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import cv2
import onnx
import onnxruntime
import numpy as np
from tqdm import tqdm
# https://github.com/yahoo/open_nsfw
def prepare_image(img):
img = cv2.resize(img, (224,224)).astype('float32')
img -= np.array([104, 117, 123], dtype=np.float32)
img = np.expand_dims(img, axis=0)
return img
class NSFWChecker:
def __init__(self, model_path=None, provider=["CPUExecutionProvider"], session_options=None):
model = onnx.load(model_path)
self.input_name = model.graph.input[0].name
self.session_options = session_options
if self.session_options == None:
self.session_options = onnxruntime.SessionOptions()
self.session = onnxruntime.InferenceSession(model_path, sess_options=self.session_options, providers=provider)
def check_image(self, image, threshold=0.9):
if isinstance(image, str):
image = cv2.imread(image)
img = prepare_image(image)
score = self.session.run(None, {self.input_name:img})[0][0][1]
if score >= threshold:
return True
return False
def check_video(self, video_path, threshold=0.9, max_frames=100):
cap = cv2.VideoCapture(video_path)
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
max_frames = min(total_frames, max_frames)
indexes = np.arange(total_frames, dtype=int)
shuffled_indexes = np.random.permutation(indexes)[:max_frames]
for idx in tqdm(shuffled_indexes, desc="Checking"):
cap.set(cv2.CAP_PROP_POS_FRAMES, int(idx))
valid_frame, frame = cap.read()
if valid_frame:
img = prepare_image(frame)
score = self.session.run(None, {self.input_name:img})[0][0][1]
if score >= threshold:
cap.release()
return True
cap.release()
return False
def check_image_paths(self, image_paths, threshold=0.9, max_frames=100):
total_frames = len(image_paths)
max_frames = min(total_frames, max_frames)
indexes = np.arange(total_frames, dtype=int)
shuffled_indexes = np.random.permutation(indexes)[:max_frames]
for idx in tqdm(shuffled_indexes, desc="Checking"):
frame = cv2.imread(image_paths[idx])
img = prepare_image(frame)
score = self.session.run(None, {self.input_name:img})[0][0][1]
if score >= threshold:
return True
return False |