Upload folder using huggingface_hub (#1)

- Upload folder using huggingface_hub (81984557c151c3b6fe28fc91b4fca23988812e04)

.dockerignore

@@ -0,0 +1,6 @@
+Dockerfile
+README.md
+*.pyc
+*.pyo
+*.pyd
+__pycache__

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+static/sample_images/twitter_image.png filter=lfs diff=lfs merge=lfs -text
+white_box_cartoonizer/saved_models/model-33999.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,4 @@
+__pycache__/
+.vscode/*
+static/uploaded_videos/*.mp4
+static/cartoonized_images/*.jpg

Dockerfile

@@ -0,0 +1,25 @@
+# Use the official lightweight Python image.
+# https://hub.docker.com/_/python
+FROM python:3.7-slim
+
+# Copy local code to the container image.
+ENV APP_HOME /app
+WORKDIR $APP_HOME
+COPY . ./
+
+ENV GOOGLE_APPLICATION_CREDENTIALS "./token.json"
+
+RUN apt-get update && apt-get install -y \
+    libglib2.0-0 \
+    libsm6 \
+    libxext6 \
+    libxrender-dev \
+    ffmpeg
+# Install production dependencies.
+RUN pip install -r requirements.txt
+
+# Run the web service on container startup. Here we use the gunicorn
+# webserver, with one worker process and 8 threads.
+# For environments with multiple CPU cores, increase the number of workers
+# to be equal to the cores available.
+CMD exec gunicorn --bind 0.0.0.0:8080 --workers 1 --threads 8 --timeout 0 app:app

LICENSE

@@ -0,0 +1,104 @@
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README.md

@@ -1,11 +1,165 @@
+# Cartoonizer
+
+> Convert images and videos into a cartoon!
+
+The webapp is deployed here - https://cartoonize-lkqov62dia-de.a.run.app
+<div style="text-align:center"><img height="100" alt="Powered by Algorithmia" style="border-width:0" src="static/sample_images/algorithmia.jpeg" /></div>
+
+You can find a writeup on this webapp's architecture [here](https://medium.com/@Niraj_pandkar/how-we-built-an-inexpensive-scalable-architecture-to-cartoonize-the-world-8610050f90a0)!
+
+---
+
+## Contents
+
+- [Prerequisites for Google Cloud and Algorithmia](#prerequisites-for-google-cloud-and-algorithmia)
+- [Installation](#installation)
+  - [Docker](#using-docker)
+  - [VirtualEnv](#using-virtualenv)
+  - [Google Colab](#using-google-colab)
+- [Sample Image and Video](#sample-image-and-video)
+
+---
+
+## Prerequisites for Google Cloud and Algorithmia
+
+**These are important steps if you want to leverage Google buckets, signed URLs and Algorithmia's platform. Skip this if you want to run locally / colab.**
+
+### Cloud Run authentication
+To use any functionalities pertaining to Google Cloud, you'll need a global authentication file (JSON). You can obtain this JSON by following the steps given here - [Getting started with authentication](https://cloud.google.com/docs/authentication/getting-started)
+
+After you get the JSON file, rename it to `token.json` (so that it's compatible with the codebase). 
+
+Set the environment variable in your terminal -
+```
+export GOOGLE_APPLICATION_CREDENTIALS="/path/to/token.json"
+```
+**Notes**:
+- You can set it permanently by adding this line to `~/.bashrc`.
+- `Dockerfile` already includes the setting of this particular environment variable. :)
+
+
+### Algorithmia
+We used the Serveless AI Layer product of [Algorithmia](https://algorithmia.com/serverless-ai-layer) for inference on videos.
+To learn more on how to deploy your model in Algorithmia, check here - https://algorithmia.com/developers
+
+---
+
+## Installation
+
+### Application tested on:
+
+- python 3.7
+- tensorflow 2.1.0 
+- tf_slim 1.1.0
+- ffmpeg 3.4.8
+- Cuda version 10.1
+- OS: Linux (Ubuntu 18.04)
+
+### Using Docker
+
+The easiest way to get the webapp running is by using the Dockerfile:
+
+1. `cd` into the root directory and build the image
+```
+docker build -t cartoonize .
+```
+**Note**: Set the appropriate values in `config.yaml` before building the image.
+
+2. Run the container by exposing the appropriate ports
+```
+docker run -p 8080:8080 cartoonize
+```
+
+
+### Using `virtualenv`
+
+1. Make a virtual environment using `virutalenv` and activate it
+```
+virtualenv -p python3 cartoonize
+source cartoonize/bin/activate
+```
+2. Install python dependencies
+```
+pip install -r requirements.txt
+```
+3. Run the webapp. Be sure to set the appropriate values in `config.yaml` file before running the application.
+```
+python app.py
+```
+
+### Using [Google Colab](https://colab.research.google.com/drive/1oDhMEVMcsRbe7bt-2A7cDsx44KQpQwuB?usp=sharing)
+1. Clone the repository using either of the below mentioned way:
+   - Using Command:
+        - Create a new Notebook in Colab and in the cell execute the below command.  
+        
+        ```
+         ! git clone https://github.com/experience-ml/cartoonize.git
+        ```
+        **Note:** Don't forget to add `!` at the beginning of the command
+        
+    - From Colab User Interface
+ ```
+        Open Colab
+            └── File
+                 └── Open Notebook
+                          └── Github
+                                └── paste the Url of the repository
+ ```
+ Note :  Before running the application change the runtime to GPU for processing videos but you for images CPU shall also work just fine.
+ ```
+            Runtime
+               └── Change runtime type
+                           └── Select GPU
+ ```
+2. After cloning the repository navigate to the `/cartoonize` using below command in the notebook cell:
+
+   ```
+   %cd cartoonize
+   ```
+3. Run the below commands in the notebook cell to install the requirements. 
+
+   ```
+   !pip install -r requirements.txt
+   ```
+
+
+4. In config.yaml file set: 
+
+   ``` 
+   colab-mode: true 
+   ``` 
+   
+5. Launch the flask app on ngrok
+
+   ```
+   !python app.py
+   ```
+
+#### Note : Sample [Google Colab Notebook](https://colab.research.google.com/drive/1oDhMEVMcsRbe7bt-2A7cDsx44KQpQwuB?usp=sharing) for reference
+
 ---
-title: Cartoonize
-emoji: 📈
-colorFrom: blue
-colorTo: purple
-sdk: static
-pinned: false
-license: mit
+
+## Sample Image and Video
+
+### Emma Watson Cartoonized
+<img alt="Emma Watson Cartoonized" style="border-width:0" src="static/sample_images/twitter_image.png" />
+
+### Youtube Video of Avenger's Bar Scene Cartoonized
+[![Cartoonized version of Avenger's bar scene](http://img.youtube.com/vi/GqduSLcmhto/0.jpg)](http://www.youtube.com/watch?v=GqduSLcmhto "AVENGERS BAR SCENE [Cartoonized Version]")
+
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## License
+
+1. Copyright © Cartoonizer ([Demo webapp](https://cartoonize-lkqov62dia-de.a.run.app/))
+
+    - Authors: [Niraj Pandkar](https://twitter.com/Niraj_pandkar) and [Tejas Mahajan](https://twitter.com/tjdevWorks).
+
+    - Licensed under the [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode) 
+    - Commercial application is prohibited by license
+
+
+2. Copyright (C) Xinrui Wang, Jinze Yu. ([White box cartoonization](https://github.com/SystemErrorWang/White-box-Cartoonization))
+    - All rights reserved. 
+    - Licensed under the CC BY-NC-SA 4.0 
+    - Also, Commercial application is prohibited license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).

