> ## Documentation Index
> Fetch the complete documentation index at: https://docs.visual-layer.com/llms.txt
> Use this file to discover all available pages before exploring further.

# YOLOv5 Object Detection Video Analysis

> In this tutorial, we will use fastdup with a pretrained yolov5 object detection model to detect and crop from videos.

Follow along this tutorial by running this [Colab Notebook](https://colab.research.google.com/github/visual-layer/fastdup/blob/main/examples/video-yolov5-detection.ipynb).

# Setting Up

```shell theme={"theme":"monokai"}
!pip install pip -U
!pip install fastdup
```

Verify the installation

```python theme={"theme":"monokai"}
import fastdup
fastdup.__version__
```

```
'0.910'
```

# Download & Extract Dataset

We downloaded a few random TikTok videos for the purpose of this demonstration. Feel free to use your own video dataset.

Let's download the TikTok videos into our local folder.

```shell theme={"theme":"monokai"}
!gdown  --fuzzy https://drive.google.com/file/d/1fzmOgmRu557aU4lEbzL7XCf78KntFCeQ/view
!unzip data.zip
```

# From Videos to Images

fastdup works on images. We must first extract frames from the videos using a one-liner fastdup utility function.

```python theme={"theme":"monokai"}
fastdup.extract_video_frames(input_dir="data", work_dir="frames")
```

This should create a`frames/`folder which stores all the frames extracted from the videos.

# Run fastdup

Now that we have the frames of images, let's run fastdup and analyze the frames.

```python theme={"theme":"monokai"}
fd = fastdup.create(input_dir='frames', work_dir='face_detection_work_dir')
```

```python theme={"theme":"monokai"}
fd.run(bounding_box='yolov5s', overwrite=True)
```

```
FastDup Software, (C) copyright 2022 Dr. Amir Alush and Dr. Danny Bickson.
2023-03-29 17:11:55 [INFO] Going to loop over dir frames
2023-03-29 17:11:55 [INFO] Found total 99 images to run on
FastDup Software, (C) copyright 2022 Dr. Amir Alush and Dr. Danny Bickson.utes 0 Features
2023-03-29 17:11:58 [INFO] Going to loop over dir /tmp/crops_input.csv
2023-03-29 17:11:58 [INFO] Found total 66 images to run on
2023-03-29 17:11:59 [INFO] Found total 66 images to run onEstimated: 0 Minutes 0 Features
Finished histogram 0.091
Finished bucket sort 0.106
2023-03-29 17:11:59 [INFO] 10) Finished write_index() NN model
2023-03-29 17:11:59 [INFO] Stored nn model index file face_detection_work_dir/nnf.index
2023-03-29 17:11:59 [INFO] Total time took 1017 ms
2023-03-29 17:11:59 [INFO] Found a total of 0 fully identical images (d>0.990), which are 0.00 %
2023-03-29 17:11:59 [INFO] Found a total of 2 nearly identical images(d>0.980), which are 1.01 %
2023-03-29 17:11:59 [INFO] Found a total of 28 above threshold images (d>0.900), which are 14.14 %
2023-03-29 17:11:59 [INFO] Found a total of 6 outlier images         (d<0.050), which are 3.03 %
2023-03-29 17:11:59 [INFO] Min distance found 0.528 max distance 0.982
2023-03-29 17:11:59 [INFO] Running connected components for ccthreshold 0.960000 
.0
 ########################################################################################

Dataset Analysis Summary: 

    Dataset contains 66 images
    Valid images are 100.00% (66) of the data, invalid are 0.00% (0) of the data
    Similarity:  3.03% (2) belong to 2 similarity clusters (components).
    96.97% (64) images do not belong to any similarity cluster.
    Largest cluster has 6 (9.09%) images.
    For a detailed analysis, use `.connected_components()`
(similarity threshold used is 0.9, connected component threshold used is 0.96).

    Outliers: 6.06% (4) of images are possible outliers, and fall in the bottom 5.00% of similarity values.
    For a detailed list of outliers, use `.outliers()`.
```

# Components Gallery

We can visualize the cluster of similar detections using the components gallery view. Specify `draw_bbox=True` to see the detection bounding box on the original image.

```python theme={"theme":"monokai"}
fd.vis.component_gallery(draw_bbox=True)
```

![](https://files.readme.io/85284e6-_home_dnth_Downloads_components.html.png)

# Similarity Gallery

Using the `similarity_gallery` view, we can find similar looking faces (bounding boxes) across all the extracted frames.

```python theme={"theme":"monokai"}
fd.vis.similarity_gallery(draw_bbox=False)
```

![](https://files.readme.io/91a4d17-_home_dnth_Downloads_components.html.png)

# Duplicates Gallery

With the `duplicates_gallery` view, visualize duplicate image pairs across videos.

```
fd.vis.duplicates_gallery()
```

![](https://files.readme.io/bfcb45a-_home_dnth_Downloads_duplicates.html.png)

# Outliers Gallery

Using the `outliers_gallery` we can also visualize faces (detections) that looks visually different from others.

```python theme={"theme":"monokai"}
fd.vis.outliers_gallery()
```

![](https://files.readme.io/a1452c6-_home_dnth_Downloads_outliers.html.png)

# Stats Gallery

You can visualize the faces using various metrics using the `stats_gallery`.

## Dark Objects

Specifying `metric='dark'` sorts the detections in an ascending order of image `mean` value.

```python theme={"theme":"monokai"}
fd.vis.stats_gallery(metric='dark')
```

![](https://files.readme.io/50b604c-_home_dnth_Downloads_mean.html.png)

## Bright Objects

Conversely, specifying `metric='bright'`sorts the detections in descending order of image `mean` value.

```python theme={"theme":"monokai"}
fd.vis.stats_gallery(metric='bright')
```

![](https://files.readme.io/df24893-_home_dnth_Downloads_mean.html.png)

## Blurry Objects

Finally, specifying `metric='blur'`ranks the images in ascending 'blurriness'.

```python theme={"theme":"monokai"}
fd.vis.stats_gallery(metric='blur')
```

![](https://files.readme.io/9949859-_home_dnth_Downloads_blur.html.png)