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()