app.py

@@ -0,0 +1,178 @@
+import os
+import io
+import uuid
+import sys
+import yaml
+import traceback
+
+with open('./config.yaml', 'r') as fd:
+    opts = yaml.safe_load(fd)
+
+sys.path.insert(0, './white_box_cartoonizer/')
+
+import cv2
+from flask import Flask, render_template, make_response, flash
+import flask
+from PIL import Image
+import numpy as np
+import skvideo.io
+if opts['colab-mode']:
+    from flask_ngrok import run_with_ngrok #to run the application on colab using ngrok
+
+
+from cartoonize import WB_Cartoonize
+
+if not opts['run_local']:
+    if 'GOOGLE_APPLICATION_CREDENTIALS' in os.environ:
+        from gcloud_utils import upload_blob, generate_signed_url, delete_blob, download_video
+    else:
+        raise Exception("GOOGLE_APPLICATION_CREDENTIALS not set in environment variables")
+    from video_api import api_request
+    # Algorithmia (GPU inference)
+    import Algorithmia
+
+app = Flask(__name__)
+if opts['colab-mode']:
+    run_with_ngrok(app)   #starts ngrok when the app is run
+
+app.config['UPLOAD_FOLDER_VIDEOS'] = 'static/uploaded_videos'
+app.config['CARTOONIZED_FOLDER'] = 'static/cartoonized_images'
+
+app.config['OPTS'] = opts
+
+## Init Cartoonizer and load its weights 
+wb_cartoonizer = WB_Cartoonize(os.path.abspath("white_box_cartoonizer/saved_models/"), opts['gpu'])
+
+def convert_bytes_to_image(img_bytes):
+    """Convert bytes to numpy array
+
+    Args:
+        img_bytes (bytes): Image bytes read from flask.
+
+    Returns:
+        [numpy array]: Image numpy array
+    """
+    
+    pil_image = Image.open(io.BytesIO(img_bytes))
+    if pil_image.mode=="RGBA":
+        image = Image.new("RGB", pil_image.size, (255,255,255))
+        image.paste(pil_image, mask=pil_image.split()[3])
+    else:
+        image = pil_image.convert('RGB')
+    
+    image = np.array(image)
+    
+    return image
+
+@app.route('/')
+@app.route('/cartoonize', methods=["POST", "GET"])
+def cartoonize():
+    opts = app.config['OPTS']
+    if flask.request.method == 'POST':
+        try:
+            if flask.request.files.get('image'):
+                img = flask.request.files["image"].read()
+                
+                ## Read Image and convert to PIL (RGB) if RGBA convert appropriately
+                image = convert_bytes_to_image(img)
+
+                img_name = str(uuid.uuid4())
+                
+                cartoon_image = wb_cartoonizer.infer(image)
+                
+                cartoonized_img_name = os.path.join(app.config['CARTOONIZED_FOLDER'], img_name + ".jpg")
+                cv2.imwrite(cartoonized_img_name, cv2.cvtColor(cartoon_image, cv2.COLOR_RGB2BGR))
+                
+                if not opts["run_local"]:
+                    # Upload to bucket
+                    output_uri = upload_blob("cartoonized_images", cartoonized_img_name, img_name + ".jpg", content_type='image/jpg')
+
+                    # Delete locally stored cartoonized image
+                    os.system("rm " + cartoonized_img_name)
+                    cartoonized_img_name = generate_signed_url(output_uri)
+                    
+
+                return render_template("index_cartoonized.html", cartoonized_image=cartoonized_img_name)
+
+            if flask.request.files.get('video'):
+                
+                filename = str(uuid.uuid4()) + ".mp4"
+                video = flask.request.files["video"]
+                original_video_path = os.path.join(app.config['UPLOAD_FOLDER_VIDEOS'], filename)
+                video.save(original_video_path)
+                
+                modified_video_path = os.path.join(app.config['UPLOAD_FOLDER_VIDEOS'], filename.split(".")[0] + "_modified.mp4")
+                
+                ## Fetch Metadata and set frame rate
+                file_metadata = skvideo.io.ffprobe(original_video_path)
+                original_frame_rate = None
+                if 'video' in file_metadata:
+                    if '@r_frame_rate' in file_metadata['video']:
+                        original_frame_rate = file_metadata['video']['@r_frame_rate']
+
+                if opts['original_frame_rate']:
+                    output_frame_rate = original_frame_rate
+                else:
+                    output_frame_rate = opts['output_frame_rate']    
+
+                output_frame_rate_number = int(output_frame_rate.split('/')[0])
+
+                #change the size if you want higher resolution :
+                ############################
+                # Recommnded width_resize  #
+                ############################
+                #width_resize = 1920 for 1080p: 1920x1080.
+                #width_resize = 1280 for 720p: 1280x720.
+                #width_resize = 854 for 480p: 854x480.
+                #width_resize = 640 for 360p: 640x360.
+                #width_resize = 426 for 240p: 426x240.
+                width_resize=opts['resize-dim']
+
+                # Slice, Resize and Convert Video as per settings
+                if opts['trim-video']:
+                    #change the variable value to change the time_limit of video (In Seconds)
+                    time_limit = opts['trim-video-length']
+                    if opts['original_resolution']:
+                        os.system("ffmpeg -hide_banner -loglevel warning -ss 0 -i '{}' -t {} -filter:v scale=-1:-2 -r {} -c:a copy '{}'".format(os.path.abspath(original_video_path), time_limit, output_frame_rate_number, os.path.abspath(modified_video_path)))
+                    else:
+                        os.system("ffmpeg -hide_banner -loglevel warning -ss 0 -i '{}' -t {} -filter:v scale={}:-2 -r {} -c:a copy '{}'".format(os.path.abspath(original_video_path), time_limit, width_resize, output_frame_rate_number, os.path.abspath(modified_video_path)))
+                else:
+                    if opts['original_resolution']:
+                       os.system("ffmpeg -hide_banner -loglevel warning -ss 0 -i '{}' -filter:v scale=-1:-2 -r {} -c:a copy '{}'".format(os.path.abspath(original_video_path), output_frame_rate_number, os.path.abspath(modified_video_path)))
+                    else:
+                        os.system("ffmpeg -hide_banner -loglevel warning -ss 0 -i '{}' -filter:v scale={}:-2 -r {} -c:a copy '{}'".format(os.path.abspath(original_video_path), width_resize, output_frame_rate_number, os.path.abspath(modified_video_path)))
+                
+                audio_file_path = os.path.join(app.config['UPLOAD_FOLDER_VIDEOS'], filename.split(".")[0] + "_audio_modified.mp4")
+                os.system("ffmpeg -hide_banner -loglevel warning -i '{}' -map 0:1 -vn -acodec copy -strict -2  '{}'".format(os.path.abspath(modified_video_path), os.path.abspath(audio_file_path)))
+
+                if opts["run_local"]:
+                    cartoon_video_path = wb_cartoonizer.process_video(modified_video_path, output_frame_rate)
+                else:
+                    data_uri = upload_blob("processed_videos_cartoonize", modified_video_path, filename, content_type='video/mp4', algo_unique_key='cartoonizeinput')
+                    response = api_request(data_uri)
+                    # Delete the processed video from Cloud storage
+                    delete_blob("processed_videos_cartoonize", filename)
+                    cartoon_video_path = download_video('cartoonized_videos', os.path.basename(response['output_uri']), os.path.join(app.config['UPLOAD_FOLDER_VIDEOS'], filename.split(".")[0] + "_cartoon.mp4"))
+                
+                ## Add audio to the cartoonized video
+                final_cartoon_video_path = os.path.join(app.config['UPLOAD_FOLDER_VIDEOS'], filename.split(".")[0] + "_cartoon_audio.mp4")
+                os.system("ffmpeg -hide_banner -loglevel warning -i '{}' -i '{}' -codec copy -shortest '{}'".format(os.path.abspath(cartoon_video_path), os.path.abspath(audio_file_path), os.path.abspath(final_cartoon_video_path)))
+
+                # Delete the videos from local disk
+                os.system("rm {} {} {} {}".format(original_video_path, modified_video_path, audio_file_path, cartoon_video_path))
+
+                return render_template("index_cartoonized.html", cartoonized_video=final_cartoon_video_path)
+        
+        except Exception:
+            print(traceback.print_exc())
+            flash("Our server hiccuped :/ Please upload another file! :)")
+            return render_template("index_cartoonized.html")
+    else:
+        return render_template("index_cartoonized.html")
+
+if __name__ == "__main__":
+    # Commemnt the below line to run the Appication on Google Colab using ngrok
+    if opts['colab-mode']:
+        app.run()
+    else:
+        app.run(debug=False, host='0.0.0.0', port=int(os.environ.get('PORT', 8080)))

