amirziai/kissing-detector
Kissing Detector - CS231n project
repo name | amirziai/kissing-detector |
repo link | https://github.com/amirziai/kissing-detector |
homepage | |
language | Jupyter Notebook |
size (curr.) | 17182 kB |
stars (curr.) | 50 |
created | 2019-05-16 |
license | MIT License |
Kissing Detector
Detect kissing scenes in a movie using both audio and video features.
Project for Stanford CS231N
Resources
Running the code
Use Python 3.6+
python3 experiments.py
this will run the experiments in params.py
specified by the experiments
dictionary.
Requirements
This is a PyTorch project. Look at requirements.txt
for more details.
Build dataset
The following will build the dataset for training. You need to provide path to video segments.
from pipeline import BuildDataset
videos_and_labels = [
# (file name in base_path, label) where label is 1 for kissing and 0 for not kissing
('movies_casino_royale_2006_kissing_1.mp4', 1),
('movies_casino_royale_2006_kissing_2.mp4', 1),
('movies_casino_royale_2006_kissing_3.mp4', 1),
('movies_casino_royale_2006_not_1.mp4', 0),
('movies_casino_royale_2006_not_2.mp4', 0),
('movies_casino_royale_2006_not_3.mp4', 0),
('movies_goldeneye_1995_kissing_1.mp4', 1),
('movies_goldeneye_1995_kissing_2.mp4', 1),
('movies_goldeneye_1995_kissing_3.mp4', 1),
('movies_goldeneye_1995_not_1.mp4', 0),
('movies_goldeneye_1995_not_2.mp4', 0),
('movies_goldeneye_1995_not_3.mp4', 0),
]
builder = BuildDataset(base_path='path/to/movies',
videos_and_labels=videos_and_labels,
output_path='/path/to/output',
test_size=1 / 3) # set aside 1 / 3 of data for validation
builder.build_dataset()
Detect kissing segments in a given video
from segmentor import Segmentor
import utils
# download model.pkl from https://drive.google.com/file/d/1RlvvdInTXtJikGv_ZbHcKoblCypN1Z0A/view?usp=sharing
# or train your own
model = utils.unpickle('model.pkl') # pickled PyTorch model
s = Segmentor(model, min_frames=10, threshold=0.7)
# For YouTube clip Hot Summer Nights - Kiss Scene (Maika Monroe and Timothee Chalamet)
# at https://www.youtube.com/watch?v=GG5HmLQ_Fx0
# v=XXX is the YouTube ID, pass that here
s.visualize_segments_youtube('GG5HmLQ_Fx0')
# alternatively you can provide a path to a local mp4 file
s.visualize_segments('path/to/file.mp4')
See examples in examples/detector.ipynb.