paddlevideo 0.0.1

Awesome Video toolkits based on PaddlePaddle

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paddlevideo package

Get started quickly

install package

install by pypi

pip install paddlevideo==0.0.1

note: you may have difficulty in installing opencv-python,you can try:

pip install opencv-python==4.2.0.32 -i https://pypi.doubanio.com/simple

build own whl package and install

python3 setup.py bdist_wheel
pip3 install dist/paddlevideo-x.x.x-py3-none-any.whl

1. Quick Start

• Assign video_file='data/example.mp4', Use inference model that Paddle provides model_name='ppTSM'

from ppvideo import PaddleVideo
clas = PaddleVideo(model_name='ppTSM',use_gpu=False,use_tensorrt=False)
video_file='data/example.mp4.'
result=clas.predict(video_file)
print(result)

    >>> result
    [{'videoname': 'data/example.mp4', 'class_ids': [5], 'scores': [0.999963], 'label_names': ['archery']}]

• Using command line interactive programming

ppvideo --model_name='ppTSM' --video_file='data/example.mp4'

    >>> result
    **********data/example.mp4**********
    [{'videoname': 'data/example.mp4', 'class_ids': [5], 'scores': [0.999963], 'label_names': ['archery']}]

2. Definition of Parameters

• model_name(str): model's name. If not assigning model_fileandparams_file, you can assign this param. If using inference model based on Kinectics-400 provided by Paddle, set as default='ppTSM'.
• video_file(str): video's path. Support assigning single local video, internet video and folder containing series of videos. Also Support numpy.ndarray.
• use_gpu(bool): Whether to use GPU or not, defalut=False.
• num_seg(int): Number of segments while using the sample strategies proposed in TSN.
• seg_len(int): Number of frames for each segment.
• short_size(int): resize the minima between height and width into resize_short(int), default=256.
• target_size(int): resize image into resize(int), default=224.
• normalize(bool): whether normalize image or not, default=True.
• model_file(str): path of inference.pdmodel. If not assign this param，you need assign model_name for downloading.
• params_file(str): path of inference.pdiparams. If not assign this param，you need assign model_name for downloading.
• batch_size(int): batch number, default=1.
• use_fp16(bool): Whether to use float16 in memory or not, default=False.
• use_tensorrt(bool): whether to open tensorrt or not. Using it can greatly promote predict preformance, default=False.
• gpu_mem(int): GPU memory usages，default=8000.
• top_k(int): Assign top_k, default=1.
• enable_mkldnn(bool): whether enable MKLDNN or not, default=False.

3. Different Usages of Codes

We provide two ways to use: 1. Python interative programming 2. Bash command line programming

• check help information

ppvideo -h

• Use user-specified model, you need to assign model's path model_file and parameters's pathparams_file

python

from ppvideo import PaddleVideo
clas = PaddleVideo(model_file='user-specified model path',
    params_file='parmas path', use_gpu=False, use_tensorrt=False)
video_file = ''
result=clas.predict(video_file)
print(result)

bash

ppvideo --model_file='user-specified model path' --params_file='parmas path' --video_file='video path'

• Use inference model which PaddlePaddle provides to predict, you need to choose one of model when initializing ppvideo to assign model_name. You may not assign model_file , and the model you chosen will be download in BASE_INFERENCE_MODEL_DIR ,which will be saved in folder named by model_name,avoiding overlay different inference model.

python

from ppvideo import PaddleVideo
clas = PaddleVideo(model_name='ppTSM',use_gpu=False, use_tensorrt=False)
video_file = ''
result=clas.predict(video_file)
print(result)

bash

ppvideo --model_name='ppTSM' --video_file='video path'

• You can assign input as formatnp.ndarray which has been preprocessed --video_file=np.ndarray.

python

from ppvideo import PaddleVideo
clas = PaddleVideo(model_name='ppTSM',use_gpu=False, use_tensorrt=False)
video_file =np.ndarray
result=clas.predict(video_file)

bash

ppvideo --model_name='ppTSM' --video_file=np.ndarray

• You can assign video_file as a folder path containing series of videos, also can assign top_k.

python

from ppvideo import PaddleVideo
clas = PaddleVideo(model_name='ppTSM',use_gpu=False, use_tensorrt=False,top_k=5)
video_file = '' # it can be video_file folder path which contains all of videos you want to predict.
result=clas.predict(video_file)
print(result)

bash

paddleclas --model_name='ResNet50' --video_file='video path' --top_k=5

• You can assign --label_name_path as your own label_dict_file, format should be as(class_idclass_name<\n>).

0 abseiling
1 air_drumming
2 answering_questions
3 applauding
4 applying_cream
5 archery
......

• If you use inference model that Paddle provides, you do not need assign label_name_path. Program will take data/k400/Kinetics-400_label_list.txt as defaults. If you hope using your own training model, you can provide label_name_path outputing 'label_name' and scores, otherwise no 'label_name' in output information.

python

from ppvideo import PaddleVideo
clas = PaddleVideo(model_file= './inference.pdmodel',params_file = './inference.pdiparams',label_name_path='./data/k400/Kinetics-400_label_list.txt',use_gpu=False)
video_file = '' # it can be video_file folder path which contains all of videos you want to predict.
result=clas.predict(video_file)
print(result)

bash

ppvideo --model_file= './inference.pdmodel' --params_file = './inference.pdiparams' --video_file='video path' --label_name_path='./data/k400/Kinetics-400_label_list.txt'

python

from ppvideo import PaddleVideo
clas = PaddleVideo(model_name='ppTSM',use_gpu=False)
video_file = '' # it can be video_file folder path which contains all of videos you want to predict.
result=clas.predict(video_file)
print(result)

bash

ppvideo --model_name='ppTSM' --video_file='video path'