001_down_data.py

#!/usr/bin/env python3

import requests
import os
import shutil

"""
Script to download
Wider Face Training Images
Wider Face Validation Images
from Google Drive using Python3.6

http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/

Wider_face_split is included in repo

"""

# credits: https://stackoverflow.com/a/16664766
# https://stackoverflow.com/questions/16664526/howto-download-file-from-drive-api-using-python-script

def download_file_from_google_drive(id, destination):
    def get_confirm_token(response):
        for key, value in response.cookies.items():
            if key.startswith('download_warning'):
                return value

        return None

    def save_response_content(response, destination):
        CHUNK_SIZE = 32768

        with open(destination, "wb") as f:
            for chunk in response.iter_content(CHUNK_SIZE):
                if chunk: # filter out keep-alive new chunks
                    f.write(chunk)

    URL = "https://docs.google.com/uc?export=download"

    session = requests.Session()

    response = session.get(URL, params = { 'id' : id }, stream = True)
    token = get_confirm_token(response)

    if token:
        params = { 'id' : id, 'confirm' : token }
        response = session.get(URL, params = params, stream = True)

    save_response_content(response, destination)


# The script
curr_path = os.getcwd()
models_path = os.path.join(curr_path,"data")

# make dir => wider_data in folder
try:
    os.makedirs(models_path)
except Exception as e:
    pass

if os.path.exists(os.path.join(models_path,"train.zip")) == False:
    print("downloading.. train.zip -- 1.47GB")
    download_file_from_google_drive("0B6eKvaijfFUDQUUwd21EckhUbWs", os.path.join(models_path,"train.zip"))

if os.path.exists(os.path.join(models_path,"val.zip")) == False:
    print("downloading.. val.zip -- 362.8MB")
    download_file_from_google_drive("0B6eKvaijfFUDd3dIRmpvSk8tLUk", os.path.join(models_path,"val.zip"))

print("files downloaded")

# unzip the files
import zipfile

if os.path.exists(os.path.join(models_path,"WIDER_train")) == False:
    with zipfile.ZipFile(os.path.join(models_path,"train.zip"),"r") as zip_ref:
        zip_ref.extractall(models_path)

if os.path.exists(os.path.join(models_path,"WIDER_val")) == False:
    with zipfile.ZipFile(os.path.join(models_path,"val.zip"),"r") as zip_ref:
        zip_ref.extractall(models_path)

print("files unziped")













# downloading from: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
url = 'http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_11_06_2017.tar.gz'

if os.path.exists(os.path.join(models_path,"ssd_mobilenet_v1_coco_11_06_2017.tar.gz")) == False:
    response = requests.get(url, stream=True)
    with open(os.path.join(models_path,"ssd_mobilenet_v1_coco_11_06_2017.tar.gz"), 'wb') as out_file:
        shutil.copyfileobj(response.raw, out_file)
    del response


import tarfile
filePath = os.path.join(models_path,"ssd_mobilenet_v1_coco_11_06_2017.tar.gz")
os.chdir(models_path)


if (filePath.endswith("tar.gz")):
    tar = tarfile.open(filePath, "r:gz")
    tar.extractall()
    tar.close()
elif (filePath.endswith("tar")):
    tar = tarfile.open(filePath, "r:")
    tar.extractall()
    tar.close()


print("done")