n",
-    "img_PIL = Image.fromarray(imgNew, 'L')\n",
-    "\n",
-    "# Save filtered image from pillow image object\n",
-    "filePath = root+'filtered_foetus_maskSize_126.png'\n",
-    "img_PIL.save(filePath, 'PNG')\n",
-    "print(\"Saved filtered image to... \\n{}\\n\\n\".format(filePath))"
+    "handler.plotFigs([img, imgNew], ahe)"
    ]
   },
   {