config.yaml

@@ -0,0 +1,10 @@
+---
+run_local: true #Set this to true if you are running locally, false if you have configured the Google buckets and algorithmia code. 
+gpu: true #Set this to true if you want to use the GPU
+trim-video: true #Set this to false if you want to process full video
+trim-video-length: 15 #Max number of seconds you want to trim the video from start
+original_frame_rate: false #If False output_frame_rate will be used else original video detected frame rate will be used, if no metadata found will use output_frame_rate  
+output_frame_rate: '24/1' #Set the output frame rate, if original resolution
+colab-mode: false #Set true if you are executing in colab mode
+original_resolution: false # Set to true if you don't want to resize the original video
+resize-dim: 720 #The width of the video will be resized to specified number maintaining aspect ratio

gcloud_utils.py

@@ -0,0 +1,68 @@
+"""
+Google Cloud Util Functions:
+- Upload to Google Storage Bucket
+- Delete from Google Storage Bucket
+- Generate signed URL
+"""
+import os
+import datetime
+
+from google.cloud import storage
+from google.cloud.storage.blob import Blob
+
+## Google Storage Client
+client = storage.Client()
+
+def upload_blob(bucket_name, source_file_name, destination_blob_name, content_type='', algo_unique_key=''):
+    """Uploading File to Google Storage Bucket
+
+    Args:
+        bucket_name (str): Google Storage Bucket Name
+        source_file_name (str): Local Absolute Filpath to upload
+        destination_blob_name (str): File Name used to store in bucket
+        content_type (str, optional): The content type of the file being uploaded
+        algo_unique_key (str, optional): [description]. Defaults to ''. Algorithmia Data Source Bucket Unique Key
+
+    Returns:
+        [str]: Google Storage Object URI, if algorithmia key is given the same is modified.
+    """
+    
+    bucket = client.get_bucket(bucket_name)
+    blob = bucket.blob(destination_blob_name)
+    blob.upload_from_filename(source_file_name, content_type=content_type)
+
+    data_uri = blob.self_link
+    
+    if algo_unique_key!="":
+        return os.path.join("gs+{}://{}".format(algo_unique_key, bucket_name), data_uri.split("/")[-1])
+    
+    return os.path.join("gs://{}".format(bucket_name), data_uri.split("/")[-1])
+
+def delete_blob(bucket_name, blob_name):
+    """Deletes a blob from the bucket."""
+    # bucket_name = "your-bucket-name"
+    # blob_name = "your-object-name"
+
+    bucket = client.bucket(bucket_name)
+    blob = bucket.blob(blob_name)
+    blob.delete()
+
+    print("Blob {} deleted.".format(blob_name))
+
+def download_video(bucket_name, filename, output_filename):
+    bucket = client.get_bucket(bucket_name)
+    # Create a blob object from the filepath
+    blob = bucket.blob(filename)
+    # Download the file to a destination
+    blob.download_to_filename(output_filename)
+
+    return output_filename
+
+def generate_signed_url(output_uri):
+    expiration_time = datetime.timedelta(minutes=5)
+
+    blob = Blob.from_string(output_uri, client=client)
+    
+    signed_url = blob.generate_signed_url(expiration=expiration_time, version='v4', response_disposition='attachment')
+
+    return signed_url            

