app.py

import os
os.system("git clone https://github.com/google-research/frame-interpolation")
import sys
sys.path.append("frame-interpolation")

import cv2
import numpy as np
import tensorflow as tf
import mediapy
from PIL import Image
from eval import interpolator, util
import gradio as gr

from huggingface_hub import snapshot_download

from image_tools.sizes import resize_and_crop


model = snapshot_download(repo_id="akhaliq/frame-interpolation-film-style")

interpolator = interpolator.Interpolator(model, None)

ffmpeg_path = util.get_ffmpeg_path()
mediapy.set_ffmpeg(ffmpeg_path)

def resize(width,img):
  basewidth = width
  img = Image.open(img)
  wpercent = (basewidth/float(img.size[0]))
  hsize = int((float(img.size[1])*float(wpercent)))
  img = img.resize((basewidth,hsize), Image.ANTIALIAS)
  return img
  

def resize_img(img1,img2):
 img_target_size = Image.open(img1)
 img_to_resize = resize_and_crop(
     img2, 
     (img_target_size.size[0],img_target_size.size[1]), #set width and height to match img1
     crop_origin="middle"
     )
 img_to_resize.save('resized_img2.png')

  
sketch1 = gr.Image(image_mode="RGB",
        source="canvas",
        type="filepath",
        shape=None,
        invert_colors=False)

sketch2 = gr.Image(image_mode="RGB",
        source="canvas",
        type="filepath",
        shape=None,
        invert_colors=False)

slider = gr.inputs.Slider(minimum=2,maximum=4,step=1)

        
def predict(frame1, frame2, times_to_interpolate):
   
    frame1 = resize(256,frame1)
    frame2 = resize(256,frame2)

    frame1.save("test1.png")
    frame2.save("test2.png")

    resize_img("test1.png","test2.png")
    input_frames = ["test1.png", "resized_img2.png"]

    frames = list(
        util.interpolate_recursively_from_files(
            input_frames, times_to_interpolate, interpolator))
    print(frames)  
    mediapy.write_video("out.mp4", frames, fps=30)

    # video.mp4 is a video of 9 seconds
    filename = "out.mp4"
        
    cap = cv2.VideoCapture(filename)
    cap.set(cv2.CAP_PROP_POS_AVI_RATIO,0)
    frameCount = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    frameWidth = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    frameHeight = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    videoFPS = int(cap.get(cv2.CAP_PROP_FPS))
    
    print (f"frameCount: {frameCount}")
    print (f"frameWidth: {frameWidth}")
    print (f"frameHeight: {frameHeight}")
    print (f"videoFPS: {videoFPS}")
    
    buf = np.empty((
        frameCount,
        frameHeight,
        frameWidth,
        3), np.dtype('uint8'))
    
    fc = 0
    ret = True
    
    while (fc < frameCount):
        ret, buf[fc] = cap.read()
        fc += 1
    
    cap.release()
    videoArray = buf
    
    print (f"DURATION: {frameCount/videoFPS}")
    print (videoArray)
    
    return "out.mp4", videoArray
    
    
    
title="sketch-frame-interpolation"
description="This is a fork of the Gradio demo for FILM: Frame Interpolation for Large Scene Motion from @akhaliq, but using sketches instead of images. This could be very useful for the animation industry :) <br /> To use it, simply draw your sketches and add the times to interpolate number. Read more at the links below."
article = "<p style='text-align: center'><a href='https://film-net.github.io/' target='_blank'>FILM: Frame Interpolation for Large Motion</a> | <a href='https://github.com/google-research/frame-interpolation' target='_blank'>Github Repo</a></p>"
custom_css = "style.css"

gr.Interface(predict,[sketch1,sketch2,slider],outputs=["playable_video", "gallery"],title=title,description=description,article=article, css=custom_css).launch(enable_queue=True)