Merge pull request larymak#230 from sohamdc/patch-1

larymak · web-flow · commit 928390275e9d · 2022-11-03T08:49:58.000+03:00
Add Trafic Sign Recognition using CNN and Keras.
diff --git a/MachineLearning Projects/traffic sign recognition using CNN and Keras/Readme.md b/MachineLearning Projects/traffic sign recognition using CNN and Keras/Readme.md
@@ -0,0 +1,12 @@
+Download the CSV file from the following link https://www.kaggle.com/datasets/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign
+You've probably heard about self-driving automobiles, in which the driver is completely dependent on the vehicle for transportation. But for vehicles to reach level 5 autonomy, all traffic regulations must be understood and adhered to.
+Speed limits, no entry, traffic signals, turns to the left or right, children crossing, no passage of heavy trucks, etc. are just a few examples of the various types of traffic signs. The process of determining which class a traffic sign belongs to is known as categorization of traffic signs.
+
+![traffic signjpg1](https://user-images.githubusercontent.com/61727479/198211059-8629caa2-e441-4c5e-9f24-d12feaf2199a.jpg)
+![traffic signjpg2](https://user-images.githubusercontent.com/61727479/198211180-66c94d0e-1586-44d1-adec-ca9de1f1122c.jpg)
+![traffic signjpg3](https://user-images.githubusercontent.com/61727479/198211202-d636ddd4-94ba-4e34-92ff-20f67076e084.jpg)
+
+Follow the instructions below to run the project:-
+*install Python, jupyter notebook to run the project.
+*Import the relative path of the dataset clone the project and install all the required libraries and run the ipynb file in jupyter notebook.
+*Download the CSV file from the following link https://www.kaggle.com/datasets/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign.
diff --git a/MachineLearning Projects/traffic sign recognition using CNN and Keras/gui.py b/MachineLearning Projects/traffic sign recognition using CNN and Keras/gui.py
@@ -0,0 +1,106 @@
+import tkinter as tk
+from tkinter import filedialog
+from tkinter import *
+from PIL import ImageTk, Image
+
+import numpy
+#load the trained model to classify sign
+from keras.models import load_model
+model = load_model('traffic_classifier.h5')
+
+#dictionary to label all traffic signs class.
+classes = { 1:'Speed limit (20km/h)',
+            2:'Speed limit (30km/h)',      
+            3:'Speed limit (50km/h)',       
+            4:'Speed limit (60km/h)',      
+            5:'Speed limit (70km/h)',    
+            6:'Speed limit (80km/h)',      
+            7:'End of speed limit (80km/h)',     
+            8:'Speed limit (100km/h)',    
+            9:'Speed limit (120km/h)',     
+           10:'No passing',   
+           11:'No passing veh over 3.5 tons',     
+           12:'Right-of-way at intersection',     
+           13:'Priority road',    
+           14:'Yield',     
+           15:'Stop',       
+           16:'No vehicles',       
+           17:'Veh > 3.5 tons prohibited',       
+           18:'No entry',       
+           19:'General caution',     
+           20:'Dangerous curve left',      
+           21:'Dangerous curve right',   
+           22:'Double curve',      
+           23:'Bumpy road',     
+           24:'Slippery road',       
+           25:'Road narrows on the right',  
+           26:'Road work',    
+           27:'Traffic signals',      
+           28:'Pedestrians',     
+           29:'Children crossing',     
+           30:'Bicycles crossing',       
+           31:'Beware of ice/snow',
+           32:'Wild animals crossing',      
+           33:'End speed + passing limits',      
+           34:'Turn right ahead',     
+           35:'Turn left ahead',       
+           36:'Ahead only',      
+           37:'Go straight or right',      
+           38:'Go straight or left',      
+           39:'Keep right',     
+           40:'Keep left',      
+           41:'Roundabout mandatory',     
+           42:'End of no passing',      
+           43:'End no passing veh > 3.5 tons' }
+                 
+#initialise GUI
+top=tk.Tk()
+top.geometry('800x600')
+top.title('Traffic sign classification')
+top.configure(background='#CDCDCD')
+
+label=Label(top,background='#CDCDCD', font=('arial',15,'bold'))
+sign_image = Label(top)
+
+def classify(file_path):
+    global label_packed
+    image = Image.open(file_path)
+    image = image.resize((30,30))
+    image = numpy.expand_dims(image, axis=0)
+    image = numpy.array(image)
+    print(image.shape)
+    pred = model.predict_classes([image])[0]
+    sign = classes[pred+1]
