Delete nsfw_checker/opennsfw.py

nsfw_checker/opennsfw.py

@@ -1,37 +0,0 @@
-import cv2
-import torch
-import onnx
-import onnxruntime
-import numpy as np
-from tqdm import tqdm
-
-# https://github.com/chemistzombie/stable-diffusion-unfiltered.git
-
-class NSFWChecker:
-    def __init__(self, model_path=None, providers=["CPUExecutionProvider"]):
-        model = onnx.load(model_path)
-        self.input_name = model.graph.input[0].name
-        session_options = onnxruntime.SessionOptions()
-        self.session = onnxruntime.InferenceSession(model_path, sess_options=session_options, providers=providers)
-
-    def is_nsfw(self, img_paths, threshold = 0.85):
-        skip_step = 1
-        total_len = len(img_paths)
-        if total_len < 100: skip_step = 1
-        if total_len > 100 and total_len < 500: skip_step = 10
-        if total_len > 500 and total_len < 1000: skip_step = 20
-        if total_len > 1000 and total_len < 10000: skip_step = 50
-        if total_len > 10000: skip_step = 100
-
-        for idx in tqdm(range(0, total_len, skip_step), total=int(total_len // skip_step), desc="Checking for NSFW contents"):
-            img = cv2.imread(img_paths[idx])
-            img = cv2.resize(img, (224,224)).astype('float32')
-            img -= np.array([104, 117, 123], dtype=np.float32)
-            img = np.expand_dims(img, axis=0)
-
-            score = self.session.run(None, {self.input_name:img})[0][0][1]
-
-            if score > threshold:
-                print(f"Detected nsfw score:{score}")
-                return False
-        return False