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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() | |