import cv2
import numpy as np
import pickle as pickle
import os

def resize(max_side, img):
    if img.shape[1] > img.shape[0]:
        r = max_side / img.shape[1]
        dim = (max_side, int(img.shape[0] * r))
    else:
        r = max_side / img.shape[0]
        dim = (int(img.shape[1] * r), max_side)

    # perform the actual resizing of the image and show it
    resized = cv2.resize(img, dim, interpolation=cv2.INTER_AREA)
    return resized


def pickle_keypoints(keypoints, descriptors):
    i = 0
    temp_array = []
    for point in keypoints:
        temp = (point.pt, point.size, point.angle, point.response, point.octave,
        point.class_id, descriptors[i])
        i += 1
        temp_array.append(temp)
    return temp_array


def unpickle_keypoints(array):
    keypoints = []
    descriptors = []
    for point in array:
        temp_feature = cv2.KeyPoint(x=point[0][0],y=point[0][1], size=point[1], angle=point[2], response=point[3], octave=point[4], class_id=point[5])
        temp_descriptor = point[6]
        keypoints.append(temp_feature)
        descriptors.append(temp_descriptor)
    return keypoints, np.array(descriptors)


def load(lf_path):
    print('loading LF dataset ' + lf_path)
    ser_dataset = pickle.load(open(lf_path, "rb"))
    lf_dataset = []
    for item in ser_dataset:
        kp, desc = unpickle_keypoints(item)
        lf_dataset.append((kp, desc))
    return lf_dataset


def save(lf_data, lf_path):
    data = []
    for lf in lf_data:
        data.append(pickle_keypoints(lf[0], lf[1]))
    pickle.dump(data, open(lf_path, 'wb'))