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Euclidean distance as a loss function
655956f6ba89 c934d44cdf5c 655956f6ba89 655956f6ba89 655956f6ba89 006c6f1aab13 006c6f1aab13 655956f6ba89 655956f6ba89 655956f6ba89 655956f6ba89 006c6f1aab13 c934d44cdf5c 655956f6ba89 006c6f1aab13 006c6f1aab13 655956f6ba89 655956f6ba89 655956f6ba89 9483b964f560 655956f6ba89 655956f6ba89 655956f6ba89 d9cf0ed8e7fd 655956f6ba89 c934d44cdf5c 9483b964f560 9483b964f560 9483b964f560 c934d44cdf5c c934d44cdf5c 006c6f1aab13 655956f6ba89 655956f6ba89 ecf98a415d97 655956f6ba89 655956f6ba89 655956f6ba89 006c6f1aab13 655956f6ba89 ecf98a415d97 655956f6ba89 | import argparse
import logging as log
import numpy as np
from keras.models import load_model
import keras.losses
import keras.metrics
from prepare_data import loadDataset,Sample
from analyzer.epoint import EPoint
from analyzer.corners import Corners
from k_util import averageDistance
import config as cfg
keras.losses.averageDistance=averageDistance
keras.metrics.averageDistance=averageDistance
parser=argparse.ArgumentParser()
parser.add_argument("model")
parser.add_argument("data")
args=parser.parse_args()
model=load_model(args.model)
model.summary()
log.info("loading data...")
with np.load(args.data) as data:
testImages=data["testImages"]
testLabels=data["testLabels"]
log.info("done")
log.info(model.evaluate(testImages.reshape((-1,224,224,1)),testLabels.reshape((-1,4,2))))
for img in testImages:
label=model.predict(np.reshape(img,(1,224,224,1)))
print(label)
points=[]
for i in range(4):
points.append(EPoint((label[0][i][0]+1)*112,(label[0][i][1]+1)*112))
corners=Corners(points)
sample=Sample(np.uint8((img+1)*128),corners)
sample.show()
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