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--- |
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license: other |
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task_categories: |
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- text-to-image |
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language: |
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- en |
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pretty_name: Peanuts Dataset (Snoopy and Co.) |
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size_categories: |
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- 10K<n<100K |
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dataset_info: |
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features: |
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- name: image |
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dtype: image |
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- name: panel_name |
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dtype: string |
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- name: characters |
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sequence: string |
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- name: themes |
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sequence: string |
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- name: color |
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dtype: string |
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- name: year |
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dtype: int64 |
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- name: caption |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 2948640650.848 |
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num_examples: 77456 |
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download_size: 4601323640 |
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dataset_size: 2948640650.848 |
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--- |
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# Peanut Comic Strip Dataset (Snoopy & Co.) |
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This is a dataset Peanuts comic strips from `1950/10/02` to `2000/02/13`. |
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There are `77,457` panels extracted from `17,816` comic strips. |
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The dataset size is approximately `4.4G`. |
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Each row in the dataset contains the following fields: |
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- `image`: `PIL.Image` containing the extracted panel. |
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- `panel_name`: unique identifier for the row. |
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- `characters`: `tuple[str, ...]` of characters included in the comic strip the panel is part of. |
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- `themes`: `tuple[str, ...]` of theme in the comic strip the panel is part of. |
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- `color`: `str` indicating whether the panel is grayscale or in color. |
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- `caption`: [BLIP-2_OPT_6.7B](https://huggingface.co/docs/transformers/main/model_doc/blip-2) generated caption from the panel. |
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- `year`: `int` storing the year the specific panel was released. |
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> **OPT-6.7B has a non-commercial use license and so this dataset cannot be used for commercial projects. If you need a dataset for commercial use please see [this similar dataset](https://huggingface.co/datasets/afmck/peanuts-flan-t5-xl) that uses Flan-T5-XL, which allows for commercial use.** |
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Character and theme information was extracted from [Peanuts Wiki (Fandom)](https://peanuts.fandom.com/wiki/Peanuts_Wiki) using [Beautiful Soup](https://www.crummy.com/software/BeautifulSoup/bs4/doc/). |
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Images were extracted from [Peanuts Search](https://peanuts-search.com/). |
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Only strips with the following characters were extracted: |
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``` |
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- "Charlie Brown" |
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- "Sally Brown" |
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- "Joe Cool" # Snoopy alter-ego |
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- "Franklin" |
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- "Violet Gray" |
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- "Eudora" |
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- "Frieda" |
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- "Marcie" |
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- "Peppermint Patty" |
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- "Patty" |
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- "Pig-Pen" |
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- "Linus van Pelt" |
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- "Lucy van Pelt" |
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- "Rerun van Pelt" |
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- "Schroeder" |
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- "Snoopy" |
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- "Shermy" |
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- "Spike" |
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- "Woodstock" |
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- "the World War I Flying Ace" # Snoopy alter-ego |
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``` |
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### Extraction Details |
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Panel detection and extraction was done using the following codeblock: |
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```python |
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def check_contour(cnt): |
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area = cv2.contourArea(cnt) |
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if area < 600: |
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return False |
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_, _, w, h = cv2.boundingRect(cnt) |
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if w / h < 1 / 2: return False |
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if w / h > 2 / 1: return False |
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return True |
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def get_panels_from_image(path): |
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panels = [] |
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original_img = cv2.imread(path) |
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gray = cv2.cvtColor(original_img, cv2.COLOR_BGR2GRAY) |
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blur = cv2.GaussianBlur(gray, (5,5), 0) |
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thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1] |
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3)) |
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opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=1) |
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invert = 255 - opening |
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cnts, _ = cv2.findContours(invert, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) |
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idx = 0 |
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for cnt in cnts: |
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if not check_contour(cnt): continue |
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idx += 1 |
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x,y,w,h = cv2.boundingRect(cnt) |
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roi = original_img[y:y+h,x:x+w] |
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panels.append(roi) |
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return panels |
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``` |
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`check_contour` will reject panels with `area < 600` or with aspect ratios larger than `2` or smaller than `0.5`. |
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Grayscale detection was done using the following codeblock: |
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```python |
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def is_grayscale(panel): |
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LAB_THRESHOLD = 10. |
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img = cv2.cvtColor(panel, cv2.COLOR_RGB2LAB) |
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_, ea, eb = cv2.split(img) |
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de = abs(ea - eb) |
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mean_e = np.mean(de) |
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return mean_e < LAB_THRESHOLD |
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``` |
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Captioning was done using the standard BLIP-2 pipeline shown in the [Huggingface docs](https://huggingface.co/docs/transformers/main/model_doc/blip-2) using beam search over 10 beams and a repetition penalty of `2.0`. |
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Raw captions are extracted and no postprocessing is applied. You may wish to normalise captions (such as replacing "cartoon" with "peanuts cartoon") or incorporate extra metadata into prompts. |
