52 lines
1.8 KiB
Python
52 lines
1.8 KiB
Python
import os
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import pydload
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import numpy as np
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import tensorflow as tf
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from .image_utils import load_images
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class LiteClassifier:
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def __init__(self):
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url = "https://github.com/notAI-tech/NudeNet/releases/download/v0/classifier.tflite"
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home = os.path.expanduser("~")
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model_folder = os.path.join(home, ".NudeNet/")
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if not os.path.exists(model_folder):
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os.mkdir(model_folder)
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model_path = os.path.join(model_folder, "lite_classifier")
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if not os.path.exists(model_path):
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print("Downloading the checkpoint to", model_path)
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pydload.dload(url, save_to_path=model_path, max_time=None)
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self.interpreter = tf.lite.Interpreter(model_path=model_path)
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self.interpreter.allocate_tensors()
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def classify(self, image_paths, size=(256, 256)):
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if isinstance(image_paths, str):
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image_paths = [image_paths]
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input_details = self.interpreter.get_input_details()
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output_details = self.interpreter.get_output_details()
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loaded_images, _ = load_images(image_paths, size, image_paths)
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result = {}
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for image_path, img in zip(image_paths, loaded_images):
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img = np.expand_dims(img, axis=0)
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input_data = np.array(img, dtype=np.float32)
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self.interpreter.set_tensor(input_details[0]["index"], input_data)
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self.interpreter.invoke()
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# The function `get_tensor()` returns a copy of the tensor data.
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# Use `tensor()` in order to get a pointer to the tensor.
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output_data = self.interpreter.get_tensor(output_details[0]["index"])
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result[image_path] = {
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"unsafe": output_data[0][0],
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"safe": output_data[0][1],
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}
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return result
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