add requiremetns for LLavaTagger

DanbooruTagger/DanbooruTagger.py

@@ -0,0 +1,105 @@
+import warnings
+from deepdanbooru_onnx import DeepDanbooru
+from PIL import Image
+import argparse
+import cv2
+import os
+from multiprocessing import Process, Queue
+import json
+from tqdm import tqdm
+
+
+image_ext_ocv = [".bmp", ".jpeg", ".jpg", ".png"]
+
+
+def find_image_files(path: str) -> list[str]:
+	paths = list()
+	for root, dirs, files in os.walk(path):
+		for filename in files:
+			name, extension = os.path.splitext(filename)
+			if extension.lower() in image_ext_ocv:
+				paths.append(os.path.join(root, filename))
+	return paths
+
+
+def image_loader(paths: list[str]):
+	for path in paths:
+		name, extension = os.path.splitext(path)
+		extension = extension.lower()
+		imagebgr = cv2.imread(path)
+		image = cv2.cvtColor(imagebgr, cv2.COLOR_BGR2RGB)
+		if image is None:
+			print(f"Warning: could not load {path}")
+		else:
+			image_pil = Image.fromarray(image)
+			yield image_pil, path
+
+
+def pipeline(queue: Queue, image_paths: list[str], device: int):
+	danbooru = DeepDanbooru()
+
+	for path in image_paths:
+		imageprompt = ""
+		tags = danbooru(path)
+		for tag in tags:
+			imageprompt = imageprompt + ", " + tag
+
+		queue.put({"file_name": path, "text": imageprompt})
+
+
+def split_list(input_list, count):
+	target_length = int(len(input_list) / count)
+	for i in range(0, count - 1):
+		yield input_list[i * target_length: (i + 1) * target_length]
+	yield input_list[(count - 1) * target_length: len(input_list)]
+
+
+def save_meta(meta_file, meta, reldir, common_description):
+	meta["file_name"] = os.path.relpath(meta["file_name"], reldir)
+	if common_description is not None:
+		meta["text"] = common_description + meta["text"]
+	meta_file.write(json.dumps(meta) + '\n')
+
+
+if __name__ == "__main__":
+	parser = argparse.ArgumentParser("A script to tag images via DeepDanbooru")
+	parser.add_argument('--batch', '-b', default=4, type=int, help="Batch size to use for inference")
+	parser.add_argument('--common_description', '-c', help="An optional description that will be preended to the ai generated one")
+	parser.add_argument('--image_dir', '-i', help="A directory containg the images to tag")
+	args = parser.parse_args()
+
+	nparalell = 2
+
+	image_paths = find_image_files(args.image_dir)
+	image_path_chunks = list(split_list(image_paths, nparalell))
+
+	print(f"Will use {nparalell} processies to create tags")
+
+	queue = Queue()
+	processies = list()
+	for i in range(0, nparalell):
+		processies.append(Process(target=pipeline, args=(queue, image_path_chunks[i], i)))
+		processies[-1].start()
+
+	progress = tqdm(desc="Generateing tags", total=len(image_paths))
+	exit = False
+	with open(os.path.join(args.image_dir, "metadata.jsonl"), mode='w') as output_file:
+		while not exit:
+			if not queue.empty():
+				meta = queue.get()
+				save_meta(output_file, meta, args.image_dir, args.common_description)
+				progress.update()
+			exit = True
+			for process in processies:
+				if process.is_alive():
+					exit = False
+					break
+
+		while not queue.empty():
+			meta = queue.get()
+			save_meta(output_file, meta, args.image_dir, args.common_description)
+			progress.update()
+
+	for process in processies:
+		process.join()
+

DanbooruTagger/deepdanbooru_onnx/__init__.py

@@ -0,0 +1,3 @@
+from .deepdanbooru_onnx import DeepDanbooru
+from .deepdanbooru_onnx import process_image
+__version__ = '0.0.8'

