feat: replace ccrestoration with cccv

Final2x_core/SRclass.py

@@ -2,16 +2,16 @@
 
 import cv2
 import numpy as np
-from ccrestoration import AutoModel, SRBaseModel
+from cccv import AutoModel, SRBaseModel
 from loguru import logger
 
 from Final2x_core.config import SRConfig
 from Final2x_core.util import PrintProgressLog, get_device
 
 
-class CCRestoration:
+class SRWrapper:
     """
-    Super-resolution class for processing images, using ccrestoration.
+    Super-resolution class for processing images, using cccv.
 
     :param config: SRConfig
     """
@@ -24,7 +24,7 @@ def __init__(self, config: SRConfig) -> None:
         PrintProgressLog().set(len(self.config.input_path), 1)
 
         self._SR_class: SRBaseModel = AutoModel.from_pretrained(
-            pretrained_model_name=self.config.pretrained_model_name,
+            self.config.pretrained_model_name,
             device=get_device(self.config.device),
             fp16=False,
             tile=tile,
@@ -41,6 +41,8 @@ def process(self, img: np.ndarray) -> np.ndarray:
         :return:
         """
 
+        _origin_size = (img.shape[1], img.shape[0])
+
         _target_size = (
             math.ceil(img.shape[1] * self.config.target_scale),
             math.ceil(img.shape[0] * self.config.target_scale),
@@ -49,9 +51,10 @@ def process(self, img: np.ndarray) -> np.ndarray:
         img = self._SR_class.inference_image(img)
         PrintProgressLog().printProgress()
 
-        if abs(float(self.config.target_scale) - float(self.config.cc_model_scale)) < 1e-3:  # type: ignore
-            return img
+        # calculate current size
+        _current_size = (img.shape[1], img.shape[0])
 
-        img = cv2.resize(img, _target_size, interpolation=cv2.INTER_LINEAR)
+        if _current_size != _target_size:
+            img = cv2.resize(img, _target_size, interpolation=cv2.INTER_LINEAR)
 
         return img

Final2x_core/SRqueue.py

@@ -6,7 +6,7 @@
 from loguru import logger
 
 from Final2x_core.config import SRConfig
-from Final2x_core.SRclass import CCRestoration
+from Final2x_core.SRclass import SRWrapper
 from Final2x_core.util import PrintProgressLog
 
 
@@ -20,7 +20,7 @@ def sr_queue(config: SRConfig) -> None:
     input_path: List[Path] = config.input_path
     output_path: Path = config.output_path / "outputs"
     output_path.mkdir(parents=True, exist_ok=True)  # create output folder
-    sr = CCRestoration(config)
+    sr = SRWrapper(config)
 
     logger.info("Processing------[ 0.0% ]")
 

Final2x_core/__init__.py

@@ -1,3 +1,3 @@
-from Final2x_core.SRqueue import sr_queue  # noqa
-from Final2x_core.SRclass import CCRestoration  # noqa
-from Final2x_core.config import SRConfig  # noqa
+from Final2x_core.config import SRConfig
+from Final2x_core.SRclass import SRWrapper
+from Final2x_core.SRqueue import sr_queue

Final2x_core/config.py

@@ -4,7 +4,7 @@
 from typing import Any, List, Optional, Union
 
 import yaml
-from ccrestoration import AutoConfig, BaseConfig, ConfigType
+from cccv import ConfigType
 from pydantic import BaseModel, DirectoryPath, FilePath, field_validator
 
 
@@ -17,7 +17,6 @@ class SRConfig(BaseModel):
     output_path: DirectoryPath
     input_path: List[FilePath]
     save_format: Optional[str] = ".png"
-    cc_model_scale: Optional[int] = None
 
     @classmethod
     def from_yaml(cls, yaml_path: Union[Path, str]) -> Any:
@@ -28,11 +27,8 @@ def from_yaml(cls, yaml_path: Union[Path, str]) -> Any:
                 raise ValueError(f"Error loading config: {e}")
 
         cfg = cls(**config)
-        c: BaseConfig = AutoConfig.from_pretrained(pretrained_model_name=cfg.pretrained_model_name)
-
-        cfg.cc_model_scale = c.scale
         if cfg.target_scale is None or cfg.target_scale <= 0:
-            cfg.target_scale = c.scale
+            cfg.target_scale = 2
         return cfg
 
     @classmethod
@@ -43,11 +39,8 @@ def from_json_str(cls, json_str: str) -> Any:
             raise ValueError(f"Error loading config: {e}")
 
         cfg = cls(**config)
-        c: BaseConfig = AutoConfig.from_pretrained(pretrained_model_name=cfg.pretrained_model_name)
-
-        cfg.cc_model_scale = c.scale
         if cfg.target_scale is None or cfg.target_scale <= 0:
-            cfg.target_scale = c.scale
+            cfg.target_scale = 2
         return cfg
 
     @classmethod

Final2x_core/util/__init__.py

@@ -1,3 +1,3 @@
-from Final2x_core.util.progressLog import PrintProgressLog  # noqa
-from Final2x_core.util.singleton import singleton  # noqa
-from Final2x_core.util.device import get_device  # noqa
+from Final2x_core.util.device import get_device
+from Final2x_core.util.progressLog import PrintProgressLog
+from Final2x_core.util.singleton import singleton

