dcgan_generator.py

import os
import numpy as np
import onnxruntime as ort
from PIL import Image

LATENT_FEATURES = 512
MODEL_PATH = os.path.join("model", "batik_dcgan.onnx")

model = ort.InferenceSession(MODEL_PATH)
input_name = model.get_inputs()[0].name

def generate_dcgan():
    noise = np.random.randn(1, LATENT_FEATURES, 1, 1).astype(np.float32)

    output = model.run(None, {
        input_name: noise
    })

    image = output[0][0]
    image = (image * 0.5 + 0.5) * 255
    image = image.astype(np.uint8)
    image = np.transpose(image, (1, 2, 0))
    pil_img = Image.fromarray(image, 'RGB')
    
    return pil_img.resize((512, 512), Image.LANCZOS)