import hashlib import os import time from PIL import Image from lama_cleaner.helper import encode_pil_to_base64, gen_frontend_mask from lama_cleaner.plugins.anime_seg import AnimeSeg from lama_cleaner.schema import RunPluginRequest from lama_cleaner.tests.utils import check_device, current_dir, save_dir os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" import cv2 import pytest from lama_cleaner.plugins import ( RemoveBG, RealESRGANUpscaler, GFPGANPlugin, RestoreFormerPlugin, InteractiveSeg, ) img_p = current_dir / "bunny.jpeg" img_bytes = open(img_p, "rb").read() bgr_img = cv2.imread(str(img_p)) rgb_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB) rgb_img_base64 = encode_pil_to_base64(Image.fromarray(rgb_img), 100, {}) bgr_img_base64 = encode_pil_to_base64(Image.fromarray(bgr_img), 100, {}) def _save(img, name): cv2.imwrite(str(save_dir / name), img) def test_remove_bg(): model = RemoveBG() rgba_np_img = model.gen_image( rgb_img, RunPluginRequest(name=RemoveBG.name, image=rgb_img_base64) ) res = cv2.cvtColor(rgba_np_img, cv2.COLOR_RGBA2BGRA) _save(res, "test_remove_bg.png") bgr_np_img = model.gen_mask( rgb_img, RunPluginRequest(name=RemoveBG.name, image=rgb_img_base64) ) res_mask = gen_frontend_mask(bgr_np_img) _save(res_mask, "test_remove_bg_frontend_mask.png") assert len(bgr_np_img.shape) == 2 _save(bgr_np_img, "test_remove_bg_mask.jpeg") def test_anime_seg(): model = AnimeSeg() img = cv2.imread(str(current_dir / "anime_test.png")) img_base64 = encode_pil_to_base64(Image.fromarray(img), 100, {}) res = model.gen_image(img, RunPluginRequest(name=AnimeSeg.name, image=img_base64)) assert len(res.shape) == 3 assert res.shape[-1] == 4 _save(res, "test_anime_seg.png") res = model.gen_mask(img, RunPluginRequest(name=AnimeSeg.name, image=img_base64)) assert len(res.shape) == 2 _save(res, "test_anime_seg_mask.png") @pytest.mark.parametrize("device", ["cuda", "cpu", "mps"]) def test_upscale(device): check_device(device) model = RealESRGANUpscaler("realesr-general-x4v3", device) res = model.gen_image( rgb_img, RunPluginRequest(name=RealESRGANUpscaler.name, image=rgb_img_base64, scale=2), ) _save(res, f"test_upscale_x2_{device}.png") res = model.gen_image( rgb_img, RunPluginRequest(name=RealESRGANUpscaler.name, image=rgb_img_base64, scale=4), ) _save(res, f"test_upscale_x4_{device}.png") @pytest.mark.parametrize("device", ["cuda", "cpu", "mps"]) def test_gfpgan(device): check_device(device) model = GFPGANPlugin(device) res = model.gen_image( rgb_img, RunPluginRequest(name=GFPGANPlugin.name, image=rgb_img_base64) ) _save(res, f"test_gfpgan_{device}.png") @pytest.mark.parametrize("device", ["cuda", "cpu", "mps"]) def test_restoreformer(device): check_device(device) model = RestoreFormerPlugin(device) res = model.gen_image( rgb_img, RunPluginRequest(name=RestoreFormerPlugin.name, image=rgb_img_base64) ) _save(res, f"test_restoreformer_{device}.png") @pytest.mark.parametrize("device", ["cuda", "cpu", "mps"]) def test_segment_anything(device): check_device(device) model = InteractiveSeg("vit_l", device) new_mask = model.gen_mask( rgb_img, RunPluginRequest( name=InteractiveSeg.name, image=rgb_img_base64, clicks=([[448 // 2, 394 // 2, 1]]), ), ) save_name = f"test_segment_anything_{device}.png" _save(new_mask, save_name)