def main(args):
listModels = []
models_list = args.models.split(",")
print("Models to be run: ", models_list)
if 'mask_rcnn' in models_list:
maskRcnn = testTimeAugmentation.MaskRCNNPred(
'/mnt/src/mask_rcnn_coco.h5', '/mnt/src/coco.names')
listModels.append(maskRcnn)
if 'retinanet' in models_list:
retinaResnet50 = testTimeAugmentation.RetinaNetResnet50Pred(
'/mnt/src/resnet50_coco_best_v2.1.0.h5', '/mnt/src/coco.csv')
listModels.append(retinaResnet50)
if 'yolo_darknet' in models_list:
yoloDarknet = testTimeAugmentation.DarknetYoloPred(
'/mnt/src/yolov3.weights', '/mnt/src/coco.names',
'/mnt/src/yolov3.cfg')
listModels.append(yoloDarknet)
if 'ssd_resnet' in models_list:
ssdResnet = testTimeAugmentation.MXnetSSD512Pred(
'/mnt/src/ssd_512_resnet50_v1_voc-9c8b225a.params',
'/mnt/src/classesMXnet.txt')
listModels.append(ssdResnet)
if 'faster_resnet' in models_list:
fasterResnet = testTimeAugmentation.MXnetFasterRCNNPred(
'/mnt/src/faster_rcnn_resnet50_v1b_voc-447328d8.params',
'/mnt/src/classesMXnet.txt')
listModels.append(fasterResnet)
# listaModels = [retinaResnet50, maskRcnn]
models(listModels, args.images_path, args.option, args.combine)
print(os.listdir('/mnt/src/'))
if notebook is False:
shutil.rmtree(pathImg+'/../salida/')
if __name__== "__main__":
#Enter the path of the folder that will contain the images
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required=True, help="path to the dataset of images")
ap.add_argument("-o", "--option", default='consensus', help="option to the ensemble: affirmative, consensus or unanimous")
notebook = False
args = vars(ap.parse_args())
pathImg= args["dataset"]
option = args["option"]
#fichs = os.listdir(pathImg)
imgFolder = pathImg
#the user define configurations fichs
yoloDarknet = testTimeAugmentation.DarknetYoloPred('/home/ancasag/Codigo/General/ensembleObjectDetection/peso/AlvaroPrueba1_600train_65000.weights', '../peso/vocEstomas.names','../peso/yolov3Estomas.cfg',0.7)
ssdResnet = testTimeAugmentation.MXnetSSD512Pred('/home/ancasag/Codigo/General/ensembleObjectDetection/peso/ssd_512_resnet50_v1_voc-9c8b225a.params', '../peso/classesMXnet.txt',0.7)
fasterResnet = testTimeAugmentation.MXnetFasterRCNNPred('/home/ancasag/Codigo/General/ensembleObjectDetection/peso/faster_rcnn_resnet50_v1b_voc-447328d8.params', '../peso/classesMXnet.txt',0.7)
yoloResnet = testTimeAugmentation.MXnetYoloPred('/home/ancasag/Codigo/General/ensembleObjectDetection/peso/yolo3_darknet53_voc-f5ece5ce.params', '../peso/classesMXnet.txt',0.7)
retinaResnet50 = testTimeAugmentation.RetinaNetResnet50Pred('/home/ancasag/Codigo/General/ensembleObjectDetection/peso/resnet50_coco_best_v2.1.0.h5', '../peso/coco.csv',0.7)
maskRcnn = testTimeAugmentation.MaskRCNNPred('/home/ancasag/Codigo/General/ensembleObjectDetection/peso/mask_rcnn_coco.h5', '../peso/coco.names',0.7)
listaModels = [retinaResnet50, maskRcnn,yoloResnet,yoloDarknet,fasterResnet,ssdResnet]
models(listaModels,pathImg,option)
"--option",
default='consensus',
help="option to the ensemble: affirmative, consensus or unanimous")
args = vars(ap.parse_args())
pathImg = args["dataset"]
option = args["option"]
#fichs = os.listdir(pathImg)
imgFolder = pathImg
#the user define configurations fichs
yoloDarknet = testTimeAugmentation.DarknetYoloPred(
'/home/master/Desktop/peso/AlvaroPrueba1_600train_65000.weights',
'../peso/vocEstomas.names', '../peso/yolov3Estomas.cfg')
