├── examples ├── example3.jpg ├── example4.jpg ├── example5.jpg ├── example6.jpg ├── receipt_sample.jpg └── document_sample.png ├── Dockerfile ├── pyproject.toml ├── .dockerignore ├── LICENSE ├── README.md ├── .gitignore ├── app.py ├── requirements.txt └── uv.lock /examples/example3.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/evibhm/ImageCorrection/HEAD/examples/example3.jpg -------------------------------------------------------------------------------- /examples/example4.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/evibhm/ImageCorrection/HEAD/examples/example4.jpg -------------------------------------------------------------------------------- /examples/example5.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/evibhm/ImageCorrection/HEAD/examples/example5.jpg -------------------------------------------------------------------------------- /examples/example6.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/evibhm/ImageCorrection/HEAD/examples/example6.jpg -------------------------------------------------------------------------------- /examples/receipt_sample.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/evibhm/ImageCorrection/HEAD/examples/receipt_sample.jpg -------------------------------------------------------------------------------- /examples/document_sample.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/evibhm/ImageCorrection/HEAD/examples/document_sample.png -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | FROM python:3.13-slim 2 | WORKDIR /app 3 | RUN sed -i 's@deb.debian.org@mirrors.tuna.tsinghua.edu.cn@g' /etc/apt/sources.list.d/debian.sources 4 | RUN apt-get update && apt-get install -y libglib2.0-0 libgl1-mesa-glx 5 | COPY pyproject.toml . 6 | RUN pip install . 7 | COPY . . 8 | EXPOSE 7860 9 | 10 | CMD ["python", "app.py"] -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "imagecorrection" 3 | version = "1.0.0" 4 | description = "这是一个基于 Python 和 Gradio 构建的交互式图像处理Web应用。用户可以上传图片（如包含文档、票据等），系统会自动进行边缘检测、透视矫正，并应用自适应二值化来增强图像。应用界面还提供了多个可调节参数，以便用户实时观察不同参数设置对处理结果的影响。" 5 | readme = "README.md" 6 | requires-python = ">=3.13" 7 | dependencies = [ 8 | "gradio>=5.29.1", 9 | "numpy>=2.2.5", 10 | "opencv-python>=4.11.0.86", 11 | "scipy>=1.15.3", 12 | ] 13 | -------------------------------------------------------------------------------- /.dockerignore: -------------------------------------------------------------------------------- 1 | # .dockerignore 2 | 3 | # Python 缓存和编译文件 4 | 5 | **pycache**/ 6 | \*.pyc 7 | \*.pyo 8 | \*.pyd 9 | \*.egg-info/ 10 | 11 | # 虚拟环境文件夹 12 | 13 | env/ 14 | venv/ 15 | .venv/ 16 | 17 | # Git 相关 18 | 19 | .git/ 20 | .gitignore 21 | .gitattributes 22 | 23 | # GitHub Actions 和 VSCode 等编辑器配置 24 | 25 | .github/ 26 | .vscode/ 27 | .idea/ 28 | 29 | # 操作系统特定文件 30 | 31 | \*.DS\_Store 32 | Thumbs.db 33 | 34 | # 构建输出和打包文件 (如果在本机构建过) 35 | 36 | dist/ 37 | build/ 38 | \*.tar.gz 39 | \*.zip 40 | \*.exe 41 | \*.dmg 42 | \*.deb 43 | \*.rpm 44 | 45 | # 日志文件 46 | 47 | \*.log 48 | logs/ 49 | 50 | # 其他不需要包含在镜像中的文件 51 | 52 | # local\_settings.py 53 | 54 | # secrets.txt -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 evibhm 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 图像边缘检测、矫正与增强锐化系统 2 | 3 | 这是一个基于 Python 和 Gradio 构建的交互式图像处理Web应用。用户可以上传图片（如包含文档、票据等），系统会自动进行边缘检测、透视矫正，并应用自适应二值化来增强图像。应用界面还提供了多个可调节参数，以便用户实时观察不同参数设置对处理结果的影响。 4 | 5 | ## 主要功能 6 | 7 | * **图像上传**: 支持通过文件上传、摄像头拍摄或剪贴板粘贴的方式导入图片。 8 | * **边缘检测**: 使用 Canny 边缘检测算法识别图像中的主要边缘。 9 | * **轮廓查找与筛选**: 找到最大的轮廓，通常对应图像中的主要对象（如文档边缘）。 10 | * **透视矫正**: 根据检测到的轮廓角点，对图像进行透视变换，以校正倾斜或变形的图像。 11 | * **自适应二值化**: 对矫正后的图像应用自适应阈值处理，生成清晰的黑白二值图像。 12 | * **参数可调**: 13 | * Canny 边缘检测的低阈值和高阈值。 14 | * 边缘膨胀操作的迭代次数。 15 | * 自适应阈值处理的窗口大小和 Beta 系数。 16 | * **实时预览**: 17 | * Canny 边缘检测结果图。 18 | * 在原图上绘制检测到的轮廓图。 19 | * 透视矫正后的彩色效果图。 20 | * 最终处理的二值化结果图。 21 | * **界面友好**: 使用 Gradio 构建，提供简洁直观的 Web 用户界面，包括滑块调节参数和示例图片加载。 22 | 23 | ## 如何运行 24 | 25 | 1. **克隆/下载项目**: 26 | 获取项目代码到您的本地计算机。 27 | 28 | 2. **安装依赖**: 29 | 确保您的 Python 环境中安装了必要的库。您可以使用 pip 来安装它们： 30 | ```bash 31 | pip install . 32 | ``` 33 | 或者 34 | ```bash 35 | pip install -r requirements.txt 36 | ``` 37 | 38 | 3. **运行应用**: 39 | 在项目根目录下，通过命令行运行 Python 脚本 (假设脚本文件名为 `app.py`，请根据您的实际文件名修改): 40 | ```bash 41 | python app.py 42 | ``` 43 | 44 | 45 | 4. **访问应用**: 46 | 脚本成功运行后，终端会显示一个本地 URL (通常是 `http://127.0.0.1:7860` 或类似的地址)。在您的网页浏览器中打开此 URL 即可开始使用该应用。 47 | 48 | ## 文件结构 (主要) 49 | 50 | * `app.py` (或您的主脚本文件名): 包含所有图像处理逻辑和 Gradio 界面定义的 Python 脚本。 51 | * `examples/`: (可选/自动生成) 存放示例图片的文件夹。 52 | 53 | ## 可调节参数详解 54 | 55 | 通过界面左侧的滑块，您可以调整以下参数： 56 | 57 | * **Canny 低阈值 (Thresh1)**: Canny 算法的第一个阈值，用于边缘连接。较小的值会检测到更多较弱的边缘。 58 | * **Canny 高阈值 (Thresh2)**: Canny 算法的第二个阈值，用于初步标记强边缘。建议 Thresh2 的值是 Thresh1 的 2 到 3 倍。 59 | * **边缘膨胀次数 (Dilation Iter.)**: 对检测到的边缘进行膨胀处理的次数。增加此值可以使边缘线条更粗，有助于闭合小的断裂。设置为 0 则不进行膨胀。 60 | * **自适应阈值窗口 (Win Size - odd)**: 计算局部均值时使用的邻域窗口大小。必须是奇数。窗口越大，对局部光照变化的适应性越粗略。 61 | * **自适应阈值 Beta (Beta Coeff.)**: 一个常数，从计算出的均值中减去（或乘以一个系数后比较）。调整此值可以使二值化结果整体偏亮或偏暗。 62 | 63 | ## 输出结果说明 64 | 65 | 应用界面右侧会以 2x2 网格的形式显示以下四个中间及最终处理结果： 66 | 67 | 1. **Canny 边缘**: 经过 Canny 边缘检测算法处理后的灰度图像，显示检测到的边缘。 68 | 2. **轮廓检测**: 在调整尺寸后的原始彩色图像上，用绿色线条绘制出的面积最大的轮廓。 69 | 3. **透视矫正图**: 对原始彩色图像应用透视变换后的结果，主要对象应被校正为矩形。 70 | 4. **最终结果**: 对透视矫正图应用自适应二值化后的黑白图像。 71 | 72 | ## 注意事项 73 | 74 | * 图像处理的效果很大程度上取决于输入图像的质量和内容。 75 | * 对于非常复杂或低对比度的图像，可能需要仔细调整参数才能获得理想结果。 76 | * 如果透视变换所需的四个角点无法准确检测（例如，对象边缘不清晰或被遮挡），矫正效果可能会不佳。 77 | 78 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /app.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import gradio as gr 3 | import numpy as np 4 | from scipy import signal 5 | 6 | 7 | def resizeImg(image, height=900): 8 | """ 9 | 根据指定高度等比例缩放图像。 10 | Resizes an image proportionally to a specified height. 11 | """ 12 | h, w = image.shape[:2] 13 | if h == 0: # 防止除以零 Prevent division by zero 14 | return image 15 | pro = height / h 16 | size = (int(w * pro), int(height)) 17 | img = cv2.resize(image, size) 18 | return img 19 | 20 | 21 | def getCanny(image, thresh1, thresh2, dilate_iterations): 22 | """ 23 | 对图像进行边缘检测。 24 | Performs Canny edge detection on an image. 25 | Input 'image' is expected to be BGR for this function's context. 26 | Parameters: 27 | - image: Input BGR image 28 | - thresh1: First threshold for the hysteresis procedure. 29 | - thresh2: Second threshold for the hysteresis procedure. 30 | - dilate_iterations: Number of iterations for dilation. 