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quicklook_download.py
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quicklook_download.py
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import argparse
import logging
import os
import re
import sys
import pandas as pd
import requests
def main(csv_folder, output_folder, wrs2_tiles=None, years=None, months=None,
skip_list_path=None, overwrite_flag=False, id_type='product'):
"""Download Landsat Collection 1 quicklook images
Parameters
----------
csv_folder : str
Folder path of the Landsat metadata CSV files.
output_folder : str
Folder path where the quicklook images will be saved.
wrs2_tiles : list, optional
Landsat WRS2 tiles (path/rows) to download images for.
The default is None which will download images for all tiles.
Example: ['p043r032', 'p043r033']
years : list, optional
Comma separated values or ranges of years to download.
The default is None which will download images for all years.
Example: ['1984', '2000-2015']
months : list, optional
Comma separated values or ranges of months to download.
The default is None which will download images for all months.
Example: ['1', '2', '3-5']
skip_list_path : str, optional
File path of an existing Landsat skip list (the default is None).
overwrite_flag : bool, optional
If True, overwrite existing files (the default is False).
id_type : str, optional
Landsat ID type (the default is 'product').
Returns
-------
None
Notes
-----
Additional filtering can be manually specified in the scripts
"""
logging.info('\nDownload Landsat Collection 1 Quicklooks')
cloud_folder_name = 'cloudy'
if wrs2_tiles is not None:
wrs2_tile_list = sorted([
x.strip() for w in wrs2_tiles for x in w.split(',') if x.strip()])
else:
wrs2_tile_list = []
if years is not None:
year_list = sorted([x for y in years for x in parse_int_set(y)])
else:
year_list = []
if months is not None:
month_list = sorted([x for m in months for x in parse_int_set(m)])
else:
month_list = []
path_list = []
row_list = []
csv_file_list = [
'LANDSAT_8_C1.csv',
'LANDSAT_ETM_C1.csv',
'LANDSAT_TM_C1.csv',
]
csv_years = {
'LANDSAT_8_C1.csv': set(range(2013, 2099)),
'LANDSAT_ETM_C1.csv': set(range(1999, 2099)),
'LANDSAT_TM_C1.csv': set(range(1984, 2012)),
}
wrs2_tile_fmt = 'p{:03d}r{:03d}'
# Input fields
acq_date_col = 'ACQUISITION_DATE'
browse_url_col = 'BROWSE_REFLECTIVE_PATH'
col_category_col = 'COLLECTION_CATEGORY'
col_number_col = 'COLLECTION_NUMBER'
product_id_col = 'LANDSAT_PRODUCT_ID'
scene_id_col = 'LANDSAT_SCENE_ID'
data_type_col = 'DATA_TYPE_L1'
wrs2_path_col = 'WRS_PATH'
wrs2_row_col = 'WRS_ROW'
wrs2_tile_col = 'WRS2_TILE'
# Only load the following columns from the CSV
input_cols = [
acq_date_col, browse_url_col, col_category_col, col_number_col,
data_type_col, product_id_col, scene_id_col, wrs2_path_col,
wrs2_row_col, wrs2_tile_col]
# All other data types and categories will be written to cloudy folder
# DEADBEEF Should OLI_L1TP (no TIRS) be included in clear images?
data_types = ['OLI_TIRS_L1TP', 'OLI_L1TP', 'ETM_L1TP', 'TM_L1TP', 'L1TP']
# data_types = ['OLI_TIRS_L1TP', 'ETM_L1TP', 'TM_L1TP', 'L1TP']
# "A1" isn't documented but appear to be good Landsat 7 L1TP images
categories = ['T1', 'RT', 'A1']
if id_type.lower() == 'short':
logging.info('\nUsing shortened Landsat ID')
# Setup and validate the path/row lists
wrs2_tile_list, path_list, row_list = check_wrs2_tiles(
wrs2_tile_list, path_list, row_list)
# Error checking
if not os.path.isdir(csv_folder):
logging.error('The CSV folder {} doesn\'t exists'.format(csv_folder))
sys.exit()
if skip_list_path and not os.path.isfile(skip_list_path):
logging.error('The skip list file {} doesn\'t exists'.format(
skip_list_path))
sys.exit()
# Read in skip list
skip_list = []
if skip_list_path:
with open(skip_list_path, 'r') as skip_f:
skip_list = skip_f.readlines()
skip_list = [item.strip() for item in skip_list]
logging.info('\nReading metadata CSV files')
download_list = []
for csv_name in csv_file_list:
logging.info('{}'.format(csv_name))
csv_path = os.path.join(csv_folder, csv_name)
if year_list and not csv_years[csv_name].intersection(set(year_list)):
logging.info(' No data for target year(s), skipping file')
continue
elif not os.path.isfile(csv_path):
logging.info(' The CSV file does not exist, skipping')
try:
input_df = pd.read_csv(
csv_path, usecols=input_cols, parse_dates=[acq_date_col])
except Exception as e:
logging.warning(' The CSV file could not be read, skipping')
logging.debug(' Exception: {}'.format(e))
continue
if input_df.empty:
logging.debug(' Empty DataFrame, skipping file')
continue
# logging.debug(input_df.head())
logging.debug(' Fields: {}'.format(', '.join(input_df.columns.values)))
logging.debug(' Initial scene count: {}'.format(len(input_df)))
# Filter scenes first by path and row separately
if path_list:
logging.debug(' Filtering by path')
input_df = input_df[input_df[wrs2_path_col] <= max(path_list)]
input_df = input_df[input_df[wrs2_path_col] >= min(path_list)]
input_df = input_df[input_df[wrs2_path_col].isin(path_list)]
if row_list:
logging.debug(' Filtering by row')
input_df = input_df[input_df[wrs2_row_col] <= max(row_list)]
input_df = input_df[input_df[wrs2_row_col] >= min(row_list)]
input_df = input_df[input_df[wrs2_row_col].isin(row_list)]
# Then filter by path/row combined
# DEADBEEF - WRS2_TILE should already be in the file
try:
input_df[wrs2_tile_col] = input_df[[wrs2_path_col, wrs2_row_col]] \
.apply(lambda x: wrs2_tile_fmt.format(x[0], x[1]), axis=1)
except ValueError:
logging.info(' Possible empty DataFrame, skipping file')
continue
if wrs2_tile_list:
logging.debug(' Filtering by path/row')
input_df = input_df[input_df[wrs2_tile_col].isin(wrs2_tile_list)]
# Filter by year
if year_list:
logging.debug(' Filtering by year')
input_df = input_df[input_df[acq_date_col].dt.year.isin(year_list)]
# Skip early/late months
if month_list:
logging.debug(' Filtering by month')
input_df = input_df[
input_df[acq_date_col].dt.month.isin(month_list)]
# if start_month:
# logging.debug(' Filtering by start month')
# input_df = input_df[input_df[date_col].dt.month >= start_month]
# if end_month:
# logging.debug(' Filtering by end month')
# input_df = input_df[input_df[date_col].dt.month <= end_month]
# # Skip scenes that don't have a browse image
# if browse_url_col in input_df.columns.values:
# logging.debug(' Filtering images without a quicklook')
# input_df = input_df[input_df[browse_url_col] != 'N']
logging.debug(' Final scene count: {}'.format(len(input_df)))
if input_df.empty:
logging.debug(' Empty DataFrame, skipping file')
continue
