rename to iopaint

iopaint/tests/utils.py

@@ -0,0 +1,77 @@
+from pathlib import Path
+import cv2
+import pytest
+import torch
+
+from iopaint.helper import encode_pil_to_base64
+from iopaint.schema import LDMSampler, HDStrategy, InpaintRequest, SDSampler
+from PIL import Image
+
+current_dir = Path(__file__).parent.absolute().resolve()
+save_dir = current_dir / "result"
+save_dir.mkdir(exist_ok=True, parents=True)
+
+
+def check_device(device: str) -> int:
+    if device == "cuda" and not torch.cuda.is_available():
+        pytest.skip("CUDA is not available, skip test on cuda")
+    if device == "mps" and not torch.backends.mps.is_available():
+        pytest.skip("mps is not available, skip test on mps")
+    steps = 1 if device == "cpu" else 20
+    return steps
+
+
+def assert_equal(
+    model,
+    config: InpaintRequest,
+    gt_name,
+    fx: float = 1,
+    fy: float = 1,
+    img_p=current_dir / "image.png",
+    mask_p=current_dir / "mask.png",
+):
+    img, mask = get_data(fx=fx, fy=fy, img_p=img_p, mask_p=mask_p)
+    print(f"Input image shape: {img.shape}")
+    res = model(img, mask, config)
+    ok = cv2.imwrite(
+        str(save_dir / gt_name),
+        res,
+        [int(cv2.IMWRITE_JPEG_QUALITY), 100, int(cv2.IMWRITE_PNG_COMPRESSION), 0],
+    )
+    assert ok, save_dir / gt_name
+
+    """
+    Note that JPEG is lossy compression, so even if it is the highest quality 100, 
+    when the saved images is reloaded, a difference occurs with the original pixel value. 
+    If you want to save the original images as it is, save it as PNG or BMP.
+    """
+    # gt = cv2.imread(str(current_dir / gt_name), cv2.IMREAD_UNCHANGED)
+    # assert np.array_equal(res, gt)
+
+
+def get_data(
+    fx: float = 1,
+    fy: float = 1.0,
+    img_p=current_dir / "image.png",
+    mask_p=current_dir / "mask.png",
+):
+    img = cv2.imread(str(img_p))
+    img = cv2.cvtColor(img, cv2.COLOR_BGRA2RGB)
+    mask = cv2.imread(str(mask_p), cv2.IMREAD_GRAYSCALE)
+    img = cv2.resize(img, None, fx=fx, fy=fy, interpolation=cv2.INTER_AREA)
+    mask = cv2.resize(mask, None, fx=fx, fy=fy, interpolation=cv2.INTER_NEAREST)
+    return img, mask
+
+
+def get_config(**kwargs):
+    data = dict(
+        sd_sampler=kwargs.get("sd_sampler", SDSampler.uni_pc),
+        ldm_steps=1,
+        ldm_sampler=LDMSampler.plms,
+        hd_strategy=kwargs.get("strategy", HDStrategy.ORIGINAL),
+        hd_strategy_crop_margin=32,
+        hd_strategy_crop_trigger_size=200,
+        hd_strategy_resize_limit=200,
+    )
+    data.update(**kwargs)
+    return InpaintRequest(image="", mask="", **data)