requirements.txt

@@ -0,0 +1,11 @@
+Flask==1.0.2
+gunicorn==20.0.4
+Pillow==6.2.0
+opencv_python==4.2.0.34
+tensorflow==2.1.0
+google-cloud-storage==1.29.0
+algorithmia==1.3.0
+scikit-video==1.1.11
+tf_slim==1.1.0
+PyYaml==5.3.1
+flask-ngrok

static/cartoonized_images/folder.txt

@@ -0,0 +1,3 @@
+when the days are cold
+and the cards all fold 
+and the saints we see are made of gold!

static/uploaded_videos/folder_required.txt

@@ -0,0 +1 @@
+Required for purposes of github.

templates/index_cartoonized.html

@@ -0,0 +1,425 @@
+<!DOCTYPE html>
+<html>
+<head>
+    <!-- Global site tag (gtag.js) - Google Analytics -->
+    <script async src="https://www.googletagmanager.com/gtag/js?id=UA-173468417-1"></script>
+    <script>
+    window.dataLayer = window.dataLayer || [];
+    function gtag(){dataLayer.push(arguments);}
+    gtag('js', new Date());
+
+    gtag('config', 'UA-173468417-1');
+    </script>
+
+    <meta charset="utf-8" />
+    <meta http-equiv="X-UA-Compatible" content="IE=edge">
+
+    <meta name="twitter:card" content="summary_large_image">
+    <meta name="twitter:site" content="@Niraj_pandkar">
+    <meta name="twitter:title" content="Cartoonized your world!">
+    <meta name="twitter:description" content="Want to see your cartoonized self? You can try image or video.">
+    <meta name="twitter:creator" content="@Niraj_pandkar">
+    <meta name="twitter:image" content="static/sample_images/twitter_image.png">
+    <meta name="twitter:domain" content="https://cartoonize-lkqov62dia-de.a.run.app/cartoonize">
+
+    <title>Cartoonizer</title>
+    <meta name="viewport" content="width=device-width, initial-scale=1">
+
+    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/semantic-ui@2.3.3/dist/semantic.min.css">
+    <script
+        src="https://code.jquery.com/jquery-3.1.1.min.js"
+        integrity="sha256-hVVnYaiADRTO2PzUGmuLJr8BLUSjGIZsDYGmIJLv2b8="
+        crossorigin="anonymous">
+    </script>
+    <script src="https://cdn.jsdelivr.net/npm/semantic-ui@2.3.3/dist/semantic.min.js"></script>
+    <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
+    <style>
+        html {
+        box-sizing: border-box;
+        }
+        *, *:before, *:after {
+        box-sizing: inherit;
+        }
+        body{
+            background-color: whitesmoke;
+        }
+
+        iframe[src*=youtube] {
+                    display: block;
+                    margin: 0 auto;
+                    max-width: 100%;
+                    padding-bottom: 10px;
+                }
+    </style>
+</head>
+
+<body>
+        <div id="loader" class="ui disabled dimmer">
+                <div class="ui text loader">Preparing your cartoon! May take an extra few seconds for video :)</div>
+              </div>
+
+
+<div class='ui padded centered grid'>
+    <!-- Messaging system -->
+    <div class="row">
+        <div class="center aligned column">
+            {% with messages = get_flashed_messages(with_categories=true) %}
+                {% if messages %}
+                    <div style="height:10%; display:flex; align-items: center; justify-content: center">
+                        {% for category, message in messages %}
+                            {% if category == error%}
+                                <h3 style="color:red">{{ message }}</h3>
+                            {% else %}
+                                <h3 style="color:green">{{ message }}</h3>
+                            {% endif %}
+                        {% endfor %}
+                    </div>
+                {% endif %}
+            {% endwith %}
+        </div>
+    </div>
+    
+
+    <!-- Heading of the page -->
+    <div class="row">
+        <div class='center aligned column'>
+            <h1>Cartoonize your world!</h1>
+        </div>
+    </div>
+    
+    <!-- Submission form -->
+    <div class="row">
+        <div class='center aligned column'>
+            <form id='formsubmit' method="post" action="cartoonize" enctype = "multipart/form-data">
+    
+                <div class="ui buttons">
+                    <div id='uploadimage' class="ui button" style="align-items: center;">
+                        <i class="image icon"></i>
+                        Image
+                    </div>
+                    <div class="or"></div>
+                    <div id='uploadvideo' class="ui button" style="align-items: center;">
+                        <i class="video icon"></i>
+                        Video
+                        <span style="font-size: 10px;">(Max 30MB)</span>
+                    </div>
+                </div>
+        
+                <input type='file' id='hiddeninputfile' accept="image/*" name = 'image' style="display: none"/>
+                <input type="file" id="hiddeninputvideo" accept="video/*" name = 'video' style="display: none">
+                <!-- <input id='submitbutton' class='ui button' type='submit'/> -->
+            </form>
+        </div>
+    </div>
+    {%if cartoonized_image or cartoonized_video%}
+    <div class="row">
+        <div class="column">
+            <!-- Nested grid -->
+            <div class="ui centered grid">
+                <div class="row">
+                    <div class="center aligned column">
+                        {%if cartoonized_image%}
+                        <div class="ui centered card">
+                            <div class="image">
+                                <img src="{{ cartoonized_image }}">
+                            </div>
+                        </div>
+                        {%endif%}
+                    
+                        {%if cartoonized_video%}
+                            <video id="player" width="320" height="240" controls>
+                                <source type="video/mp4" src="{{cartoonized_video}}">
+                            </video>
+                        {%endif%}
+                    </div>
+                </div>
+                <div class="row">
+                    {%if cartoonized_video%}
+                        <a href={{cartoonized_video}} download>
+                            
+                            <button class="ui primary button">
+                                <i class="download icon"></i>
+                                Download
+                            </button>
+                        </a>
+                    {%endif%}
+                    {%if cartoonized_image%}
+                        <a href={{cartoonized_image}} download>
+                            <button class="ui primary button">
+                                <i class="download icon"></i>
+                                Download
+                            </button><br>
+                            (Valid for 5 minutes only)
+                        </a>
+                    {%endif%}
+                    
+                </div>
+                <div class="row">
+                    <div class="ui stackable three column grid"> 
+                        <div class="three column row">
+                            <div class="center aligned column">
