utilities/python/evaluateScene.py at master · SpaceNetChallenge/utilities · GitHub

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from spaceNetUtilities import evalTools as eT

from spaceNetUtilities import geoTools as gT

import numpy as np

import csv

import multiprocessing

import time

import argparse

import os

import glob

from osgeo import ogr, osr, gdal

def writeAOISummaryToCSV(resultsDict,csvwriter):

csvwriter.writerow(['TruthFile', resultsDict['TruthFile']])

csvwriter.writerow(['ProposalFile', resultsDict['ProposalFile']])

csvwriter.writerow(['AOI_Name', resultsDict['AOI_Name']])

csvwriter.writerow(['Summary Results'])

csvwriter.writerow(['F1Score Total', resultsDict['F1ScoreTotal']])

csvwriter.writerow(['Precision', resultsDict['PrecisionTotal']])

csvwriter.writerow(['Recall', resultsDict['RecalTotal']])

csvwriter.writerow(['True Positive Total', resultsDict['TruePositiveTotal']])

csvwriter.writerow(['False Positive Total', resultsDict['FalsePositiveTotal']])

csvwriter.writerow(['False Negative Total', resultsDict['FalseNegativeTotal']])

csvwriter.writerow([''])

# resultsDict = {'AOI_Name': aoiName

# 'TruthFile': truth_fp,

# 'ProposalFile': test_fp,

# 'F1ScoreTotal': F1ScoreTotal,

# 'PrecisionTotal': precision,

# 'RecalTotal': recall,

# 'TruePositiveTotal': true_pos_total,

# 'FalsePositiveTotal': false_pos_total,

# 'FalseNegativeTotal': false_neg_total,

# 'PerImageStatsResultList': result_list,

# 'OutputSummaryFile': resultsOutputFile}

def writePerChipToCSV(resultsDictList, csvwriter):

resultsDict = resultsDictList[0]

csvwriter.writerow(['ImageId', 'F1Score', 'True Positive Count', 'False Positive Count', 'False Negative Count'])

for result in resultsDict['PerImageStatsResultList']:

tmpList = [result[1]]

tmpList.extend(result[0])

csvwriter.writerow(tmpList)

def writeResultsToScreen(resultsDict):

print('AOI of Interest', resultsDict['AOI_Name'])

print('True_Pos_Total', resultsDict['TruePositiveTotal'])

print('False_Pos_Total', resultsDict['FalsePositiveTotal'])

print('False_Neg_Total', resultsDict['FalseNegativeTotal'])

print('F1ScoreTotal', resultsDict['F1ScoreTotal'])

# resultsDict = {'AOI_Name': aoiName

# 'TruthFile': truth_fp,

# 'ProposalFile': test_fp,

# 'F1ScoreTotal': F1ScoreTotal,

# 'PrecisionTotal': precision,

# 'RecalTotal': recall,

# 'TruePositiveTotal': true_pos_total,

# 'FalsePositiveTotal': false_pos_total,

# 'FalseNegativeTotal': false_neg_total,

# 'PerImageStatsResultList': result_list,

# 'OutputSummaryFile': resultsOutputFile}

def evaluateSpaceNetSolution(summaryTruthFile, summaryProposalFile, resultsOutputFile='', processgeoJson=False,

useParallelProcessing=False, minPolygonSize=0,

iouThreshold=0.5,

AOIList=['Total',

'AOI_1_Rio',

'AOI_2_Vegas',

'AOI_3_Paris',

'AOI_4_Shanghai',

'AOI_5_Khartoum']

truth_fp = summaryTruthFile

test_fp = summaryProposalFile

# check for cores available

if useParallelProcessing:

max_cpu = multiprocessing.cpu_count()

parallel = True

else:

max_cpu = 1

parallel = False

# initialize scene counts

true_pos_counts = []

false_pos_counts = []

false_neg_counts = []

t0 = time.time()

# Start Ingest Of Truth and Test Case

if processgeoJson:

sol_polys = gT.import_summary_geojson(truth_fp, removeNoBuildings=False)

prop_polys = gT.import_summary_geojson(test_fp)

polyFlag = 'poly'

else:

sol_polys = gT.readwktcsv(truth_fp, removeNoBuildings=False)

prop_polys = gT.readwktcsv(test_fp, groundTruthFile=False)

polyFlag = 'polyPix'

t1 = time.time()

total = t1 - t0

print('time of ingest: ', total)

# inspect polygons to ensure they are not too small

sol_polys = [item for item in sol_polys if item["ImageId"] > 0 and

item[polyFlag].GetArea()> minPolygonSize]

prop_polys = [item for item in prop_polys if item["ImageId"] > 0 ]

