简介
使用雅虎开源的 TensorFlow 2 Open-NSFW 模型，NSFW：not safe for work，工作场所不宜
实践
1.环境准备，
Python 3.7 及以上，安装 opennsfw2 库。图片素材请参考 小结中地址进行下载。
pip install opennsfw2
2.代码实践
图片识别 代码如下：
import opennsfw2 as n2
# 将自动下载预训练模型 open_nsfw_weights.h5 到 C:\Users\Administrator\.opennsfw2\weights
# pip install opennsfw2
# 单张预测
image_path = '1.jpg'
nsfw_probability = n2.predict_image(image_path)
print(nsfw_probability)
# 0.16282974183559418
# 批量预测
image_paths = ['1.jpg', '2.jpg']
nsfw_probabilities = n2.predict_images(image_paths)
print(nsfw_probabilities)
# [0.16282965242862701, 0.8638442158699036]
视频识别 代码如下：
import opennsfw2 as n2
video_path = '1.mp4'
elapsed_seconds, nsfw_probabilities = n2.predict_video_frames(video_path)
for second, probability in zip(elapsed_seconds, nsfw_probabilities):
print(f'{
second:.2f}s: {
probability * 100:.0f} %')
# 0.03s: 1%
# ...
# 10.01s: 87.00%
# ...
# 10.64s: 69.00%
高级用法
1. 加载的方式
import numpy as np
from PIL import Image
from opennsfw2._model import make_open_nsfw_model
from opennsfw2._image import preprocess_image, Preprocessing
image_path = '1.jpg'
image = preprocess_image(Image.open(image_path), Preprocessing.YAHOO)
model = make_open_nsfw_model()
nsfw_probability = float(model.predict(np.expand_dims(image, 0), batch_size=1)[0][1])
print(nsfw_probability)
# 0.16282974183559418
2.车速检测
import time
import numpy as np
import tkinter as tk
from pathlib import Path
from tkinter import filedialog
from tkinter import messagebox
from PIL import ImageTk, Image
from opennsfw2._model import make_open_nsfw_model
from opennsfw2._image import preprocess_image, Preprocessing
begin = time.time()
model = make_open_nsfw_model() # 加载模型
elapsed = time.time() - begin # 加载模型耗时
initialdir = Path.cwd() # 初始化目录，可切换为图片Path.home() / 'Pictures'
img = None # 当前打开的图片
def scale(size, width=None, height=None):
"""获取按比例缩放后的宽高"""
if not width and not height:
width, height = size
if not width or not height:
_width, _height = size
height = width * _height / _width if width else height
width = height * _width / _height if height else width
return int(width), int(height)
def img_resize(event=None):
"""显示图片"""
global img
if img:
_img = img.resize(scale(img.size, height=win.winfo_height()))
_img = ImageTk.PhotoImage(_img)
label.config(image=_img)
label.image = _img
def on_closing():
"""关闭事件"""
if messagebox.askokcancel('关闭', '是否退出程序？'):
win.destroy()
def open_file(event=None):
"""打开图片"""
global initialdir
global img
file_path = filedialog.askopenfilename(title='选择图片', initialdir=initialdir,
filetypes=[('image files', ('.png', '.jpg', '.jpeg', '.gif'))])
if file_path:
statusbar.config(text='正在加载...')
