egohands_dataset_clean.py

import scipy.io as sio
import numpy as np
import os
import gc
import six.moves.urllib as urllib
import cv2
import time
import xml.etree.cElementTree as ET
import random
import shutil as sh
from shutil import copyfile
import zipfile

import csv


def save_csv(csv_path, csv_content):
    with open(csv_path, 'w') as csvfile:
        wr = csv.writer(csvfile)
        for i in range(len(csv_content)):
            wr.writerow(csv_content[i])


def get_bbox_visualize(base_path, dir):
    image_path_array = []
    for root, dirs, filenames in os.walk(base_path + dir):
        for f in filenames:
            if(f.split(".")[1] == "jpg"):
                img_path = base_path + dir + "/" + f
                image_path_array.append(img_path)

    #sort image_path_array to ensure its in the low to high order expected in polygon.mat
    image_path_array.sort()
    boxes = sio.loadmat(
        base_path + dir + "/polygons.mat")
    # there are 100 of these per folder in the egohands dataset
    polygons = boxes["polygons"][0]
    # first = polygons[0]
    # print(len(first))
    pointindex = 0
    for first in polygons:
        index = 0

        font = cv2.FONT_HERSHEY_SIMPLEX

        img_id = image_path_array[pointindex]
        img = cv2.imread(img_id)

        img_params = {}
        img_params["width"] = np.size(img, 1)
        img_params["height"] = np.size(img, 0)
        head, tail = os.path.split(img_id)
        img_params["filename"] = tail
        img_params["path"] = os.path.abspath(img_id)
        img_params["type"] = "train"
        pointindex += 1
        boxarray = []
        csvholder = []
        for pointlist in first:
            pst = np.empty((0, 2), int)
            max_x = max_y = min_x = min_y = height = width = 0

            findex = 0
            for point in pointlist:
                if(len(point) == 2):
                    x = int(point[0])
                    y = int(point[1])

                    if(findex == 0):
                        min_x = x
                        min_y = y
                    findex += 1
                    max_x = x if (x > max_x) else max_x
                    min_x = x if (x < min_x) else min_x
                    max_y = y if (y > max_y) else max_y
                    min_y = y if (y < min_y) else min_y
                    # print(index, "====", len(point))
                    appeno = np.array([[x, y]])
                    pst = np.append(pst, appeno, axis=0)

            hold = {}
            hold['minx'] = min_x
            hold['miny'] = min_y
            hold['maxx'] = max_x
            hold['maxy'] = max_y
            text_file = open("labels/" + tail[:-4] + ".txt", "a")
                #        min_x  max_y  max_x  min_y
                #        left   top,   right  bottom
            text_file.write("Car 0 0 0 " + str(min_x) + " " + str(min_y) + " " + str(max_x) + " " + str(max_y) + " 0 0 0 0 0 0 0\n" )
            text_file.close()
            print("===== saving txt file for ", tail)

#            cv2.putText(img, ".", (x, y), font, 0.7,
#                        (255, 255, 255), 2, cv2.LINE_AA)
#            cv2.polylines(img, [pst], True, (0, 255, 255), 1)
#            cv2.rectangle(img, (min_x, max_y),
#                            (max_x, min_y), (0, 255, 0), 1)
#
#            cv2.putText(img, "DIR : " + dir + " - " + tail, (20, 50),
#                                        cv2.FONT_HERSHEY_SIMPLEX, 0.75, (77, 255, 9), 2)
#            cv2.imwrite(tail, img)

#            csv_path = img_id.split(".")[0]

def create_directory(dir_path):
    if not os.path.exists(dir_path):
        os.makedirs(dir_path)