+                                <a href="https://twitter.com/share?ref_src=twsrc%5Etfw" class="twitter-share-button" data-text="Check your cartoonized version using the link below!" data-url="https://bit.ly/2CjaaJs" data-hashtags="cartoon" data-show-count="false" data-size="large">Tweet</a><script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
+                            </div>
+                    
+                            <div class="center aligned column">
+                                <a href="https://api.whatsapp.com/send?text=Try%20this%20awesome%20AI%20cartoonizer%20using%20-%20https%3A%2F%2Fbit.ly%2F2CjaaJs" target="_blank">
+                                    <button class="mini ui green button">
+                                        <i class="whatsapp icon"></i>Share
+                                    </button>
+                                </a>
+                            </div>
+                            <div class="center aligned column"">
+                                <iframe src="https://www.facebook.com/plugins/share_button.php?href=https%3A%2F%2Fbit.ly%2F2CjaaJs&layout=button&size=large&width=77&height=28&appId" width="77" height="28" style="border:none;overflow:hidden" scrolling="no" frameborder="0" allowTransparency="true" allow="encrypted-media"></iframe>
+                            </div>
+                        </div>
+                       
+                    </div>
+                </div>
+
+            </div>
+        </div>
+    </div>
+    {%endif%}
+
+    <div class="ui divider"></div>
+    <!-- Sample Images -->
+    <div class="row">
+        <!-- <div class="ui stackable grid">
+            <div class="five wide column">
+                <img class="ui medium centered image" src="/static/sample_images/emma2.jpg">
+            </div>
+            <div class="five wide column">
+                <img class="ui medium centered image" src="/static/sample_images/emma2_cartoonized.jpg">
+            </div>
+        </div> -->
+        <div class="five wide column">
+            <img class="ui medium centered image" src="/static/sample_images/emma2.jpg">
+        </div>
+        <div class="five wide column">
+            <img class="ui medium centered image" src="/static/sample_images/emma2_cartoonized.jpg">
+        </div>
+    </div>
+    <div class="row">
+        <div class="five wide column">
+            <img class="ui medium centered image" src="/static/sample_images/spice.jpeg">
+        </div>
+        <div class="five wide column">
+            <img class="ui medium centered image" src="/static/sample_images/spice_cartoonized.jpeg">
+        </div>
+    </div>
+    <div class="row">
+        <div class="five wide column">
+            <img class="ui medium centered image" src="/static/sample_images/cake.jpeg">
+        </div>
+        <div class="five wide column">
+            <img class="ui medium centered image" src="/static/sample_images/cake_cartoonized.jpeg">
+        </div>
+    </div>
+    <div class="row">
+        <div class="center aligned column">
+            <iframe width="560" height="315" src="https://www.youtube.com/embed/omenajmDBm8" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+        </div>
+    </div>
+    <div class="row">
+        <div class="center aligned column">
+            <iframe width="560" height="315" src="https://www.youtube.com/embed/GqduSLcmhto" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+        </div>
+    </div>
+    <div class="row">
+        <div class="center aligned column">
+            <iframe width="560" height="315" src="https://www.youtube.com/embed/0Y29Z7-urqA" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+        </div>
+    </div>
+    <!-- <div class="row">
+            <div class="five wide column">
+                <img src="/static/sample_images/tenor.gif">
+            </div>
+            <div class="five wide column">
+                <img src="/static/sample_images/tenor.gif">
+            </div>
+        </div> -->
+    <!-- FAQs -->
+    <div class="row">
+        <div class="column" style="padding-right: 25px; padding-left: 25px;">
+            <div class="ui centered styled accordion" style="margin:auto;">
+                    <div class="title">
+                        <i class="dropdown icon"></i>
+                        Which paper is this demo based on?
+                    </div>
+                    <div class="content">
+                        <p class="transition hidden">Due credits to the incredible paper - <a target="_blank" href="http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Learning_to_Cartoonize_Using_White-Box_Cartoon_Representations_CVPR_2020_paper.pdf">Learning to Cartoonize Using White-box Cartoon Representations</a>
+                        <p>Official implementation of the paper by the author - <a target="_blank" href="https://github.com/SystemErrorWang/White-box-Cartoonization">Github Link</a></p>
+                    </div>
+
+                    <div class="title">
+                        <i class="dropdown icon"></i>
+                        What are the restrictions of video processing and image processing?
+                    </div>
+                    <div class="content">
+                        <ul class="ui list">
+                            <li>We are currently processing only upto <b>10 second</b> videos, if you happened to upload a video greater than 10 seconds, only <b>first 10 seconds</b> will be considered.</li>
+                            <li>Video File Size Limitation: <b>30MB</b></li>
+                            <li>Image File Formats Supported: <b>jpeg, png</b></li>
+                            <li>Video File Formats Supported: <b>mp4, webm, avi, mkv</b></li>
+                            <li>GIF/TIFF Images are not supported.</li>
+                        </ul>
+                    </div>
+                    
+                    <div class="title">
+                        <i class="dropdown icon"></i>
+                        Is my data stored on your servers?
+                    </div>
+                    <div class="content">
+                        Your data is deleted in the pipeline as you run the demo.
+                    </div>
+
+                    <div class="title">
+                        <i class="dropdown icon"></i>
+                        Where could be Cartoonizer used?
+                    </div>
+                    <div class="content">
+                        <p>Some of the areas where we think it could be applied to -</p>
+                        <ul class="ui list">
+                            <li>Churn out <b>quick prototypes</b> or sprites for animes, cartoons and games</li>
+                            <li>Since it subdues facial features and information in general, it can be used to generate <b>minimal art</b></li>
+                            <li>Games can import short <b>cut scenes</b> very easily <b>without using motion-capture</b></li>
+                            <li>Can be modelled as an assistant to graphic designers or animators.</li>
+                        </ul>
