import glob
import pickle
import sys
from random import shuffle

import numpy as np
import pandas as pd
import tensorflow as tf
from PIL import Image

import cv2


def load_image(addr):
    img = np.array(Image.open(addr).resize((224, 224), Image.ANTIALIAS))
    img = img.astype(np.uint8)
    return img


def _float_feature(value):
    return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))


def _bytes_feature(value):
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))


def load_pickle(pickle_file):
    with open(pickle_file, "rb") as f:
        pickle_data = pickle.load(f, encoding="latin1")
        df = pd.DataFrame(pickle_data)
        df.reset_index(inplace=True)
        del df["interview"]
        df.columns = [
            "VideoName",
            "ValueExtraversion",
            "ValueNeuroticism",
            "ValueAgreeableness",
            "ValueConscientiousness",
            "ValueOpenness",
        ]
    return df


##### TRAINING DATA ####
df = load_pickle("Annotations/annotation_training.pkl")
NUM_VID = len(df)
addrs = []
labels = []
for i in range(NUM_VID):
    filelist = glob.glob(
        "ImageData/trainingData/"
        + (df["VideoName"].iloc[i]).split(".mp4")[0]
        + "/*.jpg"
    )
    addrs += filelist
    labels += [
        np.array(df.drop(["VideoName"], 1, inplace=False).iloc[i]).astype(np.float32)
    ] * 100


c = list(zip(addrs, labels))
shuffle(c)
train_addrs, train_labels = zip(*c)
train_filename = "train_full.tfrecords"  # address to save the TFRecords file
# open the TFRecords file
writer = tf.python_io.TFRecordWriter(train_filename)
for i in range(len(train_addrs)):
    # print how many images are saved every 1000 images
    if not i % 1000:
        print("Train data: {}/{}".format(i, len(train_addrs)))
        sys.stdout.flush()
    # Load the image
    img = load_image(train_addrs[i])
    label = train_labels[i]
    # Create a feature
    feature = {
        "train/label": _bytes_feature(tf.compat.as_bytes(label.tostring())),
        "train/image": _bytes_feature(tf.compat.as_bytes(img.tostring())),
    }
    # Create an example protocol buffer
    example = tf.train.Example(features=tf.train.Features(feature=feature))

    # Serialize to string and write on the file
    writer.write(example.SerializeToString())


writer.close()
sys.stdout.flush()
##### TRAINING DATA ####

print(len(train_addrs), "training images saved.. ")


##### VALIDATION DATA ####
df = load_pickle("Annotations/annotation_validation.pkl")
NUM_VID = len(df)
addrs = []
labels = []
for i in range(NUM_VID):
    filelist = glob.glob(
        "ImageData/validationData/"
        + (df["VideoName"].iloc[i]).split(".mp4")[0]
        + "/*.jpg"
    )
    addrs += filelist
    labels += [
        np.array(df.drop(["VideoName"], 1, inplace=False).iloc[i]).astype(np.float32)
    ] * 100

c = list(zip(addrs, labels))
shuffle(c)
val_addrs, val_labels = zip(*c)

val_filename = "val_full.tfrecords"  # address to save the TFRecords file
# open the TFRecords file
writer = tf.python_io.TFRecordWriter(val_filename)

for i in range(len(val_addrs)):
    # print how many images are saved every 1000 images
    if not i % 1000:
        print("Val data: {}/{}".format(i, len(val_addrs)))
        sys.stdout.flush()
    # Load the image
    img = load_image(val_addrs[i])
    label = val_labels[i].astype(np.float32)
    feature = {
        "val/label": _bytes_feature(tf.compat.as_bytes(label.tostring())),
        "val/image": _bytes_feature(tf.compat.as_bytes(img.tostring())),
    }
    # Create an example protocol buffer
    example = tf.train.Example(features=tf.train.Features(feature=feature))

    # Serialize to string and write on the file
    writer.write(example.SerializeToString())


writer.close()
sys.stdout.flush()
##### VALIDATION DATA ####

print(len(val_addrs), "validation images saved.. ")