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predict.py
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predict.py
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import pandas as pd
import numpy as np
import cv2
import os
import pickle
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from keras.models import Sequential, load_model
from keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten
from matplotlib import pyplot as plt
print("load all")
from digits import plate_segmentation
# Load model
model = load_model('cnn_classifier.h5')
# Detect chars
digits = plate_segmentation('demo/plates/asd.jpe')
# Predict
for d in digits:
d = np.reshape(d, (1,28,28,1))
out = model.predict(d)
# Get max pre arg
p = []
precision = 0
for i in range(len(out)):
z = np.zeros(36)
z[np.argmax(out[i])] = 1.
precision = max(out[i])
p.append(z)
prediction = np.array(p)
# Inverse one hot encoding
alphabets = ['0','1','2','3','4','5','6','7','8','9','A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z']
classes = []
for a in alphabets:
classes.append([a])
ohe = OneHotEncoder(handle_unknown='ignore', categorical_features=None)
ohe.fit(classes)
pred = ohe.inverse_transform(prediction)
if precision > 0.6:
print('Prediction : ' + str(pred[0][0]) + ' , Precision : ' + str(precision))