Data Analysis of CardiActivites Dataset

Advance/opencv-inpainting/opencv_inpainting.ipynb

@@ -0,0 +1,286 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# USAGE\n",
+    "# python opencv_inpainting.py --image examples/example01.png --mask examples/mask01.png\n",
+    "\n",
+    "# import the necessary packages\n",
+    "import argparse\n",
+    "import cv2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "usage: ipykernel_launcher.py [-h] -i IMAGE -m MASK [-a {telea,ns}] [-r RADIUS]\n",
+      "ipykernel_launcher.py: error: the following arguments are required: -i/--image, -m/--mask\n"
+     ]
+    },
+    {
+     "ename": "SystemExit",
+     "evalue": "2",
+     "output_type": "error",
+     "traceback": [
+      "An exception has occurred, use %tb to see the full traceback.\n",
+      "\u001b[1;31mSystemExit\u001b[0m\u001b[1;31m:\u001b[0m 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# construct the argument parser and parse the arguments\n",
+    "ap = argparse.ArgumentParser()\n",
+    "ap.add_argument(\"-i\", \"--image\", type=str, required=True,\n",
+    "\thelp=\"path input image on which we'll perform inpainting\")\n",
+    "ap.add_argument(\"-m\", \"--mask\", type=str, required=True,\n",
+    "\thelp=\"path input mask which corresponds to damaged areas\")\n",
+    "ap.add_argument(\"-a\", \"--method\", type=str, default=\"telea\",\n",
+    "\tchoices=[\"telea\", \"ns\"],\n",
+    "\thelp=\"inpainting algorithm to use\")\n",
+    "ap.add_argument(\"-r\", \"--radius\", type=int, default=3,\n",
+    "\thelp=\"inpainting radius\")\n",
+    "args = vars(ap.parse_args())\n",
+    "\n",
+    "# initialize the inpainting algorithm to be the Telea et al. method\n",
+    "flags = cv2.INPAINT_TELEA\n",
+    "\n",
+    "# check to see if we should be using the Navier-Stokes (i.e.,Bertalmio\n",
+    "# et al.) method for inpainting\n",
+    "if args[\"method\"] == \"ns\":\n",
+    "\tflags = cv2.INPAINT_NS\n",
+    "    \n",
+    "# load the (1) input image (i.e., the image we're going to perform\n",
+    "# inpainting on) and (2) the  mask which should have the same input\n",
+    "# dimensions as the input image -- zero pixels correspond to areas\n",
+    "# that *will not* be inpainted while non-zero pixels correspond to\n",
+    "# \"damaged\" areas that inpainting will try to correct\n",
+    "image = cv2.imread(args[\"image\"])\n",
+    "mask = cv2.imread(args[\"mask\"])\n",
+    "mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)\n",
+    "\n",
+    "# perform inpainting using OpenCV\n",
+    "output = cv2.inpaint(image, mask, args[\"radius\"], flags=flags)\n",
+    "\n",
+    "# show the original input image, mask, and output image after\n",
+    "# applying inpainting\n",
+    "cv2.imshow(\"Image\", image)\n",
+    "cv2.imshow(\"Mask\", mask)\n",
+    "cv2.imshow(\"Output\", output)\n",
+    "cv2.waitKey(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

Advance/opencv-inpainting/opencv_inpainting.py

@@ -0,0 +1,46 @@
+# USAGE
+# python opencv_inpainting.py --image examples/example01.png --mask examples/mask01.png
+
+# import the necessary packages
+import argparse
+import cv2
+
+# construct the argument parser and parse the arguments
+ap = argparse.ArgumentParser()
+ap.add_argument("-i", "--image", type=str, required=True,
+	help="path input image on which we'll perform inpainting")
+ap.add_argument("-m", "--mask", type=str, required=True,
+	help="path input mask which corresponds to damaged areas")
+ap.add_argument("-a", "--method", type=str, default="telea",
+	choices=["telea", "ns"],
+	help="inpainting algorithm to use")
+ap.add_argument("-r", "--radius", type=int, default=3,
+	help="inpainting radius")
+args = vars(ap.parse_args())
+
+# initialize the inpainting algorithm to be the Telea et al. method
+flags = cv2.INPAINT_TELEA
+
+# check to see if we should be using the Navier-Stokes (i.e.,Bertalmio
+# et al.) method for inpainting
+if args["method"] == "ns":
+	flags = cv2.INPAINT_NS
+
+# load the (1) input image (i.e., the image we're going to perform
+# inpainting on) and (2) the  mask which should have the same input
+# dimensions as the input image -- zero pixels correspond to areas
+# that *will not* be inpainted while non-zero pixels correspond to
+# "damaged" areas that inpainting will try to correct
+image = cv2.imread(args["image"])
+mask = cv2.imread(args["mask"])
+mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
+
+# perform inpainting using OpenCV
+output = cv2.inpaint(image, mask, args["radius"], flags=flags)
+
+# show the original input image, mask, and output image after
+# applying inpainting
+cv2.imshow("Image", image)
+cv2.imshow("Mask", mask)
+cv2.imshow("Output", output)
+cv2.waitKey(0)