def classify(self,
video_path: str,
data_home: str,
output_path: str = None,
compress_rate: float = 0.2,
limit: int = None):
# TODO model?
cut_result_json = os.path.join(data_home, 'cut_result.json')
res = None
stable = None
if os.path.isfile(cut_result_json):
res = VideoCutResult.load(cut_result_json)
stable, _ = res.get_range(limit=limit)
cl = SVMClassifier(compress_rate=compress_rate)
cl.load(data_home)
cl.train()
classify_result = cl.classify(video_path, stable)
# --- draw ---
r = Reporter()
r.add_dir_link(data_home)
r.draw(
classify_result,
report_path=os.path.join(output_path or data_home, 'report.html'),
cut_result=res,
)
def one_step(self,
video_path: str,
output_path: str = None,
threshold: float = 0.95,
frame_count: int = 5,
compress_rate: float = 0.2,
limit: int = None):
"""
one step => cut, classifier, draw
:param video_path: your video path
:param output_path: output path (dir)
:param threshold: float, 0-1, default to 0.95. decided whether a range is stable. larger => more unstable ranges
:param frame_count: default to 5, and finally you will get 5 frames for each range
:param compress_rate: before_pic * compress_rate = after_pic. default to 0.2
:param limit: ignore some ranges which are too short, 5 means ignore stable ranges which length < 5
:return:
"""
# --- cutter ---
cutter = VideoCutter()
res = cutter.cut(video_path, compress_rate=compress_rate)
stable, unstable = res.get_range(
threshold=threshold,
limit=limit,
)
data_home = res.pick_and_save(stable, frame_count, to_dir=output_path)
res_json_path = os.path.join(data_home, 'cut_result.json')
res.dump(res_json_path)
# --- classify ---
cl = SVMClassifier(compress_rate=compress_rate)
cl.load(data_home)
cl.train()
classify_result = cl.classify(video_path, stable)
# --- draw ---
r = Reporter()
r.add_dir_link(data_home)
r.draw(
classify_result,
report_path=os.path.join(data_home, 'report.html'),
cut_result=res,
)
# 在加载数据完成之后需要先训练
cl.train()
# # 在训练后你可以把模型保存起来
# cl.save_model('model.pkl')
# # 或者直接读取已经训练好的模型
# cl.load_model('model.pkl')
# 开始分类
res = cl.classify(
'../test.mp4',
# 步长，可以自行设置用于平衡效率与颗粒度
# 默认为1，即每帧都检测
step=1,
)
# 为了更方便的可读性，stagesepx已经内置了图表绘制功能
# 你可以直接把分析结果绘制成图表
report = Reporter()
# 你可以将把一些文件夹路径插入到报告中
# 这样你可以很方便地从报告中查看各项相关内容
# 当然，你需要想好这些路径与报告最后所在位置之间的相对位置，以确保他们能够被访问到
report.add_dir_link(data_home)
report.draw(
res,
report_path='report.html',
)
data_home = res.pick_and_save(stable, 3)
# classify
cl = SVMClassifier()
cl.load(data_home)
cl.train()
# 注意，如果在classify方法指定了范围
# 那么分析时只会分析处于范围内的帧！
# 例如，这里只传入了stable的范围，那么非stable范围内的帧都会被忽略掉，标记为 -1
classify_result = cl.classify(
video_path,
stable,
# 步长，可以自行设置用于平衡效率与颗粒度
# 默认为1，即每帧都检测
step=1)
# draw
r = Reporter()
r.add_dir_link(data_home)
# 你可以将 thumbnail 直接嵌入到report中
for each in unstable:
r.add_thumbnail(
f'{each.start}({each.start_time}) - {each.end}({each.end_time})',
res.thumbnail(each))
# 在0.3.2及之后的版本，你可以在报告中加入一些自定义内容 （https://github.com/williamfzc/stagesepx/issues/13）
# r.add_extra('here is title', 'here is content')
r.draw(classify_result)