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--- |
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language: |
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- en |
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pretty_name: 'Comics: Pick-A-Panel' |
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tags: |
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- comics |
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dataset_info: |
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- config_name: caption_relevance |
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features: |
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- name: sample_id |
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dtype: string |
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- name: context |
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sequence: image |
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- name: options |
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sequence: image |
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- name: index |
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dtype: int32 |
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- name: solution_index |
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dtype: int32 |
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- name: split |
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dtype: string |
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- name: task_type |
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dtype: string |
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- name: previous_panel_caption |
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dtype: string |
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splits: |
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- name: val |
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num_bytes: 530485241.0 |
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num_examples: 262 |
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- name: test |
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num_bytes: 1670410617.0 |
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num_examples: 932 |
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download_size: 2200220497 |
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dataset_size: 2200895858.0 |
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- config_name: char_coherence |
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features: |
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- name: sample_id |
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dtype: string |
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|
- name: context |
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sequence: image |
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|
- name: options |
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sequence: image |
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|
- name: index |
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|
dtype: int32 |
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|
- name: solution_index |
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|
dtype: int32 |
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|
- name: split |
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|
dtype: string |
|
|
- name: task_type |
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|
dtype: string |
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|
- name: previous_panel_caption |
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dtype: string |
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|
splits: |
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|
- name: val |
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|
num_bytes: 379249617.0 |
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|
num_examples: 143 |
|
|
- name: test |
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|
num_bytes: 1139813763.0 |
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num_examples: 489 |
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download_size: 1519137617 |
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dataset_size: 1519063380.0 |
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- config_name: sequence_filling |
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features: |
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- name: sample_id |
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dtype: string |
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|
- name: context |
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sequence: image |
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|
- name: options |
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|
sequence: image |
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|
- name: index |
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|
dtype: int32 |
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|
- name: solution_index |
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|
dtype: int32 |
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|
- name: split |
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|
dtype: string |
|
|
- name: task_type |
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|
dtype: string |
|
|
- name: previous_panel_caption |
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|
dtype: string |
|
|
splits: |
|
|
- name: val |
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|
num_bytes: 1230082746.0 |
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|
num_examples: 262 |
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download_size: 1153097954 |
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dataset_size: 1230082746.0 |
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- config_name: text_closure |
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features: |
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- name: sample_id |
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dtype: string |
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|
- name: context |
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|
sequence: image |
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|
- name: options |
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|
sequence: image |
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|
- name: index |
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|
dtype: int32 |
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|
- name: solution_index |
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|
dtype: int32 |
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|
- name: split |
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|
dtype: string |
|
|
- name: task_type |
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|
dtype: string |
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|
- name: previous_panel_caption |
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|
dtype: string |
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|
splits: |
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|
- name: val |
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|
num_bytes: 952974973.0 |
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|
num_examples: 274 |
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download_size: 930660064 |
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dataset_size: 952974973.0 |
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configs: |
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- config_name: caption_relevance |
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data_files: |
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- split: val |
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path: caption_relevance/val-* |
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- split: test |
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path: caption_relevance/test-* |
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- config_name: char_coherence |
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data_files: |
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- split: val |
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path: char_coherence/val-* |
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- split: test |
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path: char_coherence/test-* |
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- config_name: sequence_filling |
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data_files: |
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- split: val |
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path: sequence_filling/val-* |
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- config_name: text_closure |
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data_files: |
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- split: val |
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path: text_closure/val-* |
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--- |
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# Comics: Pick-A-Panel |
