def handle(self, video_path: str) -> bool:
super(KerasHandler, 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)
# --- classify ---
cl = KerasClassifier()
if self.model_path:
logger.info("load existed pre-train model")
cl.load_model(self.model_path)
else:
data_home = res.pick_and_save(stable,
self.frame_count,
to_dir=self.result_path)
cl.train(data_home)
self.classifier_result = cl.classify(video, stable)
# --- draw ---
r = Reporter()
r.draw(self.classifier_result, report_path=self.result_report_path)
return True
def keras_train(
train_data_path: str,
model_path: str,
# options
epochs: int = 10,
target_size: str = "600x800",
overwrite: bool = False,
**kwargs,
):
from stagesepx.classifier.keras import KerasClassifier
# handle args
target_size: typing.Sequence[int] = [
int(each) for each in target_size.split("x")
]
cl = KerasClassifier(
# 轮数
epochs=epochs,
# 保证数据集的分辨率统一性
target_size=target_size,
**kwargs,
)
cl.train(train_data_path)
# file existed
while os.path.isfile(model_path):
logger.warning(f"file {model_path} already existed")
model_path = f"{uuid.uuid4()}.h5"
logger.debug(f"trying to save it to {model_path}")
cl.save_model(model_path, overwrite=overwrite)
def keras_train(
train_data_path: str,
model_path: str,
# options
epochs: int = 10,
target_size: str = "600x800",
overwrite: bool = False,
**kwargs,
):
from stagesepx.classifier.keras import KerasClassifier
assert not os.path.isfile(model_path), f"file {model_path} already existed"
# handle args
target_size: typing.Sequence[int] = [
int(each) for each in target_size.split("x")
]
cl = KerasClassifier(
# 轮数
epochs=epochs,
# 保证数据集的分辨率统一性
target_size=target_size,
**kwargs,
)
cl.train(train_data_path)
cl.save_model(model_path, overwrite=overwrite)
def test_keras():
# set epochs to 1 for quickly training (test only)
cl = KerasClassifier(epochs=1)
cl.train(CUTTER_RESULT_DIR)
cl.save_model("haha.h5")
# recreate
cl = KerasClassifier()
cl.load_model("haha.h5")
stable, _ = cutter_res.get_range()
classify_result = cl.classify(VIDEO_PATH, stable, keep_data=True)
assert classify_result.to_dict()
def train_model_Keras(_train_picture_path, _model_file_name):
cl = KerasClassifier(
# 轮数
epochs=10,
compress_rate=0.2,
# 保证数据集的分辨率统一性
# target_size=(600, 800),
)
cl.train(_train_picture_path)
cl.save_model(_model_file_name, overwrite=True)
return cl
# overwrite this method to design your own model structure!
# model = Sequential()
# ...
pass
def train(self, data_path: str, *_, **__):
# ...
pass
# or use the default one
# and then init it
# epochs=1 is just a example
cl = KerasClassifier(epochs=1)
# train model and save weights
cl.train(data_home)
cl.save_model("keras_model.h5")
# you would better reuse the trained model for less time cost
# keras model takes much more time than SVM
# cl.load_model("keras_model.h5")
classify_result = cl.classify(video, stable, keep_data=True)
result_dict = classify_result.to_dict()
# --- draw ---
r = Reporter()
r.draw(classify_result)
'''
* @Description : 开始根据图像数据训练模型及预测
* @Autor : Tommy
* @Date : 2021-02-21 18:51:11
* @LastEditors : Tommy
* @LastEditTime : 2021-02-26 14:46:49
'''
from stagesepx.classifier.keras import KerasClassifier
# 训练模型文件
cl = KerasClassifier(
# 设置这个数，需要保证小于总样本数，否则报错。
# nb_train_samples=30,
# 训练轮数
epochs=30)
cl.train('./stable_frame')
cl.save_model('./model.h5', overwrite=True)
cutter = VideoCutter()
# 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,
100,
to_dir='./picture/train_stable_frame',
meaningful_name=True)
# 训练模型文件
cl = KerasClassifier(
# 训练轮数
epochs=10)
cl.train('./train_stable_frame')
cl.save_model('./model.h5', overwrite=True)
# 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