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from typing import Dict, List, Tuple | |
import numpy as np | |
import onnxruntime as ort | |
from PIL import Image | |
from PIL.Image import Image as PILImage | |
class BaseSession: | |
def __init__(self, model_name: str, inner_session: ort.InferenceSession): | |
self.model_name = model_name | |
self.inner_session = inner_session | |
def normalize( | |
self, | |
img: PILImage, | |
mean: Tuple[float, float, float], | |
std: Tuple[float, float, float], | |
size: Tuple[int, int], | |
) -> Dict[str, np.ndarray]: | |
im = img.convert("RGB").resize(size, Image.LANCZOS) | |
im_ary = np.array(im) | |
im_ary = im_ary / np.max(im_ary) | |
tmpImg = np.zeros((im_ary.shape[0], im_ary.shape[1], 3)) | |
tmpImg[:, :, 0] = (im_ary[:, :, 0] - mean[0]) / std[0] | |
tmpImg[:, :, 1] = (im_ary[:, :, 1] - mean[1]) / std[1] | |
tmpImg[:, :, 2] = (im_ary[:, :, 2] - mean[2]) / std[2] | |
tmpImg = tmpImg.transpose((2, 0, 1)) | |
return { | |
self.inner_session.get_inputs()[0] | |
.name: np.expand_dims(tmpImg, 0) | |
.astype(np.float32) | |
} | |
def predict(self, img: PILImage) -> List[PILImage]: | |
raise NotImplementedError | |