DanbooruTagger/deepdanbooru_onnx/deepdanbooru_onnx.py

@@ -0,0 +1,244 @@
+import onnxruntime as ort
+from PIL import Image
+import numpy as np
+import os
+from tqdm import tqdm
+import requests
+import hashlib
+from typing import List, Union
+import shutil
+from pathlib import Path
+
+
+def process_image(image: Image.Image) -> np.ndarray:
+    """
+    Convert an image to a numpy array.
+    :param image: the image to convert
+    :return: the numpy array
+    """
+
+    image = image.convert("RGB").resize((512, 512))
+    image = np.array(image).astype(np.float32) / 255
+    image = image.transpose((2, 0, 1)).reshape(1, 3, 512, 512).transpose((0, 2, 3, 1))
+    return image
+
+
+def download(url: str, save_path: str, md5: str, length: str) -> bool:
+    """
+    Download a file from url to save_path.
+    If the file already exists, check its md5.
+    If the md5 matches, return True,if the md5 doesn't match, return False.
+    :param url: the url of the file to download
+    :param save_path: the path to save the file
+    :param md5: the md5 of the file
+    :param length: the length of the file
+    :return: True if the file is downloaded successfully, False otherwise
+    """
+
+    try:
+        response = requests.get(url=url, stream=True)
+        with open(save_path, "wb") as f:
+            with tqdm.wrapattr(
+                response.raw, "read", total=length, desc="Downloading"
+            ) as r_raw:
+                shutil.copyfileobj(r_raw, f)
+        return (
+            True
+            if hashlib.md5(open(save_path, "rb").read()).hexdigest() == md5
+            else False
+        )
+    except Exception as e:
+        print(e)
+        return False
+
+
+def download_model():
+    """
+    Download the model and tags file from the server.
+    :return: the path to the model and tags file
+    """
+
+    model_url = (
+        "https://huggingface.co/chinoll/deepdanbooru/resolve/main/deepdanbooru.onnx"
+    )
+    tags_url = "https://huggingface.co/chinoll/deepdanbooru/resolve/main/tags.txt"
+    model_md5 = "16be4e40ebcc0b1d1915bbf31f00969f"
+    tags_md5 = "a3f764de985cdeba89f1d232a4204402"
+    model_length = 643993025
+    tags_length = 133810
+
+    home = str(Path.home()) + "/.deepdanbooru_onnx/"
+    if not os.path.exists(home):
+        os.mkdir(home)
+
+    model_name = "deepdanbooru.onnx"
+    tags_name = "tags.txt"
+
+    model_path = home + model_name
+    tags_path = home + tags_name
+    if os.path.exists(model_path):
+        if hashlib.md5(open(model_path, "rb").read()).hexdigest() != model_md5:
+            os.remove(model_path)
+            if not download(model_url, model_path, model_md5, model_length):
+                raise ValueError("Model download failed")
+
+    else:
+        if not download(model_url, model_path, model_md5, model_length):
+            raise ValueError("Model download failed")
+
+    if os.path.exists(tags_path):
+        if hashlib.md5(open(tags_path, "rb").read()).hexdigest() != tags_md5:
+            os.remove(tags_path)
+            if not download(tags_url, tags_path, tags_md5, tags_length):
+                raise ValueError("Tags download failed")
+    else:
+        if not download(tags_url, tags_path, tags_md5, tags_length):
+            raise ValueError("Tags download failed")
+    return model_path, tags_path
+
+
+class DeepDanbooru:
+    def __init__(
+        self,
+        mode: str = "auto",
+        model_path: Union[str, None] = None,
+        tags_path: Union[str, None] = None,
+        threshold: Union[float, int] = 0.6,
+        pin_memory: bool = False,
+        batch_size: int = 1,
+    ):
+        """
+        Initialize the DeepDanbooru class.
+        :param mode: the mode of the model, "cpu" or "gpu" or "auto"
+        :param model_path: the path to the model file
+        :param tags_path: the path to the tags file
+        :param threshold: the threshold of the model
+        :param pin_memory: whether to use pin memory
+        :param batch_size: the batch size of the model
+        """
+
+        providers = {
+            "cpu": "CPUExecutionProvider",
+            "gpu": "CUDAExecutionProvider",
+            "tensorrt": "TensorrtExecutionProvider",
+            "auto": (
+                "CUDAExecutionProvider"
+                if "CUDAExecutionProvider" in ort.get_available_providers()
+                else "CPUExecutionProvider"
+            ),
+        }
+
+        if not (isinstance(threshold, float) or isinstance(threshold, int)):
+            raise TypeError("threshold must be float or int")
+        if threshold < 0 or threshold > 1:
+            raise ValueError("threshold must be between 0 and 1")
+        if mode not in providers:
+            raise ValueError(
+                "Mode not supported. Please choose from: cpu, gpu, tensorrt"
+            )
+        if providers[mode] not in ort.get_available_providers():
+            raise ValueError(
+                f"Your device is not supported {mode}. Please choose from: cpu"
+            )
+        if model_path is not None and not os.path.exists(model_path):
+            raise FileNotFoundError("Model file not found")
+        if tags_path is not None and not os.path.exists(tags_path):
+            raise FileNotFoundError("Tags file not found")
+
+        if model_path is None or tags_path is None:
+            model_path, tags_path = download_model()
+
+        self.session = ort.InferenceSession(model_path, providers=[providers[mode]])
+        self.tags = [i.replace("\n", "") for i in open(tags_path, "r").readlines()]
+
+        self.input_name = self.session.get_inputs()[0].name
+        self.output_name = [output.name for output in self.session.get_outputs()]
+        self.threshold = threshold
+        self.pin_memory = pin_memory
+        self.batch_size = batch_size
+        self.mode = mode
+        self.cache = {}
+
+    def __str__(self) -> str:
+        return f"DeepDanbooru(mode={self.mode}, threshold={self.threshold}, pin_memory={self.pin_memory}, batch_size={self.batch_size})"
+
+    def __repr__(self) -> str:
+        return self.__str__()
+
+    def from_image_inference(self, image: Image.Image) -> dict:
+        image = process_image(image)
+        return self.predict(image)
+
+    def from_ndarray_inferece(self, image: np.ndarray) -> dict:
+        if image.shape != (1, 512, 512, 3):
+            raise ValueError(f"Image must be {(1, 512, 512, 3)}")
+        return self.predict(image)
+
+    def from_file_inference(self, image: str) -> dict:
+        return self.from_image_inference(Image.open(image))
+
+    def from_list_inference(self, image: Union[list, tuple]) -> List[dict]:
+        if self.pin_memory:
+            image = [process_image(Image.open(i)) for i in image]
+        for i in [
+            image[i : i + self.batch_size]
+            for i in range(0, len(image), self.batch_size)
+        ]:
+            imagelist = i
+            bs = len(i)
+            _imagelist, idx, hashlist = [], [], []
+            for j in range(len(i)):
+                img = Image.open(i[j]) if not self.pin_memory else imagelist[j]
+                image_hash = hashlib.md5(np.array(img).astype(np.uint8)).hexdigest()
+                hashlist.append(image_hash)
+                if image_hash in self.cache:
+                    continue
+                if not self.pin_memory:
+                    _imagelist.append(process_image(img))
+                else:
+                    _imagelist.append(imagelist[j])
+                idx.append(j)
+
+            imagelist = _imagelist
+            if len(imagelist) != 0:
+                _image = np.vstack(imagelist)
+                results = self.inference(_image)
+                results_idx = 0
+            else:
+                results = []
+
+            for i in range(bs):
+                image_tag = {}
+                if i in idx:
+                    hash = hashlist[i]
+                    for tag, score in zip(self.tags, results[results_idx]):
+                        if score >= self.threshold:
+                            image_tag[tag] = score
+                    results_idx += 1
+                    self.cache[hash] = image_tag
+                    yield image_tag
+                else:
+                    yield self.cache[hashlist[i]]
+
+    def inference(self, image):
+        return self.session.run(self.output_name, {self.input_name: image})[0]
+
+    def predict(self, image):
+        result = self.inference(image)
+        image_tag = {}
+        for tag, score in zip(self.tags, result[0]):
+            if score >= self.threshold:
+                image_tag[tag] = score
+        return image_tag
+
+    def __call__(self, image) -> Union[dict, List[dict]]:
+        if isinstance(image, str):
+            return self.from_file_inference(image)
+        elif isinstance(image, np.ndarray):
+            return self.from_ndarray_inferece(image)
+        elif isinstance(image, list) or isinstance(image, tuple):
+            return self.from_list_inference(image)
+        elif isinstance(image, Image.Image):
+            return self.from_image_inference(image)
+        else:
+            raise ValueError("Image must be a file path or a numpy array or list/tuple")

