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
# 默认情况下，所有的坐标都是从左上角开始的
# 如果我们需要偏移到右下角，意味着我们需要向下偏移 0.5 * height，向右偏移 0.5 * width
# offset=(0.5, 0.5),
# 当然，这里也可以指定绝对长度，同size
# offset=(100, 100),
overwrite=True)
# 在初始化完成后，你就可以将hook添加到 cutter 或 classifier 中了
# 在添加完成后，你可以发现，stagesepx 只会对你裁剪后的区域进行检测
cutter.add_hook(hook2)
# 在 0.7.1 之后，针对 CropHook 的使用场景，IgnoreHook 被加入用于对其进行进一步补充
# 与 CropHook 相反，它被用于对帧的一部分进行屏蔽
# 详见 https://github.com/williamfzc/stagesepx/issues/56
hook3 = IgnoreHook(
# 它的参数解析方式与 CropHook 是一致的，此处不赘述
# 与 CropHook 不同的是，此处指定的区域会被屏蔽掉
size=(0.5, 0.5),
offset=(0.5, 0.5),
overwrite=True)
# 为了不影响结果，在例子中先注释掉了
# cutter.add_hook(hook3)
# 开始切割
res = cutter.cut(
video_path,
# block 能够对每帧进行切割并分别进行比较，计算出更加敏感的ssim值
# 默认为2，即切为4宫格；若为4，即切为16宫格，以此类推；为1即不做切割，全图比较
# 值得注意，如果无法整除，block是会报错的
block=2,
)
# 你可以将你的cutter结果保存起来，供其他时刻使用（>=0.4.4）
# 使用Keras方法进行预测
cl = KerasClassifier()
cl.load_model('./model.h5')
# 将视频切分成帧
file_name = './video_for_forecast.mp4'
# 预加载，大幅度提升分析速度
video = VideoObject(file_name, pre_load=True)
# 新建帧，计算视频总共有多少帧，每帧多少ms
video.load_frames()
# 压缩视频
cutter = VideoCutter()
# 这个hook是干什么的，后续做解释
hook = IgnoreHook(
size=(0.05, 1),
overwrite=True,
)
cutter.add_hook(hook)
# 计算每一帧视频的每一个block的ssim和psnr。
res = cutter.cut(video, block=6)
# 计算出哪些区间是稳定的，哪些是不稳定的。判断A帧到B帧之间是稳定还是不稳定
stable, unstable = res.get_range(threshold=0.97, offset=2)
# 把分好类的稳定阶段的图片存本地
res.pick_and_save(stable,
30,
to_dir='./picture/forecast_stable',
meaningful_name=True)
res.pick_and_save(unstable,
30,
to_dir='./picture/forecast_unstable',
meaningful_name=True)