utils.py

import torch
from huggan.pytorch.lightweight_gan.lightweight_gan import LightweightGAN
# from datasets import load_dataset
# from PIL import Image
import numpy as np
# import paddlehub as hub
# import random
# from PIL import ImageDraw,ImageFont

# import streamlit as st

# @st.experimental_singleton
# def load_bg_model():
#     bg_model = hub.Module(name='U2NetP', directory='assets/models/')
#     return bg_model


# bg_model = load_bg_model()
# def remove_bg(img):
#     result = bg_model.Segmentation(
#         images=[np.array(img)[:,:,::-1]],
#         paths=None,
#         batch_size=1,
#         input_size=320,
#         output_dir=None,
#         visualization=False)
#     output = result[0]
#     mask=Image.fromarray(output['mask'])
#     front=Image.fromarray(output['front'][:,:,::-1]).convert("RGBA")
#     front.putalpha(mask)
#     return front

# meme_template=Image.open("./assets/pigeon_meme.jpg").convert("RGBA")
# def make_meme(pigeon,text="Is this a pigeon?",show_text=True,remove_background=True):
    
#     meme=meme_template.copy()
#     approx_butterfly_center=(850,30)

#     if remove_background:
#         pigeon=remove_bg(pigeon)
        
#     else:
#         pigeon=Image.fromarray(pigeon).convert("RGBA")

#     random_rotate=random.randint(-30,30)
#     random_size=random.randint(150,200)
#     pigeon=pigeon.resize((random_size,random_size)).rotate(random_rotate,expand=True)

#     meme.alpha_composite(pigeon, approx_butterfly_center)

#     #ref: https://blog.lipsumarium.com/caption-memes-in-python/
#     def drawTextWithOutline(text, x, y):
#         draw.text((x-2, y-2), text,(0,0,0),font=font)
#         draw.text((x+2, y-2), text,(0,0,0),font=font)
#         draw.text((x+2, y+2), text,(0,0,0),font=font)
#         draw.text((x-2, y+2), text,(0,0,0),font=font)
#         draw.text((x, y), text, (255,255,255), font=font)

#     if show_text:
#         draw = ImageDraw.Draw(meme)
#         font_size=52
#         font = ImageFont.truetype("assets/impact.ttf", font_size)
#         w, h = draw.textsize(text, font) # measure the size the text will take
#         drawTextWithOutline(text, meme.width/2 - w/2, meme.height - font_size*2)
#     meme = meme.convert("RGB")    
#     return meme

# def get_train_data(dataset_name="huggan/smithsonian_butterflies_subset"):
#     dataset=load_dataset(dataset_name)
#     dataset=dataset.sort("sim_score")
#     return dataset["train"]

# from transformers import BeitFeatureExtractor, BeitForImageClassification
# emb_feature_extractor = BeitFeatureExtractor.from_pretrained('microsoft/beit-base-patch16-224')
# emb_model = BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224')
# def embed(images):
#     inputs = emb_feature_extractor(images=images, return_tensors="pt")
#     outputs = emb_model(**inputs,output_hidden_states= True)
#     last_hidden=outputs.hidden_states[-1]
#     pooler=emb_model.base_model.pooler
#     final_emb=pooler(last_hidden).detach().numpy()
#     return final_emb    
    
# def build_index():    
#     dataset=get_train_data()
#     ds_with_embeddings = dataset.map(lambda x: {"beit_embeddings":embed(x["image"])},batched=True,batch_size=20)
#     ds_with_embeddings.add_faiss_index(column='beit_embeddings')
#     ds_with_embeddings.save_faiss_index('beit_embeddings', 'beit_index.faiss')

# def get_dataset():
#     dataset=get_train_data()
#     dataset.load_faiss_index('beit_embeddings', 'beit_index.faiss')
#     return dataset

def load_model(model_name='ceyda/butterfly_cropped_uniq1K_512',model_version=None):
    gan = LightweightGAN.from_pretrained(model_name,version=model_version)
    gan.eval()
    return gan
    
def generate(gan,batch_size=1):
    with torch.no_grad():
        ims = gan.G(torch.randn(batch_size, gan.latent_dim)).clamp_(0., 1.)*255
        ims = ims.permute(0,2,3,1).detach().cpu().numpy().astype(np.uint8)
    return ims

# def interpolate():
#     pass