31 | """ 32 | # 如果是彩色图像，先转为灰度图 Convert to grayscale if it's a color image 33 | gray = ( 34 | cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 35 | if len(image.shape) == 3 and image.shape[2] == 3 36 | else image 37 | ) 38 | # 高斯滤波 Gaussian blur 39 | binary = cv2.GaussianBlur(src=gray, ksize=(3, 3), sigmaX=2.0, sigmaY=2.0) 40 | # 边缘检测 Edge detection 41 | binary = cv2.Canny(binary, thresh1, thresh2, apertureSize=3) 42 | # 膨胀操作，尽量使边缘闭合 Dilate to close gaps in edges 43 | kernel = np.ones((3, 3), np.uint8) 44 | if dilate_iterations > 0: # Apply dilation only if iterations > 0 45 | binary = cv2.dilate(binary, kernel, iterations=dilate_iterations) 46 | return binary # Returns a grayscale image 47 | 48 | 49 | def findMaxContour(image): 50 | """ 51 | 找出图像中面积最大的轮廓。 52 | Finds the contour with the largest area in an image. 53 | Input 'image' is expected to be a binary image. 54 | """ 55 | contours, _ = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) 56 | max_area = 0.0 57 | max_contour = [] 58 | for contour in contours: 59 | currentArea = cv2.contourArea(contour) 60 | if currentArea > max_area: 61 | max_area = currentArea 62 | max_contour = contour 63 | return max_contour, max_area 64 | 65 | 66 | def getBoxPoint(contour): 67 | """ 68 | 通过多边形拟合凸包获取四个顶点。 69 | Gets four corner points by fitting a polygon to the convex hull of a contour. 70 | """ 71 | hull = cv2.convexHull(contour) 72 | epsilon = 0.02 * cv2.arcLength( 73 | contour, True 74 | ) # Epsilon factor could also be a parameter 75 | approx = cv2.approxPolyDP(hull, epsilon, True) 76 | approx = approx.reshape((len(approx), 2)) 77 | return approx 78 | 79 | 80 | def adaPoint(box, pro): 81 | """ 82 | 根据比例调整点集坐标。 83 | Adapts point set coordinates based on a scaling factor. 84 | 'pro' = scaled_dimension / original_dimension. Scales 'box' by 1/pro. 85 | """ 86 | box_pro = box.astype(np.float32) 87 | if pro != 0: 88 | box_pro = box_pro / pro 89 | return box_pro.astype(np.float32) 90 | 91 | 92 | def orderPoints(pts): 93 | """ 94 | 对四边形的四个顶点进行排序：[top-left, top-right, bottom-right, bottom-left]。 95 | Orders the four vertices of a quadrilateral. 96 | """ 97 | rect = np.zeros((4, 2), dtype="float32") 98 | s = pts.sum(axis=1) 99 | rect[0] = pts[np.argmin(s)] 100 | rect[2] = pts[np.argmax(s)] 101 | diff = np.diff(pts, axis=1) 102 | rect[1] = pts[np.argmin(diff)] 103 | rect[3] = pts[np.argmax(diff)] 104 | return rect 105 | 106 | 107 | def pointDistance(a, b): 108 | """ 109 | 计算两点之间的欧氏距离。 110 | Calculates the Euclidean distance between two points. 111 | """ 112 | return int(np.sqrt(np.sum(np.square(a - b)))) 113 | 114 | 115 | def warpImage(image, box): 116 | """ 117 | 对图像进行透视变换。 118 | Performs perspective warping on an image. Input 'image' is BGR. 119 | """ 120 | box = np.array(box, dtype="float32") 121 | if box.shape[0] != 4: 122 | print("警告: 透视变换需要4个角点。将返回原始图像的副本。") 123 | return image.copy() 124 | 125 | ordered_box = orderPoints(box) 126 | 127 | widthA = pointDistance(ordered_box[0], ordered_box[1]) 128 | widthB = pointDistance(ordered_box[3], ordered_box[2]) 129 | maxWidth = max(int(widthA), int(widthB)) 130 | 131 | heightA = pointDistance(ordered_box[0], ordered_box[3]) 132 | heightB = pointDistance(ordered_box[1], ordered_box[2]) 133 | maxHeight = max(int(heightA), int(heightB)) 134 | 135 | if maxWidth <= 0 or maxHeight <= 0: # Ensure dimensions are positive 136 | print("警告: 计算出的变换后图像宽度或高度无效。将返回原始图像的副本。") 137 | return image.copy() 138 | 139 | dst_rect = np.array( 140 | [[0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], 141 | dtype="float32", 142 | ) 143 | 144 | try: 145 | M = cv2.getPerspectiveTransform(ordered_box, dst_rect) 146 | warped = cv2.warpPerspective( 147 | image, M, (maxWidth, maxHeight) 148 | ) # Warped image is BGR 149 | except cv2.error as e: 150 | print(f"透视变换时发生OpenCV错误: {e}") 151 | return image.copy() # Return copy of original on error 152 | return warped # Returns BGR image 153 | 154 | 155 | def adaptive_thres(img, win, beta): 156 | """ 157 | 对图像进行自适应阈值处理。 158 | Applies adaptive thresholding. Input 'img' can be BGR or Grayscale. 159 | Parameters: 160 | - img: Input image (BGR or Grayscale) 161 | - win: Window size for local mean calculation (should be odd). 162 | - beta: Coefficient to adjust threshold based on local mean. 163 | """ 164 | if len(img.shape) == 3 and img.shape[2] == 3: 165 | img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 166 | elif len(img.shape) == 2: 167 | img_gray = img 168 | else: 169 | print("错误：adaptive_thres 的输入图像格式不正确。") 170 | return img 171 | 172 | # Ensure window size is odd 173 | if win % 2 == 0: 174 | win = win + 1 if win > 0 else 3 # Make it odd, ensure positive 175 | 176 | kern = np.ones([win, win]) 177 | sums = signal.correlate2d(img_gray, kern, "same", boundary="symm") 178 | cnts = signal.correlate2d(np.ones_like(img_gray), kern, "same", boundary="symm") 179 | 180 | cnts[cnts == 0] = 1 181 | means = sums / cnts 182 | 183 | binary_img = np.where(img_gray < means * beta, 0, 255) 184 | return binary_img.astype(np.uint8) # Returns grayscale binary image 185 | 186 | 187 | def enhance( 188 | image_bgr, 189 | canny_thresh1, 190 | canny_thresh2, 191 | dilate_iter, 192 | adaptive_win, 193 | adaptive_beta_val, 194 | ): 195 | """ 196 | 图像增强主函数。 197 | Returns: (final_output, canny_output, contour_img_output, warped_color_output) 198 | """ 199 | if image_bgr is None: 200 | print("错误：输入到 enhance 函数的图像为空。") 201 | return None, None, None, None 202 | 203 | # Default final_output to a grayscale version of the original image_bgr 204 | # This ensures that if processing fails early, final_output is still a valid image. 205 | final_output = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2GRAY) 206 | canny_intermediate = None 207 | contour_img_intermediate = None 208 | warped_color_intermediate = None 209 | 210 | original_height, original_width = image_bgr.shape[:2] 211 | if original_height == 0 or original_width == 0: 212 | print("警告：输入图像尺寸无效。") 213 | # Returns default final_output (grayscale of original), others None 214 | return final_output, None, None, None 215 | 216 | target_processing_height = 900 217 | img_for_canny = resizeImg(image_bgr.copy(), height=target_processing_height) 218 | 219 | # 1. Canny Edges using parameters 220 | canny_intermediate = getCanny( 221 | img_for_canny, canny_thresh1, canny_thresh2, dilate_iter 222 | ) 223 | 224 | if original_height == 0: 225 | scaling_ratio_for_adapoint = 1.0 226 | else: 227 | scaling_ratio_for_adapoint = target_processing_height / original_height 228 | 229 | max_contour, _ = findMaxContour(canny_intermediate) 230 | 231 | if not len(max_contour): 232 | print("警告：未找到主要轮廓。") 