grahamanderson · March 11, 2018 13:43
diff --git a/QR Code.ipynb b/QR Code.ipynb
 {
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Domain Catalog QR Code Generator\n- takes a long url with GA utm parameters\n- shortens the url\n- puts deal text (20% off) in the middle of the QR\n- Changes the Color to match the category\n\n#### Next up \n- Feed it a sku, a color, and some deal text, get back the QR code\n- Figure out how to change color of the logo"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pyqrcode\nfrom pyqrcode import QRCode\nfrom PIL import Image, ImageFont, ImageDraw\nfrom PIL import ImageFont\nimport cairosvg\nimport textwrap\n\nfrom pyshorteners import Shortener\nfrom urllib.parse import urlparse, parse_qsl, urlsplit\nfrom matplotlib.pyplot import imshow\nimport matplotlib.pyplot as mplot\nimport numpy as np\n\nimport pandas as pd\nimport numpy as np\nimport pickle\nimport random\n\n# KEYS for SERVICES\njson_data = json.load(open('./data/api_key.json'))\nGEONAMES_API_KEY = json_data['geonames-api']\nGOOGLE_API_KEY = json_data['google-api']\nCRAWLERA_API_KEY = json_data['crawlera-api']\nPUSHBULLET_API_KEY = json_data['pushbullet-api']\n\nshortener = Shortener('Google', api_key=GOOGLE_API_KEY)\n\nDEAL_FONT = './fonts/Proxima Nova/ProximaNova-Bold.otf'\nOFF_FONT = './fonts/Proxima Nova/ProximaNova-Regular.otf'\n\n# Catalog Colors\nd = {\n    'orange': (242, 126, 9),\n    'blue': (34, 72, 115),\n    'white': (255, 255, 255),\n    'brown': (82, 48, 19),\n    'green': (65, 117, 5),\n    'red': (162, 1, 21),\n    'dark_grey': (74, 74, 74)\n}\n\nPRDCAT = {\n    'top_sellers': 0,\n    'sinks': 1,\n    'adhesives': 2,\n    'surfacing': 3,\n    'tools': 4,\n    'general': 5\n}\n\nQR_IDS = random.sample(range(100000), 100000)\nCATALOG_IDS = random.sample(range(100000), 100000)",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "%matplotlib inline\n%config InlineBackend.figure_format = 'retina'",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Functions"
    },
    {
      "metadata": {
        "code_folding": [
          4,
          12,
          23,
          31
        ],
        "trusted": true
      },
      "cell_type": "code",
      "source": "def long_url_from_sku(sku, qr_id=random.choice(QR_IDS)):\n    url = 'https://inlive.domainindustries.com/?search={0}&qrid={1}'\n    return url.format(sku, qr_id)\n\ndef long_url_from_sku_old(sku,\n                      catalog_id=random.choice(CATALOG_IDS),\n                      prdcat_id=PRDCAT['top_sellers'],\n                      qr_id=random.choice(QR_IDS)):\n    url = 'https://inlive.domainindustries.com/?search={0}&catalog_id={1}&prdcatid={2}&qrid={3}'\n\n    return url.format(sku, catalog_id, prdcat_id, qr_id)\n\ndef extract_sku_from_url(long_url):\n    ''' url = 'https://domainindustries.com/kohler-vaulttm-shortened-apron-front-undermount-single-bowl-kitchen-sink?utm_source=google&catalog_id=1&product_id=K-3943-NA&cat_id=107'\n        extract_product_id_from_url(url)\n    '''\n    try:\n        query = urlsplit(long_url).query\n        params = parse_qsl(query)\n        return dict(params)\n    except Exception as e:\n        print('coudn\\'t find the product_id in url : {}'.format(long_url))\n        \ndef change_color(im, new_color):\n    pixels = im.load() # create the pixel map\n    for i in range(im.size[0]): # for every pixel:\n        for j in range(im.size[1]):\n            if pixels[i,j] != d['white']: #d['orange']:\n                pixels[i,j] = new_color\n    return im\n\ndef draw_and_center_text_on_image(im, msg, font_color='black', font_size=16): \n    ''' im = Image.open(\"./images/QR_trans.png\")\n        draw_and_center_text(im, '40%\\nOFF', font_color=d['blue'], font_size=44)\n    '''\n    im_size = im.size\n    W = im.size[0]\n    H = im.size[1]\n    deal_font = ImageFont.truetype(DEAL_FONT, font_size)\n    off_font = ImageFont.truetype(OFF_FONT, 32)\n    draw = ImageDraw.Draw(im)\n    w, h = draw.textsize(msg, font=deal_font)\n    w_off, h_off = draw.textsize('OFF', font=off_font)\n    draw.multiline_text(((W-w)/2,(H-h)/2), msg,font=deal_font, align='center',fill=font_color)\n    #draw.multiline_text(((W-w_off)/2,((H+h)/2)), text='OFF',font=off_font, align='center',fill=font_color)\n    #im.show() \n    return im\n\ndef draw_and_center_text_no_image(msg, font_color='black', font_size=16): \n    \n    deal_font = ImageFont.truetype(DEAL_FONT, font_size)\n    w, h = draw.textsize(msg, font=deal_font)\n    draw.multiline_text(((W-w)/2,(H-h)/2), msg,font=deal_font, align='center',fill=font_color)\n    #im.show() \n    return im\n\ndef qr_code(sku, color,font_size=24,msg=False ):  \n    try:\n        \n        # Search Salesdf for the provide sku\n        long_url = long_url_from_sku(sku)\n        #print(long_url)\n        \n        # Build the URL from product_name, sku, cat_id, catalog_id, and etc\n        # utm_source=google&catalog_id=1&product_id=K-3943-NA&cat_id=107\n        \n        # Extract Product_ID from the url (becomes the file name of the QR png image)\n        #sku = extract_sku_from_url(long_url)['product_id']\n\n        # Shorten URL\n        short_url = shortener.short(long_url)\n        \n        # Generate QR code from shortened url\n        #url = pyqrcode.QRCode(short_url,error = 'H')\n        url = pyqrcode.create(short_url, error = 'H')\n        url.png('./images/test.png',scale=10, module_color=color )\n        im = Image.open('./images/test.png')\n        im = im.convert(\"RGBA\")\n        #im.show()\n           \n        # Add a White Border AROUND the QR Code\n        box = (135,135,235,235)\n        im.crop(box)\n        \n        # Paste a White Box WITHIN the QR\n        region = Image.new('RGB', (100, 100), color = 'white')\n        region = region.resize((box[2] - box[0], box[3] - box[1]))\n        im.paste(region,box)\n        \n        \n        if msg==False:\n            # Put in a logo (or maybe add the slot machine icon later)\n            logo = Image.open(\"./images/domain_logo.png\")\n            logo = logo.resize((box[2] - box[0], box[3] - box[1]))\n            im.paste(logo,box)\n        \n        else:\n            # Draw the Deal Text into the White Empty Center of the QR\n            msg = msg.replace(' ','\\n')\n            deal_font = ImageFont.truetype(DEAL_FONT, font_size)\n            text_size = deal_font.getsize(msg)\n            draw = ImageDraw.Draw(im)\n            W = im.size[0]\n            H = im.size[1]\n            w, h = draw.textsize(msg, font=deal_font)\n            draw.multiline_text(((W-w)/2,(H-h)/2), msg, font=deal_font, fill=color, align='center')\n\n        # Save File\n        file = './images/qr_codes/{0}.png'.format(sku)\n        im.save(file, 'PNG')\n        #im.save(file, 'JPEG', quality=90)\n\n        # Display within Jupyter \n        pil_im = Image.open(file, 'r')\n        cur_axes = mplot.gca()\n        cur_axes.axes.get_xaxis().set_visible(False)\n        cur_axes.axes.get_yaxis().set_visible(False)\n        cur_axes.axes.axis('off')\n        imshow(np.asarray(pil_im))\n\n        return short_url, sku, file\n    except Exception as e:\n        print(\"I got here\")\n        print(e)",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Load salesdf to get random sku"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "salesdf = pd.read_pickle('./data/pickle/magento_salesdf.pkl')",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "sku = salesdf.sample(1)['sku'].iloc[0]\nqr_code(sku, d['blue'], msg='5%\\nOFF',font_size=38)",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": "('https://goo.gl/MLYFZN', 'OM-1616', './images/qr_codes/OM-1616.png')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x11b9c8cc0>",
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 235,
              "height": 235
            }
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "#OM-902TM&catalog_id=37065&prdcatid=0&qrid=19278\n\nqr_code('OM-902TM', d['blue'], msg='5%\\nOFF',font_size=38)",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "text/plain": "('https://goo.gl/GKy2qU', 'OM-902TM', './images/qr_codes/OM-902TM.png')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<matplotlib.figure.Figure at 0x11ba040b8>",
            "image/png": 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\n"
          },
          "metadata": {
            "image/png": {
              "width": 235,
              "height": 235
            }
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---\n## Random Todos\n\n#### Unique Skus for Andrew"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "cols = ['sku', 'product_name', 'product_category', 'product_subcategory','product_brand']\n\n(salesdf[cols].drop_duplicates('sku')\n                .sort_values(['product_category','product_brand','product_subcategory'])\n                .to_csv('./data/csv/_exports/skus_images_required_from_andrew.csv', index=False)\n)",
      "execution_count": 7,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "---"
    }
  ],
  "metadata": {
    "_draft": {
      "nbviewer_url": "https://gist.github.com/49c8e5a196c4256230fdb4fb5124710d"
    },
    "gist": {
      "id": "49c8e5a196c4256230fdb4fb5124710d",
      "data": {
        "description": "QR Code from Provided Sku",
        "public": true
      }
    },
    "kernelspec": {
      "name": "conda-env-jupyter-py",
      "display_name": "Python [conda env:jupyter]",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.3",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "toc": {
      "threshold": 4,
      "number_sections": true,
      "toc_cell": false,
      "toc_window_display": false,
      "toc_section_display": "block",
      "sideBar": true,
      "navigate_menu": true,
      "moveMenuLeft": true,
      "widenNotebook": false,
      "colors": {
        "hover_highlight": "#DAA520",
        "selected_highlight": "#FFD700",
        "running_highlight": "#FF0000",
        "wrapper_background": "#FFFFFF",
        "sidebar_border": "#EEEEEE",
        "navigate_text": "#333333",
        "navigate_num": "#000000"
      },
      "nav_menu": {
        "height": "12px",
        "width": "252px"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
No results found