init commit

.dockerignore

@@ -0,0 +1,182 @@
+**/.git/
+
+# Ignore run time generated artifacts
+**/uploaded_media/
+**/feedbacks.json
+
+# Ignore pycache
+**/app/__pycache__/
+# 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
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+**/.pdm.toml
+**/.pdm-python
+**/.pdm-build/
+
+# 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/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+# Ruff stuff:
+**/.ruff_cache/
+
+# PyPI configuration file
+**/.pypirc

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/preview.png filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,180 @@
+# Ignore run time generated artifacts
+uploaded_media/
+feedbacks.json
+
+# Ignore pycache
+app/__pycache__/
+# 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
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# 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/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+# Ruff stuff:
+.ruff_cache/
+
+# PyPI configuration file
+.pypirc

.python-version

@@ -0,0 +1 @@
+3.10

Dockerfile

@@ -0,0 +1,25 @@
+FROM ghcr.io/astral-sh/uv:python3.11-bookworm-slim
+
+# Install dependencies
+ENV DEBIAN_FRONTEND=noninteractive
+COPY packages.txt /app/packages.txt
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends $(cat /app/packages.txt) && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*
+
+# Install Python dependencies
+COPY requirements.txt /app/requirements.txt
+ENV VIRTUAL_ENV=/opt/venv
+RUN --mount=type=cache,target=/root/.cache/uv \
+    uv venv $VIRTUAL_ENV && \
+    uv pip install --extra-index-url https://download.pytorch.org/whl/cpu torch torchvision && \
+    uv pip install -r /app/requirements.txt
+
+# Copy application code
+ENV PATH="$VIRTUAL_ENV/bin:$PATH"
+COPY . /app
+WORKDIR /app
+EXPOSE 8501
+ENTRYPOINT ["streamlit", "run", "app.py"]
+CMD ["--server.enableCORS=false", "--server.enableXsrfProtection=false", "--server.port=8501"]

LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+the GNU General Public License is intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.  We, the Free Software Foundation, use the
+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors.  You can apply it to
+your programs, too.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
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+you modify it: responsibilities to respect the freedom of others.
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+  Developers that use the GNU GPL protect your rights with two steps:
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+authors' sake, the GPL requires that modified versions be marked as
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README.md

@@ -1,14 +1,52 @@
----
-title: Auto Hajimi Mosaic
-emoji: 💻
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 5.29.0
-app_file: app.py
-pinned: false
-license: agpl-3.0
-short_description: 一款自动为你的色图进行哈基米马赛克处理的打码器😎再也不用担心家里请不到高人了 | 自动哈基米打码器
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# AutoHajimiMosaic
+一款自动为你的色图进行哈基米马赛克处理的打码器😎
+还在因为家里请不到高人，做不出哈基米打码图而感到难过么？想不想让朋友一觉起来听到，"你的图都被我用哈基米祝福了，不用谢"？本项目帮你自动化生成哈基米打码图😎。
+
+[马上开玩Demo！](https://auto-hajimi-mosaic.streamlit.app/) [Streamlit Free部署太弱小了！如果访问不了用这个Demo](https://1b2c-129-170-197-100.ngrok-free.app/)
+## 预览
+![preview](./assets/preview.png)
+
+## 环境
+用pip装个环境
+```bash
+pip install -r requirements.txt
+```
+或者直接docker一键部署开玩WebUI
+```bash
+docker compose up --build
+```
+
+## 哈基米使用方法
+
+### Web UI
+
+1. 运行 Streamlit 应用：
+   ```bash
+   streamlit run app.py
+   ```
+2. 在浏览器中打开应用，上传图片进行处理。
+
+### 批量处理
+
+1. 确保你的输入图像放在一个文件夹中。
+2. 运行批量处理脚本：
+   ```bash
+   python batch_process.py path/to/input/folder path/to/output/folder --pattern_image path/to/pattern.png --head_image path/to/head.png
+   ```
+   - `path/to/input/folder`: 输入图像文件夹路径。
+   - `path/to/output/folder`: 输出处理后图像的文件夹路径。
+   - `--pattern_image`: （可选）填充图案图片路径。
+   - `--head_image`: （可选）覆盖头部图片路径。
+
+
+## Todo&缺陷
+
+1. 目前[模型](https://github.com/Forenche/nsfw_detector_annotator/tree/release)对已经进行打码处理，存在遮挡物的部分识别能力弱，也存在缺判的情况。虽然存在[NudeNet](https://github.com/notAI-tech/NudeNet)一类的优秀模型，但是并不支持mask只支持box。后续可以支持更强的模型或者使用SAM+Grounded的方式使用自然语言nsfw描述来获得mask。
+> 感谢Forenche的nsfw_detector_annotator项目，提供了一个很好的基础模型。
+2. 目前填充方法采用仿射变化，和通过自由变化、打点、液化的方式比还不够神人。后续会支持更多自动化的填充方法。
+3. 脱离streamlit，更加自由的interactive edit。对于1中可能漏掉的部分，之后可以写一个interactive edit的功能，允许用户手动拖拽旋转填充哈基米。这部分需要js。
+
+## Issues&Contributions
+现在的实现非常简单，欢迎大家提出处理失败的图片（全都发给我！！），或者提出更好的处理方法。也欢迎大家提交PR，添加更多的功能和处理方法。
+
+就这样，我去给朋友的图打码了，886~

app.py

@@ -0,0 +1,65 @@
+import gradio as gr
+from PIL import Image
+from util import load_models, classify_image, segment_image, apply_mask
+
+classification_model, segmentation_model = load_models()
+names = segmentation_model.names
+
+def process_image(uploaded_file, use_custom_head, custom_head_file=None):
+    image = Image.open(uploaded_file)
+
+    category = classify_image(image, classification_model)
+    category_name = classification_model.names[category[0]]
+
+    segmentation_results = segment_image(image, segmentation_model)
+    try:
+        masks = segmentation_results[0].masks.data.cpu().numpy()
+        class_ids = segmentation_results[0].boxes.cls.cpu().numpy().astype(int)
+    except AttributeError:
+        if category_name in ['porn', 'hentai']:
+            return "是色图！但是哈基米不知道遮哪里。坏😭", None
+        masks = []
+        class_ids = []
+
+    mask_options = [names[class_id] for class_id in class_ids]
+
+    pattern_image = Image.open("assets/pattern.png")
+    default_head_image = Image.open("assets/head.png").convert("RGBA")
+
+    if use_custom_head and custom_head_file is not None:
+        head_image = custom_head_file.convert("RGBA")
+    else:
+        head_image = default_head_image
+
+    image_with_fill = image.copy()
+    for i, mask in enumerate(masks):
+        if mask_options[i] in mask_options:
+            image_with_fill = apply_mask(image_with_fill, mask, pattern_image, head_image)
+
+    return image, image_with_fill
+
+def toggle_custom_head(use_custom_head):
+    return gr.update(visible=use_custom_head)
+
+with gr.Blocks() as iface:
+    gr.Markdown("## 🐱自动哈基米打码机\n上传一张图片，哈基米会自动识别区域并且覆盖上去，你可以选择不遮挡一部分，然后就可以下载下来送给朋友了！[Source Code](https://github.com/frinkleko/AutoHajimiMosaic)")
+
+    uploaded_file = gr.File(label="上传图片")
+    use_custom_head = gr.Checkbox(label="使用你自己的哈基米", value=False)
+    custom_head_file = gr.Image(type="pil", label="上传你的哈基米(推荐PNG with transparency)", visible=False)
+
+    use_custom_head.change(toggle_custom_head, inputs=use_custom_head, outputs=custom_head_file)
+
+    submit_btn = gr.Button("Submit")
+    
+    with gr.Row():
+        original_output = gr.Image(type="pil", label="原图")
+        mosaic_output = gr.Image(type="pil", label="哈基米图")
+
+    submit_btn.click(
+        process_image,
+        inputs=[uploaded_file, use_custom_head, custom_head_file],
+        outputs=[original_output, mosaic_output]
+    )
+
+iface.launch(share=True)