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This is the dataset for the [ICDAR 2025 Competition on Comics Understanding in the Era of Foundational Models](https://rrc.cvc.uab.es/?ch=31&com=introduction) |
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The dataset contains five subtask or skills: |
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### Sequence Filling |
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<details> |
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<summary>Task Description</summary> |
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 |
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Given a sequence of comic panels, a missing panel, and a set of option panels, the task is to select the panel that best fits the sequence. |
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</details> |
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### Character Coherence, Visual Closure, Text Closure |
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<details> |
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<summary>Task Description</summary> |
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These skills require understanding the context sequence to then pick the best panel to continue the story, focusing on the characters, the visual elements, and the text: |
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- Character Coherence: Given a sequence of comic panels, pick the panel from the two options that best continues the story in a coherent with the characters. Both options are the same panel, but the text in the speech bubbles is has been swapped. |
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- Visual Closure: Given a sequence of comic panels, pick the panel from the options that best continues the story in a coherent way with the visual elements. |
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- Text Closure: Given a sequence of comic panels, pick the panel from the options that best continues the story in a coherent way with the text. All options are the same panel, but with text in the speech retrieved from different panels. |
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</details> |
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### Caption Relevance |
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<details> |
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<summary>Task Description</summary> |
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 |
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Given a caption from the previous panel, select the panel that best continues the story. |
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</details> |
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## Loading the Data |
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```python |
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from datasets import load_dataset |
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skill = "sequence_filling" # "sequence_filling", "char_coherence", "visual_closure", "text_closure", "caption_relevance" |
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split = "val" # "val", "test" |
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dataset = load_dataset("VLR-CVC/ComPAP", skill, split=split) |
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``` |
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<details> |
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<summary>Map to single images</summary> |
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If your model can only process single images, you can render each sample as a single image: |
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```python |
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from PIL import Image, ImageDraw, ImageFont |
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import numpy as np |
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from datasets import Features, Value, Image as ImageFeature |
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class SingleImagePickAPanel: |
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def __init__(self, max_size=500, margin=10, label_space=20, font_path="Arial.ttf"): |
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self.max_size = max_size |
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self.margin = margin |
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self.label_space = label_space |
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# Add separate font sizes |
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self.label_font_size = 20 |
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self.number_font_size = 24 |
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self.font_path = font_path |
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def resize_image(self, img): |
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"""Resize image keeping aspect ratio if longest edge > max_size""" |
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if max(img.size) > self.max_size: |
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ratio = self.max_size / max(img.size) |
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new_size = tuple(int(dim * ratio) for dim in img.size) |
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return img.resize(new_size, Image.Resampling.LANCZOS) |
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return img |
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def create_mask_panel(self, width, height): |
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"""Create a question mark panel""" |
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mask_panel = Image.new("RGB", (width, height), (200, 200, 200)) |
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draw = ImageDraw.Draw(mask_panel) |
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font_size = int(height * 0.8) |
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try: |
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font = ImageFont.truetype(self.font_path, font_size) |
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except: |
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raise ValueError("Font file not found") |
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text = "?" |
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bbox = draw.textbbox((0, 0), text, font=font) |
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text_x = (width - (bbox[2] - bbox[0])) // 2 |
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text_y = (height - (bbox[3] - bbox[1])) // 2 |
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draw.text((text_x, text_y), text, fill="black", font=font) |
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return mask_panel |
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def draw_number_on_panel(self, panel, number, font): |
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"""Draw number on the bottom of the panel with background""" |
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draw = ImageDraw.Draw(panel) |
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# Get text size |
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bbox = draw.textbbox((0, 0), str(number), font=font) |
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text_width = bbox[2] - bbox[0] |
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text_height = bbox[3] - bbox[1] |
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# Calculate position (bottom-right corner) |
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padding = 2 |
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text_x = panel.size[0] - text_width - padding |
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text_y = panel.size[1] - text_height - padding |
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# Draw semi-transparent background |
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bg_rect = [(text_x - padding, text_y - padding), |
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(text_x + text_width + padding, text_y + text_height + padding)] |
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draw.rectangle(bg_rect, fill=(255, 255, 255, 180)) |
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# Draw text |
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draw.text((text_x, text_y), str(number), fill="black", font=font) |
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return panel |
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def map_to_single_image(self, examples): |
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"""Process a batch of examples from a HuggingFace dataset""" |
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single_images = [] |
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for i in range(len(examples['sample_id'])): |
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# Get context and options for current example |
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context = examples['context'][i] if len(examples['context'][i]) > 0 else [] |
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options = examples['options'][i] |
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# Resize all images |
