extract_RGB_feats.py

#-*- coding: utf-8 -*-

import cv2
import os
import ipdb
import numpy as np
import pandas as pd
import skimage
from cnn_util import *
from keras.applications.vgg19 import VGG19
from keras.models.import Model

def preprocess_frame(image, target_height=224, target_width=224):

    if len(image.shape) == 2:
        image = np.tile(image[:,:,None], 3)
    elif len(image.shape) == 4:
        image = image[:,:,:,0]

    image = skimage.img_as_float(image).astype(np.float32)
    height, width, rgb = image.shape
    if width == height:
        resized_image = cv2.resize(image, (target_height,target_width))

    elif height < width:
        resized_image = cv2.resize(image, (int(width * float(target_height)/height), target_width))
        cropping_length = int((resized_image.shape[1] - target_height) / 2)
        resized_image = resized_image[:,cropping_length:resized_image.shape[1] - cropping_length]

    else:
        resized_image = cv2.resize(image, (target_height, int(height * float(target_width) / width)))
        cropping_length = int((resized_image.shape[0] - target_width) / 2)
        resized_image = resized_image[cropping_length:resized_image.shape[0] - cropping_length,:]

    return cv2.resize(resized_image, (target_height, target_width))

def main():
    num_frames = 80
    # vgg_model = '/home/chenxp/caffe/models/vgg/VGG_ILSVRC_16_layers.caffemodel'
    # vgg_deploy = '/home/chenxp/caffe/models/vgg/VGG_ILSVRC_16_layers_deploy.prototxt'
    video_path = 'C:\\Users\\JSHwang\\Desktop\\2020-2TA\\ML_Chapter_4\\seq2seq_video2text\\testvideo'
    video_save_path = './rgb_feats'
    videos = os.listdir(video_path)
    videos = filter(lambda x: x.endswith('avi'), videos)

    cnn = CNN(model=vgg_model, deploy=vgg_deploy, width=224, height=224)

    for idx, video in enumerate(videos):
        print idx, video

        if os.path.exists( os.path.join(video_save_path, video) ):
            print "Already processed ... "
            continue

        video_fullpath = os.path.join(video_path, video)
        try:
            cap  = cv2.VideoCapture( video_fullpath )
        except:
            pass

        frame_count = 0
        frame_list = []

        while True:
            ret, frame = cap.read()
            if ret is False:
                break

            frame_list.append(frame)
            frame_count += 1

        frame_list = np.array(frame_list)

        if frame_count > 80:
            frame_indices = np.linspace(0, frame_count, num=num_frames, endpoint=False).astype(int)
            frame_list = frame_list[frame_indices]

        cropped_frame_list = np.array(map(lambda x: preprocess_frame(x), frame_list))
        feats = cnn.get_features(cropped_frame_list)

        save_full_path = os.path.join(video_save_path, video + '.npy')
        np.save(save_full_path, feats)

if __name__=="__main__":
    main()