# coding: utf-8

## This file implements a basic linear classifier approach to detecting action categories.

# In[26]:

import cv2, ffmpeg, subprocess

import numpy as np

import os, sys, collections, random, string

from sklearn.decomposition import PCA

from scipy import ndimage

import matplotlib.pyplot as plt

from sklearn.svm import LinearSVC

from sklearn import svm

from sklearn.multiclass import OneVsRestClassifier

import sklearn.metrics as metrics

#Personally made libraries

import classify_library

from tempfile import TemporaryFile

get_ipython().magic(u'matplotlib inline')

# In[4]:

orig_dir = "/Users/Bryan/CS/CS_Research/data/UCF101"

#playing a video saved in disk

video_pwd = os.path.join(orig_dir,"v_ApplyEyeMakeup_g01_c03.avi")

tmp_frames = "/Users/Bryan/CS/CS_Research/code/CS221/tmp_frames"

ucf101_path = "/Users/Bryan/CS/CS_Research/data/UCF101"

trainlists = '/Users/Bryan/CS/CS_Research/data/class_attributes_UCF101/ucfTrainTestlist/'

trainlist01 = os.path.join(trainlists, 'trainlist01.txt')

testlist01 = os.path.join(trainlists, 'testlist01.txt')

training_output = '/Users/Bryan/CS/CS_Research/code/CS221/tmp_frames/train'

testing_output = '/Users/Bryan/CS/CS_Research/code/CS221/tmp_frames/test'

# In[ ]:

def extract_frames(vidlist,vidDir,outputDir):

ffmpeg.extract_frame(videoName,frameName)

extract_frames(trainlist01, ucf101_path,training_output)

extract_frames(testlist01, ucf101_path,testing_output)

## Now lets we must construct training and testing matrices. We will also reduce the dimensions of the videos by projecting into the PCA basis.

# In[15]:

def rgb2gray(rgb):

return np.dot(rgb[...,:3], [0.299, 0.587, 0.144])

#Returns:

# 1. np.ndarray where of verticallys stacked fisher vectors.

# 2. np.array of class labels

# Inputs:

# videos: is a list of video jpg files

# fisher_path: path to the fisher vector directory

# class_index: dictionary from video name to the class.

def make_frame_matrix(videos, vid_dir, class_index):

return (X,Y)

# In[16]:

class_index_file = "./class_index.npz"

class_index_file_loaded = np.load(class_index_file)

class_index = class_index_file_loaded['class_index'][()]

index_class = class_index_file_loaded['index_class'][()]

training = [filename for filename in os.listdir(training_output) if filename.endswith('.jpeg')]

testing = [filename for filename in os.listdir(testing_output) if filename.endswith('.jpeg')]

training_dict = classify_library.toDict(training)

training_PCA = classify_library.limited_input1(training_dict,1)

X_train, Y_train = make_frame_matrix(training,training_output,class_index)

X_test, Y_test = make_frame_matrix(testing,testing_output,class_index)

### Reduced PCA dimension to 1000

# In[18]:

X_PCA, _ = make_frame_matrix(training_PCA, training_output, class_index)

pca = PCA(n_components=1000)

pca.fit(X_PCA)

X_train_PCA = pca.transform(X_train)

X_test_PCA = pca.transform(X_test)

# In[22]:

classifier = OneVsRestClassifier(LinearSVC(random_state=0, C=1, loss='l2', penalty='l2')).fit(X_train_PCA, Y_train)

classify_library.metric_scores(classifier, X_test_PCA, Y_test, verbose=True)

# In[27]:

baseline_file = "./baseline"

np.savez(baseline_file, X_train_PCA=X_train_PCA, X_test_PCA=X_test_PCA, Y_train=Y_train, Y_test=Y_test)

# In[ ]: