add image registration

facial_similarity_checker.ipynb

@@ -0,0 +1,181 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dependencies\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install -q tf_keras\n",
+    "# %pip install opencv-contrib-python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import Library\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from deepface import DeepFace\n",
+    "\n",
+    "import cv2\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Custom Class\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class facial_area():\n",
+    "    def __init__(self, tar=None):\n",
+    "        if (tar == None):\n",
+    "            self.x = 0\n",
+    "            self.y = 0\n",
+    "            self.width = 0\n",
+    "            self.height = 0\n",
+    "        else:\n",
+    "            self.set(tar)\n",
+    "            \n",
+    "    def __str__(self):\n",
+    "        return f\"x: {self.x}\\ty: {self.y}\\tSize: ({self.width},{self.height})\"\n",
+    "\n",
+    "    def set(self, tar):\n",
+    "        self.x = tar[\"x\"]\n",
+    "        self.y = tar[\"y\"]\n",
+    "        self.width = tar[\"w\"]\n",
+    "        self.height = tar[\"h\"]\n",
+    "        print(self)\n",
+    "        \n",
+    "    def show_result(self, img, with_size=False):\n",
+    "        img = cv2.imread(img)\n",
+    "        img = cv2.rectangle(img, (self.x, self.y), (self.x + self.width, self.y + self.height), (0, 255, 0), 2)\n",
+    "        if with_size:\n",
+    "            cv2.putText(img, f\"({self.width},{self.height})\", (self.x, self.y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)\n",
+    "        return img\n",
+    "        \n",
+    "class facial_similarity_checker():\n",
+    "    def __init__(self):\n",
+    "        self.img1 = None\n",
+    "        self.img2 = None\n",
+    "        self.img1_area = facial_area()\n",
+    "        self.img2_area = facial_area()\n",
+    "    \n",
+    "    def load_image(self, img1, img2):\n",
+    "        self.img1 = img1\n",
+    "        self.img2 = img2\n",
+    "    \n",
+    "    def check_similarity(self, img1=None, img2=None, show_detail=False):\n",
+    "        if img1 == None or img2 == None:\n",
+    "            img1 = self.img1\n",
+    "            img2 = self.img2\n",
+    "        else:\n",
+    "            self.load_image(img1, img2)\n",
+    "\n",
+    "        try :\n",
+    "            img1 = cv2.imread(img1)\n",
+    "            img2 = cv2.imread(img2)\n",
+    "        except:\n",
+    "            print(\"The image path is not correct\")\n",
+    "            return -1\n",
+    "\n",
+    "        result = DeepFace.verify(img1, img2)\n",
+    "        self.img1_area.set(result[\"facial_areas\"][\"img1\"])\n",
+    "        self.img2_area.set(result[\"facial_areas\"][\"img2\"])\n",
+    "        \n",
+    "        if show_detail:\n",
+    "            print(result)\n",
+    "\n",
+    "        return result[\"verified\"], result[\"distance\"]\n",
+    "\n",
+    "    def show_image(self, img):\n",
+    "        if (self.img1 == img):\n",
+    "            img = self.img1_area.show_result(img, with_size=True)\n",
+    "        else:\n",
+    "            img = self.img2_area.show_result(img, with_size=True)\n",
+    "            \n",
+    "        plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\n",
+    "        plt.show()\n",
+    "\n",
+    "    def show_result(self, show_detail=False):\n",
+    "        result, dis = self.check_similarity(self.img1, self.img2, show_detail)\n",
+    "        print(\"The similarity between the images is: \", dis)\n",
+    "        print(\"The images are the same person: \", result)\n",
+    "        self.show_image(self.img1)\n",
+    "        self.show_image(self.img2)\n",
+    "        \n",
+    "    def get_full_result(self):\n",
+    "        return DeepFace.verify(cv2.imread(self.img1),cv2.imread(self.img2))\n",
+    "    \n",
+    "    def get_facial_area(self):\n",
+    "        return self.img1_area, self.img2_area\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Implementation\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checker = facial_similarity_checker()\n",
+    "\n",
+    "# image1_path = \"img/mymask_cup.jpg\"\n",
+    "# image2_path = \"img/me_without_mask.jpg\"\n",
+    "\n",
+    "# checker.load_image(image1_path, image2_path)\n",
+    "# checker.show_result(show_detail=True)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}