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import os
os.system("git clone https://github.com/google-research/frame-interpolation")
import sys
sys.path.append("frame-interpolation")
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="L",
source="canvas",
type="filepath",
shape=(400, 400),
invert_colors=False)
sketch2 = gr.Image(image_mode="L",
source="canvas",
type="filepath",
shape=(400, 400),
invert_colors=False)
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))
mediapy.write_video("out.mp4", frames, fps=30)
return "out.mp4"
gr.Interface(predict,[sketch1,sketch2,gr.inputs.Slider(minimum=2,maximum=4,step=1)],"playable_video".launch(enable_queue=True) |