+    print(sign)
+    label.configure(foreground='#011638', text=sign) 
+   
+
+def show_classify_button(file_path):
+    classify_b=Button(top,text="Classify Image",command=lambda: classify(file_path),padx=10,pady=5)
+    classify_b.configure(background='#364156', foreground='white',font=('arial',10,'bold'))
+    classify_b.place(relx=0.79,rely=0.46)
+
+def upload_image():
+    try:
+        file_path=filedialog.askopenfilename()
+        uploaded=Image.open(file_path)
+        uploaded.thumbnail(((top.winfo_width()/2.25),(top.winfo_height()/2.25)))
+        im=ImageTk.PhotoImage(uploaded)
+        
+        sign_image.configure(image=im)
+        sign_image.image=im
+        label.configure(text='')
+        show_classify_button(file_path)
+    except:
+        pass
+
+upload=Button(top,text="Upload an image",command=upload_image,padx=10,pady=5)
+upload.configure(background='#364156', foreground='white',font=('arial',10,'bold'))
+
+upload.pack(side=BOTTOM,pady=50)
+sign_image.pack(side=BOTTOM,expand=True)
+label.pack(side=BOTTOM,expand=True)
+heading = Label(top, text="Know Your Traffic Sign",pady=20, font=('arial',20,'bold'))
+heading.configure(background='#CDCDCD',foreground='#364156')
+heading.pack()
+top.mainloop()
diff --git a/MachineLearning Projects/traffic sign recognition using CNN and Keras/traffic_sign.py b/MachineLearning Projects/traffic sign recognition using CNN and Keras/traffic_sign.py
@@ -0,0 +1,110 @@
+import numpy as np 
+import pandas as pd 
+import matplotlib.pyplot as plt
+import cv2
+import tensorflow as tf
+from PIL import Image
+import os
+from sklearn.model_selection import train_test_split
+from keras.utils import to_categorical
+from keras.models import Sequential, load_model
+from keras.layers import Conv2D, MaxPool2D, Dense, Flatten, Dropout
+
+data = []
+labels = []
+classes = 43
+cur_path = os.getcwd()
+
+#Retrieving the images and their labels 
+for i in range(classes):
+    path = os.path.join(cur_path,'train',str(i))
+    images = os.listdir(path)
+
+    for a in images:
+        try:
+            image = Image.open(path + '\\'+ a)
+            image = image.resize((30,30))
+            image = np.array(image)
+            #sim = Image.fromarray(image)
+            data.append(image)
+            labels.append(i)
+        except:
+            print("Error loading image")
+
+#Converting lists into numpy arrays
+data = np.array(data)
+labels = np.array(labels)
+
+print(data.shape, labels.shape)
+#Splitting training and testing dataset
+X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, random_state=42)
+
+print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
+
+#Converting the labels into one hot encoding
+y_train = to_categorical(y_train, 43)
+y_test = to_categorical(y_test, 43)
+
+#Building the model
+model = Sequential()
+model.add(Conv2D(filters=32, kernel_size=(5,5), activation='relu', input_shape=X_train.shape[1:]))
+model.add(Conv2D(filters=32, kernel_size=(5,5), activation='relu'))
+model.add(MaxPool2D(pool_size=(2, 2)))
+model.add(Dropout(rate=0.25))
+model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))
+model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))
+model.add(MaxPool2D(pool_size=(2, 2)))
+model.add(Dropout(rate=0.25))
+model.add(Flatten())
+model.add(Dense(256, activation='relu'))
+model.add(Dropout(rate=0.5))
+model.add(Dense(43, activation='softmax'))
+
+#Compilation of the model
+model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+
+epochs = 15
+history = model.fit(X_train, y_train, batch_size=32, epochs=epochs, validation_data=(X_test, y_test))
+model.save("my_model.h5")
+
+#plotting graphs for accuracy 
+plt.figure(0)
+plt.plot(history.history['accuracy'], label='training accuracy')
+plt.plot(history.history['val_accuracy'], label='val accuracy')
+plt.title('Accuracy')
+plt.xlabel('epochs')
+plt.ylabel('accuracy')
+plt.legend()
+plt.show()
+
+plt.figure(1)
+plt.plot(history.history['loss'], label='training loss')
+plt.plot(history.history['val_loss'], label='val loss')
+plt.title('Loss')
+plt.xlabel('epochs')
+plt.ylabel('loss')
+plt.legend()
+plt.show()
+
+#testing accuracy on test dataset
+from sklearn.metrics import accuracy_score
+
+y_test = pd.read_csv('Test.csv')
+
+labels = y_test["ClassId"].values
+imgs = y_test["Path"].values
+
+data=[]
+
+for img in imgs:
+    image = Image.open(img)
+    image = image.resize((30,30))
+    data.append(np.array(image))
+
+X_test=np.array(data)
+
+pred = model.predict_classes(X_test)
+
+#Accuracy with the test data
+from sklearn.metrics import accuracy_score
+print(accuracy_score(labels, pred))
