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,
offset: int = 3,
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 offset:
it will change the way to decided whether two ranges can be merged
before: first_range.end == second_range.start
after: first_range.end + offset >= secord_range.start
: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,
offset=offset,
)
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.draw(
classify_result,
report_path=os.path.join(data_home, 'report.html'),
cut_result=res,
# kwargs of get_range
# otherwise these thumbnails may become different
threshold=threshold,
limit=limit,
offset=offset,
)
def test_result():
cl = SVMClassifier()
cl.load_model(MODEL_PATH)
stable, _ = cutter_res.get_range()
classify_result = cl.classify(VIDEO_PATH, stable, keep_data=True)
assert classify_result.to_dict()
classify_result.mark_range(1, 3, "0")
classify_result.mark_range_unstable(1, 3)
classify_result.get_important_frame_list()
def test_result():
cl = SVMClassifier()
cl.load_model(MODEL_PATH)
stable, _ = cutter_res.get_range()
classify_result = cl.classify(VIDEO_PATH, stable, keep_data=True)
assert classify_result.to_dict()
classify_result.mark_range(1, 3, "0")
classify_result.mark_range_unstable(1, 3)
classify_result.get_important_frame_list()
classify_result.get_stage_range()
classify_result.get_specific_stage_range("0")
classify_result.get_not_stable_stage_range()
assert classify_result.first("1").frame_id == 20
assert classify_result.last("1").frame_id == 21
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,
)
def calculate_result(_cl, _SVM_or_Keras, _param, _from_movie_2_picture,
_model_file, _video_path_for_forecast):
if _SVM_or_Keras == '1\n':
print('使用SVM进行预测')
cl = SVMClassifier(
# 默认情况下使用 HoG 进行特征提取。你可以将其关闭从而直接对原始图片进行训练与测试：feature_type='raw'
feature_type="hog",
# 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=_param[0],
)
# 加载待训练数据
cl.load_model(_model_file)
elif _SVM_or_Keras == '2\n':
# 分析视频_聚类
print('使用KerasClassifier进行预测')
cl = KerasClassifier(
compress_rate=_param[0],
# 在使用时需要保证数据集格式统一（与训练集）。因为 train_model.py 用了 600x800，所以这里设定成一样的
# target_size=(600, 800),
)
cl.load_model(_model_file)
# 开始预测
_forecast_result = []
# 获取forecast文件夹的mp4文件列表
forecast_video_list = public_fun.get_mp4file_name(_video_path_for_forecast)
for i in forecast_video_list:
# 分析视频_切割视频
stable = get_data.get_range('forecast', i, _param,
_from_movie_2_picture)
classify_result = cl.classify(i, stable, keep_data=True)
result_dict = classify_result.to_dict()
_forecast_result.append(
public_fun.write_result_to_local(i, _from_movie_2_picture,
result_dict, classify_result))
# _forecast_result = [['5.mp4', '3.161888888888889', '1',
# '4.155888888888889', '1', '8.040888888888889', '1']]
return _forecast_result
def handle(self, video_path: str) -> bool:
super(NormalHandler, self).handle(video_path)
video = VideoObject(video_path)
if self.preload:
video.load_frames()
# --- cutter ---
cutter = VideoCutter()
res = cutter.cut(video)
stable, unstable = res.get_range(threshold=0.98, offset=3)
data_home = res.pick_and_save(stable,
self.frame_count,
to_dir=self.result_path)
# --- classify ---
cl = SVMClassifier()
cl.load(data_home)
cl.train()
self.classifier_result = cl.classify(video, stable)
# --- draw ---
r = Reporter()
r.draw(self.classifier_result, report_path=self.result_report_path)
return True
from stagesepx.classifier import SVMClassifier
from stagesepx.cutter import VideoCutter
from stagesepx.reporter import Reporter
TARGET_VIDEO = '../../demo.mp4'
# cut
# 这里依旧使用了 cut，主要目的还是为了可以比较好的处理变化中的过程
# 但这次我们不需要用到 pick_and_save，因为这次 classifier 不会使用 cutter 的数据
cutter = VideoCutter()
res = cutter.cut(TARGET_VIDEO)
stable, _ = res.get_range()
# classify
# 这里的参数需要保持与train.py一致，如果你有改动的话
cl = SVMClassifier()
cl.load_model('./model.pkl')
classify_result = cl.classify(
TARGET_VIDEO,
stable,
)
r = Reporter()
r.draw(
classify_result,
report_path='report.html',
cut_result=res,
)
