GammaAlpha's picture
Duplicate from skytnt/anime-aesthetic-predict
eea203f
import cv2
import numpy as np
import gradio as gr
import onnxruntime as rt
from huggingface_hub import hf_hub_download
def predict(img):
img = img.astype(np.float32) / 255
s = 768
h, w = img.shape[:-1]
h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
ph, pw = s - h, s - w
img_input = np.zeros([s, s, 3], dtype=np.float32)
img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h))
img_input = np.transpose(img_input, (2, 0, 1))
img_input = img_input[np.newaxis, :]
pred = model.run(None, {"img": img_input})[0].item()
return pred
if __name__ == "__main__":
model_path = hf_hub_download(repo_id="skytnt/anime-aesthetic", filename="model.onnx")
model = rt.InferenceSession(model_path, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
examples = [[f"examples/{x:02d}.jpg"] for x in range(0, 2)]
app = gr.Interface(predict, gr.Image(label="input image"), gr.Number(label="score"),title="Anime Aesthetic Predict",
allow_flagging="never", examples=examples, cache_examples=False)
app.launch()