import cv2 | |
import PIL | |
import requests | |
import numpy as np | |
from lama_cleaner.model.lama import LaMa | |
from lama_cleaner.schema import Config | |
def download_image(url): | |
image = PIL.Image.open(requests.get(url, stream=True).raw) | |
image = PIL.ImageOps.exif_transpose(image) | |
image = image.convert("RGB") | |
return image | |
img_url = "https://raw.githubusercontent.com/Sanster/lama-cleaner/main/assets/dog.jpg" | |
mask_url = "https://user-images.githubusercontent.com/3998421/202105351-9fcc4bf8-129d-461a-8524-92e4caad431f.png" | |
image = np.asarray(download_image(img_url)) | |
mask = np.asarray(download_image(mask_url).convert("L")) | |
# set to GPU for faster inference | |
model = LaMa("cpu") | |
result = model(image, mask, Config(hd_strategy="Original", ldm_steps=20, hd_strategy_crop_margin=128, hd_strategy_crop_trigger_size=800, hd_strategy_resize_limit=800)) | |
cv2.imwrite("lama_inpaint_demo.jpg", result) |