import os

import sys

import json

import subprocess

import numpy as np

import torch

from torch import nn

from opts import parse_opts

from model import generate_model

from mean import get_mean

from classify import classify_video

import hdf5storage

if __name__=="__main__":

opt = parse_opts()

opt.mean = get_mean()

opt.arch = '{}-{}'.format(opt.model_name, opt.model_depth)

opt.sample_size = 112

opt.sample_duration = 1

opt.n_classes = 400

model = generate_model(opt)

model_data = torch.load('./resnext-101-64f-kinetics.pth')

assert opt.arch == model_data['arch']

model.load_state_dict(model_data['state_dict'],False)

model.eval()

class_names = []

with open('class_names_list') as f:

for row in f:

class_names.append(row[:-1])

if os.path.exists('tmpv11'):

subprocess.call('rm -rf tmpv11', shell=True)

if not os.path.exists('resnext101-64f'):

subprocess.call('mkdir resnext101-64f', shell=True)

val_set = hdf5storage.read(path='/img_path', filename='./val_set.h5')

print(len(val_set))

for i in range(len(val_set)):

subprocess.call('mkdir tmpv11', shell=True)

for j in range(8):

img_path='./' + val_set[i][j].replace('\\', '/')

subprocess.call('cp '+img_path+' tmpv11/'+'image_{:05d}.jpg'.format(j+1),shell=True)

result = classify_video('tmpv11', str(i), class_names, model, opt)

outputs = []

for kk in range(7):

outputs.append(result['clips'][kk]['features'])

file_nm='./resnext101-64f/v_video'+str(i+1)+'.npy'

np.save(file_nm, outputs)

subprocess.call('rm -rf tmpv11', shell=True)

print(file_nm)

if os.path.exists('tmpv11'):

subprocess.call('rm -rf tmpv11', shell=True)

# with open(opt.output, 'w') as f:

# json.dump(outputs, f)