Face Recognition

Face Recognition/Face Recognition/Facerecog.ipynb

@@ -0,0 +1,155 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "import numpy as np\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = np.load(\"face_data.npy\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(data.shape, data.dtype)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = data[:, 1:].astype(int)\n",
+    "y = data[:, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = KNeighborsClassifier()\n",
+    "model.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cap = cv2.VideoCapture(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "detector = cv2.CascadeClassifier(\"haarcascade_frontalface_default.xml\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "while True:\n",
+    "\n",
+    "    ret, frame = cap.read()\n",
+    "\n",
+    "    if ret:\n",
+    "        faces = detector.detectMultiScale(frame)\n",
+    "\n",
+    "        for face in faces:\n",
+    "            x, y, w, h = face\n",
+    "\n",
+    "            cut = frame[y:y+h, x:x+w]\n",
+    "\n",
+    "            fix = cv2.resize(cut, (100, 100))\n",
+    "            gray = cv2.cvtColor(fix, cv2.COLOR_BGR2GRAY)\n",
+    "\n",
+    "            out = model.predict([gray.flatten()])\n",
+    "\n",
+    "            cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)\n",
+    "\n",
+    "            cv2.putText(frame, str(out[0]), (x, y - 10), cv2.FONT_HERSHEY_COMPLEX, 2, (255, 0, 0), 2)\n",
+    "\n",
+    "            print(out)\n",
+    "\n",
+    "            cv2.imshow(\"My Face\", gray)\n",
+    "\n",
+    "        cv2.imshow(\"My Screen\", frame)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    " key = cv2.waitKey(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "    if key == ord(\"q\"):\n",
+    "        break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cap.release()\n",
+    "cv2.destroyAllWindows()"
+   ]
+  }
+ ],
+ "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

Face Recognition/Face Recognition/Facerecog.py~

@@ -0,0 +1,52 @@
+import cv2
+import numpy as np
+from sklearn.neighbors import KNeighborsClassifier
+
+data = np.load("face_data.npy")
+
+print(data.shape, data.dtype)
+
+X = data[:, 1:].astype(int)
+y = data[:, 0]
+
+model = KNeighborsClassifier()
+model.fit(X, y)
+
+cap = cv2.VideoCapture(0)
+
+detector = cv2.CascadeClassifier("../../datasets/haarcascade_frontalface_default.xml")
+while True:
+
+    ret, frame = cap.read()
+
+    if ret:
+        faces = detector.detectMultiScale(frame)
+
+        for face in faces:
+            x, y, w, h = face
+
+            cut = frame[y:y+h, x:x+w]
+
+            fix = cv2.resize(cut, (100, 100))
+            gray = cv2.cvtColor(fix, cv2.COLOR_BGR2GRAY)
+
+            out = model.predict([gray.flatten()])
+
+            cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
+
+            cv2.putText(frame, str(out[0]), (x, y - 10), cv2.FONT_HERSHEY_COMPLEX, 2, (255, 0, 0), 2)
+
+            print(out)
+
+            cv2.imshow("My Face", gray)
+
+        cv2.imshow("My Screen", frame)
+
+
+    key = cv2.waitKey(1)
+
+    if key == ord("q"):
+        break
+
+cap.release()
+cv2.destroyAllWindows()

Face Recognition/Face/Facedetect.ipynb

@@ -0,0 +1,106 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cap = cv2.VideoCapture(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "detector = cv2.CascadeClassifier(\"haarcascade_frontalface_default.xml\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "while True:\n",
+    "\n",
+    "    ret, frame = cap.read()\n",
+    "\n",
+    "    if ret:\n",
+    "        faces = detector.detectMultiScale(frame)\n",
+    "\n",
+    "        for face in faces:\n",
+    "            x, y, w, h = face\n",
+    "\n",
+    "            cut = frame[y:y+h, x:x+w]\n",
+    "\n",
+    "            fix = cv2.resize(cut, (100, 100))\n",
+    "            gray = cv2.cvtColor(fix, cv2.COLOR_BGR2GRAY)\n",
+    "\n",
+    "        cv2.imshow(\"My Screen\", frame)\n",
+    "        cv2.imshow(\"My Face\", gray)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    " key = cv2.waitKey(1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if key == ord(\"q\"):\n",
+    "        break\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cap.release()\n",
+    "cv2.destroyAllWindows()\n"
+   ]
+  }
+ ],
+ "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}