# Speed up search by preprocessing ImageId and polygonIds

test_image_ids = [item['ImageId'] for item in prop_polys if item['ImageId'] > 0]

test_image_ids2 = [item['ImageId'] for item in sol_polys if item['ImageId'] > 0]

test_image_ids.extend(test_image_ids2)

test_image_ids = set(test_image_ids)

prop_polysIdList = np.asarray([item['ImageId'] for item in prop_polys if item["ImageId"] >= 0 and \

item['BuildingId'] != -1])

prop_polysPoly = np.asarray([item[polyFlag] for item in prop_polys if item["ImageId"] >= 0 and \

item['BuildingId'] != -1])

sol_polysIdsList = np.asarray([item['ImageId'] for item in sol_polys if item["ImageId"] >= 0 and \

item['BuildingId'] != -1])

sol_polysPoly = np.asarray([item[polyFlag] for item in sol_polys if item["ImageId"] >= 0 and \

item['BuildingId'] != -1])

bad_count = 0

F1ScoreList = []

cpu_count = min(multiprocessing.cpu_count(), max_cpu)

print('{}'.format(max_cpu))

p = multiprocessing.Pool(processes=cpu_count)

ResultList = []

eval_function_input_list = eT.create_eval_function_input((test_image_ids,

(prop_polysIdList, prop_polysPoly),

(sol_polysIdsList, sol_polysPoly)))

# Calculate Values

t3 = time.time()

print('time For DataCreation {}s'.format(t3 - t1))

# result_list = p.map(eT.evalfunction, eval_function_input_list)

if parallel == False:

result_list = []

for eval_input in eval_function_input_list:

if resultsOutputFile != '':

result_list.append(eT.evalfunction(eval_input,

resultGeoJsonName=os.path.splitext(resultsOutputFile)[0]+"_"+eval_input[0]+".geojson",

threshold=iouThreshold))

else:

result_list.append(eT.evalfunction(eval_input,

threshold=iouThreshold))

else:

result_list = p.map(eT.evalfunction, eval_function_input_list)

result_listNP = np.asarray([item[0] for item in result_list])

result_listName = [item[1] for item in result_list]

AOIIndexList = []

resultsDictList = []

for AOI in AOIList:

if AOI !='Total':

AOIIndex = [i for i, s in enumerate(result_listName) if AOI in s]

AOIIndexList.append(AOIIndex)

result_sum = np.sum(result_listNP[AOIIndex], axis=0)

else:

AOIIndex = [i for i, s in enumerate(result_listName) if '' in s]

AOIIndexList.append(AOIIndex)

result_sum = np.sum(result_listNP, axis=0)

#result_sum = np.sum(result_listNP, axis=0)

true_pos_total = result_sum[1]

false_pos_total = result_sum[2]

false_neg_total = result_sum[3]

if (float(true_pos_total) + float(false_pos_total)) > 0:

precision = float(true_pos_total) / (float(true_pos_total) + float(false_pos_total))

else:

precision = 0

if (float(true_pos_total) + float(false_neg_total)) > 0:

recall = float(true_pos_total) / (float(true_pos_total) + float(false_neg_total))

else:

recall = 0

if (precision + recall) > 0:

F1ScoreTotal = 2.0 * precision * recall / (precision + recall)

else:

F1ScoreTotal = 0

resultsDict = {'AOI_Name': AOI,

'TruthFile': truth_fp,

'ProposalFile': test_fp,

'F1ScoreTotal': F1ScoreTotal,

'PrecisionTotal': precision,

'RecalTotal': recall,

'TruePositiveTotal': true_pos_total,

'FalsePositiveTotal': false_pos_total,

'FalseNegativeTotal': false_neg_total,

'PerImageStatsResultList': result_list,

'OutputSummaryFile': resultsOutputFile}

resultsDictList.append(resultsDict)

writeResultsToScreen(resultsDict)

if resultsOutputFile != '':

with open(resultsOutputFile, 'w') as csvFile:

csvwriter = csv.writer(csvFile, delimiter=',')

for resultsDict in resultsDictList:

writeAOISummaryToCSV(resultsDict, csvwriter)

writePerChipToCSV(resultsDictList, csvwriter)

return resultsDictList

def combineGeoJsonAndConvertToWGS84(baseName, rasterLocationList,

AOIList=['Total',

'AOI_1_Rio',

'AOI_2_Vegas',

'AOI_3_Paris',

'AOI_4_Shanghai',

'AOI_5_Khartoum'],

removeGeoJsonAfter=True):

srcBaseName = os.path.splitext(baseName)[0]

geoJsonList = glob.glob(srcBaseName+"_*.geojson")

print geoJsonList

rasterList = []

for rasterLocation in rasterLocationList:

rasterList.extend(glob.glob(os.path.join(rasterLocation, '*.tif')))

for AOI in AOIList:

AOIgeoJsonList = [s for i, s in enumerate(geoJsonList) if AOI in s]

AOIimageList = [s for i, s in enumerate(rasterList) if AOI in s]

#print AOIimageList

outShapeFile = srcBaseName+"_"+AOI+"Summary.shp"

outDriver = ogr.GetDriverByName("ESRI Shapefile")

# Remove output shapefile if it already exists

if os.path.exists(outShapeFile):

outDriver.DeleteDataSource(outShapeFile)

outDataSource = outDriver.CreateDataSource(outShapeFile)

srs = osr.SpatialReference()

srs.ImportFromEPSG(4326)

outLayer = outDataSource.CreateLayer("AOI_Results", srs, geom_type=ogr.wkbPolygon)

inDataSource = ogr.Open(geoJsonList[0], 0)

inLayer = inDataSource.GetLayer()

# Add input Layer Fields to the output Layer

inLayerDefn = inLayer.GetLayerDefn()

for i in range(0, inLayerDefn.GetFieldCount()):

fieldDefn = inLayerDefn.GetFieldDefn(i)

outLayer.CreateField(fieldDefn)

# Get the output Layer's Feature Definition

outLayerDefn = outLayer.GetLayerDefn()

inLayerDefn = 0

inLayer = 0

inDataSource = 0

for AOIgeoJson in AOIgeoJsonList:

imageId = AOIgeoJson.replace(srcBaseName+'_', "").replace('.geojson', '')

rasterName = [s for i, s in enumerate(AOIimageList) if imageId in s]

if len(rasterName)>0:

rasterName = rasterName[0]

#print rasterName

inDataSource = ogr.Open(AOIgeoJson, 0)

inLayer = inDataSource.GetLayer()

targetRaster = gdal.Open(rasterName)

geomTransform = targetRaster.GetGeoTransform()

for i in range(0, inLayer.GetFeatureCount()):

# Get the input Feature

inFeature = inLayer.GetFeature(i)

# Create output Feature

outFeature = ogr.Feature(outLayerDefn)

# Add field values from input Layer

for i in range(0, outLayerDefn.GetFieldCount()):

outFeature.SetField(outLayerDefn.GetFieldDefn(i).GetNameRef(), inFeature.GetField(i))

# Set geometry as centroid

geom = inFeature.GetGeometryRef()

#print geom.ExportToWkt()

# [GeoWKT, PixWKT])

geomList = gT.pixelGeomToGeoGeom(geom, rasterName, targetSR='', geomTransform='', breakMultiPolygonPix=False)

#print geomList[0][0].ExportToWkt()

if geomList:

outFeature.SetGeometry(geomList[0][0])

# Add new feature to output Layer

outLayer.CreateFeature(outFeature)

outFeature = None

inFeature = None

if removeGeoJsonAfter:

for f in geoJsonList:

os.remove(f)

if __name__ == "__main__":

parser = argparse.ArgumentParser(description='Evaluate Score for SpaceNet')

parser.add_argument("summaryTruthFile",

help="The Location of Summary Ground Truth csv File"

"Summary File should have a header = ImageId, BuildingId, polygonPixWKT, polygonGeoPix "

"Format is '{},{},{},{}.format(ImageId, BuildingId, polygonPixWKT, polygonGeoPix)',"

"unless --geoJson flag is set"

"See spaceNet competition details for more information about file format"

)

parser.add_argument("summaryProposalFile",

help="The Location of summary Propsal csv File"

"Summary File should have a header = ImageId, BuildingId, polygonPixWKT, Confidence "

"followed by values"

"Format is '{},{},{},{}.format(ImageId, BuildingId, polygonPixWKT, Confidence)'"

"unless --geoJson flag is set"

)

parser.add_argument("--polygonMinimumPixels",

help="The minimum number of pixels a polygon must have to be considered valid"

"The minimum for spacenet round 2 is 20 pixels",

type=int,

default=20)

parser.add_argument("--iouThreshold",

help="The IOU threshold for a True Positive"

"Spacenet uses 0.5",

type=float,

default=0.5)

parser.add_argument("--resultsOutputFile",

help="If you would like summary data outwritten to a file, specify the file",

default='')

parser.add_argument("--geoJson",

help='results Submitted are in geoJson Format',

action='store_true')

parser.add_argument("--useParallelProcessing",

help='Convert Image from Native format to 8bit',

action='store_true')

parser.add_argument("--rasterLocation",

help='Image Directory List',

action='append',

default=[]

)

args = parser.parse_args()

# load Truth and Test File Locations

AOIList = ['Total',

'AOI_1_Rio',

'AOI_2_Vegas',

'AOI_3_Paris',

'AOI_4_Shanghai',

'AOI_5_Khartoum']

resultsOutputFile = args.resultsOutputFile

summaryDict = evaluateSpaceNetSolution(args.summaryTruthFile,

args.summaryProposalFile,

resultsOutputFile=resultsOutputFile,

processgeoJson=args.geoJson,

useParallelProcessing=args.useParallelProcessing,

minPolygonSize=args.polygonMinimumPixels,

iouThreshold=args.iouThreshold,

AOIList=AOIList)

if resultsOutputFile != '':

combineGeoJsonAndConvertToWGS84(resultsOutputFile,

rasterLocationList=args.rasterLocation)

utilities/python/evaluateScene.py at master · SpaceNetChallenge/utilities · GitHub

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