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import os
import sys
import numpy as np
import tensorflow as tf
import mediapy
from PIL import Image
import gradio as gr
from huggingface_hub import snapshot_download
# Clone the repository and add the path
os.system("git clone https://github.com/google-research/frame-interpolation")
sys.path.append("frame-interpolation")
# Import after appending the path
from eval import interpolator, util
def load_model(model_name):
model = interpolator.Interpolator(snapshot_download(repo_id=model_name), None)
return model
model_names = [
"akhaliq/frame-interpolation-film-style",
"NimaBoscarino/frame-interpolation_film_l1",
"NimaBoscarino/frame_interpolation_film_vgg",
]
models = {model_name: load_model(model_name) for model_name in model_names}
ffmpeg_path = util.get_ffmpeg_path()
mediapy.set_ffmpeg(ffmpeg_path)
def resize(width, img):
img = Image.fromarray(img)
wpercent = (width / float(img.size[0]))
hsize = int((float(img.size[1]) * float(wpercent)))
img = img.resize((width, hsize), Image.LANCZOS)
return img
def resize_and_crop(img_path, size, crop_origin="middle"):
img = Image.open(img_path)
img = img.resize(size, Image.LANCZOS)
return img
def resize_img(img1, img2_path):
img_target_size = Image.open(img1)
img_to_resize = resize_and_crop(
img2_path,
(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')
def predict(frame1, frame2, times_to_interpolate, model_name):
model = models[model_name]
frame1 = resize(1080, frame1)
frame2 = resize(1080, 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, model))
mediapy.write_video("out.mp4", frames, fps=30)
return "out.mp4"
title = "Sports model"
description = "Wechat:Liesle1"
article = ""
examples = [
['cat3.jpeg', 'cat4.jpeg', 2, model_names[0]],
['cat1.jpeg', 'cat2.jpeg', 2, model_names[1]],
]
gr.Interface(
fn=predict,
inputs=[
gr.Image(label="First Frame"),
gr.Image(label="Second Frame"),
gr.Number(label="Times to Interpolate", value=2),
gr.Dropdown(label="Model", choices=model_names),
],
outputs=gr.Video(label="Interpolated Frames"),
title=title,
description=description,
article=article,
examples=examples,
).launch()
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