deploy: 63a85616f5fc427cf1e1e7b425293131f2fce2b8
Browse files- README.md +161 -1
- layout-unreadability.py +5 -3
- requirements.txt +139 -90
README.md
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@@ -8,4 +8,164 @@ sdk_version: 4.36.1
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app_file: app.py
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pinned: false
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---
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app_file: app.py
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pinned: false
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---
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# Layout Unreadability
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## Description
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The Layout Unreadability metric evaluates whether text elements are placed on visually complex or non-flat background regions that could impair readability. This metric computes the non-flatness (gradient intensity) of regions where text is positioned, helping assess whether text placement respects readability principles in content-aware layout design.
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## What It Measures
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This metric computes:
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- **Background complexity under text**: Gradient intensity in regions occupied by text elements
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- **Text readability risk**: Whether text is placed on busy or complex backgrounds
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- **Content-awareness**: How well the layout avoids placing text on unsuitable regions
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Lower scores indicate better text placement on flat, readable backgrounds.
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## Metric Details
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- Uses Sobel gradient operators to detect edges and texture in background canvas
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- Computes gradient magnitude (non-flatness) in regions covered by text elements
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- Excludes underlay/decoration elements from background canvas analysis
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- From PosterLayout (Hsu et al., CVPR 2023) and CGL-GAN methodology
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- Lower gradient scores mean text is on flatter, more readable backgrounds
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## Usage
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### Installation
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```bash
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pip install evaluate opencv-python
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```
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### Basic Example
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```python
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import evaluate
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import numpy as np
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# Load the metric with canvas dimensions
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metric = evaluate.load(
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"creative-graphic-design/layout-unreadability",
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canvas_width=360,
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canvas_height=504,
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text_label_index=1,
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decoration_label_index=3
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)
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# Prepare data
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predictions = np.random.rand(1, 25, 4) # normalized ltrb coordinates
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gold_labels = np.random.randint(0, 4, size=(1, 25)) # class labels
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# Paths to canvas background images
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image_canvases = ["path/to/canvas_image.jpg"]
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score = metric.compute(
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predictions=predictions,
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gold_labels=gold_labels,
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image_canvases=image_canvases
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)
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print(score)
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```
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### Batch Processing Example
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```python
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import evaluate
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# Load the metric
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metric = evaluate.load(
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"creative-graphic-design/layout-unreadability",
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canvas_width=360,
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canvas_height=504,
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text_label_index=1,
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decoration_label_index=3
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)
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# Batch processing
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batch_size = 128
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predictions = np.random.rand(batch_size, 25, 4)
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gold_labels = np.random.randint(0, 4, size=(batch_size, 25))
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image_canvases = [f"path/to/canvas_{i}.jpg" for i in range(batch_size)]
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score = metric.compute(
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predictions=predictions,
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gold_labels=gold_labels,
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image_canvases=image_canvases
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)
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print(score)
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```
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## Parameters
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### Initialization Parameters
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- **canvas_width** (`int`, required): Width of the canvas in pixels
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- **canvas_height** (`int`, required): Height of the canvas in pixels
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- **text_label_index** (`int`, optional, default=1): Class index for text elements
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- **decoration_label_index** (`int`, optional, default=3): Class index for underlay/decoration elements to mask out
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### Computation Parameters
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- **predictions** (`list` of `lists` of `float`): Normalized bounding boxes in ltrb format (0.0 to 1.0)
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- **gold_labels** (`list` of `lists` of `int`): Class labels for each element (0 = padding)
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- **image_canvases** (`list` of `str`): File paths to canvas background images
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**Note**:
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- Canvas images should show the background content (photos, graphics) where layout will be placed
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- Underlay/decoration elements are masked out before computing gradients
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- Only text elements (text_label_index) are evaluated for readability
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## Returns
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Returns a `float` value representing the average gradient intensity under text elements (range: 0.0 to 1.0).