# Each item is a "row" of data
for row_index, row_df in input_df.iterrows():
# logging.debug(row_df)
if id_type.lower() == 'short':
product_id = row_df[product_id_col].split('_')
product_id = '_'.join([
product_id[0], product_id[2], product_id[3]])
else:
product_id = str(row_df[product_id_col])
# logging.debug(' {}'.format(product_id))
image_dt = row_df[acq_date_col].to_pydatetime()
# Quicklook image path
image_folder = os.path.join(
output_folder, row_df[wrs2_tile_col], str(image_dt.year))
image_name = '{}_{}.jpg'.format(
image_dt.strftime('%Y%m%d_%j'), product_id[:4].upper())
image_path = os.path.join(image_folder, image_name)
# "Cloudy" quicklooks are moved to a separate folder
cloud_path = os.path.join(
image_folder, cloud_folder_name, image_name)
# Remove exist
if overwrite_flag:
if os.path.isfile(image_path):
# logging.debug(' {} - removing'.format(product_id))
os.remove(image_path)
if os.path.isfile(cloud_path):
# logging.debug(' {} - removing'.format(product_id))
os.remove(cloud_path)
# Skip if file is already classified as cloud
elif os.path.isfile(cloud_path):
if os.path.isfile(image_path):
os.remove(image_path)
logging.debug(
' {} - in cloudy folder, skipping'.format(product_id))
continue
# # Download fully cloudy scenes to cloud folder
# if int(row_dict[cloud_cover_col]) >= 90:
# image_path = cloud_path[:]
# logging.info(' {} - cloud_cover >= 90, downloading to cloudy'.format(
# product_id))
# Download non-L1T quicklooks to the cloud folder
if (row_df[data_type_col].upper() not in data_types or
row_df[col_category_col].upper() not in categories):
if os.path.isfile(image_path):
os.remove(image_path)
image_path = cloud_path[:]
logging.info(' {} - not T1/L1TP, downloading to cloudy'.format(
product_id))
# Download scenes in skip list to cloudy folder
if skip_list and product_id in skip_list:
if os.path.isfile(image_path):
os.remove(image_path)
image_path = cloud_path[:]
logging.info(' {} - in skip list, downloading to cloudy'.format(
product_id))
# Check if file exists last
if os.path.isfile(image_path):
logging.debug(
' {} - image exists, skipping'.format(product_id))
continue
# Save download URL and save path
logging.debug(' {}'.format(product_id))
download_list.append([image_path, row_df[browse_url_col]])
# Download Landsat Look Images
logging.debug('')
for image_path, image_url in sorted(download_list):
logging.info('{}'.format(image_path))
logging.debug(' {}'.format(image_url))
image_folder = os.path.dirname(image_path)
if not os.path.isdir(image_folder):
os.makedirs(image_folder)
# Make cloudy image folder also
cloud_folder = os.path.join(image_folder, cloud_folder_name)
if (os.path.basename(image_folder) != cloud_folder_name and
not os.path.isdir(cloud_folder)):
os.makedirs(cloud_folder)
# Trying to catch errors when the bulk metadata site is down
download_file(image_url, image_path)
def check_wrs2_tiles(wrs2_tile_list=[], path_list=[], row_list=[]):
"""Setup path/row lists"""
wrs2_tile_fmt = 'p{:03d}r{:03d}'
wrs2_tile_re = re.compile('p(?P<PATH>\d{1,3})r(?P<ROW>\d{1,3})')
# Force path/row list to zero padded three digit numbers
if wrs2_tile_list:
wrs2_tile_list = sorted([
wrs2_tile_fmt.format(int(m.group('PATH')), int(m.group('ROW')))
for wrs2_tile in wrs2_tile_list
for m in [wrs2_tile_re.match(wrs2_tile)] if m])
# If path_list and row_list were specified, force to integer type
# Declare variable as an empty list if it does not exist
try:
path_list = list(sorted(map(int, path_list)))
except ValueError:
logging.error(
'\nERROR: The path list could not be converted to integers, '
'exiting\n {}'.format(path_list))
sys.exit()
try:
row_list = list(sorted(map(int, row_list)))
except ValueError:
logging.error(
'\nERROR: The row list could not be converted to integers, '
'exiting\n {}'.format(row_list))
sys.exit()
