File size: 1,986 Bytes
e60e640 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | import cv2
import tempfile
import requests
import os
from PIL import Image
from transformers import pipeline
classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection")
def download_file(url):
response = requests.get(url, stream=True)
tmp = tempfile.NamedTemporaryFile(delete=False)
for chunk in response.iter_content(1024):
tmp.write(chunk)
tmp.close()
return tmp.name
def get_video_duration(video_path):
cap = cv2.VideoCapture(video_path)
fps = cap.get(cv2.CAP_PROP_FPS)
frame_count = cap.get(cv2.CAP_PROP_FRAME_COUNT)
cap.release()
return frame_count / fps if fps > 0 else 0
def extract_frame(video_path, second):
cap = cv2.VideoCapture(video_path)
fps = cap.get(cv2.CAP_PROP_FPS)
frame_number = int(fps * second)
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_number)
success, frame = cap.read()
cap.release()
if not success:
return None
tmp_file = tempfile.NamedTemporaryFile(suffix=".jpg", delete=False)
cv2.imwrite(tmp_file.name, frame)
return tmp_file.name
def get_frame_times(duration, file_size_mb):
if duration <= 2:
return [1, 2]
elif duration <= 10:
return [2]
elif duration <= 15:
return [4, 9, 13]
if file_size_mb > 14:
return [4, 9, 13]
return [2]
def check_image_nsfw(image_path):
image = Image.open(image_path).convert("RGB")
result = classifier(image)
for r in result:
if r["label"] == "nsfw" and r["score"] > 0.5:
return True
return False
def check_video_nsfw(video_path):
size_mb = os.path.getsize(video_path) / (1024 * 1024)
duration = get_video_duration(video_path)
times = get_frame_times(duration, size_mb)
for t in times:
frame = extract_frame(video_path, t)
if frame:
if check_image_nsfw(frame):
return True # 🚨 return immediately if ANY frame is NSFW
return False |