statusbar.update_idletasks()
begin = time.time()
path = Path(file_path)
initialdir = path.parent
img = Image.open(file_path)
img_resize()
_img = preprocess_image(Image.open(file_path), Preprocessing.YAHOO)
probability = float(model.predict(np.expand_dims(_img, 0), batch_size=1)[0][1])
print(probability)
end = time.time()
statusbar.config(text=f'{
path.name} 耗时: {
end - begin:.2f}s 概率: {
probability * 100:.2f} %')
win = tk.Tk()
win.title('黄图检测') # 标题
menu = tk.Menu(win)
menu.add_command(label='打开', command=open_file)
win.config(menu=menu)
win.bind('<Configure>', img_resize)
win.geometry('600x300+300+300')
win.minsize(200, 200)
win.protocol('WM_DELETE_WINDOW', on_closing)
statusbar = tk.Label(win, text=f'加载模型耗时: {
elapsed:.2f}s', bd=1, relief=tk.SUNKEN, anchor=tk.W, name='statusbar')
statusbar.pack(side=tk.BOTTOM, fill=tk.X)
label = tk.Label(win, text='双击打开图片')
label.bind('<Double-Button-1>', open_file)
label.pack(fill=tk.BOTH, expand=True)
win.mainloop()
3. 视频车速检测(无声)
import time
import threading
import tkinter as tk
from pathlib import Path
from tkinter import filedialog
from tkinter import messagebox
from timeit import default_timer as timer
import imageio
import numpy as np
from PIL import ImageTk, Image
from opennsfw2._model import make_open_nsfw_model
from opennsfw2._image import preprocess_image, Preprocessing
# pip install imageio-ffmpeg
begin = time.time()
model = make_open_nsfw_model() # 加载模型
elapsed = time.time() - begin # 加载模型耗时
initialdir = Path.cwd() # 初始化目录，可切换为图片Path.home() / 'Pictures'
reader = None # 视频读取器
accum_time = 0
curr_fps = 0
last_fps = 0
prev_time = timer()
def on_closing():
"""关闭事件"""
if messagebox.askokcancel('关闭', '是否退出程序？'):
win.destroy()
def play():
global reader
global prev_time, accum_time, curr_fps
for image in reader:
image = Image.fromarray(image)
frame_image = ImageTk.PhotoImage(image)
label.config(image=frame_image)
label.image = frame_image
_img = preprocess_image(image, Preprocessing.YAHOO)
probability = float(model.predict(np.expand_dims(_img, 0), batch_size=1)[0][1])
# FPS
curr_time = timer()
exec_time = curr_time - prev_time
prev_time = curr_time
accum_time = accum_time + exec_time
curr_fps = curr_fps + 1
if accum_time > 1:
accum_time = accum_time - 1
last_fps = curr_fps
curr_fps = 0
statusbar.config(text=f'概率: {
probability * 100:.2f} % FPS: {
last_fps}')
def open_file(event=None):
"""打开视频"""
global initialdir
global reader
file_path = filedialog.askopenfilename(title='选择视频', initialdir=initialdir,
filetypes=[('Select files', ('.mp4', '.mkv', '.avi', '.wmv'))])
if file_path:
statusbar.config(text='正在加载...')
statusbar.update_idletasks()
path = Path(file_path)
initialdir = path.parent
reader = imageio.get_reader(path)
thread = threading.Thread(target=play, daemon=True)
thread.start()
win = tk.Tk()
win.title('黄图检测') # 标题
menu = tk.Menu(win)
menu.add_command(label='打开', command=open_file)
win.config(menu=menu)
win.geometry('1280x720+300+300')
win.minsize(200, 200)
win.protocol('WM_DELETE_WINDOW', on_closing)
statusbar = tk.Label(win, text=f'加载模型耗时: {
elapsed:.2f}s', bd=1, relief=tk.SUNKEN, anchor=tk.W, name='statusbar')
statusbar.pack(side=tk.BOTTOM, fill=tk.X)
label = tk.Label(win, text='双击打开视频')
label.bind('<Double-Button-1>', open_file)
label.pack(fill=tk.BOTH, expand=True)
win.mainloop()
4. 视频车速检测（有声）
import io
import pyglet
import numpy as np
from PIL import Image
from opennsfw2._model import make_open_nsfw_model
from opennsfw2._image import preprocess_image, Preprocessing
# pip install pyglet
model = make_open_nsfw_model()
filename = '1.mp4'
source = pyglet.media.load(filename)
video_format = source.video_format
width, height = video_format.width, video_format.height
title = 'Video Player'
window = pyglet.window.Window(width, height, title)
player = pyglet.media.Player()
player.queue(source)
player.play()
@window.event
def on_draw():
window.clear()
if player.source and player.source.video_format:
player.get_texture().blit(0, 0, width=width, height=height)
image_data = player.get_texture().get_image_data()
pitch = -(image_data.width * len('RGB'))
data = image_data.get_data('RGB', pitch)
_img = preprocess_image(Image.frombytes('RGB', (width, height), data, 'raw'), Preprocessing.YAHOO)
probability = float(model.predict(np.expand_dims(_img, 0), batch_size=1)[0][1])
print(probability)
pyglet.app.run()
运行结果：
效率分析：
get_data() 0.941 s
frombytes() 0.001 s
preprocess_image() 0.006 s
predict() 0.052 s
小结
参考： https://blog.csdn.net/lly1122334/article/details/121247781
https://github.com/bhky/opennsfw2
资源： https://img-blog.csdnimg.cn/20210702231858370.jpg
http://www.lenna.org/full/len_full.jpg