Final2x_core/util/device.py

@@ -1,7 +1,7 @@
 from typing import Union
 
 import torch
-from ccrestoration.util.device import default_device
+from cccv.util.device import DEFAULT_DEVICE
 
 
 def get_device(device: str) -> Union[torch.device, str]:
@@ -13,7 +13,7 @@ def get_device(device: str) -> Union[torch.device, str]:
     device = device.lower()
 
     if device.startswith("auto"):
-        return default_device()
+        return DEFAULT_DEVICE
     elif device.startswith("cpu"):
         return torch.device("cpu")
     elif device.startswith("cuda"):
@@ -27,5 +27,4 @@ def get_device(device: str) -> Union[torch.device, str]:
     elif device.startswith("xpu"):
         return torch.device("xpu")
     else:
-        print(f"Unknown device: {device}, use auto instead.")
-        return default_device()
+        return DEFAULT_DEVICE

README.md

@@ -15,7 +15,7 @@ Final2x-core is a cross-platform image super-resolution CLI tool for [Final2x](h
 
 Download in [Release](https://github.com/EutropicAI/Final2x-core/releases) or use pip to install (Linux User).
 
-Make sure you have Python >= 3.9 and PyTorch >= 1.13 installed
+Make sure you have Python >= 3.9 and PyTorch >= 2.0 installed
 
 ```shell
 pip install Final2x-core
@@ -50,6 +50,12 @@ make gen
 
 The following references were used in the development of this project:
 
+After v4.0.0, we use cccv as the algorithm implementation, which implemented most of the features from ccrestoration
+
+- [cccv](https://github.com/EutropicAI/cccv)
+
+---
+
 After v3.0.0, ncnn will be deprecated, and the project will use ccrestoration(PyTorch) as the algorithm implementation.
 
 - [ccrestoration](https://github.com/EutropicAI/ccrestoration)

pyproject.toml

@@ -5,7 +5,6 @@ requires = ["hatchling"]
 [dependency-groups]
 dev = [
   "numpy~=1.26.4",
-  "ccrestoration==0.2.2",
   "opencv-python~=4.10.0.84",
   "torch~=2.7.1",
   "torchvision~=0.22.1",
@@ -34,7 +33,7 @@ classifiers = [
 ]
 dependencies = [
   "PyYAML",
-  "ccrestoration==0.2.2",
+  "cccv==0.0.4",
   "loguru",
   "pydantic"
 ]
@@ -94,7 +93,17 @@ quote-style = "double"
 skip-magic-trailing-comma = false
 
 [tool.ruff.lint]
-extend-ignore = ["B018", "B019", "RUF001", "PGH003", "PGH004", "RUF003", "E402", "RUF002", "B904"]
+extend-ignore = [
+  "B018",
+  "B019",
+  "RUF001",
+  "PGH003",
+  "PGH004",
+  "RUF003",
+  "E402",
+  "RUF002",
+  "B904"
+]
 extend-select = [
   "I",  # isort
   "B",  # flake8-bugbear
@@ -112,6 +121,9 @@ combine-as-imports = true
 [tool.ruff.lint.mccabe]
 max-complexity = 10
 
+[tool.ruff.lint.per-file-ignores]
+"**/__init__.py" = ["F401", "F403"]
+
 [tool.uv]
 default-groups = [
   "dev",

scripts/gen_config.py

@@ -2,7 +2,7 @@
 from pathlib import Path
 
 import yaml
-from ccrestoration import ConfigType
+from cccv import ConfigType
 
 projectPATH = Path(__file__).resolve().parent.parent.absolute()
 

scripts/gen_pretrained_model_name.py

@@ -1,7 +1,7 @@
 from pathlib import Path
 from typing import List
 
-from ccrestoration import ConfigType, ModelType
+from cccv import ConfigType, ModelType
 
 projectPATH = Path(__file__).resolve().parent.parent.absolute()
 

tests/test_config.py

@@ -2,7 +2,7 @@
 
 import pytest
 
-from Final2x_core.config import SRConfig
+from Final2x_core import SRConfig
 
 from .util import CONFIG_PATH
 

tests/test_targetscale.py

@@ -1,6 +1,6 @@
-from ccrestoration import ConfigType
+from cccv import ConfigType
 
-from Final2x_core import CCRestoration, SRConfig
+from Final2x_core import SRConfig, SRWrapper
 
 from .util import CONFIG_PATH, calculate_image_similarity, compare_image_size, load_image
 
@@ -11,7 +11,7 @@ def test_case_targetscale_positive(self) -> None:
         config.pretrained_model_name = ConfigType.RealESRGAN_AnimeJaNai_HD_V3_Compact_2x
         for t in [7.99999, 1, 2, 2.5, 4, 5.6619, 8, 0.673]:
             config.target_scale = t
-            SR = CCRestoration(config=config)
+            SR = SRWrapper(config=config)
             img1 = load_image()
             img2 = SR.process(img1)
             assert calculate_image_similarity(img1, img2)