Nyako/nsfw-vid-detect
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Hell13Cat
2022-06-30 22:15:11 +03:00
parent 8c3ee11b4e
commit ae846f2e3e
6 changed files with 102 additions and 0 deletions
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.gitignore
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# Test files
data/vids/*
data/screen/*
data/json/*
data/*.*
# Byte-compiled / optimized / DLL files # Byte-compiled / optimized / DLL files
__pycache__/ __pycache__/
*.py[cod] *.py[cod]
data/model/nsfw_mobilenet2.224x224.h5
BIN
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data/templates/index.html
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<html>
<head>
<title>{file_name}</title>
</head>
<h2>{file_name}</h2><br>
<h3>{resolution}</h3><br>
<h3>{duration}</h3><br>
<video height="50%" src="{file_vid}" controls></video><br>
<hr>
{frame_info}
</html>
data/templates/one_frame.html
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<div>
<p>{file_name}</p>
<img src="{file_name}" alt="{file_name}" height="50%"><br>
<p>{info_res}</p>
</div>
<hr>
html_create.py
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import json
def create(file_name):
datas = json.load(open("data/json/"+file_name+".json", "r", encoding='utf-8'))
text_no_format = open("data/templates/index.html", "r").read()
text_f_no_format = open("data/templates/one_frame.html", "r").read()
list_frame_id = datas["frames"].keys()
text_f_format = ""
for ii in list_frame_id:
res_info=""
for dd in datas["frames"][ii].keys():
if dd == "file":
pass
else:
res_add = dd + " : " +str(round(datas["frames"][ii][dd]*100, 2))+"<br>"
res_info += res_add
text_add = text_f_no_format.format(
file_name=datas["frames"][ii]["file"],
info_res=res_info
)
text_f_format += text_add
text_format = text_no_format.format(
file_name=file_name,
resolution=str(round(datas["height"]))+"x"+str(round(datas["width"])),
duration=str(round(datas["duration"], 2))+" s.",
file_vid="vids/"+file_name,
frame_info=text_f_format
)
open("data/"+file_name+".html", "w").write(text_format)
main.py
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import PIL.Image as Image
from nsfw_detector import predict
import cv2
import os
import json
import html_create
def save_frame(screen_folder, secs, vid):
save_name = screen_folder + "/" + str(secs) + ".jpg"
fps = vid.get(cv2.CAP_PROP_FPS)
vid.set(cv2.CAP_PROP_POS_FRAMES, fps*secs)
ret, frame = vid.read()
cv2.imwrite(save_name, frame)
return save_name
model = predict.load_model('./data/model/nsfw_mobilenet2.224x224.h5')
file_path = "./data/vids/5.mp4"
vid = cv2.VideoCapture( file_path )
height = vid.get(cv2.CAP_PROP_FRAME_HEIGHT)
width = vid.get(cv2.CAP_PROP_FRAME_WIDTH)
fps = vid.get(cv2.CAP_PROP_FPS)
totalNoFrames = vid.get(cv2.CAP_PROP_FRAME_COUNT)
durationInSeconds = float(totalNoFrames) / float(fps)
datas = {"height":height, "width":width, "duration":durationInSeconds}
print(height, width, durationInSeconds)
screen_folder = "data/screen/" + file_path.split("/")[-1]
try:
if not os.path.exists(screen_folder):
os.makedirs(screen_folder)
except OSError:
print('Error: Creating directory of data')
save_frame(screen_folder, 1, vid)
current_sec = 1
datas_frames = {}
while True:
if current_sec < durationInSeconds:
file_name = save_frame(screen_folder, current_sec, vid)
image = Image.open(file_name)
data_res = predict.classify(model, file_name)
data_one = data_res[list(data_res.keys())[0]]
data_one["file"] = file_name.replace("data/", "", 1)
datas_frames[str(current_sec)] = data_one
else:
break
current_sec += 5
datas["frames"] = datas_frames
json.dump(datas, open("data/json/"+file_path.split("/")[-1]+".json", "w", encoding='utf-8'), ensure_ascii=False, indent=4)
html_create.create(file_path.split("/")[-1])