google crawl img.py

'''# install module
import requests
import random
import shutil
import bs4

#Get URL to save
def image(data,num):
    #Get URL for Google Image Search
    res = requests.get("https://www.google.com/search?hl=jp&q=" + data + "&btnG=Google+Search&tbs=0&safe=off&tbm=isch")
    html = res.text   #Text conversion
    soup = bs4.BeautifulSoup(html,'lxml')   #Plastic surgery
    links = soup.find_all("img")   #Get img element
    link = links[num].get("src")   #Get numth srcURL
    return link

#Download from the corresponding URL
def download(url,file_name):
    req = requests.get(url, stream=True)
    if req.status_code == 200:
        with open(file_name + ".jpg ",'wb') as f:   #Export png to file with bin
            req.raw.decode_content = True
            shutil.copyfileobj(req.raw, f)   #Copy png image data to file

#Pick up the name of the child to search for
name = input("What child are you looking for?:")
for i in range(1,5):   #20 sheets for convenience
    link = image(name,i)
    download(link,name + "-"+str(i))
    print(link)   #Repeat 20 times'''
    
from pyexpat import XML_PARAM_ENTITY_PARSING_ALWAYS
import numpy as np
import random
import os
import cv2 as cv
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from keras.models import Sequential, load_model
from keras.layers import Conv2D, Dense, Flatten, Rescaling, AveragePooling2D, Dropout

#source:https://towardsdatascience.com/classification-of-traffic-signs-with-lenet-5-cnn-cb861289bd62
images = []
labels = []
classes = 43

current_path = 'C:\\Users\\ADMIN\\Downloads\\GTSRB_Final_Training_Images\\GTSRB\\Final_Training\\Images\\00002'

for i in range(classes):
   # path = os.path.join(current_path, str(str(i).zfill(5)))
    img_folder = os.listdir(current_path)
    for j in img_folder:
        try:
            image = cv.imread(str(current_path+'\\'+j))
            image = cv.resize(image, (32, 32))
            image = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
            image = np.array(image)
            images.append(image)
            label = np.zeros(classes)
            label[i] = 1.0
            labels.append(label)
        except:
            pass
images = np.array(images)
images = images/255
labels = np.array(labels)
print('Images shape:', images.shape)
print('Labels shape:', labels.shape)
images = np.array(images)
images = images/255
labels = np.array(labels)
print('Images shape:', images.shape)
print('Labels shape:', labels.shape)

X = images.astype(np.float32)
y = labels.astype(np.float32)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)

print('X_train shape:', X_train.shape)
print('y_train shape:', y_train.shape)
print('X_test shape:', X_test.shape)
print('y_test shape:', y_test.shape)
plt.figure(figsize=(12, 12))
start_index = 0
for i in range(25):
    plt.subplot(5, 5, i+1)
    plt.grid(False)
    plt.xticks([])
    plt.yticks([])
    
    label = np.argmax(y_train[start_index+i])
    
    plt.xlabel('i={}, label={}'.format(start_index+i, label))
    plt.imshow(X_train[start_index+i], cmap='gray')
plt.show()
# Building the model
model = Sequential([
    Rescaling(1, input_shape=(32, 32, 1)),
    Conv2D(filters=6, kernel_size=(5, 5), activation='relu'),
    AveragePooling2D(pool_size=(2, 2)),
    Conv2D(filters=16, kernel_size=(5, 5), activation='relu'),
    AveragePooling2D(pool_size=(2, 2)),
    Conv2D(filters=120, kernel_size=(5, 5), activation='relu'),
    Dropout(0.2),
    Flatten(),
    Dense(units=120, activation='relu'),
    Dense(units=43, activation='softmax')
])

# Compilation of the model
model.compile(
    optimizer='adam',
    loss='categorical_crossentropy',
    metrics=['accuracy']
)

# Model architecture
model.summary()

history = model.fit(X_train, y_train, epochs=50,
                    validation_data=(X_test, y_test))

val_loss, val_acc = model.evaluate(X_test, y_test, verbose=2)
print('\nValifdation accuracy:', val_acc)
print('\nValidation loss:', val_loss)

plt.figure(0)
plt.plot(history.history['accuracy'], label='accuracy')
plt.plot(history.history['val_accuracy'], label = 'val_accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.ylim([0.8, 1])
plt.legend(loc='lower right')

plt.figure(1)
plt.plot(history.history['loss'], label='loss')
plt.plot(history.history['val_loss'], label = 'val_loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.ylim([0, 0.2])
plt.legend(loc='lower right')

preds = model.predict(X_test)

plt.figure(figsize=(12, 12))
start_index = random.randint(0, 7800)
for i in range(25):
    plt.subplot(5, 5, i+1)
    plt.grid(False)
    plt.xticks([])
    plt.yticks([])
    
    pred = np.argmax(preds[start_index+i])
    gt = np.argmax(y_test[start_index+i])
    
    col = 'g'
    if pred != gt:
        col = 'r'
    
    plt.xlabel('i={}, pred={}, gt={}'.format(start_index+i, pred, gt), color=col)
    plt.imshow(X_test[start_index+i], cmap='gray')
plt.show()