# Convert wrs2_tile_list to path_list and row_list if not set
# Pre-filtering on path and row separately is faster than building wrs2_tile
# This is a pretty messy way of doing this...
if wrs2_tile_list and not path_list:
path_list = sorted(list(set([
int(wrs2_tile_re.match(wrs2_tile).group('PATH'))
for wrs2_tile in wrs2_tile_list
if wrs2_tile_re.match(wrs2_tile)])))
if wrs2_tile_list and not row_list:
row_list = sorted(list(set([
int(wrs2_tile_re.match(wrs2_tile).group('ROW'))
for wrs2_tile in wrs2_tile_list
if wrs2_tile_re.match(wrs2_tile)])))
if path_list:
logging.debug(' Paths: {}'.format(
' '.join(list(map(str, path_list)))))
if row_list:
logging.debug(' Rows: {}'.format(' '.join(list(map(str, row_list)))))
if wrs2_tile_list:
logging.debug(' WRS2 Tiles: {}'.format(
' '.join(list(map(str, wrs2_tile_list)))))
return wrs2_tile_list, path_list, row_list
def download_file(file_url, file_path):
""""""
logging.debug(' Downloading file')
logging.debug(' {}'.format(file_url))
try:
r = requests.get(file_url)
with open(file_path, 'wb') as output_f:
for chunk in r.iter_content(chunk_size=128):
output_f.write(chunk)
except Exception as e:
logging.info(' {}\n Try manually checking the quicklook '
'URL\n'.format(e))
# urlrequest.urlretrieve(file_url, file_path)
# def get_csv_path(workspace):
# import Tkinter, tkFileDialog
# root = Tkinter.Tk()
# ini_path = tkFileDialog.askopenfilename(
# initialdir=workspace, parent=root, filetypes=[('XML files', '.xml')],
# title='Select the target XML file')
# root.destroy()
# return ini_path
def is_valid_file(parser, arg):
if not os.path.isfile(os.path.abspath(arg)):
parser.error('The file {} does not exist!'.format(arg))
else:
return arg
def is_valid_folder(parser, arg):
if not os.path.isdir(os.path.abspath(arg)):
parser.error('The folder {} does not exist!'.format(arg))
else:
return arg
def parse_int_set(nputstr=""):
"""Return list of numbers given a string of ranges
http://thoughtsbyclayg.blogspot.com/2008/10/parsing-list-of-numbers-in-python.html
"""
selection = set()
invalid = set()
# tokens are comma separated values
tokens = [x.strip() for x in nputstr.split(',')]
for i in tokens:
try:
# typically tokens are plain old integers
selection.add(int(i))
except:
# if not, then it might be a range
try:
token = [int(k.strip()) for k in i.split('-')]
if len(token) > 1:
token.sort()
# we have items separated by a dash
# try to build a valid range
first = token[0]
last = token[len(token) - 1]
for x in range(first, last + 1):
selection.add(x)
except:
# not an int and not a range...
invalid.add(i)
# Report invalid tokens before returning valid selection
# print "Invalid set: " + str(invalid)
return selection
def arg_parse():
""""""
parser = argparse.ArgumentParser(
description='Download Landsat Collection 1 quicklook images',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
'--csv', default=os.getcwd(), metavar='FOLDER',
type=lambda x: is_valid_folder(parser, x),
help='Landsat metadata CSV folder')
parser.add_argument(
'--output', default=os.getcwd(), metavar='FOLDER',
type=lambda x: is_valid_folder(parser, x),
help='Output folder')
parser.add_argument(
'-pr', '--wrs2', default=None, nargs='+', metavar='pXXXrYYY',
help='Space/comma separated list of Landsat WRS2 tiles to download '
'(i.e. --wrs2 p043r032 p043r033)')
parser.add_argument(
'-y', '--years', default=None, nargs='+',
help='Space/comma separated list of years or year_ranges to download '
'(i.e. "--years 1984 2000-2015")')
parser.add_argument(
'-m', '--months', default=None, nargs='+',
help='Space/comma separated list of months or month ranges to download '
'(i.e. "--months 1 2 3-5")')
parser.add_argument(
'--skiplist', default=None, metavar='FILE',
type=lambda x: is_valid_file(parser, x),
help='File path of scene IDs that should be downloaded directly to '
'the "cloudy" scenes folder')
parser.add_argument(
'-id', '--id_type', default='product', choices=['product', 'short'],
help='Landsat ID type')
parser.add_argument(
'-o', '--overwrite', default=False, action='store_true',
help='Overwite existing quicklooks')
parser.add_argument(
'-d', '--debug', default=logging.INFO, const=logging.DEBUG,
help='Debug level logging', action='store_const', dest='loglevel')
args = parser.parse_args()
# Convert relative paths to absolute paths
if args.csv and os.path.isdir(os.path.abspath(args.csv)):
args.csv = os.path.abspath(args.csv)
if args.output and os.path.isdir(os.path.abspath(args.output)):
args.output = os.path.abspath(args.output)
if args.skiplist and os.path.isfile(os.path.abspath(args.skiplist)):
args.skiplist = os.path.abspath(args.skiplist)
return args
if __name__ == '__main__':
args = arg_parse()
logging.basicConfig(level=args.loglevel, format='%(message)s')
main(csv_folder=args.csv, output_folder=args.output,
wrs2_tiles=args.wrs2, years=args.years, months=args.months,
skip_list_path=args.skiplist, id_type=args.id_type,
overwrite_flag=args.overwrite)