# 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
from towhee import pipe, ops, DataCollection
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)

p = (
    pipe.input('src', 'dest') \
        .map(('src', 'dest'), ('range', 'range_score'), ops.video_copy_detection.temporal_network()) \
        .output('src', 'dest', 'range', 'range_score')
)

DataCollection(p(videos_embeddings, videos_embeddings)).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).



# More Resources

- [DNA Sequence Classification based on Milvus - Zilliz blog](https://zilliz.com/blog/dna-sequence-classification-based-on-milvus): Use Milvus, an open-source vector database, to recognize gene families of DNA sequences. Less space but higher accuracy.
- [Vector Database Use Cases: Video Similarity Search - Zilliz](https://zilliz.com/vector-database-use-cases/video-similarity-search): Experience a 10x performance boost and unparalleled precision when your video similarity search system is powered by Zilliz Cloud.
- [How to Get the Right Vector Embeddings - Zilliz blog](https://zilliz.com/blog/how-to-get-the-right-vector-embeddings): A comprehensive introduction to vector embeddings and how to generate them with popular open-source models.
- [What is a Convolutional Neural Network? An Engineer's Guide](https://zilliz.com/glossary/convolutional-neural-network): Convolutional Neural Network is a type of deep neural network that processes images, speeches, and videos. Let's find out more about CNN.
- [The guide to clip-vit-base-patch32 | OpenAI](https://zilliz.com/ai-models/clip-vit-base-patch32): clip-vit-base-patch32: a CLIP multimodal model variant by OpenAI for image and text embedding.
- [Understanding ImageNet: A Key Resource for Computer Vision and AI Research](https://zilliz.com/glossary/imagenet): The large-scale image database with over 14 million annotated images. Learn how this dataset supports advancements in computer vision.
- [Build a Multimodal Search System with Milvus - Zilliz blog](https://zilliz.com/blog/how-vector-dbs-are-revolutionizing-unstructured-data-search-ai-applications): Implementing a Multimodal Similarity Search System Using Milvus, Radient, ImageBind, and Meta-Chameleon-7b
- [Unlock Advanced Recommendation Engines with Milvus' New Range Search - Zilliz blog](https://zilliz.com/blog/unlock-advanced-recommendation-engines-with-milvus-new-range-search): Exploring Milvus’s newly released range search feature, how it differs from the traditional KNN search, and when to use it.
- [Similarity Metrics for Vector Search - Zilliz blog](https://zilliz.com/blog/similarity-metrics-for-vector-search): Exploring five similarity metrics for vector search: L2 or Euclidean distance, cosine distance, inner product, and hamming distance.