LLavaTagger/requirements.txt

@@ -0,0 +1,11 @@
+accelerate==0.29.0
+bitsandbytes
+huggingface-hub==0.22.2
+ninja==1.11.1.1
+safetensors==0.4.2
+tokenizers==0.15.2
+transformers
+pytorch
+opencv-python
+numpy
+tqdm

PersonDatasetAssembler/PersonDatasetAssembler.py

@@ -1,4 +1,24 @@
 #!/bin/python3
+
+# PersonDatasetAssembler - A tool to assmble images of a specific person from a
+# directory of images or from a video file
+# Copyright (C) 2024 Carl Philipp Klemm
+#
+# This file is part of PersonDatasetAssembler.
+#
+# PersonDatasetAssembler is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# PersonDatasetAssembler is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with PersonDatasetAssembler.  If not, see <http://www.gnu.org/licenses/>.
+
 import argparse
 import os
 from typing import Iterator

SmartCrop/facerecognizer.cpp

@@ -1,3 +1,23 @@
+//
+// SmartCrop - A tool for content aware croping of images
+// Copyright (C) 2024 Carl Philipp Klemm
+//
+// This file is part of SmartCrop.
+//
+// SmartCrop is free software: you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation, either version 3 of the License, or
+// (at your option) any later version.
+//
+// SmartCrop is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU General Public License
+// along with SmartCrop.  If not, see <http://www.gnu.org/licenses/>.
+//
+
 #include "facerecognizer.h"
 #include <filesystem>
 

SmartCrop/facerecognizer.h

@@ -1,3 +1,22 @@
+/* * SmartCrop - A tool for content aware croping of images
+ * Copyright (C) 2024 Carl Philipp Klemm
+ *
+ * This file is part of SmartCrop.
+ *
+ * SmartCrop is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * SmartCrop is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with SmartCrop.  If not, see <http://www.gnu.org/licenses/>.
+ */
+
 #pragma once
 #include <exception>
 #include <opencv2/core/mat.hpp>

SmartCrop/incbin.h

@@ -1,3 +1,22 @@
+/* * SmartCrop - A tool for content aware croping of images
+ * Copyright (C) 2024 Carl Philipp Klemm
+ *
+ * This file is part of SmartCrop.
+ *
+ * SmartCrop is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * SmartCrop is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with SmartCrop.  If not, see <http://www.gnu.org/licenses/>.
+ */
+
 /**
  * @file incbin.h
  * @author Dale Weiler

SmartCrop/intelligentroi.cpp

@@ -1,3 +1,23 @@
+//
+// SmartCrop - A tool for content aware croping of images
+// Copyright (C) 2024 Carl Philipp Klemm
+//
+// This file is part of SmartCrop.
+//
+// SmartCrop is free software: you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation, either version 3 of the License, or
+// (at your option) any later version.
+//
+// SmartCrop is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU General Public License
+// along with SmartCrop.  If not, see <http://www.gnu.org/licenses/>.
+//
+
 #include "intelligentroi.h"
 
 #include <opencv2/imgproc.hpp>

SmartCrop/intelligentroi.h

@@ -1,3 +1,22 @@
+/* * SmartCrop - A tool for content aware croping of images
+ * Copyright (C) 2024 Carl Philipp Klemm
+ *
+ * This file is part of SmartCrop.
+ *
+ * SmartCrop is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * SmartCrop is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with SmartCrop.  If not, see <http://www.gnu.org/licenses/>.
+ */
+
 #pragma once
 
 #include <opencv2/imgproc.hpp>

SmartCrop/main.cpp

@@ -1,3 +1,23 @@
+//
+// SmartCrop - A tool for content aware croping of images
+// Copyright (C) 2024 Carl Philipp Klemm
+//
+// This file is part of SmartCrop.
+//
+// SmartCrop is free software: you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation, either version 3 of the License, or
+// (at your option) any later version.
+//
+// SmartCrop is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU General Public License
+// along with SmartCrop.  If not, see <http://www.gnu.org/licenses/>.
+//
+
 #include <filesystem>
 #include <iostream>
 #include <opencv2/core.hpp>
@@ -430,7 +450,7 @@ int main(int argc, char* argv[])
 	std::vector<std::thread> threads;
 	std::vector<std::vector<std::filesystem::path>> imagePathParts = splitVector(imagePaths, std::thread::hardware_concurrency());
 
-	for(size_t i = 0; i < std::thread::hardware_concurrency(); ++i)
+	for(size_t i = 0; i < imagePathParts.size(); ++i)
 		threads.push_back(std::thread(threadFn, imagePathParts[i], std::ref(config),  recognizer, std::ref(recognizerMutex), std::ref(debugOutputPath)));
 
 	for(std::thread& thread : threads)

SmartCrop/options.h

@@ -1,3 +1,22 @@
+/* * SmartCrop - A tool for content aware croping of images
+ * Copyright (C) 2024 Carl Philipp Klemm
+ *
+ * This file is part of SmartCrop.
+ *
+ * SmartCrop is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * SmartCrop is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with SmartCrop.  If not, see <http://www.gnu.org/licenses/>.
+ */
+
 #pragma once
 #include <string>
 #include <vector>

