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Resolves #14
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@2320sharon
2320sharon committed Jun 11, 2024
1 parent 2d64635 commit 97caa10
Showing 1 changed file with 71 additions and 121 deletions.
192 changes: 71 additions & 121 deletions src/coastsat/SDS_download.py
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Original file line number Diff line number Diff line change
Expand Up @@ -141,6 +141,7 @@ def debug_kill_wifi():

os.system("netsh wlan disconnect")


def get_images_list_from_collection(ee_col, polygon, dates):
"""
Retrieves a list of images from a given Earth Engine collection within a specified polygon and date range.
Expand Down Expand Up @@ -195,10 +196,13 @@ def split_date_range(start_date, end_date, num_splits):
Raises:
ValueError: If the number of splits is greater than the range of days available.
"""
# Convert strings to datetime objects
start_date = datetime.strptime(start_date, '%Y-%m-%d')
end_date = datetime.strptime(end_date, '%Y-%m-%d')

# Check if start_date and end_date are strings and convert them if necessary
if isinstance(start_date, str):
start_date = datetime.strptime(start_date, '%Y-%m-%d')

if isinstance(end_date, str):
end_date = datetime.strptime(end_date, '%Y-%m-%d')

# Calculate the total duration of the range
total_duration = (end_date - start_date).days

Expand Down Expand Up @@ -615,6 +619,66 @@ def merge_image_tiers(inputs, im_dict_T1, im_dict_T2):
return im_dict_T1


def check_images_available(inputs, months_list=None, scene_cloud_cover=0.95,tier1=True,tier2=False):
"""
Scan the GEE collections to see how many images are available for each
satellite mission (L5, L7, L8, L9, S2), collection (C02) and tier (T1, T2).
KV WRL 2018
Arguments:
-----------
inputs: dict
inputs dictionary
months_list: list of int
list of months to filter the images by and only keep images in these months (default is None)
example: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
scene_cloud_cover: int
maximum cloud cover percentage for the scene (default is 95)
Note: this is the entire scene not just the ROI
Returns:
-----------
im_dict_T1: list of dict
list of images in Tier 1 and Level-1C
im_dict_T2: list of dict
list of images in Tier 2 (Landsat only)
"""
if months_list is None:
months_list = list(range(1, 13))

dates = [datetime.strptime(_, "%Y-%m-%d") for _ in inputs["dates"]]
dates_str = inputs["dates"]
polygon = inputs["polygon"]

im_dict_T2 = {}
im_dict_T1 = {}

check_dates_order(dates)
initialize_ee()
# check_collection(inputs["landsat_collection"])

print("Number of images available between %s and %s:" % (dates_str[0], dates_str[1]), end="\n")

if tier1:
im_dict_T1 = get_tier1_images(inputs, polygon, dates, scene_cloud_cover, months_list)
im_dict_T1 = remove_existing_images_if_needed(inputs, im_dict_T1)

sum_img = sum(len(im_dict_T1[satname]) for satname in im_dict_T1)
print(" Total images available to download from Tier 1: %d images" % sum_img)

if len(inputs["sat_list"]) == 1 and inputs["sat_list"][0] == "S2":
return im_dict_T1, []

if tier2:
im_dict_T2 = get_tier2_images(inputs, polygon, dates_str, scene_cloud_cover, months_list)
im_dict_T2 = remove_existing_images_if_needed(inputs, im_dict_T2)

sum_img = sum(len(im_dict_T2[satname]) for satname in im_dict_T2)
print(" Total images available to download from Tier 2: %d images" % sum_img)

return im_dict_T1, im_dict_T2

def retrieve_images(
inputs,
cloud_threshold: float = 0.95,
Expand Down Expand Up @@ -1551,115 +1615,6 @@ def remove_existing_imagery(image_dict:dict, metadata:dict,sat_list:list[str])->
return image_dict


def check_images_available(inputs,months_list=None,scene_cloud_cover=0.95):
"""
Scan the GEE collections to see how many images are available for each
satellite mission (L5,L7,L8,L9,S2), collection (C02) and tier (T1,T2).
KV WRL 2018
Arguments:
-----------
inputs: dict
inputs dictionary
months_list: list of int
list of months to filter the images by and only keep images in these months (default is None)
example: [1,2,3,4,5,6,7,8,9,10,11,12]
scene_cloud_cover: int
maximum cloud cover percentage for the scene (default is 95)
Note: this is the entire scene not just the ROI
Returns:
-----------
im_dict_T1: list of dict
list of images in Tier 1 and Level-1C
im_dict_T2: list of dict
list of images in Tier 2 (Landsat only)
"""
if months_list is None:
months_list = list(range(1, 13))
dates = [datetime.strptime(_, "%Y-%m-%d") for _ in inputs["dates"]]
dates_str = inputs["dates"]
polygon = inputs["polygon"]

