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| import gradio as gr | |
| from transformers import pipeline | |
| import io, base64 | |
| from PIL import Image | |
| import numpy as np | |
| import tensorflow as tf | |
| import mediapy | |
| import os | |
| import sys | |
| from huggingface_hub import snapshot_download | |
| from image_tools.sizes import resize_and_crop | |
| os.system("git clone https://github.com/google-research/frame-interpolation") | |
| sys.path.append("frame-interpolation") | |
| from eval import interpolator, util | |
| ffmpeg_path = util.get_ffmpeg_path() | |
| mediapy.set_ffmpeg(ffmpeg_path) | |
| model = snapshot_download(repo_id="akhaliq/frame-interpolation-film-style") | |
| interpolator = interpolator.Interpolator(model, None) | |
| 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, output_name): | |
| img_target_size = Image.open(img1) | |
| img_to_resize = resize_and_crop( | |
| img2, | |
| (img_target_size.size[0], img_target_size.size[1]), | |
| crop_origin="middle" | |
| ) | |
| img_to_resize.save(output_name) | |
| def generate_interpolation(frame1, frame2, frame3, frame4, frame5, frame6, times_to_interpolate, fps): | |
| frame1 = resize(256, frame1) | |
| frame2 = resize(256, frame2) | |
| frame3 = resize(256, frame3) | |
| frame4 = resize(256, frame4) | |
| frame5 = resize(256, frame5) | |
| frame6 = resize(256, frame6) | |
| frame1.save("test1.png") | |
| frame2.save("test2.png") | |
| frame3.save("test3.png") | |
| frame4.save("test4.png") | |
| frame5.save("test5.png") | |
| frame6.save("test6.png") | |
| resize_img("test1.png", "test2.png", "resized_img2.png") | |
| resize_img("test1.png", "test3.png", "resized_img3.png") | |
| resize_img("test1.png", "test4.png", "resized_img4.png") | |
| resize_img("test1.png", "test5.png", "resized_img5.png") | |
| resize_img("test1.png", "test6.png", "resized_img6.png") | |
| input_frames = ["test1.png", "resized_img2.png", "resized_img3.png", "resized_img4.png", "resized_img5.png", "resized_img6.png"] | |
| frames = list(util.interpolate_recursively_from_files(input_frames, times_to_interpolate, interpolator)) | |
| mediapy.write_video("out.mp4", frames, fps=fps) | |
| return "out.mp4" | |
| demo = gr.Blocks() | |
| with demo: | |
| with gr.Row(): | |
| # Left column (inputs) | |
| with gr.Column(): | |
| with gr.Row(): | |
| # upload images and get image strings | |
| input_arr = [ | |
| gr.inputs.Image(type='filepath', label="Frame 1"), | |
| gr.inputs.Image(type='filepath', label="Frame 2"), | |
| gr.inputs.Image(type='filepath', label="Frame 3"), | |
| gr.inputs.Image(type='filepath', label="Frame 4"), | |
| gr.inputs.Image(type='filepath', label="Frame 5"), | |
| gr.inputs.Image(type='filepath', label="Frame 6"), | |
| ] | |
| with gr.Row(): | |
| input_arr.append(gr.inputs.Slider(minimum=2, maximum=10, step=1, label="Times to Interpolate")) | |
| input_arr.append(gr.inputs.Slider(minimum=15, maximum=60, step=1, label="fps")) | |
| # Rows of instructions & buttons | |
| with gr.Row(): | |
| gr.Markdown("After uploading some images, hit the 'Generate Video' button to create a short video!") | |
| button_gen_video = gr.Button("Generate Video") | |
| # Right column (outputs) | |
| with gr.Column(): | |
| output_interpolation = gr.Video(label="Generated Video") | |
| # Bind functions to buttons | |
| button_gen_video.click(fn=generate_interpolation, inputs=input_arr, outputs=output_interpolation) | |
| demo.launch(debug=True, enable_queue=True) | |