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 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()
'''
* @Description : 预测
* @Autor : Tommy
* @Date : 2021-02-21 19:07:26
* @LastEditors : Tommy
* @LastEditTime : 2021-03-15 12:15:48
'''
from stagesepx.classifier.keras import KerasClassifier
from stagesepx.cutter import VideoCutter
from stagesepx.video import VideoObject
from stagesepx.reporter import Reporter
# 使用Keras方法进行预测
cl = KerasClassifier()
cl.load_model('./Color_finally.h5')
# 将视频切分成帧
file_name = './video/TapColor_5.mp4'
video = VideoObject(file_name)
# 新建帧，计算视频总共多少帧，每帧多少ms
video.load_frames()
# 压缩视频
cutter = VideoCutter()
# 计算每一帧视频的每一个block的ssim和psnr
res = cutter.cut(video)
# 判断A帧到B帧之间是否稳定还是不稳定
stable, unstable = res.get_range()
from stagesepx.cutter import VideoCutter
from stagesepx.classifier.keras import KerasClassifier
from stagesepx.reporter import Reporter
from stagesepx.video import VideoObject
video_path = "../videos/douyin1.mp4"
video = VideoObject(video_path)
video.load_frames()
# --- cutter ---
cutter = VideoCutter()
res = cutter.cut(video)
stable, unstable = res.get_range()
# --- classifier ---
cl = KerasClassifier(
# 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), )
model_file = "./keras_model.h5"
cl.load_model(model_file)
classify_result = cl.classify(video, stable, keep_data=True)
result_dict = classify_result.to_dict()
# --- draw ---
r = Reporter()
r.draw(classify_result)
# 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
hook = IgnoreHook(
size=(0.05, 1),
overwrite=True,
)
cutter.add_hook(hook)