233 | # Returns default final_output, canny_intermediate, others None 234 | return final_output, canny_intermediate, None, None 235 | 236 | boxes_on_resized = getBoxPoint(max_contour) 237 | if boxes_on_resized.shape[0] < 4: 238 | print("警告: 轮廓点不足4个，无法形成四边形。") 239 | return final_output, canny_intermediate, None, None 240 | 241 | boxes_on_original_scale = adaPoint(boxes_on_resized, scaling_ratio_for_adapoint) 242 | ordered_boxes_on_original = orderPoints(boxes_on_original_scale.astype(np.float32)) 243 | 244 | contour_img_intermediate = image_bgr.copy() 245 | cv2.drawContours( 246 | contour_img_intermediate, 247 | [ordered_boxes_on_original.astype(np.int32)], 248 | -1, 249 | (0, 255, 0), 250 | 3, # Green contour 251 | ) 252 | 253 | warped_color_intermediate = warpImage(image_bgr, ordered_boxes_on_original) 254 | 255 | if ( 256 | warped_color_intermediate is None 257 | or warped_color_intermediate.size < 100 # Check for minimal size 258 | or np.array_equal(warped_color_intermediate, image_bgr.copy()) 259 | ): # Check if warp returned original 260 | print("警告：透视变换失败或结果无效。") 261 | if np.array_equal(warped_color_intermediate, image_bgr.copy()): 262 | warped_color_intermediate = ( 263 | None # Explicitly set to None if it's just a copy 264 | ) 265 | # Return current state, final_output is still default (grayscale of original) 266 | return ( 267 | final_output, 268 | canny_intermediate, 269 | contour_img_intermediate, 270 | warped_color_intermediate, 271 | ) 272 | 273 | # 7. Final Result using parameters, only if warped_color_intermediate is valid 274 | final_output = adaptive_thres( 275 | warped_color_intermediate, adaptive_win, adaptive_beta_val 276 | ) 277 | 278 | return ( 279 | final_output, 280 | canny_intermediate, 281 | contour_img_intermediate, 282 | warped_color_intermediate, 283 | ) 284 | 285 | 286 | # --- Gradio 接口函数 --- 287 | def process_image_gradio( 288 | input_image_rgb, 289 | canny_t1, 290 | canny_t2, 291 | dilate_iterations, 292 | adaptive_window, 293 | adaptive_beta_slider, 294 | ): 295 | """ 296 | Gradio 接口的处理函数。 297 | Returns: (canny_gray, contour_rgb, warped_rgb, final_gray) 298 | """ 299 | if input_image_rgb is None: # If no image is uploaded or image is cleared 300 | # Return None for all 4 outputs to clear them 301 | return None, None, None, None 302 | 303 | input_image_bgr = cv2.cvtColor(input_image_rgb, cv2.COLOR_RGB2BGR) 304 | 305 | # Call the enhance function with all parameters 306 | # enhance returns: (final_gray, canny_gray, contour_bgr, warped_bgr) 307 | final_result_gray, canny_result_gray, contour_result_bgr, warped_result_bgr = ( 308 | enhance( 309 | input_image_bgr, 310 | int(canny_t1), 311 | int(canny_t2), 312 | int(dilate_iterations), 313 | int(adaptive_window), 314 | float(adaptive_beta_slider), 315 | ) 316 | ) 317 | 318 | # Convert BGR results to RGB for Gradio display 319 | contour_result_rgb = None 320 | if contour_result_bgr is not None: 321 | contour_result_rgb = cv2.cvtColor(contour_result_bgr, cv2.COLOR_BGR2RGB) 322 | 323 | warped_result_rgb = None 324 | if warped_result_bgr is not None: 325 | if ( 326 | len(warped_result_bgr.shape) == 3 and warped_result_bgr.shape[2] == 3 327 | ): # Check if it's a color image 328 | warped_result_rgb = cv2.cvtColor(warped_result_bgr, cv2.COLOR_BGR2RGB) 329 | else: # It might be grayscale if warpImage returned an error image that was grayscale 330 | warped_result_rgb = warped_result_bgr # Pass as is if already grayscale 331 | 332 | # Handle cases where enhance might return None for final_result_gray (e.g., early exit) 333 | # Ensure a valid grayscale image is returned for the final output slot if processing fails partially 334 | if final_result_gray is None and input_image_rgb is not None: 335 | final_result_gray = cv2.cvtColor(input_image_rgb, cv2.COLOR_RGB2GRAY) 336 | 337 | # Return the four processed images 338 | return (canny_result_gray, contour_result_rgb, warped_result_rgb, final_result_gray) 339 | 340 | 341 | # 定义 Gradio 界面 342 | with gr.Blocks(theme=gr.Theme()) as demo: 343 | gr.Markdown("# 图片边缘检测、矫正与增强锐化系统") 344 | gr.Markdown("上传图片并调整参数以查看不同处理阶段的效果。") 345 | 346 | with gr.Row(): 347 | # Input Controls Column 348 | with gr.Column(scale=1, min_width=350): 349 | input_image_rgb = gr.Image( 350 | type="numpy", 351 | label="上传图片 (Upload Image)", 352 | sources=["upload", "webcam", "clipboard"], 353 | ) 354 | 355 | gr.Markdown("#### Canny 边缘检测参数") 356 | canny_t1 = gr.Slider( 357 | minimum=1, maximum=200, value=60, step=1, label="低阈值 (Thresh1)" 358 | ) 359 | canny_t2 = gr.Slider( 360 | minimum=50, maximum=400, value=240, step=1, label="高阈值 (Thresh2)" 361 | ) 362 | dilate_iterations = gr.Slider( 363 | minimum=0, 364 | maximum=10, 365 | value=1, 366 | step=1, 367 | label="膨胀次数 (Dilation Iter.)", 368 | ) 369 | 370 | gr.Markdown("#### 自适应阈值参数") 371 | adaptive_window = gr.Slider( 372 | minimum=3, 373 | maximum=51, 374 | value=9, 375 | step=2, 376 | label="窗口大小 (Win Size - odd)", 377 | ) 378 | adaptive_beta_slider = gr.Slider( 379 | minimum=0.8, 380 | maximum=1.2, 381 | value=0.9, 382 | step=0.01, 383 | label="Beta 系数 (Beta Coeff.)", 384 | ) 385 | 386 | # Output Images Column 387 | with gr.Column(scale=3): 388 | gr.Markdown("### 处理结果预览 (Processing Results Preview)") 389 | # 2x2 grid for the four output images 390 | with gr.Row(): 391 | canny_out = gr.Image( 392 | type="numpy", label="1. Canny 边缘", interactive=False, height=250 393 | ) 394 | contour_out = gr.Image( 395 | type="numpy", label="2. 轮廓检测", interactive=False, height=250 396 | ) 397 | with gr.Row(): 398 | warped_out = gr.Image( 399 | type="numpy", label="3. 透视矫正图", interactive=False, height=250 400 | ) 401 | final_out = gr.Image( 402 | type="numpy", label="4. 