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context = [self.resize_image(img) for img in context] |
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options = [self.resize_image(img) for img in options] |
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# Calculate common panel size (use median size to avoid outliers) |
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all_panels = context + options |
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if len(all_panels) > 0: |
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widths = [img.size[0] for img in all_panels] |
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heights = [img.size[1] for img in all_panels] |
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panel_width = int(np.median(widths)) |
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panel_height = int(np.median(heights)) |
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# Resize all panels to common size |
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context = [img.resize((panel_width, panel_height)) for img in context] |
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options = [img.resize((panel_width, panel_height)) for img in options] |
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# Create mask panel for sequence filling tasks if needed |
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if 'index' in examples and len(context) > 0: |
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mask_idx = examples['index'][i] |
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mask_panel = self.create_mask_panel(panel_width, panel_height) |
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context.insert(mask_idx, mask_panel) |
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# Calculate canvas dimensions based on whether we have context |
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if len(context) > 0: |
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context_row_width = panel_width * len(context) + self.margin * (len(context) - 1) |
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options_row_width = panel_width * len(options) + self.margin * (len(options) - 1) |
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canvas_width = max(context_row_width, options_row_width) |
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canvas_height = (panel_height * 2 + |
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self.label_space * 2) |
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else: |
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# Only options row for caption_relevance |
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canvas_width = panel_width * len(options) + self.margin * (len(options) - 1) |
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canvas_height = (panel_height + |
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self.label_space) |
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# Create canvas |
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final_image = Image.new("RGB", (canvas_width, canvas_height), "white") |
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draw = ImageDraw.Draw(final_image) |
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try: |
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label_font = ImageFont.truetype(self.font_path, self.label_font_size) |
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number_font = ImageFont.truetype(self.font_path, self.number_font_size) |
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except: |
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raise ValueError("Font file not found") |
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current_y = 0 |
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# Add context section if it exists |
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if len(context) > 0: |
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# Draw "Context" label |
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bbox = draw.textbbox((0, 0), "Context", font=label_font) |
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text_x = (canvas_width - (bbox[2] - bbox[0])) // 2 |
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draw.text((text_x, current_y), "Context", fill="black", font=label_font) |
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current_y += self.label_space |
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# Paste context panels |
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x_offset = (canvas_width - (panel_width * len(context) + |
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self.margin * (len(context) - 1))) // 2 |
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for panel in context: |
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final_image.paste(panel, (x_offset, current_y)) |
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x_offset += panel_width + self.margin |
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current_y += panel_height |
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# Add "Options" label |
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bbox = draw.textbbox((0, 0), "Options", font=label_font) |
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text_x = (canvas_width - (bbox[2] - bbox[0])) // 2 |
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draw.text((text_x, current_y), "Options", fill="black", font=label_font) |
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current_y += self.label_space |
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# Paste options with numbers on panels |
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x_offset = (canvas_width - (panel_width * len(options) + |
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self.margin * (len(options) - 1))) // 2 |
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for idx, panel in enumerate(options): |
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# Create a copy of the panel to draw on |
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panel_with_number = panel.copy() |
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if panel_with_number.mode != 'RGBA': |
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panel_with_number = panel_with_number.convert('RGBA') |
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# Draw number on panel |
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panel_with_number = self.draw_number_on_panel( |
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panel_with_number, |
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idx, |
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number_font |
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) |
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# Paste the panel with number |
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final_image.paste(panel_with_number, (x_offset, current_y), panel_with_number) |
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x_offset += panel_width + self.margin |
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# Convert final_image to PIL Image format (instead of numpy array) |
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single_images.append(final_image) |
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# Prepare batch output |
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examples['single_image'] = single_images |
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return examples |
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from datasets import load_dataset |
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skill = "sequence_filling" # "sequence_filling", "char_coherence", "visual_closure", "text_closure", "caption_relevance" |
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split = "val" # "val", "test" |
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dataset = load_dataset("VLR-CVC/ComPAP", skill, split=split) |
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processor = SingleImagePickAPanel() |
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dataset = dataset.map( |
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processor.map_to_single_image, |
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batched=True, |
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batch_size=32, |
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remove_columns=['context', 'options'] |
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) |
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dataset.save_to_disk(f"ComPAP_{skill}_{split}_single_images") |
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``` |
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</details> |
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## Summit Results and Leaderboard |
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The competition is hosted in the [Robust Reading Competition website](https://rrc.cvc.uab.es/?ch=31&com=introduction) and the leaderboard is available [here](https://rrc.cvc.uab.es/?ch=31&com=evaluation). |
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## Citation |
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_coming soon_ |
