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| import gradio as gr | |
| import PIL | |
| import numpy as np | |
| import re | |
| from actors_matching.api import analyze_image, load_annoy_index | |
| from pathlib import Path | |
| annoy_index, actors_mapping = load_annoy_index() | |
| def get_image_html(actor: dict): | |
| url = actor["url"] | |
| name = actor["name"] | |
| imdb_url = f"https://www.imdb.com/name/{actor['nconst']}/" | |
| return f""" | |
| <div style="position: relative; text-align: center; color: white;"> | |
| <img src="{url}" alt="{name} matches the input image" style="height: 500px"> | |
| <div style="padding: 0.2em; position: absolute; bottom: 16px; left: 16px; background-color: #aacccccc; font-size: 2em;"> | |
| <p>{name}</p> | |
| <p style="font-size:0.5em"><a href={imdb_url} target="_blank">Click to see on IMDb</></p> | |
| </div> | |
| </div> | |
| """ | |
| def no_faces_found_html(): | |
| return f"""<div>No faces found in the picture</div>""" | |
| def get_best_matches(image, n_matches: int): | |
| return analyze_image(image, annoy_index=annoy_index, n_matches=n_matches) | |
| def resize_image_keep_ratio(input_image: np.array, size: tuple): | |
| resized_image = PIL.Image.fromarray(input_image) | |
| resized_image.thumbnail(size, PIL.Image.ANTIALIAS) | |
| return np.array(resized_image) | |
| def get_article_text(): | |
| article = Path("README.md").read_text() | |
| # Remove the HuggingFace Space app information from the README | |
| article = re.sub(r"^---.+---\s+", "", article, flags=re.MULTILINE + re.DOTALL) | |
| return article | |
| def find_matching_actors(input_img, title, n_matches: int = 10): | |
| resized_image = resize_image_keep_ratio(input_img, (512, 512)) | |
| best_matches_list = get_best_matches(resized_image, n_matches=n_matches) | |
| # TODO: allow looping through characters | |
| if best_matches_list: | |
| best_matches = best_matches_list[0] | |
| # TODO: Show how the initial image was parsed (ie: which person is displayed) | |
| # Build htmls to display the result | |
| output_htmls = [] | |
| for match in best_matches["matches"]: | |
| actor = actors_mapping[match] | |
| output_htmls.append(get_image_html(actor)) | |
| return output_htmls | |
| # No matches | |
| return [no_faces_found_html()] | |
| iface = gr.Interface( | |
| find_matching_actors, | |
| title="Which actor or actress looks like you?", | |
| description="""Who is the best person to play a movie about you? Upload a picture and find out! | |
| Or maybe you'd like to know who would best interpret your favorite historical character? | |
| Give it a shot or try one of the sample images below. | |
| Built with ❤️ using great open-source libraries such as dlib, face_recognition and Annoy. | |
| Please read below for more information on biases | |
| and limitations of the tool!""", | |
| article=get_article_text(), | |
| inputs=[ | |
| gr.inputs.Image(shape=None, label="Your image"), | |
| gr.inputs.Textbox( | |
| label="Who's that?", placeholder="Optional, you can leave this blank" | |
| ), | |
| # gr.inputs.Slider(minimum=1, maximum=10, step=1, default=5, label="Number of matches"), | |
| ], | |
| outputs=gr.outputs.Carousel(gr.outputs.HTML(), label="Matching actors & actresses"), | |
| examples=[ | |
| ["images/example_rb_ginsburg.jpg", "RB Ginsburg in 1977"], | |
| [ | |
| "images/example_hannibal_barca.jpg", | |
| "Hannibal (the one with the elephants...)", | |
| ], | |
| ["images/example_frederick_douglass.jpg", "Frederik Douglass"], | |
| ["images/example_leonardo_davinci.jpg", "Leonoardo da Vinci"], | |
| ["images/example_joan_of_arc.jpg", "Jeanne d'Arc"], | |
| ["images/example_sun_tzu.jpg", "Sun Tzu"], | |
| ], | |
| ) | |
| iface.launch() | |