+                    </div>
+                    
+
+                    <div class="title">
+                        <i class="dropdown icon"></i>
+                        Video and Image attributes?
+                    </div>
+                    <div class="content">
+                        <p>Cake and food assortment photos are OC (Original Content). Other than that -</p> 
+                        <ul class="ui list">
+                            <li>Emma Watson Image: <a target="_blank" href="https://static.independent.co.uk/s3fs-public/thumbnails/image/2019/12/20/15/emma-watson-little-women.jpg?w968">Independent UK</a></li>
+                            <li>Rick Astley - Never Gonna Give You Up (Original Video): <a target="_blank" href="https://www.youtube.com/watch?v=dQw4w9WgXcQ">YouTube Link</a></li>
+                            <li>Avengers (Original Video): <a target="_blank" href="https://www.youtube.com/watch?v=u7JO1RCE3Zk">YouTube Link</a></li>
+                            <li>Joey (Original Video): <a target="_blank" href="https://www.youtube.com/watch?v=tHuQiUP-kyQ">YouTube Link</a></li>
+                        </ul>
+                    </div>
+
+                    <div class="title">
+                        <i class="dropdown icon"></i>
+                        I want to know more about cartoonization using AI.
+                    </div>
+                    <div class="content">
+                        <p>The <a href="">author</a> of the above mentioned paper can probably indulge you in some detailed resoursces. Other than that here are some we found - </p>
+                        <ul class="ui list">
+                            <li>CartoonGAN
+                                <ul>
+                                    <li><a target="_blank" href="https://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_CartoonGAN_Generative_Adversarial_CVPR_2018_paper.pdf">Paper</a></li>
+                                    <li><a target="_blank" href="https://github.com/mnicnc404/CartoonGan-tensorflow">Github Link</a></li>
+                                </ul>
+                            </li>
+                            <li>AnimeGAN
+                                <ul>
+                                    <li><a target="_blank" href="https://link.springer.com/chapter/10.1007/978-981-15-5577-0_18">Paper</a></li>
+                                    <li><a target="_blank" href="https://github.com/TachibanaYoshino/AnimeGAN">Github Link</a></li>
+                                </ul>
+                            </li>   
+                        </ul>
+                    </div>
+
+                    <div class="title">
+                        <i class="dropdown icon"></i>
+                        Why did we make this demo?
+                    </div>
+                    <div class="content">
+                        <p>Honestly we thought this was a cool application of GAN but didn't find any demo available. 
+                            Our friends wanted to try it so we made a quick demo for images; which then later got extended to videos.</p>
+                        <p>Also we wanted to learn the deployment architecture which would allow us to serve such power hungry inference in the least money hogging method possible. (Blog post coming soon!)</p>
+                    </div>
+                    
+                    <div class="title">
+                        <i class="dropdown icon"></i>
+                        Help us pay the bills?
+                    </div>
+                    <div class="content">
+                        If you liked our demo and want to support us, please donate - <a href="https://www.paypal.me/tjdevworks">Paypal Link</a>
+                    </div>
+
+                    <div class="title">
+                        <i class="dropdown icon"></i>
+                        Do you want to share your feedback?
+                    </div>
+                    <div class="content">
+                        <iframe src="https://docs.google.com/forms/d/e/1FAIpQLSevnAJeRc0JvoXAY_wNOu4jKb5tM3PKmwZMzH5tDnxVr1bXzQ/viewform?embedded=true" width="550" height="605" frameborder="0" marginheight="0" marginwidth="0">Loading…</iframe>
+                    </div>
+
+
+
+
+                    <!-- <div class="title">
+                        <i class="dropdown icon"></i>
+                        What is the deployment architecture of this project?
+                    </div>
+                    <div class="content">
+                        <p>This is a Flask app which resides on a Cloud Run instance with Cloud Storage integration. We've leveraged Algorithmia's community cloud AI layer for our inference.</p>
+                        <!-- <p><a href="https://github.com/nirajpandkar/x-ize/tree/wb_cartoonizer">Github Link</a></p> -->
+                    <!-- </div> -->
+            </div>
+        </div>
+    </div>
+
+    <div class="row">
+        <div class="center aligned column">
+            <h3><i class="copyright outline icon"></i> 2020 Cartoonizer</h3>
+            <h3>Made with <i class="heart icon"></i> by <a target="_blank" href="https://twitter.com/Niraj_pandkar">Niraj</a> and <a target="_blank" href="https://twitter.com/tjdevWorks">Tejas</a></h3>
+        </div>
+        
+    </div>
+    <!-- <div class="row">
+        <h3>Made with <i class="heart icon"></i> by <a href="https://www.linkedin.com/in/nirajpandkar/">Niraj</a> and <a href="https://www.linkedin.com/in/tejas-mahajan-21175a118/">Tejas</a></h3>
+    </div> -->
+    
+</div>
+
+
+<script>
+    $('.ui.accordion')
+  .accordion()
+;
+    $("#uploadimage").on("click", function() {
+        $('#hiddeninputfile').click(); 
+    });
+
+    $("#uploadvideo").on("click", function() {
+        $('#hiddeninputvideo').click(); 
+    });
+
+    document.getElementById("hiddeninputfile").onchange = function() {
+        $('#loader').removeClass('disabled').addClass('active');
+        document.getElementById("formsubmit").submit();
+        
+    };
+    document.getElementById("hiddeninputvideo").onchange = function() {
+        const fi = document.getElementById('hiddeninputvideo'); 
+        // Check if any file is selected. 
+        if (fi.files.length > 0) { 
+            for (const i = 0; i <= fi.files.length - 1; i++) { 
+  
+                const fsize = fi.files.item(i).size; 
+                const file = Math.round((fsize / 1024)); 
+                // The size of the file.
+                //Change the max_file_size as per your need 
+                const max_file_size = 30720;
+                if (file >= max_file_size) { 
+                    alert( 
+                      "File too Big, please select a file less than 30mb (10 sec at 1080p or 5 sec at 4k)"); 
+                } else { 
+                    $('#loader').removeClass('disabled').addClass('active');
+                    document.getElementById("formsubmit").submit();
+                } 
+            } 
+        } 
+        
+        
+    };
+    var recorder = document.getElementById('recorder');
+</script> 
+</body>
+</html>