ssdResnet = testTimeAugmentation.MXnetSSD512Pred(
'/home/master/Desktop/peso/ssd_512_resnet50_v1_voc-9c8b225a.params',
'../peso/classesMXnet.txt')
fasterResnet = testTimeAugmentation.MXnetFasterRCNNPred(
'/home/master/Desktop/peso/faster_rcnn_resnet50_v1b_voc-447328d8.params',
'../peso/classesMXnet.txt')
yoloResnet = testTimeAugmentation.MXnetYoloPred(
'/home/master/Desktop/peso/yolo3_darknet53_voc-f5ece5ce.params',
'../peso/classesMXnet.txt')
retinaResnet50 = testTimeAugmentation.RetinaNetResnet50Pred(
'/home/master/Desktop/peso/resnet50_coco_best_v2.1.0.h5',
'../peso/coco.csv')
maskRcnn = testTimeAugmentation.MaskRCNNPred(
'/home/master/Desktop/peso/mask_rcnn_coco.h5', '../peso/coco.names')
#Enter the path of the folder that will contain the images
ap = argparse.ArgumentParser()
ap.add_argument("-d",
"--dataset",
required=True,
help="path to the dataset of images")
ap.add_argument(
"-o",
"--option",
default='consensus',
help="option to the ensemble: affirmative, consensus or unanimous")
notebook = False
args = vars(ap.parse_args())
pathImg = args["dataset"]
option = args["option"]
imgFolder = pathImg
# the user define configurations fichs
yoloDarknet = testTimeAugmentation.DarknetYoloPred(
'/home/ancasag/Codigo/General/ensembleObjectDetection/peso/yolov3.weights',
'/home/ancasag/Codigo/General/ensembleObjectDetection/peso/coco.names',
'/home/ancasag/Codigo/General/ensembleObjectDetection/peso/yolov3.cfg',
0.7)
# ssdResnet = testTimeAugmentation.MXnetSSD512Pred('weights/ssd_512_resnet50_v1_voc-9c8b225a.params', 'weights/classesMXnet.txt',0.7)
# fasterResnet = testTimeAugmentation.MXnetFasterRCNNPred('weights/Desktop/peso/faster_rcnn_resnet50_v1b_voc-447328d8.params', 'weights/classesMXnet.txt',0.7)
# yoloResnet = testTimeAugmentation.MXnetYoloPred('weights/Desktop/peso/yolo3_darknet53_voc-f5ece5ce.params', 'weights/classesMXnet.txt',0.7)
# retinaResnet50 = testTimeAugmentation.RetinaNetResnet50Pred('weights/resnet50_coco_best_v2.1.0.h5', 'weights/coco.csv',0.7)
# maskRcnn = testTimeAugmentation.MaskRCNNPred('weights/mask_rcnn_coco.h5', 'weights/coco.names',0.7)
myTechniques = ["histo", "hflip", "none"]
tta(yoloDarknet, myTechniques, pathImg, option)
help="path to the dataset of images")
ap.add_argument(
"-o",
"--option",
default='consensus',
help="option to the ensemble: affirmative, consensus or unanimous")
args = vars(ap.parse_args())
pathImg = args["dataset"]
option = args["option"]
#2. the user define the techniques and configurations fichs
myTechniques = ["histo", "vflip", "gamma"]
yoloDarknet = testTimeAugmentation.DarknetYoloPred(
'/home/master/Desktop/peso/yolov3.weights', '../peso/coco.names',
'../peso/yolov3.cfg')
ssdResnet = testTimeAugmentation.MXnetSSD512Pred(
'/home/master/Desktop/peso/ssd_512_resnet50_v1_voc-9c8b225a.params',
'../peso/classesMXnet.txt')
fasterResnet = testTimeAugmentation.MXnetFasterRCNNPred(
'/home/master/Desktop/peso/faster_rcnn_resnet50_v1b_voc-447328d8.params',
'../peso/classesMXnet.txt')
yoloResnet = testTimeAugmentation.MXnetYoloPred(
'/home/master/Desktop/peso/yolo3_darknet53_voc-f5ece5ce.params',
'../peso/classesMXnet.txt')
retinaResnet50 = testTimeAugmentation.RetinaNetResnet50Pred(
'/home/master/Desktop/peso/resnet50_coco_best_v2.1.0.h5',
'../peso/coco.csv')
maskRcnn = testTimeAugmentation.MaskRCNNPred(
'/home/master/Desktop/peso/mask_rcnn_coco.h5', '../peso/coco.names')