def test_hook():
# init hook
hook = ExampleHook()
hook1 = ExampleHook(overwrite=True)
hook2 = IgnoreHook(size=(0.5, 0.5), overwrite=True)
frame_home = os.path.join(PROJECT_PATH, 'frame_save_dir')
hook3 = FrameSaveHook(frame_home)
hook4 = CropHook(
size=(0.5, 0.5),
offset=(0., 0.5),
overwrite=True,
)
hook5 = RefineHook()
hook6 = InvalidFrameDetectHook()
hook7 = TemplateCompareHook({
'amazon': IMAGE_PATH,
})
# --- cutter ---
cutter = VideoCutter(compress_rate=0.8)
# add hook
cutter.add_hook(hook)
cutter.add_hook(hook1)
cutter.add_hook(hook2)
cutter.add_hook(hook3)
cutter.add_hook(hook4)
cutter.add_hook(hook5)
cutter.add_hook(hook6)
cutter.add_hook(hook7)
res = cutter.cut(VIDEO_PATH)
stable, unstable = res.get_range()
assert len(stable) == 2, 'count of stable range is not correct'
data_home = res.pick_and_save(
stable,
5,
)
assert os.path.isdir(data_home), 'result dir not existed'
# --- classify ---
cl = SVMClassifier()
cl.load(data_home)
cl.train()
classify_result = cl.classify(VIDEO_PATH, stable)
# --- draw ---
r = Reporter()
report_path = os.path.join(data_home, 'report.html')
r.draw(
classify_result,
report_path=report_path,
cut_result=res,
)
assert os.path.isfile(report_path)
# hook check
assert os.path.isdir(frame_home)
assert hook6.result
assert hook7.result
def test_hook():
# init hook
hook = ExampleHook()
hook1 = ExampleHook()
hook2 = IgnoreHook(size=(0.5, 0.5))
frame_home = os.path.join(PROJECT_PATH, "frame_save_dir")
hook3 = FrameSaveHook(frame_home)
hook4 = CropHook(size=(0.5, 0.5), offset=(0.0, 0.5))
hook5 = RefineHook()
hook6 = InterestPointHook()
hook7 = TemplateCompareHook({"amazon": IMAGE_PATH})
# --- cutter ---
cutter = VideoCutter(compress_rate=0.9)
# add hook
cutter.add_hook(hook)
cutter.add_hook(hook1)
cutter.add_hook(hook2)
cutter.add_hook(hook3)
cutter.add_hook(hook4)
cutter.add_hook(hook5)
cutter.add_hook(hook6)
cutter.add_hook(hook7)
res = cutter.cut(VIDEO_PATH)
stable, unstable = res.get_range()
assert len(stable) == 2, "count of stable range is not correct"
data_home = res.pick_and_save(stable, 5)
assert os.path.isdir(data_home), "result dir not existed"
# --- classify ---
cl = SVMClassifier()
cl.load(data_home)
cl.train()
classify_result = cl.classify(VIDEO_PATH, stable)
# --- draw ---
r = Reporter()
report_path = os.path.join(data_home, "report.html")
r.draw(classify_result, report_path=report_path, cut_result=res)
assert os.path.isfile(report_path)
# hook check
assert os.path.isdir(frame_home)
assert hook6.result
assert hook7.result
# 步长，默认为1，通过它可以自行把握效率与颗粒度
# 设定为2时，会以2帧为一个单位进行遍历
# 即跳过一帧
step=1,
# 默认为0.2，即将图片缩放为0.2倍
# 主要为了提高计算效率
# 如果你担心影响分析效果，可以将其提高
compress_rate=0.2,
# 或者直接指定尺寸
# 当压缩率与指定尺寸同时传入时，优先以指定尺寸为准
# target_size=(200, 400),
)
# 在 0.4.2 之后，hook特性正式被加入：https://williamfzc.github.io/stagesepx/#/pages/3_how_it_works?id=hook
# 使用极其简单，你只需要初始化 hook
hook = ExampleHook()
# 再将 hook 添加到 cutter 或者 classifier 中去
cutter.add_hook(hook)
# 支持多个hook，他们会按顺序执行
# 当 overwrite 被设置为 true 时，hook的修改将会持续影响到后续的分析
# 否则 hook 操作的都是 frame 的副本
hook1 = ExampleHook(overwrite=True)
cutter.add_hook(hook1)
# 在 0.7.0 之后，CropHook加入，被用于局部检测
# 它能够对帧进行裁剪，使用户能够只对视频的其中一部分进行分析
# 例如，它能够顺利解决轮播图问题：https://github.com/williamfzc/stagesepx/issues/55
# 它采用两种参数，size 与 offset，分别对应 裁剪区域大小 与 偏移量
# 例如，你希望裁剪出画面右下角的 1/4
hook2 = CropHook(
# 高度为 0.5 * height，宽度为 0.5 * width
def test_preload_with_hook():
v = VideoObject(VIDEO_PATH)
hook = ExampleHook()
v.add_preload_hook(hook)
v.load_frames()
