def evaluate(args):
# Get current path to set default folders
iouThreshold = args.iouThreshold
# Arguments validation
errors = []
# Validate formats
gtFormat = ValidateFormats(args.gtFormat, '-gtformat', errors)
detFormat = ValidateFormats(args.detFormat, '-detformat', errors)
# Groundtruth folder
if ValidateMandatoryArgs(args.gtFolder, '-gt/--gtfolder', errors):
gtFolder = ValidatePaths(args.gtFolder, '-gt/--gtfolder', errors)
else:
# errors.pop()
gtFolder = os.path.join(currentPath, 'groundtruths')
if os.path.isdir(gtFolder) is False:
errors.append('folder %s not found' % gtFolder)
# Coordinates types
gtCoordType = ValidateCoordinatesTypes(args.gtCoordinates,
'-gtCoordinates', errors)
detCoordType = ValidateCoordinatesTypes(args.detCoordinates,
'-detCoordinates', errors)
imgSize = (0, 0)
if gtCoordType == CoordinatesType.Relative: # Image size is required
imgSize = ValidateImageSize(args.imgSize, '-imgsize', '-gtCoordinates',
errors)
if detCoordType == CoordinatesType.Relative: # Image size is required
imgSize = ValidateImageSize(args.imgSize, '-imgsize',
'-detCoordinates', errors)
# Detection folder
if ValidateMandatoryArgs(args.detFolder, '-det/--detfolder', errors):
detFolder = ValidatePaths(args.detFolder, '-det/--detfolder', errors)
else:
# errors.pop()
detFolder = os.path.join(currentPath, 'detections')
if os.path.isdir(detFolder) is False:
errors.append('folder %s not found' % detFolder)
if args.savePath is not None:
savePath = ValidatePaths(args.savePath, '-sp/--savepath', errors)
else:
savePath = os.path.join(currentPath, 'results')
# Validate savePath
# If error, show error messages
if len(errors) != 0:
print(
"""usage: Object Detection Metrics [-h] [-v] [-gt] [-det] [-t] [-gtformat]
[-detformat] [-save]""")
print('Object Detection Metrics: error(s): ')
[print(e) for e in errors]
sys.exit()
# Create directory to save results
shutil.rmtree(savePath, ignore_errors=True) # Clear folder
os.makedirs(savePath)
# Show plot during execution
showPlot = args.showPlot
# Get groundtruth boxes
allBoundingBoxes, allClasses = getBoundingBoxesXML(gtFolder,
True,
gtFormat,
gtCoordType,
imgSize=imgSize)
# Get detected boxes
allBoundingBoxes, allClasses = getBoundingBoxes(detFolder,
False,
detFormat,
detCoordType,
allBoundingBoxes,
allClasses,
imgSize=imgSize)
allClasses.sort()
evaluator = Evaluator()
acc_AP = 0
validClasses = 0
# Plot Precision x Recall curve
detections = evaluator.PlotPrecisionRecallCurve(
allBoundingBoxes, # Object containing all bounding boxes (ground truths and detections)
IOUThreshold=iouThreshold, # IOU threshold
method=MethodAveragePrecision.EveryPointInterpolation,
showAP=True, # Show Average Precision in the title of the plot
showInterpolatedPrecision=
False, # Don't plot the interpolated precision curve
savePath=savePath,
showGraphic=showPlot)
f = open(os.path.join(savePath, 'results.txt'), 'w')
f.write('Object Detection Metrics\n')
f.write('https://github.com/rafaelpadilla/Object-Detection-Metrics\n\n\n')
f.write('Average Precision (AP), Precision and Recall per class:')
# each detection is a class
for metricsPerClass in detections:
# Get metric values per each class
cl = metricsPerClass['class']
ap = metricsPerClass['AP']
precision = metricsPerClass['precision']
recall = metricsPerClass['recall']
totalPositives = metricsPerClass['total positives']
total_TP = metricsPerClass['total TP']
total_FP = metricsPerClass['total FP']
if totalPositives > 0:
validClasses = validClasses + 1
acc_AP = acc_AP + ap
prec = ['%.2f' % p for p in precision]
rec = ['%.2f' % r for r in recall]
ap_str = "{0:.2f}%".format(ap * 100)
# ap_str = "{0:.4f}%".format(ap * 100)
print('AP: %s (%s)' % (ap_str, cl))
f.write('\n\nClass: %s' % cl)
f.write('\nAP: %s' % ap_str)
f.write('\nPrecision: %s' % prec)
f.write('\nRecall: %s' % rec)
mAP = acc_AP / validClasses
mAP_str = "{0:.2f}%".format(mAP * 100)
print('mAP: %s' % mAP_str)
f.write('\n\n\nmAP: %s' % mAP_str)