video_api.py

@@ -0,0 +1,19 @@
+import Algorithmia
+
+with open("algo.txt", "r") as infile:
+   client_key = infile.read()
+
+client = Algorithmia.client(client_key)
+algo = client.algo('tjdevworks/cartoonizer/2.2.2')
+algo.set_options(timeout=300)
+
+def api_request(input_file_uri):
+    # API call for cartoonization.
+    input = {"data_uri": input_file_uri,
+            "data_type": 1,
+            "datastore": ""
+            }
+    
+    response = algo.pipe(input).result
+    
+    return response

white_box_cartoonizer/cartoonize.py

@@ -0,0 +1,133 @@
+"""
+Internal code snippets were obtained from https://github.com/SystemErrorWang/White-box-Cartoonization/
+
+For it to work tensorflow version 2.x changes were obtained from https://github.com/steubk/White-box-Cartoonization 
+"""
+import os
+import uuid
+import time
+import subprocess
+import sys
+
+import cv2
+import numpy as np
+import skvideo.io
+try:
+    import tensorflow.compat.v1 as tf
+except ImportError:
+    import tensorflow as tf
+
+import network
+import guided_filter
+
+class WB_Cartoonize:
+    def __init__(self, weights_dir, gpu):
+        if not os.path.exists(weights_dir):
+            raise FileNotFoundError("Weights Directory not found, check path")
+        self.load_model(weights_dir, gpu)
+        print("Weights successfully loaded")
+    
+    def resize_crop(self, image):
+        h, w, c = np.shape(image)
+        if min(h, w) > 720:
+            if h > w:
+                h, w = int(720*h/w), 720
+            else:
+                h, w = 720, int(720*w/h)
+        image = cv2.resize(image, (w, h),
+                            interpolation=cv2.INTER_AREA)
+        h, w = (h//8)*8, (w//8)*8
+        image = image[:h, :w, :]
+        return image
+
+    def load_model(self, weights_dir, gpu):
+        try:
+            tf.disable_eager_execution()
+        except:
+            None
+
+        tf.reset_default_graph()
+
+        
+        self.input_photo = tf.placeholder(tf.float32, [1, None, None, 3], name='input_image')
+        network_out = network.unet_generator(self.input_photo)
+        self.final_out = guided_filter.guided_filter(self.input_photo, network_out, r=1, eps=5e-3)
+
+        all_vars = tf.trainable_variables()
+        gene_vars = [var for var in all_vars if 'generator' in var.name]
+        saver = tf.train.Saver(var_list=gene_vars)
+        
+        if gpu:
+            gpu_options = tf.GPUOptions(allow_growth=True)
+            device_count = {'GPU':1}
+        else:
+            gpu_options = None
+            device_count = {'GPU':0}
+        
+        config = tf.ConfigProto(gpu_options=gpu_options, device_count=device_count)
+        
+        self.sess = tf.Session(config=config)
+
+        self.sess.run(tf.global_variables_initializer())
+        saver.restore(self.sess, tf.train.latest_checkpoint(weights_dir))
+
+    def infer(self, image):
+        image = self.resize_crop(image)
+        batch_image = image.astype(np.float32)/127.5 - 1
+        batch_image = np.expand_dims(batch_image, axis=0)
+        
+        ## Session Run
+        output = self.sess.run(self.final_out, feed_dict={self.input_photo: batch_image})
+        
+        ## Post Process
+        output = (np.squeeze(output)+1)*127.5
+        output = np.clip(output, 0, 255).astype(np.uint8)
+        
+        return output
+    
+    def process_video(self, fname, frame_rate):
+        ## Capture video using opencv
+        cap = cv2.VideoCapture(fname)
+
+        target_size = (int(cap.get(3)),int(cap.get(4)))
+        output_fname = os.path.abspath('{}/{}-{}.mp4'.format(fname.replace(os.path.basename(fname), ''),str(uuid.uuid4())[:7],os.path.basename(fname).split('.')[0]))
+
+        out = skvideo.io.FFmpegWriter(output_fname, inputdict={'-r':frame_rate}, outputdict={'-r':frame_rate})
+
+        while True:
+            ret, frame = cap.read()
+            
+            if ret:
+                
+                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+                
+                frame = self.infer(frame)
+                
+                frame = cv2.resize(frame, target_size)
+                
+                out.writeFrame(frame)
+                
+            else:
+                break
+        cap.release()
+        out.close()
+        
+        final_name = '{}final_{}'.format(fname.replace(os.path.basename(fname), ''), os.path.basename(output_fname))
+
+        p = subprocess.Popen(['ffmpeg','-i','{}'.format(output_fname), "-pix_fmt", "yuv420p", final_name])
+        p.communicate()
+        p.wait()
+
+        os.system("rm "+output_fname)
+
+        return final_name
+
+if __name__ == '__main__':
+    gpu = len(sys.argv) < 2 or sys.argv[1] != '--cpu'
+    wbc = WB_Cartoonize(os.path.abspath('white_box_cartoonizer/saved_models'), gpu)
+    img = cv2.imread('white_box_cartoonizer/test.jpg')
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    cartoon_image = wbc.infer(img)
+    import matplotlib.pyplot as plt
+    plt.imshow(cartoon_image)
+    plt.show()

