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import json
from typing import Any, Dict, List
import tensorflow as tf
from tensorflow import keras
from huggingface_hub import from_pretrained_keras, hf_hub_download
import base64
import io
import numpy as np
from PIL import Image
class PreTrainedPipeline():
def __init__(self, model_id: str):
self.model = keras.models.load_model("./tf_model.h5")
def __call__(self, inputs: "Image.Image")-> List[Dict[str, Any]]:
with Image.open(inputs) as img:
img = np.array(img)
im = tf.image.resize(img, (128, 128))
im = tf.cast(im, tf.float32) / 255.0
pred_mask = model.predict(im[tf.newaxis, ...])
pred_mask_arg = tf.argmax(pred_mask, axis=-1)
labels = []
binary_masks = {}
mask_codes = {}
for cls in range(pred_mask.shape[-1]):
binary_masks[f"mask_{cls}"] = np.zeros(shape = (pred_mask.shape[1], pred_mask.shape[2]))
for row in range(pred_mask_arg[0][1].get_shape().as_list()[0]):
for col in range(pred_mask_arg[0][2].get_shape().as_list()[0]):
if pred_mask_arg[0][row][col] == cls:
binary_masks[f"mask_{cls}"][row][col] = 1
else:
binary_masks[f"mask_{cls}"][row][col] = 0
mask = binary_masks[f"mask_{cls}"]
mask *= 255
img = Image.fromarray(mask.astype(np.int8), mode="L")
with io.BytesIO() as out:
img.save(out, format="PNG")
png_string = out.getvalue()
mask = base64.b64encode(png_string).decode("utf-8")
mask_codes[f"mask_{cls}"] = mask
labels.append({
"label": f"LABEL_{cls}",
"mask": mask_codes[f"mask_{cls}"],
"score": 1.0,
})
return labels |