in_file = "/home/user/FinalAssignment/output/InputMap.tif"
#Data source
url = "https://api.planet.com/v0/scenes/ortho/"
key = #Add own key
#Coordinates of point
point = (-122.486, 37.698)
#Set map type and file location
map_type = "visual"
crop_file = "/home/user/FinalAssignment/Downloads/DownloadFile_"+map_type+".tif"
#Download the image
try:
select_image(url, key, point, map_type, crop_file)
except:
print "ERROR: No map available"
#Cut to size
cut_size(crop_file, 1500, 1000, 1500, 2000, in_file)
#Creating model to classify trees
create_model(in_file, statistics_file, training_poly, output_model, confusion_matrix)
#Apply model
classify(output_model, in_file, statistics_file, output_map)
#Delete all none trees from dataset
select_trees(output_map, selection_map)
out_file_all = "/home/user/FinalAssignment/output/OutputMapObjectAll.shp"
#Data source
url = "https://api.planet.com/v0/scenes/ortho/"
key =#Add own key
#Coordinates of point
point_obj = (-118.4, 34.084)
#Set map type and file location
map_type_obj = "visual"
crop_file_obj = "/home/user/FinalAssignment/Downloads/DownloadFileObject_"+map_type_obj+".tif"
#Download the image
try:
select_image(url, key, point_obj, map_type_obj, crop_file_obj)
except:
print "ERROR: No map available"
#Cut to size
cut_size(crop_file_obj, 1250, 1500, 750, 750, in_file_obj)
##Use object based selection
min_size = 5
#Expression to select all dark-green objects
expression = "(p1b2 < 100) and ((p1b2 > p1b1)or(p1b2>p1b3)) and (p1b1 < 90) and (p1b3<90)"
#Select all objects
OBIA_none = ''
select_object(in_file_obj, expression, min_size, OBIA_none, out_file_all)
#Select all round objects
OBIA = "SHAPE_RegionRatio > 0.65"