white_box_cartoonizer/guided_filter.py

@@ -0,0 +1,94 @@
+"""
+Code copyrights are with: https://github.com/SystemErrorWang/White-box-Cartoonization/
+
+To adapt the code with tensorflow v2 changes obtained from: https://github.com/steubk/White-box-Cartoonization 
+"""
+try:
+    import tensorflow.compat.v1 as tf
+except ImportError:
+    import tensorflow as tf
+
+import numpy as np
+
+
+def tf_box_filter(x, r):
+    k_size = int(2*r+1)
+    ch = x.get_shape().as_list()[-1]
+    weight = 1/(k_size**2)
+    box_kernel = weight*np.ones((k_size, k_size, ch, 1))
+    box_kernel = np.array(box_kernel).astype(np.float32)
+    output = tf.nn.depthwise_conv2d(x, box_kernel, [1, 1, 1, 1], 'SAME')
+    return output
+
+
+
+def guided_filter(x, y, r, eps=1e-2):
+    
+    x_shape = tf.shape(x)
+    #y_shape = tf.shape(y)
+
+    N = tf_box_filter(tf.ones((1, x_shape[1], x_shape[2], 1), dtype=x.dtype), r)
+
+    mean_x = tf_box_filter(x, r) / N
+    mean_y = tf_box_filter(y, r) / N
+    cov_xy = tf_box_filter(x * y, r) / N - mean_x * mean_y
+    var_x  = tf_box_filter(x * x, r) / N - mean_x * mean_x
+
+    A = cov_xy / (var_x + eps)
+    b = mean_y - A * mean_x
+
+    mean_A = tf_box_filter(A, r) / N
+    mean_b = tf_box_filter(b, r) / N
+
+    output = tf.add(mean_A * x, mean_b, name='final_add')
+
+    return output
+
+
+
+def fast_guided_filter(lr_x, lr_y, hr_x, r=1, eps=1e-8):
+    
+    #assert lr_x.shape.ndims == 4 and lr_y.shape.ndims == 4 and hr_x.shape.ndims == 4
+   
+    lr_x_shape = tf.shape(lr_x)
+    #lr_y_shape = tf.shape(lr_y)
+    hr_x_shape = tf.shape(hr_x)
+    
+    N = tf_box_filter(tf.ones((1, lr_x_shape[1], lr_x_shape[2], 1), dtype=lr_x.dtype), r)
+
+    mean_x = tf_box_filter(lr_x, r) / N
+    mean_y = tf_box_filter(lr_y, r) / N
+    cov_xy = tf_box_filter(lr_x * lr_y, r) / N - mean_x * mean_y
+    var_x  = tf_box_filter(lr_x * lr_x, r) / N - mean_x * mean_x
+
+    A = cov_xy / (var_x + eps)
+    b = mean_y - A * mean_x
+
+    mean_A = tf.image.resize_images(A, hr_x_shape[1: 3])
+    mean_b = tf.image.resize_images(b, hr_x_shape[1: 3])
+
+    output = mean_A * hr_x + mean_b
+    
+    return output
+
+
+if __name__ == '__main__':
+    import cv2
+    from tqdm import tqdm
+
+    input_photo = tf.placeholder(tf.float32, [1, None, None, 3])
+    #input_superpixel = tf.placeholder(tf.float32, [16, 256, 256, 3])
+    output = guided_filter(input_photo, input_photo, 5, eps=1)
+    image = cv2.imread('output_figure1/cartoon2.jpg')
+    image = image/127.5 - 1
+    image = np.expand_dims(image, axis=0)
+
+    config = tf.ConfigProto()
+    config.gpu_options.allow_growth = True
+    sess = tf.Session(config=config)
+    sess.run(tf.global_variables_initializer())
+
+    out = sess.run(output, feed_dict={input_photo: image})
+    out = (np.squeeze(out)+1)*127.5
+    out = np.clip(out, 0, 255).astype(np.uint8)
+    cv2.imwrite('output_figure1/cartoon2_filter.jpg', out)

white_box_cartoonizer/network.py

@@ -0,0 +1,72 @@
+"""
+Code copyrights are with: https://github.com/SystemErrorWang/White-box-Cartoonization/
+
+To adapt the code with tensorflow v2 changes obtained from: https://github.com/steubk/White-box-Cartoonization 
+"""
+try:
+    import tensorflow.compat.v1 as tf
+    import tf_slim as slim
+except ImportError:
+    import tensorflow as tf
+    import tensorflow.contrib.slim as slim
+
+import numpy as np
+
+
+
+def resblock(inputs, out_channel=32, name='resblock'):
+    
+    with tf.variable_scope(name):
+        
+        x = slim.convolution2d(inputs, out_channel, [3, 3], 
+                               activation_fn=None, scope='conv1')
+        x = tf.nn.leaky_relu(x)
+        x = slim.convolution2d(x, out_channel, [3, 3], 
+                               activation_fn=None, scope='conv2')
+        
+        return x + inputs
+
+
+
+
+def unet_generator(inputs, channel=32, num_blocks=4, name='generator', reuse=False):
+    with tf.variable_scope(name, reuse=reuse):
+        
+        x0 = slim.convolution2d(inputs, channel, [7, 7], activation_fn=None)
+        x0 = tf.nn.leaky_relu(x0)
+        
+        x1 = slim.convolution2d(x0, channel, [3, 3], stride=2, activation_fn=None)
+        x1 = tf.nn.leaky_relu(x1)
+        x1 = slim.convolution2d(x1, channel*2, [3, 3], activation_fn=None)
+        x1 = tf.nn.leaky_relu(x1)
+        
+        x2 = slim.convolution2d(x1, channel*2, [3, 3], stride=2, activation_fn=None)
+        x2 = tf.nn.leaky_relu(x2)
+        x2 = slim.convolution2d(x2, channel*4, [3, 3], activation_fn=None)
+        x2 = tf.nn.leaky_relu(x2)
+        
+        for idx in range(num_blocks):
+            x2 = resblock(x2, out_channel=channel*4, name='block_{}'.format(idx))
+            
+        x2 = slim.convolution2d(x2, channel*2, [3, 3], activation_fn=None)
+        x2 = tf.nn.leaky_relu(x2)
+        
+        h1, w1 = tf.shape(x2)[1], tf.shape(x2)[2]
+        x3 = tf.image.resize_bilinear(x2, (h1*2, w1*2))
+        x3 = slim.convolution2d(x3+x1, channel*2, [3, 3], activation_fn=None)
+        x3 = tf.nn.leaky_relu(x3)
+        x3 = slim.convolution2d(x3, channel, [3, 3], activation_fn=None)
+        x3 = tf.nn.leaky_relu(x3)
+
+        h2, w2 = tf.shape(x3)[1], tf.shape(x3)[2]
+        x4 = tf.image.resize_bilinear(x3, (h2*2, w2*2))
+        x4 = slim.convolution2d(x4+x0, channel, [3, 3], activation_fn=None)
+        x4 = tf.nn.leaky_relu(x4)
+        x4 = slim.convolution2d(x4, 3, [7, 7], activation_fn=None)
+        
+        return x4
+
+if __name__ == '__main__':
+    
+
+    pass

white_box_cartoonizer/saved_models/checkpoint

@@ -0,0 +1,3 @@
+model_checkpoint_path: "model-33999"
+all_model_checkpoint_paths: "model-33999"
+all_model_checkpoint_paths: "model-37499"

white_box_cartoonizer/saved_models/model-33999.data-00000-of-00001

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1e2df1a5aa86faa4f979720bfc2436f79333a480876f8d6790b7671cf50fe75b
+size 5868300