def read_pixel_data(field_no):
startDate, endDate = "2017-05-01", "2017-09-30"
arrays = reader.parseKML("2017_polygons.kml")
array = arrays[field_no]
data, dates = getSAR.arrayToSAR(array, startDate, endDate, returnDates=True)
res_date, res_px = ([], [])
for i in range(0, len(data), 2):
a1 = data[i][0][data[i][0] != 0]
a2 = data[i+1][0][data[i+1][0] != 0]
tmp = np.array([[np.mean(a1), np.mean(a2)]])
res_px.append(tmp)
res_date.append(f'{(dates[i//2]):%B %d, %Y}')
return res_date, res_px
def getDataset():
Xs = []
ys = []
for year in ["2017", "2018", "2019"]:
counter = 0
fields = parseKML(year + "_polygons.kml")
total = str(len(fields))
for array in fields:
try:
X, y = pixelPairs(array, year + "-05-01", year + "-09-30", CLOUDY_PIXEL_PERCENTAGE=100)
Xs.append(X)
ys.append(y)
# print([np.concatenate(Xs), np.concatenate(ys)])
except ee.ee_exception.EEException as e:
# TODO: fix exception (exception is related to some empty ee.Array)
print(e)
print("Exception occurred, skipping")
print(year + ": " + str(counter) + "/" + total)
counter += 1
return np.concatenate(Xs), np.concatenate(ys)
def generateFieldImages(field_no, regr_model, year="2017"):
try:
startDate, endDate = f"{year}-05-01", f"{year}-09-30"
arrays = reader.parseKML(f"{year}_polygons.kml")
array = arrays[field_no]
data, mask = getPair.arrayToPairs(array, startDate, endDate, False, 0.5)
ndvi_images = [data[i][0] for i in range(0, len(data), 3)]
vv_images = [data[i+1][0] for i in range(0, len(data), 3)]
vh_images = [data[i+2][0] for i in range(0, len(data), 3)]
for i in range(len(ndvi_images)):
print(f"Processing image #{i+1}...")
y = imagePredict(regr, vv_images[i], vh_images[i], mask)
ndvi_mes, ndvi_pred = ndvi_images[i], y
img_mes, img_pred = fieldToImages(ndvi_mes, ndvi_pred)
#img_mes.show()
#img_pred.show()
img_mes.save(f"images/field_NDVI/{year}/measured/field{field_no+1}_{i+1}.jpg", "JPEG")
img_pred.save(f"images/field_NDVI/{year}/predicted/field{field_no+1}_{i+1}.jpg", "JPEG")
except ValueError:
return
import ee
import model
from pixelPairs import pixelPairs
from reader import parseKML
if __name__ == "__main__":
# Trigger the authentication flow.
# ee.Authenticate()
# Initialize the library.
ee.Initialize()
for array in parseKML("2017_polygons.kml"):
X, y = pixelPairs(array, "2017-05-01", "2017-09-30")
if len(X):
model.train_and_test(X, y)
[common.fastLatLonImg(ee.Image(l_NDVI.get(NDVI)), area) for NDVI in sorted_pairs])
LatLonImgsSAR = ee.List([LatLonImgVHVV(ee.Image(l_SAR.get(SAR)), area) for SAR in range(SAR_size)])
both_lists = ee.List([LatLonImgsNDVI, LatLonImgsSAR]).getInfo()
LatLonImgsNDVI = both_lists[0]
LatLonImgsSAR = both_lists[1]
for NDVI in range(len(LatLonImgsNDVI)):
i = sorted_pairs[NDVI]
for SAR in pairs_i[i]:
ndvi_temp = (LatLonImgsNDVI[NDVI][0], LatLonImgsNDVI[NDVI][1], LatLonImgsNDVI[NDVI][2])
if SAR not in precomputed_SAR:
lats = LatLonImgsSAR[SAR][0]
lons = LatLonImgsSAR[SAR][1]
vh = LatLonImgsSAR[SAR][2]
vv = LatLonImgsSAR[SAR][3]
precomputed_SAR[SAR] = []
precomputed_SAR[SAR].append((lats, lons, vh) + (f'SAR (VH) {l_SAR_dates[SAR]:%B %d, %Y}',))
precomputed_SAR[SAR].append((lats, lons, vv) + (f'SAR (VV) {l_SAR_dates[SAR]:%B %d, %Y}',))
arr.append(ndvi_temp + (f'NDVI {l_NDVI_dates[NDVI]:%B %d, %Y}',))
arr.extend(precomputed_SAR[SAR])
return rasteriser.rasteriseImages(arr)
if __name__ == "__main__":
import reader
for array in reader.parseKML("2017_polygons.kml"):
p = arrayToPairs(array, "2017-05-01", "2017-09-30")
common.plot(p[0])