from tensorflow.keras.models import load_model
import tensorflow.keras.backend as K
import tensorflow as tf
from PIL import Image
import numpy as np
import glob
import os
#import cv2
import configparser
import math
import time
from shutil import copyfile
class ReadAnalogNeedle:
def __init__(self):
config = configparser.ConfigParser()
config.read('./config/config.ini')
self.log_Image = ''
self.LogNames = ''
if config.has_option('Analog_Counter', 'LogImageLocation'):
self.log_Image = config['Analog_Counter']['LogImageLocation']
if config.has_option('Analog_Counter', 'LogNames'):
zw_LogNames = config.get('Analog_Counter', 'LogNames').split(',')
self.LogNames = []
for nm in zw_LogNames:
self.LogNames.append(nm.strip())
else:
self.LogNames = ''
else:
self.log_Image = ''
self.model_file = config['Analog_Counter']['Modelfile']
self.CheckAndLoadDefaultConfig()
self.model = load_model(self.model_file)
def CheckAndLoadDefaultConfig(self):
defaultdir = "./config_default/"
targetdir = './config/'
if not os.path.exists(self.model_file):
zerlegt = self.model_file.split('/')
pfad = zerlegt[0]
for i in range(1, len(zerlegt)-1):
pfad = pfad + '/' + zerlegt[i]
if not os.path.exists(pfad):
os.makedirs(pfad)
defaultmodel = self.model_file.replace(targetdir, defaultdir)
copyfile(defaultmodel, self.model_file)
if len(self.log_Image) > 0:
if not os.path.exists(self.log_Image):
zerlegt = self.log_Image.split('/')
pfad = zerlegt[0]
for i in range(1, len(zerlegt)):
pfad = pfad + '/' + zerlegt[i]
if not os.path.exists(pfad):
os.makedirs(pfad)
def Readout(self, PictureList, logtime):
self.result = []
for image in PictureList:
value = self.ReadoutSingleImage(image[1])
if len(self.log_Image) > 0:
self.saveLogImage(image, value, logtime)
self.result.append(value)
return self.result
def ReadoutSingleImage(self, image):
test_image = image.resize((32, 32), Image.NEAREST)
test_image.save('./image_tmp/resize.jpg', "JPEG")
test_image = np.array(test_image, dtype="float32")
img = np.reshape(test_image,[1,32,32,3])
classes = self.model.predict(img)
out_sin = classes[0][0]
out_cos = classes[0][1]
K.clear_session()
result = np.arctan2(out_sin, out_cos)/(2*math.pi) % 1
result = result * 10
return result
def saveLogImage(self, image, value, logtime):
if (len(self.LogNames) > 0) and (not image[0] in self.LogNames):
return
speichername = "{:.1f}".format(value) + '_' + image[0] + '_' + logtime + '.jpg'
speichername = self.log_Image + '/' + speichername
image[1].save(speichername, "JPEG")
