#!/usr/bin/env python
# coding: utf-8

# # Face Detection from Videos
# 
# In this tutorial, we will use fastdup with a face detection model to detect and crop from videos. Following that we analyze the cropped faces for issues such as duplicates, near-duplicates, outliers, bright/dark/blurry faces.
# 
# > This is an advanced functionality of fastdup. Sign up for free to be an beta tester and get early access at info@visual-layer.com .

# ## Installation & Setting Up

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get_ipython().system('pip install pip -U')
get_ipython().system('pip install fastdup')


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import fastdup
fastdup.__version__


# ## Download & Extract Dataset

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get_ipython().system('gdown  --fuzzy https://drive.google.com/file/d/1fzmOgmRu557aU4lEbzL7XCf78KntFCeQ/view')


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get_ipython().system('unzip data.zip')


# ## Video to Images
# 
# fastdup works on images. We must first turn the videos into frames of images.
# 
# We can use a one-liner fastdup utility function to turn all the videos in a folder into frames:

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fastdup.extract_video_frames(input_dir="data", work_dir="frames")


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

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fd = fastdup.create(input_dir='frames', work_dir='face_detection_work_dir')


# As this is an advance functionality of fastdup, you'd need a license key to use this function, sign up and get your license key for free at info@visual-layer.com .

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fd.run(bounding_box='face', license='your_license_key', overwrite=True)


# ## 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.

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fd.vis.component_gallery(draw_bbox=True)


# If you'd like to view just the cropped bounding box images, specify `draw_bbox=False`

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fd.vis.component_gallery(draw_bbox=False)


# ## Find Similar Faces Across Videos
# 
# Using the `similarity_gallery` view, we can find similar looking faces (bounding boxes) across all the extracted frames.

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fd.vis.similarity_gallery(draw_bbox=False)


# ## Find Outliers
# 
# Useing the `outliers_gallery` we can also viaualize faces (detections) that looks visually different from others.

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fd.vis.outliers_gallery()


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

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fd.vis.duplicates_gallery()


# ## Dark Faces
# 
# Using the `stats_gallery` view, we can sort the faces (detections) following a desired `metric` such as 'dark', 'bright' and 'blur'. 

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fd.vis.stats_gallery(metric='dark')


# ## Bright Faces

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fd.vis.stats_gallery(metric='bright')


# ## Blurry Faces

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fd.vis.stats_gallery(metric='blur')


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