def test_ssim_classifier():
cl = SSIMClassifier()
cl.load(CUTTER_RESULT_DIR)
classify_result = cl.classify(VIDEO_PATH)
# --- draw ---
_draw_report(classify_result)
def test_load_from_range_list():
cl = SSIMClassifier()
cl.load(cutter_res.range_list)
classify_result = cl.classify(VIDEO_PATH)
# --- draw ---
_draw_report(classify_result)
def test_ssim_classifier():
cl = SSIMClassifier()
cl.load(CUTTER_RESULT_DIR)
cl.classify(VIDEO_PATH, boost_mode=False)
from stagesepx.reporter import Reporter
# 在运行这个例子前需要有前置数据
# 你可以先从 cut.py 开始
# 这里用的分类器是默认的SSIM分类器
# 更多的分类器会在稳定之后逐步加入
cl = SSIMClassifier()
# cut.py会把数据生成在这个路径下
# 如果你改动了，这里也要做相应修改
data_home = './cut_result'
cl.load(data_home)
# 开始分析即可
res = cl.classify(
'../demo.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)
# 分类出来的结果是一个 list，里面包含 ClassifierResult 对象
# 你可以用它进行二次开发
for each in res:
# 它的帧编号
print(each.frame_id)
# 它的时间戳
print(each.timestamp)
# 它被划分为什么类型
print(each.stage)
break
# 为了更方便的可读性，stagesepx已经内置了图表绘制功能
from stagesepx.cutter import VideoCutter
from stagesepx.classifier import SSIMClassifier
from stagesepx.reporter import Reporter
# cut
video_path = '../test.mp4'
cutter = VideoCutter()
res = cutter.cut(video_path)
stable = res.get_stable_range()
# classify
cl = SSIMClassifier()
cl.load(stable)
res = cl.classify(
video_path,
stable,
)
# draw
Reporter.draw(res)
from stagesepx.cutter import VideoCutter
from stagesepx.classifier import SSIMClassifier
from stagesepx.reporter import Reporter
# # cut
video_path = 'test.mp4'
cutter = VideoCutter(step=2)
res = cutter.cut(video_path)
stable = res.get_stable_range()
data_home = res.pick_and_save(stable, 3)
# classify
cl = SSIMClassifier()
cl.load(data_home)
res = cl.classify(video_path)
# draw
Reporter.draw(res, report_path=f'{data_home}/report.html')