cross_vali_data_convert_merge.py

import numpy as np, numpy
import csv
import glob
import os

window_size = 1000
threshold = 60
slide_size = 200  # less than window_size!!!


def dataimport(path1, path2):
    xx = np.empty([0, window_size, 90], float)
    yy = np.empty([0, 8], float)

    ###Input data###
    # data import from csv
    input_csv_files = sorted(glob.glob(path1))
    for f in input_csv_files:
        print("input_file_name=", f)
        data = [[float(elm) for elm in v] for v in csv.reader(open(f, "r"))]
        tmp1 = np.array(data)
        x2 = np.empty([0, window_size, 90], float)

        # data import by slide window
        k = 0
        while k <= (len(tmp1) + 1 - 2 * window_size):
            x = np.dstack(np.array(tmp1[k:k + window_size, 1:91]).T)
            x2 = np.concatenate((x2, x), axis=0)
            k += slide_size

        xx = np.concatenate((xx, x2), axis=0)
    xx = xx.reshape(len(xx), -1)

    ###Annotation data###
    # data import from csv
    annotation_csv_files = sorted(glob.glob(path2))
    for ff in annotation_csv_files:
        print("annotation_file_name=", ff)
        ano_data = [[str(elm) for elm in v] for v in csv.reader(open(ff, "r"))]
        tmp2 = np.array(ano_data)

        # data import by slide window
        y = np.zeros(((len(tmp2) + 1 - 2 * window_size) // slide_size + 1, 8))
        k = 0
        while k <= (len(tmp2) + 1 - 2 * window_size):
            y_pre = np.stack(np.array(tmp2[k:k + window_size]))
            bed = 0
            fall = 0
            walk = 0
            pickup = 0
            run = 0
            sitdown = 0
            standup = 0
            noactivity = 0
            for j in range(window_size):
                if y_pre[j] == "bed":
                    bed += 1
                elif y_pre[j] == "fall":
                    fall += 1
                elif y_pre[j] == "walk":
                    walk += 1
                elif y_pre[j] == "pickup":
                    pickup += 1
                elif y_pre[j] == "run":
                    run += 1
                elif y_pre[j] == "sitdown":
                    sitdown += 1
                elif y_pre[j] == "standup":
                    standup += 1
                else:
                    noactivity += 1

            if bed > window_size * threshold / 100:
                y[k / slide_size, :] = np.array([0, 1, 0, 0, 0, 0, 0, 0])
            elif fall > window_size * threshold / 100:
                y[k / slide_size, :] = np.array([0, 0, 1, 0, 0, 0, 0, 0])
            elif walk > window_size * threshold / 100:
                y[k / slide_size, :] = np.array([0, 0, 0, 1, 0, 0, 0, 0])
            elif pickup > window_size * threshold / 100:
                y[k / slide_size, :] = np.array([0, 0, 0, 0, 1, 0, 0, 0])
            elif run > window_size * threshold / 100:
                y[k / slide_size, :] = np.array([0, 0, 0, 0, 0, 1, 0, 0])
            elif sitdown > window_size * threshold / 100:
                y[k / slide_size, :] = np.array([0, 0, 0, 0, 0, 0, 1, 0])
            elif standup > window_size * threshold / 100:
                y[k / slide_size, :] = np.array([0, 0, 0, 0, 0, 0, 0, 1])
            else:
                y[k / slide_size, :] = np.array([2, 0, 0, 0, 0, 0, 0, 0])
            k += slide_size

        yy = np.concatenate((yy, y), axis=0)
    print(xx.shape, yy.shape)
    return (xx, yy)


#### Main ####
if not os.path.exists("input_files/"):
    os.makedirs("input_files/")

for i, label in enumerate(["bed", "fall", "pickup", "run", "sitdown", "standup", "walk"]):
    filepath1 = "dataset/input_*" + str(label) + "*.csv"
    filepath2 = "dataset/annotation_*" + str(label) + "*.csv"
    outputfilename1 = "input_files/xx_" + str(window_size) + "_" + str(threshold) + "_" + label + ".csv"
    outputfilename2 = "input_files/yy_" + str(window_size) + "_" + str(threshold) + "_" + label + ".csv"

    x, y = dataimport(filepath1, filepath2)

    with open(outputfilename1, "w") as f:
        writer = csv.writer(f, lineterminator="\n")
        writer.writerows(x)
    with open(outputfilename2, "w") as f:
        writer = csv.writer(f, lineterminator="\n")
        writer.writerows(y)
    print(label + "finish!")