processor.py

''' This module extracts frames a Video
performs preprocessing on the frames and stores them in a Numpy array for
furthur use by the spatiotemporal autoencoder

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Dependencies: ffmpeg

If you dont have ffmpeg installed:

Install it with :


1. sudo apt-get install ffmpeg for Linux Users
2. brew install ffmpeg for macOS

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Usage:

python3 processor.py video_dir_path time_in_seconds_to_extract_one_frame

eg;python3 processor.py ./train 5   will search for train directory and for each video in train directory

It will extract 1 frame every 5 seconds and store it.



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Author: Harsh Tiku
'''

import cv2
import ffmpeg
from keras.preprocessing.image import img_to_array, load_img
import numpy as np
import glob
import os
from PIL import Image

# from scipy.misc import imresize
import argparse

imagestore = []

# parser=argparse.ArgumentParser(description='Source Video path')

# parser=argparse.ArgumentParser('train')

# parser.add_argument('train')
# parser.add_argument('fps',type=int)
# parser.add_argument(3)

# args=parser.parse_args()

# video_source_path= args.source_vid_path
# fps=args.fps
video_source_path = 'train'
fps = 5


def create_dir(path):
    if not os.path.exists(path):
        os.makedirs(path)


def remove_old_images(path):
    filelist = glob.glob(os.path.join(path, "*.png"))
    for f in filelist:
        os.remove(f)


def store(image_path):
    img = load_img(image_path)
    img = img_to_array(img)

    # Resize the Image to (227,227,3) for the network to be able to process it.

    # frame = Image.fromarray(frame).resize(size=(227, 227, 3))

    img = Image.fromarray(img).resize(size=(227, 227, 3))

    # Convert the Image to Grayscale

    gray = 0.2989 * img[:, :, 0] + 0.5870 * img[:, :, 1] + 0.1140 * img[:, :, 2]

    imagestore.append(gray)


# List of all Videos in the Source Directory.
videos = os.listdir(video_source_path)
print("Found ", len(videos), " training video")

# Make a temp dir to store all the frames
create_dir(video_source_path + '/frames')

# Remove old images
remove_old_images(video_source_path + '/frames')

framepath = video_source_path + '/frames'

# for video in videos:
#     # os.system("ffmpeg -i {}/{} -r 1/{}  {}/frames/%03d.jpg".format(video_source_path, video, fps, video_source_path))
#     os.system(f"ffmpeg -i {video_source_path}/{video} -r 0.25 output_%04d.jpg")
#     images = os.listdir(framepath)
#     for image in images:
#         image_path = framepath + '/' + image
#         store(image_path)

currentframe = 0
for video in videos:
    cap = cv2.VideoCapture(video)
    print("cap is {}".format(cap))
    while (True):
        ret, frame = cap.read()
        if ret:
            #   "ffmpeg -i {}/{} -r 1/{}  {}/frames/%03d.jpg".format(video_source_path,video,fps,video_source_path)
            x = 'train/frames/' + str(currentframe) + '.jpg'
            # print(x)
            cv2.imwrite(x, frame)
            # store_inarray(x,frame)
            store(x)
            currentframe += 1
        else:
            break

imagestore = np.array(imagestore)
a, b, c = imagestore.shape
# Reshape to (227,227,batch_size)
imagestore.resize(b, c, a)
# Normalize
imagestore = (imagestore - imagestore.mean()) / (imagestore.std())
# Clip negative Values
imagestore = np.clip(imagestore, 0, 1)
np.save('training.npy', imagestore)
# Remove Buffer Directory
os.system('rm -r {}'.format(framepath))