def create_thumbnails(scene_id, landsat_id):
"""Creates thumbnails based on scene_id
Args:
scene_id (str): id of the scene (e.g. LC81351172016273LGN00)
Returns:
List[Thumbnail]
"""
path = get_landsat_url(landsat_id)
small_url = path + '_'.join([landsat_id, 'thumb', 'small']) + '.jpg'
large_url = path + '_'.join([landsat_id, 'thumb', 'large']) + '.jpg'
return [
Thumbnail(organization, 228, 233, 'SMALL', small_url,
sceneId=scene_id),
Thumbnail(organization,
1143,
1168,
'LARGE',
large_url,
sceneId=scene_id)
]
def get_planet_thumbnail(organization_id, thumbnail_uri, planet_key, scene_id):
"""Download planet thumbnail, push to S3, create RF thumbnail
Args:
organization_id (str): organization requiring thumbnail
thumbnail_uri (str): source thumbnail
planet_key (str): planet API key for authentication
scene_id (str): scene to attach thumbnail to
Returns:
Thumbnail
"""
_, local_filepath = tempfile.mkstemp()
params = {'api_key': planet_key}
logger.info('Downloading thumbnail for Planet scene: %s', thumbnail_uri)
response = requests.get(thumbnail_uri, params=params, stream=True)
with open(local_filepath, 'wb') as filehandle:
for chunk in response.iter_content(1024):
filehandle.write(chunk)
thumbnail_key = '{}.png'.format(scene_id)
thumbnail_uri = '/thumbnails/{}.png'.format(scene_id)
logger.info('Uploading thumbnails to S3: %s', thumbnail_key)
s3_bucket_name = os.getenv('THUMBNAIL_BUCKET')
s3_bucket = boto3.resource('s3').Bucket(s3_bucket_name)
s3_bucket.upload_file(local_filepath, thumbnail_key,
{'ContentType': 'image/png'})
delete_file(local_filepath)
return Thumbnail(organization_id,
256,
256,
'SMALL',
thumbnail_uri,
sceneId=scene_id)
def create_thumbnails(scene_id, tile_path):
"""Creates thumbnail based on tile_path
Args:
tile_path (str): path to tile directory (e.g. tiles/54/M/XB/2016/9/25/0)
Returns:
List[Thumbnail]
"""
key_path = '{tile_path}/preview.jpg'.format(tile_path=tile_path)
thumbnail_url = base_http_path.format(key_path=key_path)
if not s3_obj_exists(thumbnail_url):
return []
else:
return [
Thumbnail(organization,
343,
343,
'SQUARE',
thumbnail_url,
sceneId=scene_id)
]
def create_thumbnails(tif_path, scene_id, organization_id):
"""Creates thumbnails based on a geotiff path and scene
Args:
tif_path (str): path of local tiff file
scene_id (str): uuid of the scene
Returns:
List[Thumbnail]
"""
r_tif_path = os.path.join(
os.path.dirname(tif_path),
'resampled_{}'.format(os.path.basename(tif_path))
)
rp_tif_path = os.path.join(
os.path.dirname(tif_path),
'reprojected_{}'.format(os.path.basename(tif_path))
)
dim = get_geotiff_dimensions(tif_path)
max_dim = float(max(get_geotiff_dimensions(tif_path)))
size_large = 1024
size_small = 256
scale_large = size_large / max_dim
scale_small = size_small / max_dim
dim_large = (dim[0] * scale_large, dim[1] * scale_large)
dim_small = (dim[0] * scale_small, dim[1] * scale_small)
# Create paths for each size thumb
path_large = '{}-LARGE.png'.format(scene_id)
path_small = '{}-SMALL.png'.format(scene_id)
# Create urls for each size Thumbnail
url_large = '/thumbnails/{}'.format(path_large)
url_small = '/thumbnails/{}'.format(path_small)
try_to_remove_files([r_tif_path, rp_tif_path, path_large, path_small])
if os.path.isfile(tif_path):
try:
# Resample tif
subprocess.check_call([
'gdalwarp', tif_path, r_tif_path,
'-ts', str(dim_large[0]), str(dim_large[1]),
'-q'
])
# Reproject tif
subprocess.check_call([
'gdalwarp', r_tif_path, rp_tif_path,
'-t_srs', 'EPSG:3857',
'-q'
])
# Create a temporary env object
mod_env = os.environ.copy()
# Add variable to avoid sidecar files
mod_env['GDAL_PAM_ENABLED'] = 'NO'
# Convert tif to pngs (large)
subprocess.check_call([
'gdal_translate', rp_tif_path, path_large,
'-b', '1',
'-b', '2',
'-b', '3',
'-outsize', str(dim_large[0]), str(dim_large[1]),
'-of', 'PNG',
'-q'
], env=mod_env)
# Convert tif to pngs (small)
subprocess.check_call([
'gdal_translate', rp_tif_path, path_small,
'-b', '1',
'-b', '2',
'-b', '3',
'-outsize', str(dim_small[0]), str(dim_small[1]),
'-of', 'PNG',
'-q',
], env=mod_env)
except:
# If any subprocess calls fail, we need to clean up before exiting
try_to_remove_files([r_tif_path, rp_tif_path, path_large, path_small])
raise
if os.path.isfile(path_large) and os.path.isfile(path_small):
s3_bucket_name = os.getenv('THUMBNAIL_BUCKET')
s3_bucket = boto3.resource('s3').Bucket(s3_bucket_name)
s3_bucket.upload_file(path_large, path_large, {'ContentType': 'image/png'})
s3_bucket.upload_file(path_small, path_small, {'ContentType': 'image/png'})
try_to_remove_files([r_tif_path, rp_tif_path, path_large, path_small])
else:
return
# Return List[Thumbnail]
return [
Thumbnail(
organization_id,
dim_small[0],
dim_small[1],
'SMALL',
url_small,
sceneId=scene_id
),
Thumbnail(
organization_id,
dim_large[0],
dim_large[1],
'LARGE',
url_large,
sceneId=scene_id
)
]
def create_thumbnails(tif_path, scene_id, organization_id):
"""Creates thumbnails based on a geotiff path and scene
Args:
tif_path (str): path of local tiff file
scene_id (str): uuid of the scene
Returns:
List[Thumbnail]
"""
r_tif_path = os.path.join(
os.path.dirname(tif_path),
'resampled_{}'.format(os.path.basename(tif_path)))
rp_tif_path = os.path.join(
os.path.dirname(tif_path),
'reprojected_{}'.format(os.path.basename(tif_path)))
dim = get_geotiff_dimensions(tif_path)
max_dim = float(max(get_geotiff_dimensions(tif_path)))
size_large = 1024
size_small = 256
scale_large = size_large / max_dim
scale_small = size_small / max_dim
dim_large = (int(dim[0] * scale_large), int(dim[1] * scale_large))
dim_small = (int(dim[0] * scale_small), int(dim[1] * scale_small))
tempdir = os.path.dirname(tif_path)
# Create paths for each size thumb
path_large = os.path.join(tempdir, '{}-LARGE.png'.format(scene_id))
path_small = os.path.join(tempdir, '{}-SMALL.png'.format(scene_id))
# Color Corrected
path_cc_large = os.path.join(tempdir, '{}-CC-LARGE.png'.format(scene_id))
path_cc_small = os.path.join(tempdir, '{}-CC-SMALL.png'.format(scene_id))
# Create urls for each size Thumbnail
url_large = '/thumbnails/{}'.format(os.path.basename(path_cc_large))
url_small = '/thumbnails/{}'.format(os.path.basename(path_cc_small))
try_to_remove_files([
r_tif_path, rp_tif_path, path_large, path_small, path_cc_large,
path_cc_small
])
if os.path.isfile(tif_path):
try:
logger.info('Performing thumbnail resampling')
subprocess.check_call([
'gdal_translate',
tif_path,
r_tif_path,
'-outsize',
str(dim_large[0]),
str(dim_large[1]),
])
logger.info('Performing thumbnail warping')
subprocess.check_call([
'gdalwarp', r_tif_path, rp_tif_path, '-t_srs', 'EPSG:3857',
'-q'
])
# Create a temporary env object
mod_env = os.environ.copy()
# Add variable to avoid sidecar files
mod_env['GDAL_PAM_ENABLED'] = 'NO'
with rasterio.open(rp_tif_path) as src:
num_bands = src.count
if num_bands >= 3:
bands = ['-b', '1', '-b', '2', '-b', '3']
else:
bands = ['-b', '1']
logger.info('Creating thumbnail files')
large_thumbnail_gdal_command = [
'gdal_translate', rp_tif_path, path_large, '-outsize',
str(dim_large[0]),
str(dim_large[1]), '-of', 'PNG', '-q'
] + bands
small_thumbnail_gdal_command = [
'gdal_translate', rp_tif_path, path_small, '-outsize',
str(dim_small[0]),
str(dim_small[1]), '-of', 'PNG', '-q'
] + bands
# Convert tif to pngs (large)
subprocess.check_call(large_thumbnail_gdal_command, env=mod_env)
# Convert tif to pngs (small)
subprocess.check_call(small_thumbnail_gdal_command, env=mod_env)
# Do basic histogram normalization to improve thumbnails
subprocess.check_call(
['convert', path_small, '-normalize', path_cc_small])
# Do basic histogram normalization to improve thumbnails
subprocess.check_call(
['convert', path_large, '-normalize', path_cc_large])
except:
# If any subprocess calls fail, we need to clean up before exiting
try_to_remove_files(
[r_tif_path, rp_tif_path, path_large, path_small])
raise
if os.path.isfile(path_large) and os.path.isfile(path_small):
logger.info('Uploading thumbnails to S3')
s3_bucket_name = os.getenv('THUMBNAIL_BUCKET')
s3_bucket = boto3.resource('s3').Bucket(s3_bucket_name)
s3_bucket.upload_file(path_cc_large,
os.path.basename(path_cc_large),
{'ContentType': 'image/png'})
