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

# [![image](https://raw.githubusercontent.com/visual-layer/visuallayer/main/imgs/vl_horizontal_logo.png)](https://www.visual-layer.com)

# # Working with survailance videos, captioning frames and detecting indoor/outdoor

# In[13]:


from IPython.display import Image


# ## Download the fight detection survailance camera dataset

# In[ ]:


get_ipython().system('git clone https://github.com/seymanurakti/fight-detection-surv-dataset.git')


# ## Install fastdup

# In[1]:


get_ipython().run_line_magic('pip', 'install fastdup')
import fastdup


# ## Extract videos into frames

# In[4]:


get_ipython().system('mkdir -p frames')
fastdup.extract_video_frames('fight-detection-surv-dataset/fight/', 'frames/fight')
fastdup.extract_video_frames('fight-detection-surv-dataset/noFight/', 'frames/no-fight')


# ## Quick preview of video frames

# In[8]:


fight_files=get_ipython().getoutput("find frames/fight/ -name '*.jpg'")
no_fight_files=get_ipython().getoutput("find frames/no-fight/ -name '*.jpg'")


# In[9]:


ret = fastdup.generate_sprite_image(fight_files, 55, ".")[0]
from IPython.display import Image
Image(filename=ret)
                              


# In[12]:


ret = fastdup.generate_sprite_image(no_fight_files, 55, ".")[0]
Image(filename=ret)


# ## Build fastdup model

# In[3]:


# Cluster similar frames together
fd = fastdup.create(input_dir='frames', work_dir='out_frames')
fd.run(overwrite=True, model_path='dinov2s')


# In[10]:


fd.vis.component_gallery(num_images=10)


# ## Look at video outliers, and caption them automatically

# In[6]:


fd.vis.outliers_gallery(num_images=10, label_col='blip')


# In[25]:


# In the above reports the captions are given under the "label" field name


# ## Look at image outliers, and classify them into indoors/outdoors

# In[10]:


fd.vis.outliers_gallery(num_images=10, label_col='indoors_outdoors')


# As can be seen the indoors/outdoors classifier is 90% correct on the above example

# ## Wrap Up
# 
# Next, feel free to check out other tutorials -
# 
# + ⚡ [**Quickstart**](https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/quick-dataset-analysis.ipynb): Learn how to install fastdup, load a dataset and analyze it for potential issues such as duplicates/near-duplicates, broken images, outliers, dark/bright/blurry images, and view visually similar image clusters. If you're new, start here!
# + 🧹 [**Clean Image Folder**](https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/cleaning-image-dataset.ipynb): Learn how to analyze and clean a folder of images from potential issues and export a list of problematic files for further action. If you have an unorganized folder of images, this is a good place to start.
# + 🖼 [**Analyze Image Classification Dataset**](https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/analyzing-image-classification-dataset.ipynb): Learn how to load a labeled image classification dataset and analyze for potential issues. If you have labeled ImageNet-style folder structure, have a go!
# + 🎁 [**Analyze Object Detection Dataset**](https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/analyzing-object-detection-dataset.ipynb): Learn how to load bounding box annotations for object detection and analyze for potential issues. If you have a COCO-style labeled object detection dataset, give this example a try.

# 
# ## VL Profiler
# If you prefer a no-code platform to inspect and visualize your dataset, [**try our free cloud product VL Profiler**](https://app.visual-layer.com) - VL Profiler is our first no-code commercial product that lets you visualize and inspect your dataset in your browser. 
# 
# [Sign up](https://app.visual-layer.com) now, it's free.
# 
# [![image](https://raw.githubusercontent.com/visual-layer/fastdup/main/gallery/vl_profiler_promo.svg)](https://app.visual-layer.com)
# 
# As usual, feedback is welcome! 
# 
# Questions? Drop by our [Slack channel](https://visualdatabase.slack.com/join/shared_invite/zt-19jaydbjn-lNDEDkgvSI1QwbTXSY6dlA#/shared-invite/email) or open an issue on [GitHub](https://github.com/visual-layer/fastdup/issues).

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