batch_process.py

@@ -0,0 +1,57 @@
+import os
+import argparse
+from PIL import Image
+from util import load_models, classify_image, segment_image, apply_mask
+
+def process_images(input_folder, output_folder, pattern_image_path, head_image_path):
+    classification_model, segmentation_model = load_models()
+    names = segmentation_model.names
+
+    pattern_image = Image.open(pattern_image_path)
+    head_image = Image.open(head_image_path).convert("RGBA")
+
+    for filename in os.listdir(input_folder):
+        if filename.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.webp')):
+            image_path = os.path.join(input_folder, filename)
+            image = Image.open(image_path)
+
+            # Classify the image
+            category = classify_image(image, classification_model)
+            category_name = classification_model.names[category[0]]
+
+            # Segment the image
+            segmentation_results = segment_image(image, segmentation_model)
+            try:
+                masks = segmentation_results[0].masks.data.cpu().numpy()
+                class_ids = segmentation_results[0].boxes.cls.cpu().numpy().astype(int)
+            except AttributeError:
+                if category_name in ['porn', 'hentai']:
+                    print(f"Warning: {filename} is classified as sensitive content, but no mask was found.")
+                masks = []
+                class_ids = []
+
+            mask_options = [names[class_id] for class_id in class_ids]
+            selected_masks = mask_options  # Automatically select all masks
+
+            if selected_masks:
+                image_with_fill = image.copy()
+                for i, mask in enumerate(masks):
+                    if mask_options[i] in selected_masks:
+                        image_with_fill = apply_mask(image_with_fill, mask, pattern_image, head_image)
+
+                # Save the processed image
+                output_path = os.path.join(output_folder, f"processed_{filename}")
+                image_with_fill.save(output_path, format="PNG")
+                print(f"Processed and saved: {output_path}")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Batch process images with YOLO models.")
+    parser.add_argument("input_folder", type=str, help="Path to the input folder containing images.")
+    parser.add_argument("output_folder", type=str, help="Path to the output folder to save processed images.")
+    parser.add_argument("--pattern_image", type=str, default="assets/pattern.png", help="Path to the pattern image.")
+    parser.add_argument("--head_image", type=str, default="assets/head.png", help="Path to the head image.")
+
+    args = parser.parse_args()
+
+    os.makedirs(args.output_folder, exist_ok=True)
+    process_images(args.input_folder, args.output_folder, args.pattern_image, args.head_image)

docker-compose.yml

@@ -0,0 +1,9 @@
+name: hajimi
+
+services:
+  app:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    ports:
+      - "8501:8501"

models/classification_model.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b36a3497741d04d567b796a372701d10a9630ed3b4662f69a8a774632b29aa47
+size 20903196

models/segmentation_model.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:098c6ef3f75a9c41df2aa86ae78b700ac319d63947de6466ef728a219a3960eb
+size 11808576

packages.txt

@@ -0,0 +1,2 @@
+libgl1
+libglib2.0-0

requirements.txt

@@ -0,0 +1,5 @@
+opencv_python==4.10.0.84
+Pillow==11.1.0
+pillow_heif==0.21.0
+streamlit==1.41.1
+ultralytics==8.3.58

util.py

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+from ultralytics import YOLO
+from PIL import Image
+import numpy as np
+import cv2
+
+def load_models():
+    classification_model = YOLO('models/classification_model.pt')
+    segmentation_model = YOLO('models/segmentation_model.pt')
+    return classification_model, segmentation_model
+
+def classify_image(image, classification_model):
+    results = classification_model(image, verbose=True)
+    category = results[0].probs.top5
+    return category
+
+def segment_image(image, segmentation_model):
+    results = segmentation_model(image, agnostic_nms=True, retina_masks=True, verbose=True)
+    return results
+
+def tile_pattern(image_size, pattern_image):
+    pattern_np = np.array(pattern_image)
+    pattern_height, pattern_width = pattern_np.shape[:2]
+
+    tiled_pattern = np.tile(pattern_np, (
+        image_size[1] // pattern_height + 1,
+        image_size[0] // pattern_width + 1,
+        1
+    ))
+
+    return Image.fromarray(tiled_pattern[:image_size[1], :image_size[0]])
+
+def apply_mask(image, mask, pattern_image, head_image):
+    image_np = np.array(image)
+    mask_np = np.array(mask)
+
+    tiled_pattern = tile_pattern(image.size, pattern_image)
+    tiled_pattern_np = np.array(tiled_pattern)
+
+    mask_indices = np.argwhere(mask_np > 0)
+    if mask_indices.size > 0:
+        min_y, min_x = mask_indices.min(axis=0)
+        max_y, max_x = mask_indices.max(axis=0)
+
+        dst_points = np.float32([
+            [min_x, min_y],
+            [max_x, min_y],
+            [max_x, max_y],
+            [min_x, max_y]
+        ])
+
+        head_width, head_height = head_image.size
+        src_points = np.float32([
+            [0, 0],
+            [head_width, 0],
+            [head_width, head_height],
+            [0, head_height]
+        ])
+
+        matrix = cv2.getPerspectiveTransform(src_points, dst_points)
+        head_np = np.array(head_image)
+        transformed_head = cv2.warpPerspective(head_np, matrix, (image.size[0], image.size[1]))
+
+        overlay = Image.fromarray(transformed_head, 'RGBA')
+        image_with_head = Image.alpha_composite(Image.fromarray(image_np).convert("RGBA"), overlay)
+        return image_with_head.convert("RGB")
+    return Image.fromarray(image_np)