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Location: OneEye/exp/kerokero/test.py - annotation
4381957e967e
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text/x-python
keras checkpoint saving
655956f6ba89 c934d44cdf5c 655956f6ba89 655956f6ba89 655956f6ba89 655956f6ba89 655956f6ba89 655956f6ba89 655956f6ba89 c934d44cdf5c 655956f6ba89 655956f6ba89 655956f6ba89 655956f6ba89 9483b964f560 655956f6ba89 655956f6ba89 655956f6ba89 d9cf0ed8e7fd 655956f6ba89 c934d44cdf5c 9483b964f560 9483b964f560 9483b964f560 c934d44cdf5c c934d44cdf5c ecf98a415d97 655956f6ba89 655956f6ba89 ecf98a415d97 655956f6ba89 655956f6ba89 655956f6ba89 fad34516870e 655956f6ba89 ecf98a415d97 655956f6ba89 | import argparse
import logging as log
import numpy as np
from keras.models import load_model
from prepare_data import loadDataset,Sample
from analyzer.epoint import EPoint
from analyzer.corners import Corners
import config as cfg
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))
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*2]+1)*112,(label[0][i*2+1]+1)*112))
corners=Corners(points)
sample=Sample(np.uint8((img+1)*128),corners)
sample.show()
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