import os
import cv2
from nudenet import NudeClassifier, NudeDetector
video_path = 'p**n.mp4'
classifier = NudeClassifier()
dic = classifier.classify_video(video_path, batch_size=4)
# dic = classifier.classify('example.jpg')
# detector = NudeDetector()
# dic = detector.detect('example.jpg')
print(dic)
video = cv2.VideoCapture(video_path)
print(video.isOpened())
fps = video.get(cv2.CAP_PROP_FPS)
length = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
print("fps:", fps, "length", length)
# print(os.getcwd())
classifier = NudeClassifier()
# Classify single image
# dic = classifier.classify('test.jpg')
# print(dic)
# Returns {'path_to_image_1': {'safe': PROBABILITY, 'unsafe': PROBABILITY}}
# Classify multiple images (batch prediction)
# batch_size is optional; defaults to 4
# classifier.classify(['path_to_image_1', 'path_to_image_2'], batch_size=BATCH_SIZE)
# # Returns {'path_to_image_1': {'safe': PROBABILITY, 'unsafe': PROBABILITY},
# # 'path_to_image_2': {'safe': PROBABILITY, 'unsafe': PROBABILITY}}
# # Classify video
# # batch_size is optional; defaults to 4
dic = classifier.classify_video('p**n.mp4')
# print("\n\n\n")
# for key, value in dic['preds'].items():
# print('프레임 {}의 결과 값은 {} 입니다'.format(key, value))
count = 0
num_frames = 0
for key, value in dic['preds'].items():
if float(value['unsafe']) > 0.6:
count += 1
num_frames += 1
# print("count:{} num_frames:{}".format(count, num_frames))
prop = count / num_frames