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## Interpretation
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- **Lower is better** (range: 0.0 to 1.0)
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- **Value ~0.0**: Text placed on flat, uniform backgrounds (ideal for readability)
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- **Value 0.0-0.2**: Good text placement on relatively flat regions
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- **Value 0.2-0.4**: Moderate background complexity, may affect readability
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- **Value 0.4-0.6**: High background complexity, readability concerns
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- **Value > 0.6**: Very complex backgrounds under text (poor placement)
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### Use Cases
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- **Content-aware poster generation**: Ensure text is readable on background imagery
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- **Advertisement layout**: Place call-to-action text on suitable backgrounds
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- **Presentation slides**: Validate text visibility on photo backgrounds
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- **Magazine/flyer design**: Assess text-background contrast and readability
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### Key Insights
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- **Readability principle**: Text should be on flat or low-detail backgrounds
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- **Design solutions**: Use underlay/decoration elements to create readable regions
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- **Trade-off**: Sometimes text must go on complex backgrounds (consider semi-transparent overlays)
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- **Context matters**: Title text may tolerate more complexity than body text
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## Citations
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```bibtex
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@inproceedings{hsu2023posterlayout,
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title={Posterlayout: A new benchmark and approach for content-aware visual-textual presentation layout},
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author={Hsu, Hsiao Yuan and He, Xiangteng and Peng, Yuxin and Kong, Hao and Zhang, Qing},
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booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
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pages={6018--6026},
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year={2023}
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}
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```
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## References
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- **Paper**: [PosterLayout (Hsu et al., CVPR 2023)](https://arxiv.org/abs/2303.15937)
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- **Reference Implementation**: [PosterLayout eval.py](https://github.com/PKU-ICST-MIPL/PosterLayout-CVPR2023/blob/main/eval.py#L144-L171)
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- **Related**: CGL-GAN text readability evaluation
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## Related Metrics
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- [Layout Occlusion](../layout_occlusion/): Evaluates coverage of salient regions
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- [Layout Utility](../layout_utility/): Measures utilization of suitable space
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- [Layout Underlay Effectiveness](../layout_underlay_effectiveness/): Evaluates underlay placement
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layout-unreadability.py
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import evaluate
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import numpy as np
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import numpy.typing as npt
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from PIL import Image
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from PIL.Image import Image as PilImage
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ReqType = Literal["pil2cv", "cv2pil"]
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class LayoutUnreadability(evaluate.Metric):
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def __init__(
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self,
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if req == "pil2cv":
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assert isinstance(img, PilImage)
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color_code = color_code or cv2.COLOR_RGB2BGR
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return cv2.cvtColor(np.asarray(img), color_code)
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elif req == "cv2pil":
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assert isinstance(img, np.ndarray)
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color_code = color_code or cv2.COLOR_BGR2RGB
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filepath = filepath[0]
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canvas_pil = Image.open(filepath) # type: ignore
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canvas_pil = canvas_pil.convert("RGB")
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if canvas_pil.size != (self.canvas_width, self.canvas_height):
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canvas_pil = canvas_pil.resize((self.canvas_width, self.canvas_height))
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canvas_pil = self.img_to_g_xy(canvas_pil)
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assert isinstance(canvas_pil, PilImage)
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import evaluate
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import numpy as np
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import numpy.typing as npt
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from evaluate.utils.file_utils import add_start_docstrings
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from PIL import Image
