# Video Alignment with Temporal Network

*author: David Wang*


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## Description

This operator can compare two ordered sequences, then detect the range which features from each sequence are computationally similar in order.
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## Code Example


```python
import towhee
import numpy as np

# simulate a video feature by 10 frames of 512d vectors.  
videos_embeddings = np.random.randn(10,512)
videos_embeddings = videos_embeddings / np.linalg.norm(videos_embeddings,axis=1).reshape(10,-1)

towhee.dc['src','dest']([[videos_embeddings,videos_embeddings]]) \
      .video_copy_detection.temporal_network[('src','dest'), ('range', 'range_score')]() \
      .show()
```

<img src="./tabular.png" alt="result" style="height:60px;"/>

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## Factory Constructor

Create the operator via the following factory method

***clip(model_name, modality)***
***temporal_network(tn_max_step, tn_top_k, max_path, min_sim, min_length, max_iou)***


**Parameters:**

​   ***tn_max_step:*** *str*

​   Max step range in TN.

​   ***tn_top_k:*** *str*

​   Top k frame similarity selection in TN. 

​   ***max_path:*** *str*

​   Max loop for multiply segments detection. 

​   ***min_sim:*** *str*

​   Min average similarity score for each aligned segment. 

​   ***min_length:*** *str*

​   Min segment length. 

​   ***max_iout:*** *str*

​   Max iou for filtering overlap segments (bbox). 

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## Interface

A Temporal Network operator takes two numpy.ndarray(shape(N,D) N: number of features. D: dimension of features) and get the duplicated ranges and scores.


**Parameters:**

​	***src_video_vec*** *numpy.ndarray*

​  Source video feature vectors.

​  ***dst_video_vec:*** *numpy.ndarray*

​  Destination video feature vectors.


**Returns:**

​	***aligned_ranges:*** *List[List[Int]]*

​   The returned aligned range.

​	***aligned_scores:*** *List[float]*

​   The returned similarity scores(length same as aligned_ranges).