SmartCrop/readfile.h

@@ -1,3 +1,22 @@
+/* * SmartCrop - A tool for content aware croping of images
+ * Copyright (C) 2024 Carl Philipp Klemm
+ *
+ * This file is part of SmartCrop.
+ *
+ * SmartCrop is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * SmartCrop is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with SmartCrop.  If not, see <http://www.gnu.org/licenses/>.
+ */
+
 #pragma once
 #include <string>
 #include <filesystem>

SmartCrop/seamcarving.cpp

@@ -1,3 +1,23 @@
+//
+// SmartCrop - A tool for content aware croping of images
+// Copyright (C) 2024 Carl Philipp Klemm
+//
+// This file is part of SmartCrop.
+//
+// SmartCrop is free software: you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation, either version 3 of the License, or
+// (at your option) any later version.
+//
+// SmartCrop is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU General Public License
+// along with SmartCrop.  If not, see <http://www.gnu.org/licenses/>.
+//
+
 #include "seamcarving.h"
 
 #include <opencv2/imgcodecs.hpp>

SmartCrop/seamcarving.h

@@ -1,3 +1,22 @@
+/* * SmartCrop - A tool for content aware croping of images
+ * Copyright (C) 2024 Carl Philipp Klemm
+ *
+ * This file is part of SmartCrop.
+ *
+ * SmartCrop is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * SmartCrop is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with SmartCrop.  If not, see <http://www.gnu.org/licenses/>.
+ */
+
 #pragma once
 
 #include <opencv2/core/core.hpp>

SmartCrop/tokenize.cpp

@@ -1,3 +1,23 @@
+//
+// SmartCrop - A tool for content aware croping of images
+// Copyright (C) 2024 Carl Philipp Klemm
+//
+// This file is part of SmartCrop.
+//
+// SmartCrop is free software: you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation, either version 3 of the License, or
+// (at your option) any later version.
+//
+// SmartCrop is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU General Public License
+// along with SmartCrop.  If not, see <http://www.gnu.org/licenses/>.
+//
+
 #include "tokenize.h"
 
 

SmartCrop/tokenize.h

@@ -1,3 +1,22 @@
+/* * SmartCrop - A tool for content aware croping of images
+ * Copyright (C) 2024 Carl Philipp Klemm
+ *
+ * This file is part of SmartCrop.
+ *
+ * SmartCrop is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * SmartCrop is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with SmartCrop.  If not, see <http://www.gnu.org/licenses/>.
+ */
+
 #pragma once
 
 #include <string>

SmartCrop/utils.cpp

@@ -1,3 +1,23 @@
+//
+// SmartCrop - A tool for content aware croping of images
+// Copyright (C) 2024 Carl Philipp Klemm
+//
+// This file is part of SmartCrop.
+//
+// SmartCrop is free software: you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation, either version 3 of the License, or
+// (at your option) any later version.
+//
+// SmartCrop is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU General Public License
+// along with SmartCrop.  If not, see <http://www.gnu.org/licenses/>.
+//
+
 #include "utils.h"
 
 #include <filesystem>

SmartCrop/utils.h

@@ -1,3 +1,22 @@
+/* * SmartCrop - A tool for content aware croping of images
+ * Copyright (C) 2024 Carl Philipp Klemm
+ *
+ * This file is part of SmartCrop.
+ *
+ * SmartCrop is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * SmartCrop is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with SmartCrop.  If not, see <http://www.gnu.org/licenses/>.
+ */
+
 #pragma once
 
 #include <filesystem>

SmartCrop/yolo.cpp

@@ -1,3 +1,23 @@
+//
+// SmartCrop - A tool for content aware croping of images
+// Copyright (C) 2024 Carl Philipp Klemm
+//
+// This file is part of SmartCrop.
+//
+// SmartCrop is free software: you can redistribute it and/or modify
+// it under the terms of the GNU General Public License as published by
+// the Free Software Foundation, either version 3 of the License, or
+// (at your option) any later version.
+//
+// SmartCrop is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+// GNU General Public License for more details.
+//
+// You should have received a copy of the GNU General Public License
+// along with SmartCrop.  If not, see <http://www.gnu.org/licenses/>.
+//
+
 #include <opencv2/dnn/dnn.hpp>
 #include <algorithm>
 #include <string>

SmartCrop/yolo.h

@@ -1,3 +1,22 @@
+/* * SmartCrop - A tool for content aware croping of images
+ * Copyright (C) 2024 Carl Philipp Klemm
+ *
+ * This file is part of SmartCrop.
+ *
+ * SmartCrop is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * SmartCrop is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with SmartCrop.  If not, see <http://www.gnu.org/licenses/>.
+ */
+
 #pragma once
 
 #include <fstream>