Correction of "import keras" error

Author: jomjol

code/lib/ReadAnalogNeedleClass.py

@@ -1,84 +1,84 @@
-import keras
-from tensorflow.keras.models import load_model
-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())
-
-        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 = cv2.resize(image,(32,32), interpolation = cv2.INTER_CUBIC)
-        cv2.imwrite('./image_tmp/resize.jpg', test_image)
-        test_image = np.array(test_image, dtype="float32")
-#      test_image/=255.
-        img = np.reshape(test_image,[1,32,32,3])
-        classes = self.model.predict(img)
-        out_sin = classes[0][0]
-        out_cos = classes[0][1]
-        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
-        cv2.imwrite(speichername, image[1])
+import keras
+from tensorflow.keras.models import load_model
+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())
+
+        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 = cv2.resize(image,(32,32), interpolation = cv2.INTER_CUBIC)
+        cv2.imwrite('./image_tmp/resize.jpg', test_image)
+        test_image = np.array(test_image, dtype="float32")
+#      test_image/=255.
+        img = np.reshape(test_image,[1,32,32,3])
+        classes = self.model.predict(img)
+        out_sin = classes[0][0]
+        out_cos = classes[0][1]
+        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
+        cv2.imwrite(speichername, image[1])

code/lib/ReadDigitalDigitClass.py

@@ -1,95 +1,95 @@
-import keras
-from tensorflow.keras.models import load_model
-
-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 ReadDigitalDigit:
-    def __init__(self):
-        config = configparser.ConfigParser()
-        config.read('./config/config.ini')
-
-        self.log_Image = ''
-        self.LogNames = ''
-
-        self.model_file = config['Digital_Digit']['Modelfile']
-        if config.has_option('Digital_Digit', 'LogImageLocation'):
-            self.log_Image = config['Digital_Digit']['LogImageLocation']
-        self.CheckAndLoadDefaultConfig()
-
-        if config.has_option('Digital_Digit', 'LogImageLocation'):
-            if (os.path.exists(self.log_Image)):
-                for i in range(10):
-                    pfad = self.log_Image + '/' + str(i)
-                    if not os.path.exists(pfad):
-                        os.makedirs(pfad)
-                pfad = self.log_Image + '/NaN'
-                if not os.path.exists(pfad):
-                    os.makedirs(pfad)
-
-            if config.has_option('Digital_Digit', 'LogNames'):
-                zw_LogNames = config.get('Digital_Digit', 'LogNames').split(',')
-                self.LogNames = []
-                for nm in zw_LogNames:
-                      self.LogNames.append(nm.strip())
-
-        self.model_file = config['Digital_Digit']['Modelfile']
-        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 = cv2.resize(image,(20,32), interpolation = cv2.INTER_CUBIC)
-        cv2.imwrite('./image_tmp/resize.jpg', test_image)
-        test_image = np.array(test_image, dtype="float32")
-#      test_image/=255.
-        img = np.reshape(test_image,[1,32,20,3])
-        result = self.model.predict_classes(img)
-        if result == 10:
-            result = "NaN"
-        else:
-            result = result[0]
-        return result
-
-    def saveLogImage(self, image, value, logtime):
-        if (len(self.LogNames) > 0) and (not image[0] in self.LogNames):
-            return
-        speichername = image[0] + '_' + logtime + '.jpg'
-        speichername = self.log_Image + '/' + str(value) + '/' + speichername
-        cv2.imwrite(speichername, image[1])
+import keras
+from tensorflow.keras.models import load_model
+
+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 ReadDigitalDigit:
+    def __init__(self):
+        config = configparser.ConfigParser()
+        config.read('./config/config.ini')
+
+        self.log_Image = ''
+        self.LogNames = ''
+
+        self.model_file = config['Digital_Digit']['Modelfile']
+        if config.has_option('Digital_Digit', 'LogImageLocation'):
+            self.log_Image = config['Digital_Digit']['LogImageLocation']
+        self.CheckAndLoadDefaultConfig()
+
+        if config.has_option('Digital_Digit', 'LogImageLocation'):
+            if (os.path.exists(self.log_Image)):
+                for i in range(10):
+                    pfad = self.log_Image + '/' + str(i)
+                    if not os.path.exists(pfad):
+                        os.makedirs(pfad)
+                pfad = self.log_Image + '/NaN'
+                if not os.path.exists(pfad):
+                    os.makedirs(pfad)
+
+            if config.has_option('Digital_Digit', 'LogNames'):
+                zw_LogNames = config.get('Digital_Digit', 'LogNames').split(',')
+                self.LogNames = []
+                for nm in zw_LogNames:
+                      self.LogNames.append(nm.strip())
+
+        self.model_file = config['Digital_Digit']['Modelfile']
+        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 = cv2.resize(image,(20,32), interpolation = cv2.INTER_CUBIC)
+        cv2.imwrite('./image_tmp/resize.jpg', test_image)
+        test_image = np.array(test_image, dtype="float32")
+#      test_image/=255.
+        img = np.reshape(test_image,[1,32,20,3])
+        result = self.model.predict_classes(img)
+        if result == 10:
+            result = "NaN"
+        else:
+            result = result[0]
+        return result
+
+    def saveLogImage(self, image, value, logtime):
+        if (len(self.LogNames) > 0) and (not image[0] in self.LogNames):
+            return
+        speichername = image[0] + '_' + logtime + '.jpg'
+        speichername = self.log_Image + '/' + str(value) + '/' + speichername
+        cv2.imwrite(speichername, image[1])