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

# # A computer-assisted exploration of visual themes in Taylor Swift's music videos 
# 

# Eamonn Bell, Columbia University `<epb2125@columbia.edu>`

# ---
# For academic use only. WORK IN PROGRESS.
# 

# ---
# ## Summary
# 
# In this notebook I show how to use the [Clarifai](http://www.clarifai.com/) neural-network-driven image tagging API on music videos released by Taylor Swift in order to demonstrate the utility of this technology to the study of music videos.
# 
# ## Abstract
# 
# TBD

# In[1]:


from clarifai.client import ClarifaiApi
import glob
import os
import pandas 
import numpy as np
from matplotlib import pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')


# In[2]:


clarifai_api = ClarifaiApi()


# In[3]:


TS_WE_ARE_NEVER = 'WA4iX5D9Z64'
TS_22 = 'AgFeZr5ptV8'
video_ids = [TS_WE_ARE_NEVER, TS_22]


# In[20]:


for video_id in video_ids:
    os.system(r"youtube-dl {} -o '%(id)s'".format(video_id))


# In[28]:


for video_id in video_ids:
    os.system(r"ffmpeg -i {0}.mp4 -r 1 {0}-%3d.jpeg".format(video_id))


# In[15]:


results = {}

# http://stackoverflow.com/a/434328
def chunker(seq, size):
    return (seq[pos:pos + size] for pos in xrange(0, len(seq), size))

def tag_images_queue(queue):
    results = []
    
    for frames in queue:
        
        open_files = [open(frame) for frame in frames]
   
       
        api_response = clarifai_api.tag_images(open_files)
        
        closed = [f.close() for f in open_files]
        
        results.extend(api_response['results'])
        
    return results    

def tag_batch(frames):
    file_count = len(frames)
    
    q = []
    
    if file_count >= 127:
        for chunk in chunker(frames, 127):
            q.append(chunk)
    else:
        q.append(frames)
    
    return tag_images_queue(q)
    
for video_id in video_ids:
    frames = sorted(glob.glob('{}-*.jpeg'.format(video_id)))
    api_response_results = tag_batch(frames)
    results[video_id] = api_response_results


# In[19]:


len(results['AgFeZr5ptV8'])


# In[20]:


def parse_video_results(video_results):
    classes_seen = []
    tags_dicts = []
    
    for frame_result in video_results:
        classes_seen.extend(frame_result['result']['tag']['classes'])
        
    for frame_result in video_results:
        classes = frame_result['result']['tag']['classes']
        probs = frame_result['result']['tag']['probs']
        
        tags_dict = dict(zip(classes, probs))
        
        unseen_classes = (class_ for class_ in classes_seen if class_ not in classes)
        
        for unseen_class in unseen_classes:
            tags_dict[unseen_class] = np.nan
        
        tags_dicts.append(tags_dict)
           
    return tags_dicts            


# In[21]:


def parse_results_dict(results_dict):
    _ = []
    
    for video_id in results_dict.keys():
        video_results = results_dict[video_id]
        
        parsed_video = parse_video_results(video_results)
        video_df = pandas.DataFrame(parsed_video)
        video_df['_video_id'] = video_id
        video_df.index.names = ['frame']
        _.append(video_df)
        
    
    return pandas.concat(_)


# In[22]:


data = parse_results_dict(results)


# In[23]:


data.head()


# In[26]:


data.to_pickle('video_data.pkl')


# ## Recurring imagery

# In[103]:


def most_common_images(video_id, limit=100):
    video_data = data[data._video_id == video_id]
    return video_data.count().order(ascending=False).head(limit)


# In[104]:


most_common_images(TS_WE_ARE_NEVER)


# ## Narratology: male/female dynamics

# In[68]:


data[data._video_id == TS_WE_ARE_NEVER][['women', 'men']].plot()
plt.title('Taylor Swift - We Are Never Getting Back Together')
plt.xlabel('Video location (s)')
plt.ylabel('Tag class confidence (proprietary)')


# In[67]:


data[data._video_id == TS_22][['women', 'men']].plot()
plt.title('Taylor Swift - 22')
plt.xlabel('Video location (s)')
plt.ylabel('Tag class confidence (proprietary)')


# In[111]:


d = data[data._video_id == TS_22][['women', 'men']].interpolate()

woman_on_screen = pandas.rolling_mean(d['women'], 2) > 0.90
to_plot = woman_on_screen.apply(lambda x: 0.85 if x else 0.35)
to_plot.plot(color='red')

men_on_screen = pandas.rolling_mean(d['men'], 2) > 0.90
to_plot = men_on_screen.apply(lambda x: 0.75 if x else 0.25)
to_plot.plot(color='blue')
plt.title('Gendered Screentime : Taylor Swift - 22')


# In[108]:


d = data[data._video_id == TS_WE_ARE_NEVER][['women', 'men']].interpolate()

woman_on_screen = pandas.rolling_mean(d['women'], 2) > 0.90
to_plot = woman_on_screen.apply(lambda x: 0.85 if x else 0.35)
to_plot.plot(color='red')

men_on_screen = pandas.rolling_mean(d['men'], 2) > 0.90
to_plot = men_on_screen.apply(lambda x: 0.75 if x else 0.25)
to_plot.plot(color='blue')
plt.title('Gendered Screentime : We Are Never Getting Back Together')


# In[ ]: