import numpy as np, os

import h5py

import glob

import imgaug as ia

from imgaug import augmenters as iaa

import numpy as np

import time, csv

import cv2

import pandas

image_size = 256

test_data_path ="/home/mil/gupta/ifood18/data/test_set/"

test_label_path ="/home/mil/gupta/ifood18/data/labels/test_info.csv"

data = pandas.read_csv(test_label_path, header=None , names= ["name_of_pic"])

print(data.head())

#If you want your lists as in the question, you can now do:

#val_labels = data.noisy_label.tolist()

# read addresses and labels from the 'train' folder

test_data = data.name_of_pic.tolist()

print("Length of val data is : ",len(test_data))

#combined = zip(train_data, train_labels)

start =time.time()

data_order = 'pytorch'# 'th' for Theano, 'tf' for Tensorflow

# check the order of data and chose proper data shape to save images

# if data_order == 'th':

# train_shape = (len(train_addrs), 3,)

# val_shape = (len(val_addrs), 3, 224, 224)

# test_shape = (len(test_addrs), 3, 224, 224)

# elif

if data_order == 'pytorch':

test_shape = (len(test_data),3, image_size, image_size)

# val_shape = (len(val_data_path), image_size, image_size, 3)

# test_shape = (len(test_data_path), image_size, image_size, 3)

hdf5_path = "/home/mil/gupta/ifood18/data/h5data/test_data.h5py"

# open a hdf5 file and create earrays

hdf5_file = h5py.File(hdf5_path, mode='w')

hdf5_file.create_dataset("data", test_shape, np.float32)

hdf5_file.create_dataset("mean", test_shape[1:], np.float32)

#hdf5_file.create_dataset("labels", (len(test_data),), np.int32)

#hdf5_file["labels"][...] = val_labels

from tqdm import tqdm

# a numpy array to save the mean of the images

mean = np.zeros(test_shape[1:], np.float32)

# loop over train addresses

for i in tqdm(range(len(test_data))):

addr = os.path.join(test_data_path,test_data[i])

#print("image addres is :",addr)

img = cv2.imread(addr)

#print(img)

img = cv2.resize(img, (image_size, image_size), interpolation=cv2.INTER_CUBIC)

img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

#current_image = ia.imresize_single_image(addr, (256, 256))

image_aug = img.transpose(2,0,1)

hdf5_file["data"][i, ...] = image_aug[None]

mean += image_aug / float(len(test_data))

print("time taken for image" , time.time()-start)

hdf5_file["mean"][...] = mean

hdf5_file.close()