# combine all individual csv files for each image into a single csv file per folder.

def generate_label_files(image_dir):
    header = ['filename', 'width', 'height',
              'class', 'xmin', 'ymin', 'xmax', 'ymax']
    for root, dirs, filenames in os.walk(image_dir):
        for dir in dirs:
            csvholder = []
            csvholder.append(header)
            loop_index = 0
            for f in os.listdir(image_dir + dir):
                if(f.split(".")[1] == "csv"):
                    loop_index += 1
                    #print(loop_index, f)
                    csv_file = open(image_dir + dir + "/" + f, 'r')
                    reader = csv.reader(csv_file)
                    for row in reader:
                        csvholder.append(row)
                    csv_file.close()
                    os.remove(image_dir + dir + "/" + f)
            save_csv(image_dir + dir + "/" + dir + "_labels.csv", csvholder)
            print("Saved label csv for ", dir, image_dir +
                  dir + "/" + dir + "_labels.csv")


# Split data, copy to train/test folders
def split_data_test_eval_train(image_dir):
    create_directory("images")
    create_directory("images/train")
    create_directory("images/val")

    data_size = 4000
    loop_index = 0
    data_sampsize = int(0.1 * data_size)
    test_samp_array = random.sample(range(data_size), k=data_sampsize)

    for root, dirs, filenames in os.walk(image_dir):
        for dir in dirs:
            for f in os.listdir(image_dir + dir):
                if(f.split(".")[1] == "jpg"):
                    loop_index += 1
                    print(loop_index, f)

                    if loop_index in test_samp_array:
                        os.rename(image_dir + dir +
                                  "/" + f, "images/val/" + f)
#                        os.rename(image_dir + dir +
#                                  "/" + f.split(".")[0] + ".csv", "images/val/" + f.split(".")[0] + ".csv")
                    else:
                        os.rename(image_dir + dir +
                                  "/" + f, "images/train/" + f)
#                        os.rename(image_dir + dir +
#                                  "/" + f.split(".")[0] + ".csv", "images/train/" + f.split(".")[0] + ".csv")
                    print(loop_index, image_dir + f)
            print(">   done scanning director ", dir)
            os.remove(image_dir + dir + "/polygons.mat")
            os.rmdir(image_dir + dir)

        print("Train/test content generation complete!")
#        generate_label_files("images/")


def generate_csv_files(image_dir):
    for root, dirs, filenames in os.walk(image_dir):
        for dir in dirs:
            get_bbox_visualize(image_dir, dir)

    print("Text file generation complete!\nGenerating train/val/eval folders")
    split_data_test_eval_train("egohands/_LABELLED_SAMPLES/")


# rename image files so we can have them all in a train/test/eval folder.
def rename_files(image_dir):
    print("Renaming files")
    loop_index = 0
    for root, dirs, filenames in os.walk(image_dir):
        for dir in dirs:
            for f in os.listdir(image_dir + dir):
                if (dir not in f):
                    if(f.split(".")[1] == "jpg"):
                        loop_index += 1
                        os.rename(image_dir + dir +
                                  "/" + f, image_dir + dir +
                                  "/" + dir + "_" + f)
                else:
                    break

    generate_csv_files("egohands/_LABELLED_SAMPLES/")

def extract_folder(dataset_path):
    print("Egohands dataset already downloaded.\nGenerating CSV files")
    if not os.path.exists("egohands"):
        zip_ref = zipfile.ZipFile(dataset_path, 'r')
        print("> Extracting Dataset files")
        zip_ref.extractall("egohands")
        print("> Extraction complete")
        zip_ref.close()
        rename_files("egohands/_LABELLED_SAMPLES/")

def download_egohands_dataset(dataset_url, dataset_path):
    is_downloaded = os.path.exists(dataset_path)
    if not is_downloaded:
        print(
            "> downloading egohands dataset. This may take a while (1.3GB, say 3-5mins). Coffee break?")
        opener = urllib.request.URLopener()
        opener.retrieve(dataset_url, dataset_path)
        print("> download complete")
        extract_folder(dataset_path);

    else:
        extract_folder(dataset_path)


EGOHANDS_DATASET_URL = "http://vision.soic.indiana.edu/egohands_files/egohands_data.zip"
EGO_HANDS_FILE = "egohands_data.zip"


download_egohands_dataset(EGOHANDS_DATASET_URL, EGO_HANDS_FILE)