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