# check if dates are in chronological order
if dates[1] <= dates[0]:
raise Exception("Verify that your dates are in the correct chronological order")

# validates the inputs have references the correct collection (C02)
inputs = validate_collection(inputs)

# check if EE was initialised or not
try:
ee.ImageCollection("LANDSAT/LC08/C02/T1_TOA")
except:
ee.Initialize()

print(
"Number of images available between %s and %s:" % (dates_str[0], dates_str[1]),
end="\n",
)

# CREATES A DICTIONARY OF SATELLITES CONTAINING THE IMAGES IN TIER 1 IN COLLECTION C02
# get images in Landsat Tier 1 as well as Sentinel Level-1C
print("- In Landsat Tier 1 & Sentinel-2 Level-1C:")
col_names_T1 = {
"L5": "LANDSAT/LT05/%s/T1_TOA" % inputs["landsat_collection"],
"L7": "LANDSAT/LE07/%s/T1_TOA" % inputs["landsat_collection"],
"L8": "LANDSAT/LC08/%s/T1_TOA" % inputs["landsat_collection"],
"L9": "LANDSAT/LC09/C02/T1_TOA", # only C02 for Landsat 9
"S2": "COPERNICUS/S2_HARMONIZED",
}
im_dict_T1 = dict([])
sum_img = 0
# gets the list of images for each satellite mission
for satname in inputs["sat_list"]:
im_list=get_image_info(col_names_T1[satname], satname, polygon, dates,S2tile=inputs.get("S2tile", ""), scene_cloud_cover=scene_cloud_cover,months_list= months_list)
# S2 contains many duplicates images so filter collection to only keep images with same UTM Zone projection
if satname == "S2":
im_list = filter_S2_collection(im_list)
print(" %s: %d images" % (satname, len(im_list)))
im_dict_T1[satname] = im_list

# if the directory already exists, remove the images that already exist from the download request
filepath = os.path.join(inputs['filepath'],inputs['sitename'])
if os.path.exists(filepath):
# get the metadata and satellites that need to be filtered
sat_list = inputs["sat_list"]
metadata = get_metadata(inputs)
# remove any images that already exist from im_dict_T1 because they've already been downloaded
im_dict_T1 = remove_existing_imagery(im_dict_T1, metadata,sat_list)

# Sum all the images available in Tier 1
for satname in im_dict_T1:
sum_img = sum_img + len(im_dict_T1[satname])
print(" Total images available to download from Tier 1: %d images" % sum_img)

# if only S2 is in sat_list, stop here as no Tier 2 for Sentinel
if len(inputs["sat_list"]) == 1 and inputs["sat_list"][0] == "S2":
return im_dict_T1, []

# CREATES A DICTIONARY OF SATELLITES CONTAINING THE IMAGES IN TIER 2
# if user also requires Tier 2 images, check the T2 collections as well
col_names_T2 = {
"L5": "LANDSAT/LT05/%s/T2_TOA" % inputs["landsat_collection"],
"L7": "LANDSAT/LE07/%s/T2_TOA" % inputs["landsat_collection"],
"L8": "LANDSAT/LC08/%s/T2_TOA" % inputs["landsat_collection"],
}
print("- In Landsat Tier 2 (not suitable for time-series analysis):", end="\n")
im_dict_T2 = dict([])
sum_img = 0
for satname in inputs["sat_list"]:
if satname in ["L9", "S2"]:
continue # no Tier 2 for Sentinel-2 and Landsat 9
im_list=get_image_info(col_names_T2[satname], satname, polygon, dates_str,S2tile=inputs.get("S2tile", ""), scene_cloud_cover=scene_cloud_cover,months_list= months_list)
sum_img = sum_img + len(im_list)
print(" %s: %d images" % (satname, len(im_list)))
im_dict_T2[satname] = im_list


print(" Total images available to download from Tier 2: %d images" % sum_img)
return im_dict_T1, im_dict_T2


def get_s2cloudless(image_list: list, inputs: dict):
Expand All @@ -1680,14 +1635,9 @@ def get_s2cloudless(image_list: list, inputs: dict):
# Convert string dates to datetime objects
dates = [datetime.strptime(date, "%Y-%m-%d") for date in inputs["dates"]]
polygon = inputs["polygon"]
collection = "COPERNICUS/S2_CLOUD_PROBABILITY"
# get s2cloudless collection
s2cloudless_col = (
ee.ImageCollection(collection)
.filterBounds(ee.Geometry.Polygon(polygon))
.filterDate(dates[0], dates[1])
)
cloud_images_list = s2cloudless_col.getInfo().get("features", [])
collection_name = "COPERNICUS/S2_CLOUD_PROBABILITY"
collection = ee.ImageCollection(collection_name)
cloud_images_list = get_images_list_from_collection(collection, polygon, dates)
# Extract image IDs from the s2cloudless collection
cloud_indices = [
image["properties"]["system:index"] for image in cloud_images_list
Expand Down

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