自适应二值", interactive=False, height=250 403 | ) 404 | 405 | # Consolidate inputs and outputs lists for event handling 406 | # inputs_list remains the same 407 | inputs_list = [ 408 | input_image_rgb, 409 | canny_t1, 410 | canny_t2, 411 | dilate_iterations, 412 | adaptive_window, 413 | adaptive_beta_slider, 414 | ] 415 | # outputs_list now has 4 components 416 | outputs_list = [canny_out, contour_out, warped_out, final_out] 417 | 418 | # Event handling for live updates 419 | for comp in inputs_list: 420 | comp.change( 421 | fn=process_image_gradio, 422 | inputs=inputs_list, 423 | outputs=outputs_list, 424 | show_progress="minimal", 425 | ) 426 | 427 | # Example loading 428 | gr.Examples( 429 | examples=[ 430 | ["examples/document_sample.png", 60, 240, 1, 9, 0.9], 431 | ["examples/receipt_sample.jpg", 60, 240, 1, 9, 0.9], 432 | ["examples/example3.jpg", 25, 80, 1, 9, 0.9], 433 | ["examples/example4.jpg", 60, 240, 1, 9, 0.97], 434 | ["examples/example5.jpg", 30, 50, 5, 9, 0.9], 435 | ["examples/example6.jpg", 30, 50, 1, 9, 0.96], 436 | ], # Example data structure remains the same for inputs 437 | inputs=inputs_list, # inputs for the function call 438 | outputs=outputs_list, # components to update with the function's return values 439 | fn=process_image_gradio, 440 | label="示例图片 (Click to load an example)", 441 | ) 442 | 443 | # --- 启动 Gradio 应用 --- 444 | if __name__ == "__main__": 445 | demo.launch(server_name="0.0.0.0", server_port=7860) 446 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | # This file was autogenerated by uv via the following command: 2 | # uv export --format requirements.txt -o requirements.txt 3 | aiofiles==24.1.0 \ 4 | --hash=sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c \ 5 | --hash=sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5 6 | # via gradio 7 | annotated-types==0.7.0 \ 8 | --hash=sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53 \ 9 | --hash=sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89 10 | # via pydantic 11 | anyio==4.9.0 \ 12 | --hash=sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028 \ 13 | --hash=sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c 14 | # via 15 | # gradio 16 | # httpx 17 | # starlette 18 | audioop-lts==0.2.1 \ 19 | --hash=sha256:05da64e73837f88ee5c6217d732d2584cf638003ac72df124740460531e95e47 \ 20 | --hash=sha256:120678b208cca1158f0a12d667af592e067f7a50df9adc4dc8f6ad8d065a93fb \ 21 | --hash=sha256:161249db9343b3c9780ca92c0be0d1ccbfecdbccac6844f3d0d44b9c4a00a17f \ 22 | --hash=sha256:2aeb6f96f7f6da80354330470b9134d81b4cf544cdd1c549f2f45fe964d28059 \ 23 | --hash=sha256:2bdb3b7912ccd57ea53197943f1bbc67262dcf29802c4a6df79ec1c715d45a78 \ 24 | --hash=sha256:3827e3fce6fee4d69d96a3d00cd2ab07f3c0d844cb1e44e26f719b34a5b15455 \ 25 | --hash=sha256:4a8dd6a81770f6ecf019c4b6d659e000dc26571b273953cef7cd1d5ce2ff3ae6 \ 26 | --hash=sha256:534ce808e6bab6adb65548723c8cbe189a3379245db89b9d555c4210b4aaa9b6 \ 27 | --hash=sha256:54cd4520fc830b23c7d223693ed3e1b4d464997dd3abc7c15dce9a1f9bd76ab2 \ 28 | --hash=sha256:56b7a0a4dba8e353436f31a932f3045d108a67b5943b30f85a5563f4d8488d77 \ 29 | --hash=sha256:5b7b4ff9de7a44e0ad2618afdc2ac920b91f4a6d3509520ee65339d4acde5abf \ 30 | --hash=sha256:64562c5c771fb0a8b6262829b9b4f37a7b886c01b4d3ecdbae1d629717db08b4 \ 31 | --hash=sha256:6e899eb8874dc2413b11926b5fb3857ec0ab55222840e38016a6ba2ea9b7d5e3 \ 32 | --hash=sha256:72e37f416adb43b0ced93419de0122b42753ee74e87070777b53c5d2241e7fab \ 33 | --hash=sha256:78bfb3703388c780edf900be66e07de5a3d4105ca8e8720c5c4d67927e0b15d0 \ 34 | --hash=sha256:a351af79edefc2a1bd2234bfd8b339935f389209943043913a919df4b0f13300 \ 35 | --hash=sha256:c45317debeb64002e980077642afbd977773a25fa3dfd7ed0c84dccfc1fafcb0 \ 36 | --hash=sha256:c589f06407e8340e81962575fcffbba1e92671879a221186c3d4662de9fe804e \ 37 | --hash=sha256:d1cd3c0b6f2ca25c7d2b1c3adeecbe23e65689839ba73331ebc7d893fcda7ffe \ 38 | --hash=sha256:d2d5434717f33117f29b5691fbdf142d36573d751716249a288fbb96ba26a281 \ 39 | --hash=sha256:d2de9b6fb8b1cf9f03990b299a9112bfdf8b86b6987003ca9e8a6c4f56d39543 \ 40 | --hash=sha256:d6bd20c7a10abcb0fb3d8aaa7508c0bf3d40dfad7515c572014da4b979d3310a \ 41 | --hash=sha256:e175350da05d2087e12cea8e72a70a1a8b14a17e92ed2022952a4419689ede5e \ 42 | --hash=sha256:e1af3ff32b8c38a7d900382646e91f2fc515fd19dea37e9392275a5cbfdbff63 \ 43 | --hash=sha256:e81268da0baa880431b68b1308ab7257eb33f356e57a5f9b1f915dfb13dd1387 \ 44 | --hash=sha256:f0ed1ad9bd862539ea875fb339ecb18fcc4148f8d9908f4502df28f94d23491a \ 45 | --hash=sha256:f0f2f336aa2aee2bce0b0dcc32bbba9178995454c7b979cf6ce086a8801e14c7 \ 46 | --hash=sha256:f24865991b5ed4b038add5edbf424639d1358144f4e2a3e7a84bc6ba23e35074 \ 47 | --hash=sha256:f51bb55122a89f7a0817d7ac2319744b4640b5b446c4c3efcea5764ea99ae509 \ 48 | --hash=sha256:f626a01c0a186b08f7ff61431c01c055961ee28769591efa8800beadd27a2959 \ 49 | --hash=sha256:fbae5d6925d7c26e712f0beda5ed69ebb40e14212c185d129b8dfbfcc335eb48 \ 50 | --hash=sha256:fd1345ae99e17e6910f47ce7d52673c6a1a70820d78b67de1b7abb3af29c426a \ 51 | --hash=sha256:ff3f97b3372c97782e9c6d3d7fdbe83bce8f70de719605bd7ee1839cd1ab360a 52 | # via gradio 53 | certifi==2025.4.26 \ 54 | --hash=sha256:0a816057ea3cdefcef70270d2c515e4506bbc954f417fa5ade2021213bb8f0c6 \ 55 | --hash=sha256:30350364dfe371162649852c63336a15c70c6510c2ad5015b21c2345311805f3 56 | # via 57 | # httpcore 58 | # httpx 59 | # requests 60 | charset-normalizer==3.4.2 \ 61 | --hash=sha256:1c95a1e2902a8b722868587c0e1184ad5c55631de5afc0eb96bc4b0d738092c0 \ 62 | --hash=sha256:289200a18fa698949d2b39c671c2cc7a24d44096784e76614899a7ccf2574b7b \ 63 | --hash=sha256:32fc0341d72e0f73f80acb0a2c94216bd704f4f0bce10aedea38f30502b271ff \ 64 | --hash=sha256:3fddb7e2c84ac87ac3a947cb4e66d143ca5863ef48e4a5ecb83bd48619e4634e \ 65 | --hash=sha256:4a476b06fbcf359ad25d34a057b7219281286ae2477cc5ff5e3f70a246971148 \ 66 | --hash=sha256:5baececa9ecba31eff645232d59845c07aa030f0c81ee70184a90d35099a0e63 \ 67 | --hash=sha256:6c9379d65defcab82d07b2a9dfbfc2e95bc8fe0ebb1b176a3190230a3ef0e07c \ 68 | --hash=sha256:7f56930ab0abd1c45cd15be65cc741c28b1c9a34876ce8c17a2fa107810c0af0 \ 69 | --hash=sha256:926ca93accd5d36ccdabd803392ddc3e03e6d4cd1cf17deff3b989ab8e9dbcf0 \ 70 | --hash=sha256:98f862da73774290f251b9df8d11161b6cf25b599a66baf087c1ffe340e9bfd1 \ 71 | --hash=sha256:aa6af9e7d59f9c12b33ae4e9450619cf2488e2bbe9b44030905877f0b2324980 \ 72 | --hash=sha256:aaeeb6a479c7667fbe1099af9617c83aaca22182d6cf8c53966491a0f1b7ffb7 \ 73 | --hash=sha256:e635b87f01ebc977342e2697d05b56632f5f879a4f15955dfe8cef2448b51691 \ 74 | --hash=sha256:eba9904b0f38a143592d9fc0e19e2df0fa2e41c3c3745554761c5f6447eedabf \ 75 | --hash=sha256:ef8de666d6179b009dce7bcb2ad4c4a779f113f12caf8dc77f0162c29d20490b 76 | # via requests 77 | click==8.1.8 ; sys_platform != 'emscripten' \ 78 | --hash=sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2 \ 79 | --hash=sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a 80 | # via 81 | # typer 82 | # uvicorn 83 | colorama==0.4.6 ; sys_platform == 'win32' \ 84 | --hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \ 85 | --hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6 