s3_bucket.upload_file(path_cc_small,
os.path.basename(path_cc_small),
{'ContentType': 'image/png'})
try_to_remove_files([r_tif_path, rp_tif_path, path_large, path_small])
else:
return
# Return List[Thumbnail]
return [
Thumbnail(organization_id,
dim_small[0],
dim_small[1],
'SMALL',
url_small,
sceneId=scene_id),
Thumbnail(organization_id,
dim_large[0],
dim_large[1],
'LARGE',
url_large,
sceneId=scene_id)
]
def create_thumbnails(tif_path, scene_id):
"""Creates thumbnails based on a geotiff path and scene
Args:
tif_path (str): path of local tiff file
scene_id (str): uuid of the scene
Returns:
List[Thumbnail]
"""
r_tif_path = os.path.join(
os.path.dirname(tif_path), "resampled_{}".format(os.path.basename(tif_path))
)
rp_tif_path = os.path.join(
os.path.dirname(tif_path), "reprojected_{}".format(os.path.basename(tif_path))
)
dim = get_geotiff_dimensions(tif_path)
max_dim = float(max(get_geotiff_dimensions(tif_path)))
size_large = 1024
size_small = 256
scale_large = size_large / max_dim
scale_small = size_small / max_dim
dim_large = (int(dim[0] * scale_large), int(dim[1] * scale_large))
dim_small = (int(dim[0] * scale_small), int(dim[1] * scale_small))
tempdir = os.path.dirname(tif_path)
# Create paths for each size thumb
path_large = os.path.join(tempdir, "{}-LARGE.png".format(scene_id))
path_small = os.path.join(tempdir, "{}-SMALL.png".format(scene_id))
# Color Corrected
path_cc_large = os.path.join(tempdir, "{}-CC-LARGE.png".format(scene_id))
path_cc_small = os.path.join(tempdir, "{}-CC-SMALL.png".format(scene_id))
# Create urls for each size Thumbnail
url_large = "/thumbnails/{}".format(os.path.basename(path_cc_large))
url_small = "/thumbnails/{}".format(os.path.basename(path_cc_small))
try_to_remove_files(
[r_tif_path, rp_tif_path, path_large, path_small, path_cc_large, path_cc_small]
)
if os.path.isfile(tif_path):
try:
logger.info("Performing thumbnail resampling")
subprocess.check_call(
[
"gdal_translate",
tif_path,
r_tif_path,
"-outsize",
str(dim_large[0]),
str(dim_large[1]),
]
)
logger.info("Performing thumbnail warping")
subprocess.check_call(
["gdalwarp", r_tif_path, rp_tif_path, "-t_srs", "EPSG:3857", "-q"]
)
# Create a temporary env object
mod_env = os.environ.copy()
# Add variable to avoid sidecar files
mod_env["GDAL_PAM_ENABLED"] = "NO"
with rasterio.open(rp_tif_path) as src:
num_bands = src.count
if num_bands >= 3:
bands = ["-b", "1", "-b", "2", "-b", "3"]
else:
bands = ["-b", "1"]
logger.info("Creating thumbnail files")
large_thumbnail_gdal_command = [
"gdal_translate",
rp_tif_path,
path_large,
"-outsize",
str(dim_large[0]),
str(dim_large[1]),
"-of",
"PNG",
"-q",
] + bands
small_thumbnail_gdal_command = [
"gdal_translate",
rp_tif_path,
path_small,
"-outsize",
str(dim_small[0]),
str(dim_small[1]),
"-of",
"PNG",
"-q",
] + bands
# Convert tif to pngs (large)
subprocess.check_call(large_thumbnail_gdal_command, env=mod_env)
# Convert tif to pngs (small)
subprocess.check_call(small_thumbnail_gdal_command, env=mod_env)
# Do basic histogram normalization to improve thumbnails
subprocess.check_call(["convert", path_small, "-normalize", path_cc_small])
# Do basic histogram normalization to improve thumbnails
subprocess.check_call(["convert", path_large, "-normalize", path_cc_large])
except:
# If any subprocess calls fail, we need to clean up before exiting
try_to_remove_files([r_tif_path, rp_tif_path, path_large, path_small])
raise
if os.path.isfile(path_large) and os.path.isfile(path_small):
logger.info("Uploading thumbnails to S3")
s3_bucket_name = os.getenv("THUMBNAIL_BUCKET")
s3_bucket = boto3.resource("s3").Bucket(s3_bucket_name)
s3_bucket.upload_file(
path_cc_large,
os.path.basename(path_cc_large),
{"ContentType": "image/png"},
)
s3_bucket.upload_file(
path_cc_small,
os.path.basename(path_cc_small),
{"ContentType": "image/png"},
)
try_to_remove_files([r_tif_path, rp_tif_path, path_large, path_small])
else:
return
# Return List[Thumbnail]
return [
Thumbnail(dim_small[0], dim_small[1], "SMALL", url_small, sceneId=scene_id),
Thumbnail(dim_large[0], dim_large[1], "LARGE", url_large, sceneId=scene_id),
]