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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 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",
tool='color-sketch',
type="filepath",
shape=None,
invert_colors=False)
sketch2 = gr.Image(image_mode="RGB",
source="canvas",
tool='color-sketch',
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(512,frame1)
frame2 = resize(512,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=24)
# 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="<p style='text-align: center'>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. <br /> <img id='visitor-badge' alt='visitor badge' src='https://visitor-badge.glitch.me/badge?page_id=gradio-blocks.sketch_frame_interpolation' style='display: inline-block'/></p>"
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=[gr.Video(label="video output"),gr.Gallery(label="list of frames output").style(grid=5)],title=title,description=description,article=article,css=custom_css).launch(enable_queue=True) |