Uploading all the files

Uploading all the source and other solution files.

Creating_API_with_Flask.ipynb

@@ -0,0 +1,103 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import flask\n",
+    "from flask import request, jsonify\n",
+    "import sqlite3\n",
+    "\n",
+    "app = flask.Flask(__name__)\n",
+    "app.config[\"DEBUG\"] = True\n",
+    "\n",
+    "def dict_factory(cursor, row):\n",
+    "    d = {}\n",
+    "    for idx, col in enumerate(cursor.description):\n",
+    "        d[col[0]] = row[idx]\n",
+    "    return d\n",
+    "\n",
+    "\n",
+    "@app.route('/', methods=['GET'])\n",
+    "def home():\n",
+    "    return '''<h1>Distant Reading Archive</h1>\n",
+    "<p>A prototype API for distant reading of science fiction novels.</p>'''\n",
+    "\n",
+    "\n",
+    "@app.route('/api/v1/resources/books/all', methods=['GET'])\n",
+    "def api_all():\n",
+    "    conn = sqlite3.connect('books.db')\n",
+    "    conn.row_factory = dict_factory\n",
+    "    cur = conn.cursor()\n",
+    "    all_books = cur.execute('SELECT * FROM books;').fetchall()\n",
+    "\n",
+    "    return jsonify(all_books)\n",
+    "\n",
+    "\n",
+    "\n",
+    "@app.errorhandler(404)\n",
+    "def page_not_found(e):\n",
+    "    return \"<h1>404</h1><p>The resource could not be found.</p>\", 404\n",
+    "\n",
+    "\n",
+    "@app.route('/api/v1/resources/books', methods=['GET'])\n",
+    "def api_filter():\n",
+    "    query_parameters = request.args\n",
+    "\n",
+    "    id = query_parameters.get('id')\n",
+    "    published = query_parameters.get('published')\n",
+    "    author = query_parameters.get('author')\n",
+    "\n",
+    "    query = \"SELECT * FROM books WHERE\"\n",
+    "    to_filter = []\n",
+    "\n",
+    "    if id:\n",
+    "        query += ' id=? AND'\n",
+    "        to_filter.append(id)\n",
+    "    if published:\n",
+    "        query += ' published=? AND'\n",
+    "        to_filter.append(published)\n",
+    "    if author:\n",
+    "        query += ' author=? AND'\n",
+    "        to_filter.append(author)\n",
+    "    if not (id or published or author):\n",
+    "        return page_not_found(404)\n",
+    "\n",
+    "    query = query[:-4] + ';'\n",
+    "\n",
+    "    conn = sqlite3.connect('books.db')\n",
+    "    conn.row_factory = dict_factory\n",
+    "    cur = conn.cursor()\n",
+    "\n",
+    "    results = cur.execute(query, to_filter).fetchall()\n",
+    "\n",
+    "    return jsonify(results)\n",
+    "\n",
+    "app.run()"
+   ]
+  }
+ ],
+ "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
+}

Detecting_Image(main_file).ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "\n",
+    "face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n",
+    "eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')\n",
+    "\n",
+    "cap = cv2.VideoCapture(0)\n",
+    "\n",
+    "while True:\n",
+    "    ret, img = cap.read()\n",
+    "    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
+    "    faces = face_cascade.detectMultiScale(gray, 1.3, 5)\n",
+    "\n",
+    "    for (x, y, w, h) in faces:\n",
+    "        cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)\n",
+    "        roi_gray = gray[y:y + h, x:x + w]\n",
+    "        roi_color = img[y:y + h, x:x + w]\n",
+    "\n",
+    "        eyes = eye_cascade.detectMultiScale(roi_gray)\n",
+    "        for (ex, ey, ew, eh) in eyes:\n",
+    "            cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 2)\n",
+    "\n",
+    "    cv2.imshow('img', img)\n",
+    "    k = cv2.waitKey(30) & 0xff\n",
+    "    if k == 27:\n",
+    "        break\n",
+    "\n",
+    "cap.release()\n",
+    "cv2.destroyAllWindows()"
+   ]
+  },
+  {
+   "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

Emotion_Prediction.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fer import FER\n",
+    "import matplotlib.pyplot as plt \n",
+    "img = plt.imread(\"happy.jpg\")\n",
+    "detector = FER(mtcnn=True)\n",
+    "print(detector.detect_emotions(img))\n",
+    "plt.imshow(img)"
+   ]
+  }
+ ],
+ "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
+}

Importing_fer_libraries.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fer import Video\n",
+    "from fer import FER\n",
+    "video_filename = \"D:/python/YouTube.mp4\"\n",
+    "video = Video(video_filename)\n",
+    "# Analyze video, displaying the output\n",
+    "detector = FER(mtcnn=True)\n",
+    "video.analyze(detector, display=True)"
+   ]
+  }
+ ],
+ "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
+}

README.md

@@ -1 +1 @@
-"# DCod.X" 
+"# DCod.X" 

Stachostic_Gradient_Descent_Algorithm.ipynb

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+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

Training_images_data.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "from fer import FER\n",
+    "detector = FER(mtcnn=True) \n",
+    "image = cv2.imread(\"andrew_ng.jpg\")\n",
+    "result = detector.detect_emotions(image)"
+   ]
+  },
+  {
+   "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}