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from detectree2model.predictions.predict import run_detectree2
from polygons_processing.postpprocess_detectree2 import postprocess
from generate_tree_images.generate_tree_images import generate_tree_images
from classification.classification_predict import classify
import os
import json
def row_to_feature(row):
feature = {
"id": row["id"],
"type": "Feature",
"properties": {"Confidence_score": row["Confidence_score"], "species": row['species']},
"geometry": {"type": "Polygon", "coordinates": [row["coordinates"]]},
}
return feature
# [[first guess, prob],[second guess, prob],[third guess, prob]]
def export_geojson(df, filename):
features = [row_to_feature(row) for idx, row in df.iterrows()]
feature_collection = {
"type": "FeatureCollection",
"crs": {"type": "name", "properties": {"name": "urn:ogc:def:crs:EPSG::32720"}},
"features": features,
}
output_geojson = json.dumps(feature_collection)
with open(f"{filename}.geojson", "w") as f:
f.write(output_geojson)
print(f"GeoJSON data exported to '{filename}.geojson' file.")
"""
tif_input: the file containing a tif that we are analyzing
tif_file_name: the file name of the tif input. tif_input is the folder in which the tif file lies
(detectree2 works with that) but generate_tree_images requires path including the file hence the file name is needed
output_directory: the directory were all in-between and final files are stored
generate_tree_images stores the cutout tree images in a separate folder
"""
tif_file_name = "TreeCrownVectorDataset_761588_9673769_20_20_32720.tif"
tif_input = "/Users/jonathanseele/ETH/Hackathons/EcoHackathon/WeCanopy/test/" + tif_file_name
current_directory = os.getcwd()
output_directory = os.path.join(current_directory, "outputs")
if not os.path.exists(output_directory):
os.makedirs(output_directory)
run_detectree2(tif_input, store_path=output_directory)
processed_output_df = postprocess(output_directory + '/detectree2_delin.geojson', output_directory + '/processed_delin')
processed_geojson = output_directory + '/processed_delin.geojson'
generate_tree_images(processed_geojson, tif_input)
output_folder = './tree_images'
all_top_3_list = [] # Initialize an empty list to accumulate all top_3 lists
for file_name in os.listdir(output_folder):
file_path = os.path.join(output_folder, file_name)
probs = classify(file_path)
top_3 = probs.head(3)
top_3_list = [[cls, prob] for cls, prob in top_3.items()]
# Accumulate the top_3_list for each file
all_top_3_list.append(top_3_list)
# Assign the accumulated top_3_list to the 'species' column of the dataframe
processed_output_df['species'] = all_top_3_list
final_output_path = 'result'
export_geojson(processed_output_df, final_output_path)
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