Merge pull request #8 from curegit/pnn

Merge legacy code 2

.editorconfig

@@ -0,0 +1,19 @@
+root = true
+
+[*]
+charset = utf-8
+end_of_line = lf
+insert_final_newline = true
+trim_trailing_whitespace = true
+
+[*.py]
+indent_style = space
+indent_size = 4
+
+[*.md]
+indent_style = space
+indent_size = 2
+
+[*.toml]
+indent_style = space
+indent_size = 2

.gitignore

@@ -0,0 +1,129 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pdm
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/

README.md

@@ -14,3 +14,12 @@ This project solves it by using CNN (Convolutional Neural Network) which convert
 Here are the provisional result and comparison to the baseline (Gaussian blur).
 
 ![comparison](comparison.png)
+
+
+## Train
+
+###
+
+```
+python3 -m descreening.pitch.make JapanColor2011Coated.icc images pitch_dataset
+```

convert.py

@@ -5,6 +5,8 @@
 import numpy as np
 #import cupy as cp
 
+
+
 i = Image.open("test2.bmp")
 # ニューラルネットワークを作成
 model = RgbNet()

data/.gitignore

@@ -0,0 +1,2 @@
+*
+!.gitignore

descreen/__main__.py

@@ -0,0 +1,5 @@
+import sys
+from .cli import main
+
+if __name__ == "__main__":
+    sys.exit(main())

descreen/cli.py

@@ -0,0 +1,53 @@
+import typer
+
+def main():
+
+    	except KeyboardInterrupt:
+		eprint("KeyboardInterrupt")
+		exit_code = 130
+		return exit_code
+
+
+def con():
+    device = "cpu"
+
+
+    #from .models import UNetLikeModel
+    model = UNetLikeModel()
+    model.load_state_dict(torch.load(sys.argv[2]))
+
+
+    model.to(device)
+    model.eval()
+    print(model)
+
+    patch_size = model.output_size(512)
+    #img = read_uint16_image(sys.argv[3])
+
+    with open(sys.argv[3], "rb") as fp:
+        i = load_image(magickpng(fp.read(), png48=True), assert16=True)
+
+
+    height, width = img.shape[1:3]
+    # TODO: 4倍数にあわせる
+    ppp_h = h % 512
+    ppp_w = w % 512
+    a_h = h + ppp_h
+    a_w = w + ppp_w
+    img = img.reshape((1, 3, h, w))
+    res = np.zeros((3, a_h, a_w), dtype="float32")
+    p = model.required_padding(patch_size)
+
+    img = np.pad(img, ((0, 0), (0, 0), (p, p + ppp_h), (p, p + ppp_w)), mode="symmetric")
+    for (j, i), (k, l) in model.patch_slices(a_h, a_w, patch_size):
+        print(k)
+        x = img[:, :, j, i]
+        t = torch.from_numpy(x.astype("float32"))
+        t = t.to(device)
+        y = model(t)
+        yy = y.detach().cpu().numpy()
+        print(y.shape)
+        res[:, k, l] = yy[0]
+    res = res[:, :h, :w]
+    save_image(res, sys.argv[4])
+    #save_wide_gamut_uint16_array_as_srgb(res, sys.argv[4])

descreen/image.py

@@ -0,0 +1,53 @@
+import cv2
+import numpy as np
+from pathlib import Path
+from numpy import ndarray
+from .utilities.filesys import resolve_path
+
+
+# ファイルパス、パスオブジェクト、またはバイトを受け取り画像を配列としてロードする
+def load_image(filelike: str | Path | bytes, *, transpose: bool = True, normalize: bool = True, orient: bool = True, assert16: bool = False) -> ndarray:
+    match filelike:
+        case str() | Path() as path:
+            with open(resolve_path(path), "rb") as fp:
+                buffer = fp.read()
+        case bytes() as buffer:
+            pass
+        case _:
+            raise ValueError()
+    # OpenCV が ASCII パスしか扱えない問題を回避するためにバッファを経由する
+    bin = np.frombuffer(buffer, np.uint8)
+    flags = cv2.IMREAD_COLOR | cv2.IMREAD_ANYDEPTH
+    if not orient:
+        flags |= cv2.IMREAD_IGNORE_ORIENTATION
+    img = cv2.imdecode(bin, flags)
+    if img.ndim != 3 or img.shape[2] != 3:
+        raise RuntimeError()
+    if transpose:
+        # RGBxHxW にする
+        img = img[:, :, [2, 1, 0]].transpose(2, 0, 1)
+    match img.dtype:
+        case np.uint8:
+            if assert16:
+                raise RuntimeError()
+            if normalize:
+                return (img / (2**8 - 1)).astype(np.float32)
+            else:
+                return img
+        case np.uint16:
+            if normalize:
+                return (img / (2**16 - 1)).astype(np.float32)
+            else:
+                return img
+        case _:
+            raise RuntimeError()
+
+
+
+def to_pil_image(array):
+	srgb = rint(array * 255).clip(0, 255).astype(uint8)
+	return Image.fromarray(srgb.transpose(1, 2, 0), "RGB")
+
+def save_image(array, filepath):
+	to_pil_image(array).save(filepath)
+

descreen/models/abs.py

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+from abc import ABC, abstractmethod
+
+from ..utilities import range_chunks
+
+from torch import nn
+
+class AbsModel(ABC, nn.Module):
+
+    @abstractmethod
+    def forward(self, x):
+        raise NotImplementedError()
+
+    @abstractmethod
+    def output_size(self, input_size):
+        raise NotImplementedError()
+
+    @abstractmethod
+    def input_size(self, output_size):
+        raise NotImplementedError()
+
+    def reduced_padding(self, input_size):
+        output_size = self.output_size(input_size)
+        return (input_size - output_size) // 2
+
+    def required_padding(self, output_size):
+        input_size = self.input_size(output_size)
+        return (input_size - output_size) // 2
+
+
+    def patch_slices(self, height, width, patch_size):
+        for h_start, h_stop in range_chunks(height, patch_size):
+            for w_start, w_stop in range_chunks(width, patch_size):
+                h_pad = self.required_padding(h_stop - h_start)
+                w_pad = self.required_padding(w_stop - w_start)
+                h_s = slice(h_start, h_stop)
+                w_s = slice(w_start, w_stop)
+                h_slice = slice(h_start - h_pad + h_pad, h_stop + h_pad * 2)
+                w_slice = slice(w_start - w_pad + w_pad, w_stop + w_pad * 2)
+                yield (h_slice, w_slice), (h_s, w_s)