86 | # via 87 | # click 88 | # tqdm 89 | fastapi==0.115.12 \ 90 | --hash=sha256:1e2c2a2646905f9e83d32f04a3f86aff4a286669c6c950ca95b5fd68c2602681 \ 91 | --hash=sha256:e94613d6c05e27be7ffebdd6ea5f388112e5e430c8f7d6494a9d1d88d43e814d 92 | # via gradio 93 | ffmpy==0.5.0 \ 94 | --hash=sha256:277e131f246d18e9dcfee9bb514c50749031c43582ce5ef82c57b51e3d3955c3 \ 95 | --hash=sha256:df3799cf5816daa56d4959a023630ee53c6768b66009dae6d131519ba4b80233 96 | # via gradio 97 | filelock==3.18.0 \ 98 | --hash=sha256:adbc88eabb99d2fec8c9c1b229b171f18afa655400173ddc653d5d01501fb9f2 \ 99 | --hash=sha256:c401f4f8377c4464e6db25fff06205fd89bdd83b65eb0488ed1b160f780e21de 100 | # via huggingface-hub 101 | fsspec==2025.3.2 \ 102 | --hash=sha256:2daf8dc3d1dfa65b6aa37748d112773a7a08416f6c70d96b264c96476ecaf711 \ 103 | --hash=sha256:e52c77ef398680bbd6a98c0e628fbc469491282981209907bbc8aea76a04fdc6 104 | # via 105 | # gradio-client 106 | # huggingface-hub 107 | gradio==5.29.1 \ 108 | --hash=sha256:34a39d1d2d21e73f01912ec59d15a507d6501b158840ebf63696b5cff1a9d8a6 \ 109 | --hash=sha256:e6fb5e984c514a8a863feaa7a6d56a8c5ded16e14c301bb3716ebe2c1ca0556c 110 | # via imagecorrection 111 | gradio-client==1.10.1 \ 112 | --hash=sha256:550662eae8dc0d06d44cb8d42be74f214db1e793ad4d789d7b7ecb42e82ca045 \ 113 | --hash=sha256:fcff53f6aad3dfa9dd082adedb94256172d6b20666b1ef66480d82023e1907db 114 | # via gradio 115 | groovy==0.1.2 \ 116 | --hash=sha256:25c1dc09b3f9d7e292458aa762c6beb96ea037071bf5e917fc81fb78d2231083 \ 117 | --hash=sha256:7f7975bab18c729a257a8b1ae9dcd70b7cafb1720481beae47719af57c35fa64 118 | # via gradio 119 | h11==0.16.0 \ 120 | --hash=sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1 \ 121 | --hash=sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86 122 | # via 123 | # httpcore 124 | # uvicorn 125 | httpcore==1.0.9 \ 126 | --hash=sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55 \ 127 | --hash=sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8 128 | # via httpx 129 | httpx==0.28.1 \ 130 | --hash=sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc \ 131 | --hash=sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad 132 | # via 133 | # gradio 134 | # gradio-client 135 | # safehttpx 136 | huggingface-hub==0.31.2 \ 137 | --hash=sha256:7053561376ed7f6ffdaecf09cc54d70dc784ac6315fa4bb9b93e19662b029675 \ 138 | --hash=sha256:8138cd52aa2326b4429bb00a4a1ba8538346b7b8a808cdce30acb6f1f1bdaeec 139 | # via 140 | # gradio 141 | # gradio-client 142 | idna==3.10 \ 143 | --hash=sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9 \ 144 | --hash=sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3 145 | # via 146 | # anyio 147 | # httpx 148 | # requests 149 | jinja2==3.1.6 \ 150 | --hash=sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d \ 151 | --hash=sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67 152 | # via gradio 153 | markdown-it-py==3.0.0 ; sys_platform != 'emscripten' \ 154 | --hash=sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1 \ 155 | --hash=sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb 156 | # via rich 157 | markupsafe==3.0.2 \ 158 | --hash=sha256:131a3c7689c85f5ad20f9f6fb1b866f402c445b220c19fe4308c0b147ccd2ad9 \ 159 | --hash=sha256:15ab75ef81add55874e7ab7055e9c397312385bd9ced94920f2802310c930396 \ 160 | --hash=sha256:3d79d162e7be8f996986c064d1c7c817f6df3a77fe3d6859f6f9e7be4b8c213a \ 161 | --hash=sha256:444dcda765c8a838eaae23112db52f1efaf750daddb2d9ca300bcae1039adc5c \ 162 | --hash=sha256:4aa4e5faecf353ed117801a068ebab7b7e09ffb6e1d5e412dc852e0da018126c \ 163 | --hash=sha256:569511d3b58c8791ab4c2e1285575265991e6d8f8700c7be0e88f86cb0672094 \ 164 | --hash=sha256:6381026f158fdb7c72a168278597a5e3a5222e83ea18f543112b2662a9b699c5 \ 165 | --hash=sha256:a904af0a6162c73e3edcb969eeeb53a63ceeb5d8cf642fade7d39e7963a22ddb \ 166 | --hash=sha256:b5a6b3ada725cea8a5e634536b1b01c30bcdcd7f9c6fff4151548d5bf6b3a36c \ 167 | --hash=sha256:ba8062ed2cf21c07a9e295d5b8a2a5ce678b913b45fdf68c32d95d6c1291e0b6 \ 168 | --hash=sha256:ba9527cdd4c926ed0760bc301f6728ef34d841f405abf9d4f959c478421e4efd \ 169 | --hash=sha256:bcf3e58998965654fdaff38e58584d8937aa3096ab5354d493c77d1fdd66d7a1 \ 170 | --hash=sha256:c0ef13eaeee5b615fb07c9a7dadb38eac06a0608b41570d8ade51c56539e509d \ 171 | --hash=sha256:cabc348d87e913db6ab4aa100f01b08f481097838bdddf7c7a84b7575b7309ca \ 172 | --hash=sha256:cdb82a876c47801bb54a690c5ae105a46b392ac6099881cdfb9f6e95e4014c6a \ 173 | --hash=sha256:d16a81a06776313e817c951135cf7340a3e91e8c1ff2fac444cfd75fffa04afe \ 174 | --hash=sha256:e444a31f8db13eb18ada366ab3cf45fd4b31e4db1236a4448f68778c1d1a5a2f \ 175 | --hash=sha256:e6a2a455bd412959b57a172ce6328d2dd1f01cb2135efda2e4576e8a23fa3b0f \ 176 | --hash=sha256:ee55d3edf80167e48ea11a923c7386f4669df67d7994554387f84e7d8b0a2bf0 \ 177 | --hash=sha256:f3818cb119498c0678015754eba762e0d61e5b52d34c8b13d770f0719f7b1d79 \ 178 | --hash=sha256:f8b3d067f2e40fe93e1ccdd6b2e1d16c43140e76f02fb1319a05cf2b79d99430 179 | # via 180 | # gradio 181 | # jinja2 182 | mdurl==0.1.2 ; sys_platform != 'emscripten' \ 183 | --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 \ 184 | --hash=sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba 185 | # via markdown-it-py 186 | numpy==2.2.5 \ 187 | --hash=sha256:02f226baeefa68f7d579e213d0f3493496397d8f1cff5e2b222af274c86a552a \ 188 | --hash=sha256:059b51b658f4414fff78c6d7b1b4e18283ab5fa56d270ff212d5ba0c561846f4 \ 189 | --hash=sha256:1a161c2c79ab30fe4501d5a2bbfe8b162490757cf90b7f05be8b80bc02f7bb8e \ 190 | --hash=sha256:261a1ef047751bb02f29dfe337230b5882b54521ca121fc7f62668133cb119c9 \ 191 | --hash=sha256:2ba321813a00e508d5421104464510cc962a6f791aa2fca1c97b1e65027da80d \ 192 | --hash=sha256:352d330048c055ea6db701130abc48a21bec690a8d38f8284e00fab256dc1376 \ 193 | --hash=sha256:3d14b17b9be5f9c9301f43d2e2a4886a33b53f4e6fdf9ca2f4cc60aeeee76372 \ 194 | --hash=sha256:4520caa3807c1ceb005d125a75e715567806fed67e315cea619d5ec6e75a4191 \ 195 | --hash=sha256:47f9ed103af0bc63182609044b0490747e03bd20a67e391192dde119bf43d52f \ 196 | --hash=sha256:54088a5a147ab71a8e7fdfd8c3601972751ded0739c6b696ad9cb0343e21ab73 \ 197 | --hash=sha256:55f09e00d4dccd76b179c0f18a44f041e5332fd0e022886ba1c0bbf3ea4a18d0 \ 198 | --hash=sha256:8b4c0773b6ada798f51f0f8e30c054d32304ccc6e9c5d93d46cb26f3d385ab19 \ 199 | --hash=sha256:8dfa94b6a4374e7851bbb6f35e6ded2120b752b063e6acdd3157e4d2bb922eba \ 200 | --hash=sha256:97c8425d4e26437e65e1d189d22dff4a079b747ff9c2788057bfb8114ce1e133 \ 201 | --hash=sha256:a4cbdef3ddf777423060c6f81b5694bad2dc9675f110c4b2a60dc0181543fac7 \ 202 | --hash=sha256:a9c0d994680cd991b1cb772e8b297340085466a6fe964bc9d4e80f5e2f43c291 \ 203 | --hash=sha256:c26843fd58f65da9491165072da2cccc372530681de481ef670dcc8e27cfb066 \ 204 | --hash=sha256:c8b82a55ef86a2d8e81b63da85e55f5537d2157165be1cb2ce7cfa57b6aef38b \ 205 | --hash=sha256:d403c84991b5ad291d3809bace5e85f4bbf44a04bdc9a88ed2bb1807b3360bb8 \ 206 | --hash=sha256:d8882a829fd779f0f43998e931c466802a77ca1ee0fe25a3abe50278616b1471 \ 207 | --hash=sha256:e8b025c351b9f0e8b5436cf28a07fa4ac0204d67b38f01433ac7f9b870fa38c6 208 | # via 209 | # gradio 210 | # imagecorrection 211 | # opencv-python 212 | # pandas 213 | # scipy 214 | opencv-python==4.11.0.86 \ 215 | --hash=sha256:03d60ccae62304860d232272e4a4fda93c39d595780cb40b161b310244b736a4 \ 216 | --hash=sha256:085ad9b77c18853ea66283e98affefe2de8cc4c1f43eda4c100cf9b2721142ec \ 217 | --hash=sha256:1b92ae2c8852208817e6776ba1ea0d6b1e0a1b5431e971a2a0ddd2a8cc398202 \ 218 | --hash=sha256:432f67c223f1dc2824f5e73cdfcd9db0efc8710647d4e813012195dc9122a52a \ 219 | --hash=sha256:6b02611523803495003bd87362db3e1d2a0454a6a63025dc6658a9830570aa0d \ 220 | --hash=sha256:810549cb2a4aedaa84ad9a1c92fbfdfc14090e2749cedf2c1589ad8359aa169b \ 221 | --hash=sha256:9d05ef13d23fe97f575153558653e2d6e87103995d54e6a35db3f282fe1f9c66 222 | # via imagecorrection 223 | orjson==3.10.18 \ 224 | --hash=sha256:0315317601149c244cb3ecef246ef5861a64824ccbcb8018d32c66a60a84ffbc \ 225 | --hash=sha256:1ebeda919725f9dbdb269f59bc94f861afbe2a27dce5608cdba2d92772364d1c \ 226 | --hash=sha256:2d808e34ddb24fc29a4d4041dcfafbae13e129c93509b847b14432717d94b44f \ 227 | --hash=sha256:5adf5f4eed520a4959d29ea80192fa626ab9a20b2ea13f8f6dc58644f6927103 \ 228 | --hash=sha256:69c34b9441b863175cc6a01f2935de994025e773f814412030f269da4f7be147 \ 229 | --hash=sha256:7592bb48a214e18cd670974f289520f12b7aed1fa0b2e2616b8ed9e069e08595 \ 230 | --hash=sha256:8e4b2ae732431127171b875cb2668f883e1234711d3c147ffd69fe5be51a8012 \ 231 | --hash=sha256:ad8eacbb5d904d5591f27dee4031e2c1db43d559edb8f91778efd642d70e6bea \ 232 | --hash=sha256:aed411bcb68bf62e85588f2a7e03a6082cc42e5a2796e06e72a962d7c6310b52 \ 233 | --hash=sha256:bb70d489bc79b7519e5803e2cc4c72343c9dc1154258adf2f8925d0b60da7c58 \ 234 | --hash=sha256:c382a5c0b5931a5fc5405053d36c1ce3fd561694738626c77ae0b1dfc0242ca1 \ 235 | --hash=sha256:e0da26957e77e9e55a6c2ce2e7182a36a6f6b180ab7189315cb0995ec362e049 \ 236 | --hash=sha256:e8da3947d92123eda795b68228cafe2724815621fe35e8e320a9e9593a4bcd53 \ 237 | --hash=sha256:e9e86a6af31b92299b00736c89caf63816f70a4001e750bda179e15564d7a034 \ 238 | --hash=sha256:f54c1385a0e6aba2f15a40d703b858bedad36ded0491e55d35d905b2c34a4cc3 \ 239 | --hash=sha256:f872bef9f042734110642b7a11937440797ace8c87527de25e0c53558b579ccc 240 | # via gradio 241 | packaging==25.0 \ 242 | --hash=sha256:29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484 \ 243 | --hash=sha256:d443872c98d677bf60f6a1f2f8c1cb748e8fe762d2bf9d3148b5599295b0fc4f 244 | # via 245 | # gradio 246 | # gradio-client 247 | # huggingface-hub 248 | pandas==2.2.3 \ 249 | --hash=sha256:15c0e1e02e93116177d29ff83e8b1619c93ddc9c49083f237d4312337a61165d \ 250 | --hash=sha256:1db71525a1538b30142094edb9adc10be3f3e176748cd7acc2240c2f2e5aa3a4 \ 251 | --hash=sha256:22a9d949bfc9a502d320aa04e5d02feab689d61da4e7764b62c30b991c42c5f0 \ 252 | --hash=sha256:3508d914817e153ad359d7e069d752cdd736a247c322d932eb89e6bc84217f28 \ 253 | --hash=sha256:38cf8125c40dae9d5acc10fa66af8ea6fdf760b2714ee482ca691fc66e6fcb18 \ 254 | --hash=sha256:3b71f27954685ee685317063bf13c7709a7ba74fc996b84fc6821c59b0f06468 \ 255 | --hash=sha256:4f18ba62b61d7e192368b84517265a99b4d7ee8912f8708660fb4a366cc82667 \ 256 | --hash=sha256:61c5ad4043f791b61dd4752191d9f07f0ae412515d59ba8f005832a532f8736d \ 257 | --hash=sha256:6374c452ff3ec675a8f46fd9ab25c4ad0ba590b71cf0656f8b6daa5202bca3fb \ 258 | --hash=sha256:800250ecdadb6d9c78eae4990da62743b857b470883fa27f652db8bdde7f6659 \ 259 | --hash=sha256:ad5b65698ab28ed8d7f18790a0dc58005c7629f227be9ecc1072aa74c0c1d43a \ 260 | --hash=sha256:ba96630bc17c875161df3818780af30e43be9b166ce51c9a18c1feae342906c2 \ 261 | --hash=sha256:f00d1345d84d8c86a63e476bb4955e46458b304b9575dcf71102b5c705320015 \ 262 | --hash=sha256:f3a255b2c19987fbbe62a9dfd6cff7ff2aa9ccab3fc75218fd4b7530f01efa24 263 | # via gradio 264 | pillow==11.2.1 \ 265 | --hash=sha256:062b7a42d672c45a70fa1f8b43d1d38ff76b63421cbbe7f88146b39e8a558d91 \ 266 | --hash=sha256:191955c55d8a712fab8934a42bfefbf99dd0b5875078240943f913bb66d46d9f \ 267 | --hash=sha256:225c832a13326e34f212d2072982bb1adb210e0cc0b153e688743018c94a2681 \ 268 | --hash=sha256:31df6e2d3d8fc99f993fd253e97fae451a8db2e7207acf97859732273e108406 \ 269 | --hash=sha256:36d6b82164c39ce5482f649b437382c0fb2395eabc1e2b1702a6deb8ad647d6e \ 270 | --hash=sha256:3fe735ced9a607fee4f481423a9c36701a39719252a9bb251679635f99d0f7d2 \ 271 | --hash=sha256:4eb92eca2711ef8be42fd3f67533765d9fd043b8c80db204f16c8ea62ee1a751 \ 272 | --hash=sha256:5119225c622403afb4b44bad4c1ca6c1f98eed79db8d3bc6e4e160fc6339d66c \ 273 | --hash=sha256:63b5dff3a68f371ea06025a1a6966c9a1e1ee452fc8020c2cd0ea41b83e9037b \ 274 | --hash=sha256:74ee3d7ecb3f3c05459ba95eed5efa28d6092d751ce9bf20e3e253a4e497e691 \ 275 | --hash=sha256:750f96efe0597382660d8b53e90dd1dd44568a8edb51cb7f9d5d918b80d4de14 \ 276 | --hash=sha256:7bdb5e09068332578214cadd9c05e3d64d99e0e87591be22a324bdbc18925be0 \ 277 | --hash=sha256:8ce2e8411c7aaef53e6bb29fe98f28cd4fbd9a1d9be2eeea434331aac0536b22 \ 278 | --hash=sha256:9622e3b6c1d8b551b6e6f21873bdcc55762b4b2126633014cea1803368a9aa16 \ 279 | --hash=sha256:9ee66787e095127116d91dea2143db65c7bb1e232f617aa5957c0d9d2a3f23a7 \ 280 | --hash=sha256:a64dd61998416367b7ef979b73d3a85853ba9bec4c2925f74e588879a58716b6 \ 281 | --hash=sha256:ad275964d52e2243430472fc5d2c2334b4fc3ff9c16cb0a19254e25efa03a155 \ 282 | --hash=sha256:b0e130705d568e2f43a17bcbe74d90958e8a16263868a12c3e0d9c8162690830 \ 283 | --hash=sha256:d189ba1bebfbc0c0e529159631ec72bb9e9bc041f01ec6d3233d6d82eb823bc1 \ 284 | --hash=sha256:e0b55f27f584ed623221cfe995c912c61606be8513bfa0e07d2c674b4516d9dd \ 285 | --hash=sha256:f91ebf30830a48c825590aede79376cb40f110b387c17ee9bd59932c961044f9 \ 286 | --hash=sha256:fdec757fea0b793056419bca3e9932eb2b0ceec90ef4813ea4c1e072c389eb28 \ 287 | --hash=sha256:fe15238d3798788d00716637b3d4e7bb6bde18b26e5d08335a96e88564a36b6b 288 | # via gradio 289 | pydantic==2.11.4 \ 290 | --hash=sha256:32738d19d63a226a52eed76645a98ee07c1f410ee41d93b4afbfa85ed8111c2d \ 291 | --hash=sha256:d9615eaa9ac5a063471da949c8fc16376a84afb5024688b3ff885693506764eb 292 | # via 293 | # fastapi 294 | # gradio 295 | pydantic-core==2.33.2 \ 296 | --hash=sha256:04a1a413977ab517154eebb2d326da71638271477d6ad87a769102f7c2488c56 \ 297 | --hash=sha256:0a9f2c9dd19656823cb8250b0724ee9c60a82f3cdf68a080979d13092a3b0fef \ 298 | --hash=sha256:0fb2d542b4d66f9470e8065c5469ec676978d625a8b7a363f07d9a501a9cb36a \ 299 | --hash=sha256:1082dd3e2d7109ad8b7da48e1d4710c8d06c253cbc4a27c1cff4fbcaa97a9e3f \ 300 | --hash=sha256:1ea40a64d23faa25e62a70ad163571c0b342b8bf66d5fa612ac0dec4f069d916 \ 301 | --hash=sha256:2b0a451c263b01acebe51895bfb0e1cc842a5c666efe06cdf13846c7418caa9a \ 302 | --hash=sha256:52fb90784e0a242bb96ec53f42196a17278855b0f31ac7c3cc6f5c1ec4811849 \ 303 | --hash=sha256:5c92edd15cd58b3c2d34873597a1e20f13094f59cf88068adb18947df5455b4e \ 304 | --hash=sha256:61c18fba8e5e9db3ab908620af374db0ac1baa69f0f32df4f61ae23f15e586ac \ 305 | --hash=sha256:65132b7b4a1c0beded5e057324b7e16e10910c106d43675d9bd87d4f38dde162 \ 306 | --hash=sha256:7cb8bc3605c29176e1b105350d2e6474142d7c1bd1d9327c4a9bdb46bf827acc \ 307 | --hash=sha256:95237e53bb015f67b63c91af7518a62a8660376a6a0db19b89acc77a4d6199f5 \ 308 | --hash=sha256:9fdac5d6ffa1b5a83bca06ffe7583f5576555e6c8b3a91fbd25ea7780f825f7d \ 309 | --hash=sha256:c083a3bdd5a93dfe480f1125926afcdbf2917ae714bdb80b36d34318b2bec5d9 \ 310 | --hash=sha256:c2fc0a768ef76c15ab9238afa6da7f69895bb5d1ee83aeea2e3509af4472d0b9 \ 311 | --hash=sha256:c8e7af2f4e0194c22b5b37205bfb293d166a7344a5b0d0eaccebc376546d77d5 \ 312 | --hash=sha256:e80b087132752f6b3d714f041ccf74403799d3b23a72722ea2e6ba2e892555b9 \ 313 | --hash=sha256:f517ca031dfc037a9c07e748cefd8d96235088b83b4f4ba8939105d20fa1dcd6 314 | # via pydantic 315 | pydub==0.25.1 \ 316 | --hash=sha256:65617e33033874b59d87db603aa1ed450633288aefead953b30bded59cb599a6 \ 317 | --hash=sha256:980a33ce9949cab2a569606b65674d748ecbca4f0796887fd6f46173a7b0d30f 318 | # via gradio 319 | pygments==2.19.1 ; sys_platform != 'emscripten' \ 320 | --hash=sha256:61c16d2a8576dc0649d9f39e089b5f02bcd27fba10d8fb4dcc28173f7a45151f \ 321 | --hash=sha256:9ea1544ad55cecf4b8242fab6dd35a93bbce657034b0611ee383099054ab6d8c 322 | # via rich 323 | python-dateutil==2.9.0.post0 \ 324 | --hash=sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3 \ 325 | --hash=sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 326 | # via pandas 327 | python-multipart==0.0.20 \ 328 | --hash=sha256:8a62d3a8335e06589fe01f2a3e178cdcc632f3fbe0d492ad9ee0ec35aab1f104 \ 329 | --hash=sha256:8dd0cab45b8e23064ae09147625994d090fa46f5b0d1e13af944c331a7fa9d13 330 | # via gradio 331 | pytz==2025.2 \ 332 | --hash=sha256:360b9e3dbb49a209c21ad61809c7fb453643e048b38924c765813546746e81c3 \ 333 | --hash=sha256:5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00 334 | # via pandas 335 | pyyaml==6.0.2 \ 336 | --hash=sha256:0ffe8360bab4910ef1b9e87fb812d8bc0a308b0d0eef8c8f44e0254ab3b07133 \ 337 | --hash=sha256:17e311b6c678207928d649faa7cb0d7b4c26a0ba73d41e99c4fff6b6c3276484 \ 338 | --hash=sha256:41e4e3953a79407c794916fa277a82531dd93aad34e29c2a514c2c0c5fe971cc \ 339 | --hash=sha256:50187695423ffe49e2deacb8cd10510bc361faac997de9efef88badc3bb9e2d1 \ 340 | --hash=sha256:68ccc6023a3400877818152ad9a1033e3db8625d899c72eacb5a668902e4d652 \ 341 | --hash=sha256:70b189594dbe54f75ab3a1acec5f1e3faa7e8cf2f1e08d9b561cb41b845f69d5 \ 342 | --hash=sha256:8388ee1976c416731879ac16da0aff3f63b286ffdd57cdeb95f3f2e085687563 \ 343 | --hash=sha256:bc2fa7c6b47d6bc618dd7fb02ef6fdedb1090ec036abab80d4681424b84c1183 \ 344 | --hash=sha256:d584d9ec91ad65861cc08d42e834324ef890a082e591037abe114850ff7bbc3e \ 345 | --hash=sha256:efdca5630322a10774e8e98e1af481aad470dd62c3170801852d752aa7a783ba 346 | # via 347 | # gradio 348 | # huggingface-hub 349 | requests==2.32.3 \ 350 | --hash=sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760 \ 351 | --hash=sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6 352 | # via huggingface-hub 353 | rich==14.0.0 ; sys_platform != 'emscripten' \ 354 | --hash=sha256:1c9491e1951aac09caffd42f448ee3d04e58923ffe14993f6e83068dc395d7e0 \ 355 | --hash=sha256:82f1bc23a6a21ebca4ae0c45af9bdbc492ed20231dcb63f297d6d1021a9d5725 356 | # via typer 357 | ruff==0.11.9 ; sys_platform != 'emscripten' \ 358 | --hash=sha256:0f3f46f759ac623e94824b1e5a687a0df5cd7f5b00718ff9c24f0a894a683be7 \ 359 | --hash=sha256:440ac6a7029f3dee7d46ab7de6f54b19e34c2b090bb4f2480d0a2d635228f381 \ 360 | --hash=sha256:52edaa4a6d70f8180343a5b7f030c7edd36ad180c9f4d224959c2d689962d964 \ 361 | --hash=sha256:537c82c9829d7811e3aa680205f94c81a2958a122ac391c0eb60336ace741a70 \ 362 | --hash=sha256:5b1d18b4be8182cc6fddf859ce432cc9631556e9f371ada52f3eaefc10d878de \ 363 | --hash=sha256:66bc18ca783b97186a1f3100e91e492615767ae0a3be584e1266aa9051990722 \ 364 | --hash=sha256:71c539bac63d0788a30227ed4d43b81353c89437d355fdc52e0cda4ce5651787 \ 365 | --hash=sha256:7b27613a683b086f2aca8996f63cb3dd7bc49e6eccf590563221f7b43ded3f65 \ 366 | --hash=sha256:7fe1bc950e7d7b42caaee2a8a3bc27410547cc032c9558ee2e0f6d3b209e845a \ 367 | --hash=sha256:9e0d88756e63e8302e630cee3ce2ffb77859797cc84a830a24473939e6da3ca6 \ 368 | --hash=sha256:a31a1d143a5e6f499d1fb480f8e1e780b4dfdd580f86e05e87b835d22c5c6f8c \ 369 | --hash=sha256:bcf42689c22f2e240f496d0c183ef2c6f7b35e809f12c1db58f75d9aa8d630ca \ 370 | --hash=sha256:bd576cd06962825de8aece49f28707662ada6a1ff2db848d1348e12c580acbf1 \ 371 | --hash=sha256:c67117bc82457e4501473c5f5217d49d9222a360794bfb63968e09e70f340abd \ 372 | --hash=sha256:e4b78454f97aa454586e8a5557facb40d683e74246c97372af3c2d76901d697b \ 373 | --hash=sha256:ebd58d4f67a00afb3a30bf7d383e52d0e036e6195143c6db7019604a05335517 \ 374 | --hash=sha256:f33b15e00435773df97cddcd263578aa83af996b913721d86f47f4e0ee0ff271 \ 375 | --hash=sha256:f34847eea11932d97b521450cf3e1d17863cfa5a94f21a056b93fb86f3f3dba2 376 | # via gradio 377 | safehttpx==0.1.6 \ 378 | --hash=sha256:407cff0b410b071623087c63dd2080c3b44dc076888d8c5823c00d1e58cb381c \ 379 | --hash=sha256:b356bfc82cee3a24c395b94a2dbeabbed60aff1aa5fa3b5fe97c4f2456ebce42 380 | # via gradio 381 | scipy==1.15.3 \ 382 | --hash=sha256:0a769105537aa07a69468a0eefcd121be52006db61cdd8cac8a0e68980bbb723 \ 383 | --hash=sha256:0bdd905264c0c9cfa74a4772cdb2070171790381a5c4d312c973382fc6eaf730 \ 384 | --hash=sha256:14ed70039d182f411ffc74789a16df3835e05dc469b898233a245cdfd7f162cb \ 385 | --hash=sha256:2c620736bcc334782e24d173c0fdbb7590a0a436d2fdf39310a8902505008759 \ 386 | --hash=sha256:3ac07623267feb3ae308487c260ac684b32ea35fd81e12845039952f558047b8 \ 387 | --hash=sha256:40e54d5c7e7ebf1aa596c374c49fa3135f04648a0caabcb66c52884b943f02b4 \ 388 | --hash=sha256:50f9e62461c95d933d5c5ef4a1f2ebf9a2b4e83b0db374cb3f1de104d935922e \ 389 | --hash=sha256:5e721fed53187e71d0ccf382b6bf977644c533e506c4d33c3fb24de89f5c3ed5 \ 390 | --hash=sha256:6487aa99c2a3d509a5227d9a5e889ff05830a06b2ce08ec30df6d79db5fcd5c5 \ 391 | --hash=sha256:76ad1fb5f8752eabf0fa02e4cc0336b4e8f021e2d5f061ed37d6d264db35e3ca \ 392 | --hash=sha256:79167bba085c31f38603e11a267d862957cbb3ce018d8b38f79ac043bc92d825 \ 393 | --hash=sha256:7e11270a000969409d37ed399585ee530b9ef6aa99d50c019de4cb01e8e54e62 \ 394 | --hash=sha256:8c9ed3ba2c8a2ce098163a9bdb26f891746d02136995df25227a20e71c396ebb \ 395 | --hash=sha256:9db984639887e3dffb3928d118145ffe40eff2fa40cb241a306ec57c219ebbbb \ 396 | --hash=sha256:b90ab29d0c37ec9bf55424c064312930ca5f4bde15ee8619ee44e69319aab163 \ 397 | --hash=sha256:c9deabd6d547aee2c9a81dee6cc96c6d7e9a9b1953f74850c179f91fdc729cb7 \ 398 | --hash=sha256:dde4fc32993071ac0c7dd2d82569e544f0bdaff66269cb475e0f369adad13f11 \ 399 | --hash=sha256:eae3cf522bc7df64b42cad3925c876e1b0b6c35c1337c93e12c0f366f55b0eaf \ 400 | --hash=sha256:f77f853d584e72e874d87357ad70f44b437331507d1c311457bed8ed2b956126 401 | # via imagecorrection 402 | semantic-version==2.10.0 \ 403 | --hash=sha256:bdabb6d336998cbb378d4b9db3a4b56a1e3235701dc05ea2690d9a997ed5041c \ 404 | --hash=sha256:de78a3b8e0feda74cabc54aab2da702113e33ac9d9eb9d2389bcf1f58b7d9177 405 | # via gradio 406 | shellingham==1.5.4 ; sys_platform != 'emscripten' \ 407 | --hash=sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686 \ 408 | --hash=sha256:8dbca0739d487e5bd35ab3ca4b36e11c4078f3a234bfce294b0a0291363404de 409 | # via typer 410 | six==1.17.0 \ 411 | --hash=sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274 \ 412 | --hash=sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81 413 | # via python-dateutil 414 | sniffio==1.3.1 \ 415 | --hash=sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2 \ 416 | --hash=sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc 417 | # via anyio 418 | starlette==0.46.2 \ 419 | --hash=sha256:595633ce89f8ffa71a015caed34a5b2dc1c0cdb3f0f1fbd1e69339cf2abeec35 \ 420 | --hash=sha256:7f7361f34eed179294600af672f565727419830b54b7b084efe44bb82d2fccd5 421 | # via 422 | # fastapi 423 | # gradio 424 | tomlkit==0.13.2 \ 425 | --hash=sha256:7a974427f6e119197f670fbbbeae7bef749a6c14e793db934baefc1b5f03efde \ 426 | --hash=sha256:fff5fe59a87295b278abd31bec92c15d9bc4a06885ab12bcea52c71119392e79 427 | # via gradio 428 | tqdm==4.67.1 \ 429 | --hash=sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2 \ 430 | --hash=sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2 431 | # via huggingface-hub 432 | typer==0.15.4 ; sys_platform != 'emscripten' \ 433 | --hash=sha256:89507b104f9b6a0730354f27c39fae5b63ccd0c95b1ce1f1a6ba0cfd329997c3 \ 434 | --hash=sha256:eb0651654dcdea706780c466cf06d8f174405a659ffff8f163cfbfee98c0e173 435 | # via gradio 436 | typing-extensions==4.13.2 \ 437 | --hash=sha256:a439e7c04b49fec3e5d3e2beaa21755cadbbdc391694e28ccdd36ca4a1408f8c \ 438 | --hash=sha256:e6c81219bd689f51865d9e372991c540bda33a0379d5573cddb9a3a23f7caaef 439 | # via 440 | # fastapi 441 | # gradio 442 | # gradio-client 443 | # huggingface-hub 444 | # pydantic 445 | # pydantic-core 446 | # typer 447 | # typing-inspection 448 | typing-inspection==0.4.0 \ 449 | --hash=sha256:50e72559fcd2a6367a19f7a7e610e6afcb9fac940c650290eed893d61386832f \ 450 | --hash=sha256:9765c87de36671694a67904bf2c96e395be9c6439bb6c87b5142569dcdd65122 451 | # via pydantic 452 | tzdata==2025.2 \ 453 | --hash=sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8 \ 454 | --hash=sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9 455 | # via pandas 456 | urllib3==2.4.0 \ 457 | --hash=sha256:414bc6535b787febd7567804cc015fee39daab8ad86268f1310a9250697de466 \ 458 | --hash=sha256:4e16665048960a0900c702d4a66415956a584919c03361cac9f1df5c5dd7e813 459 | # via 460 | # gradio 461 | # requests 462 | uvicorn==0.34.2 ; sys_platform != 'emscripten' \ 463 | --hash=sha256:0e929828f6186353a80b58ea719861d2629d766293b6d19baf086ba31d4f3328 \ 464 | --hash=sha256:deb49af569084536d269fe0a6d67e3754f104cf03aba7c11c40f01aadf33c403 465 | # via gradio 466 | websockets==15.0.1 \ 467 | --hash=sha256:0f3c1e2ab208db911594ae5b4f79addeb3501604a165019dd221c0bdcabe4db8 \ 468 | --hash=sha256:229cf1d3ca6c1804400b0a9790dc66528e08a6a1feec0d5040e8b9eb14422375 \ 469 | --hash=sha256:3c714d2fc58b5ca3e285461a4cc0c9a66bd0e24c5da9911e30158286c9b5be7f \ 470 | --hash=sha256:558d023b3df0bffe50a04e710bc87742de35060580a293c2a984299ed83bc4e4 \ 471 | --hash=sha256:595b6c3969023ecf9041b2936ac3827e4623bfa3ccf007575f04c5a6aa318c22 \ 472 | --hash=sha256:5a939de6b7b4e18ca683218320fc67ea886038265fd1ed30173f5ce3f8e85675 \ 473 | --hash=sha256:746ee8dba912cd6fc889a8147168991d50ed70447bf18bcda7039f7d2e3d9151 \ 474 | --hash=sha256:756c56e867a90fb00177d530dca4b097dd753cde348448a1012ed6c5131f8b7d \ 475 | --hash=sha256:82544de02076bafba038ce055ee6412d68da13ab47f0c60cab827346de828dee \ 476 | --hash=sha256:ba9e56e8ceeeedb2e080147ba85ffcd5cd0711b89576b83784d8605a7df455fa \ 477 | --hash=sha256:e09473f095a819042ecb2ab9465aee615bd9c2028e4ef7d933600a8401c79561 \ 478 | --hash=sha256:ee443ef070bb3b6ed74514f5efaa37a252af57c90eb33b956d35c8e9c10a1931 \ 479 | --hash=sha256:f7a866fbc1e97b5c617ee4116daaa09b722101d4a3c170c787450ba409f9736f 480 | # via gradio-client 481 | -------------------------------------------------------------------------------- /uv.lock: -------------------------------------------------------------------------------- 1 | version = 1 2 | revision = 2 3 | requires-python = ">=3.13" 4 | resolution-markers = [ 5 | "sys_platform == 'darwin'", 6 | "platform_machine == 'aarch64' and sys_platform == 'linux'", 7 | "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')", 8 | ] 9 | 10 | [[package]] 11 | name = "aiofiles" 12 | version = "24.1.0" 13 | source = { registry = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple" } 14 | sdist = { url = "https://mirrors.tuna.tsinghua.edu.cn/pypi/web/packages/0b/03/a88171e277e8caa88a4c77808c20ebb04ba74cc4681bf1e9416c862de237/aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c", size = 30247, upload-time = "2024-06-24T11:02:03.584Z" } 15 | wheels = [ 16 | { url = 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