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from PIL.Image import Image as PilImage
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ReqType = Literal["pil2cv", "cv2pil"]
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@add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
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class LayoutUnreadability(evaluate.Metric):
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def __init__(
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self,
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if req == "pil2cv":
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assert isinstance(img, PilImage)
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color_code = color_code or cv2.COLOR_RGB2BGR
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return cv2.cvtColor(np.asarray(img), color_code) # type: ignore
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elif req == "cv2pil":
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assert isinstance(img, np.ndarray)
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color_code = color_code or cv2.COLOR_BGR2RGB
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filepath = filepath[0]
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canvas_pil = Image.open(filepath) # type: ignore
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canvas_pil = canvas_pil.convert("RGB") # type: ignore
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if canvas_pil.size != (self.canvas_width, self.canvas_height):
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canvas_pil = canvas_pil.resize((self.canvas_width, self.canvas_height)) # type: ignore
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canvas_pil = self.img_to_g_xy(canvas_pil)
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assert isinstance(canvas_pil, PilImage)
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requirements.txt
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# This file was autogenerated by uv via the following command:
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# uv export --package layout_unreadability --no-dev --no-hashes --format requirements-txt
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aiohappyeyeballs==2.6.1
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# via aiohttp
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aiohttp==3.13.2
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# via fsspec
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aiosignal==1.4.0
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# via aiohttp
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anyio==4.12.0
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# via httpx
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attrs==25.4.0
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# via aiohttp
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certifi==2025.11.12
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# via
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# httpcore
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# httpx
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# requests
|
| 18 |
+
charset-normalizer==3.4.4
|
| 19 |
+
# via requests
|
| 20 |
+
click==8.3.1
|
| 21 |
+
# via typer-slim
|
| 22 |
+
colorama==0.4.6 ; sys_platform == 'win32'
|
| 23 |
+
# via
|
| 24 |
+
# click
|
| 25 |
+
# tqdm
|
| 26 |
+
datasets==4.4.2
|
| 27 |
+
# via evaluate
|
| 28 |
+
dill==0.4.0
|
| 29 |
+
# via
|
| 30 |
+
# datasets
|
| 31 |
+
# evaluate
|
| 32 |
+
# multiprocess
|
| 33 |
+
evaluate==0.4.6
|
| 34 |
+
# via layout-unreadability
|
| 35 |
+
filelock==3.20.1
|
| 36 |
+
# via
|
| 37 |
+
# datasets
|
| 38 |
+
# huggingface-hub
|
| 39 |
+
frozenlist==1.8.0
|
| 40 |
+
# via
|
| 41 |
+
# aiohttp
|
| 42 |
+
# aiosignal
|
| 43 |
+
fsspec==2025.10.0
|
| 44 |
+
# via
|
| 45 |
+
# datasets
|
| 46 |
+
# evaluate
|
| 47 |
+
# huggingface-hub
|
| 48 |
+
h11==0.16.0
|
| 49 |
+
# via httpcore
|
| 50 |
+
hf-xet==1.2.0 ; platform_machine == 'AMD64' or platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'arm64' or platform_machine == 'x86_64'
|
| 51 |
+
# via huggingface-hub
|
| 52 |
+
httpcore==1.0.9
|
| 53 |
+
# via httpx
|
| 54 |
+
httpx==0.28.1
|
| 55 |
+
# via
|
| 56 |
+
# datasets
|
| 57 |
+
# huggingface-hub
|
| 58 |
+
huggingface-hub==1.2.3
|
| 59 |
+
# via
|
| 60 |
+
# datasets
|
| 61 |
+
# evaluate
|
| 62 |
+
idna==3.11
|
| 63 |
+
# via
|
| 64 |
+
# anyio
|
| 65 |
+
# httpx
|
| 66 |
+
# requests
|
| 67 |
+
# yarl
|
| 68 |
+
multidict==6.7.0
|
| 69 |
+
# via
|
| 70 |
+
# aiohttp
|
| 71 |
+
# yarl
|
| 72 |
+
multiprocess==0.70.18
|
| 73 |
+
# via
|
| 74 |
+
# datasets
|
| 75 |
+
# evaluate
|
| 76 |
+
numpy==2.2.6
|
| 77 |
+
# via
|
| 78 |
+
# datasets
|
| 79 |
+
# evaluate
|
| 80 |
+
# opencv-python
|
| 81 |
+
# pandas
|
| 82 |
+
opencv-python==4.12.0.88
|
| 83 |
+
# via layout-unreadability
|
| 84 |
+
packaging==25.0
|
| 85 |
+
# via
|
| 86 |
+
# datasets
|
| 87 |
+
# evaluate
|
| 88 |
+
# huggingface-hub
|
| 89 |
+
pandas==2.3.3
|
| 90 |
+
# via
|
| 91 |
+
# datasets
|
| 92 |
+
# evaluate
|
| 93 |
+
pillow==12.0.0
|
| 94 |
+
# via layout-unreadability
|
| 95 |
+
propcache==0.4.1
|
| 96 |
+
# via
|
| 97 |
+
# aiohttp
|
| 98 |
+
# yarl
|
| 99 |
+
pyarrow==22.0.0
|
| 100 |
+
# via datasets
|
| 101 |
+
python-dateutil==2.9.0.post0
|
| 102 |
+
# via pandas
|
| 103 |
+
pytz==2025.2
|
| 104 |
+
# via pandas
|
| 105 |
+
pyyaml==6.0.3
|
| 106 |
+
# via
|
| 107 |
+
# datasets
|
| 108 |
+
# huggingface-hub
|
| 109 |
+
requests==2.32.5
|
| 110 |
+
# via
|
| 111 |
+
# datasets
|
| 112 |
+
# evaluate
|
| 113 |
+
shellingham==1.5.4
|
| 114 |
+
# via huggingface-hub
|
| 115 |
+
six==1.17.0
|
| 116 |
+
# via python-dateutil
|
| 117 |
+
tqdm==4.67.1
|
| 118 |
+
# via
|
| 119 |
+
# datasets
|
| 120 |
+
# evaluate
|
| 121 |
+
# huggingface-hub
|
| 122 |
+
typer-slim==0.21.0
|
| 123 |
+
# via huggingface-hub
|
| 124 |
+
typing-extensions==4.15.0
|
| 125 |
+
# via
|
| 126 |
+
# aiosignal
|
| 127 |
+
# anyio
|
| 128 |
+
# huggingface-hub
|
| 129 |
+
# typer-slim
|
| 130 |
+
tzdata==2025.3
|
| 131 |
+
# via pandas
|
| 132 |
+
urllib3==2.6.2
|
| 133 |
+
# via requests
|
| 134 |
+
xxhash==3.6.0
|
| 135 |
+
# via
|
| 136 |
+
# datasets
|
| 137 |
+
# evaluate
|
| 138 |
+
yarl==1.22.0
|
| 139 |
+
# via aiohttp
|