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import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import argparse
import cv2
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
# initialize the number of epochs to train for, initial learning rate,
# and batch size
EPOCHS = 50
def parse_argument():
"""
Construct the argument parser and parse the arguments.
"""
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required=True,
help="path to English Handwritten Characters dataset")
ap.add_argument("-m", "--model", type=str, default="model",
help="path to output trained handwriting recognition model")
ap.add_argument('-s', '--show', action='store_true',
ap.add_argument("-p", "--plot", type=str, default="plot.png",
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args = vars(ap.parse_args())
return args
def load_eng_dataset(datasetPath):
"""
Helper function for train model OCR. Function will load English Handwritten
Characters dataset that should be in given path.
"""
# initialize the list of data and labels
data = []
labels = []
# loop over the rows of the A-Z handwritten digit dataset
for row in open(datasetPath):
# Skip the first row
if row == "image,label\n":
continue
# parse the label and image from the row
row = row.split(",")
imagePath = "eng_dataset/" + row[0] # hardcode the path
try:
image = cv2.imread(imagePath)
except cv2.error as e:
print("[ERROR] loading image ", row[0], " fail")
continue
label = row[1][:-1] if len(row[1]) > 1 else row[1] # remove '\n' at end
# update the list of data and labels
data.append(image)
labels.append(label)
# convert the data and labels to NumPy arrays
data = np.array(data)
labels = np.array(labels, dtype="U1")
# return a 2-tuple of the English Handwritten Characters data and labels
return (data, labels)
def process_dataset(data, labels):
"""
Help function to pre-process the dataset for ready to train model.
"""
# the architecture we're using is designed for 32x32 images,
# so we need to resize them to 32x32
data = [cv2.resize(image, (32, 32)) for image in data]
# add a channel dimension to every image in the dataset and
# scale the pixel intensities of the images from [0, 255] down to [0, 1]
data /= 255.0
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# convert the labels from integers to vectors
le = LabelBinarizer()
labels = le.fit_transform(labels)
# account for skew in the labeled data
classTotals = labels.sum(axis=0)
classWeight = {}
# loop over all classes and calculate the class weight
for i in range(0, len(classTotals)):
classWeight[i] = classTotals.max() / classTotals[i]
return data, labels, classWeight
def show_train_data(train_images, train_labels):
"""
To verify that the dataset looks correct, let's plot the first 25 images from
the training set and display the class name below each image
"""
class_names = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9',
'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'G', 'K', 'L', 'M',
'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z',
'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'g', 'k', 'l', 'm',
'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
plt.figure(figsize=(10,10))
for i in range(25):
plt.subplot(5,5,i+1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(train_images[i])
# The CIFAR labels happen to be arrays,
# which is why you need the extra index
index = np.where(train_labels[i] == 1)[0][0]
plt.xlabel(class_names[index])
plt.show()
if __name__ == "__main__":
# load arguments
args = parse_argument()
# load the English Handwritten Characters datasets
print("[INFO] loading datasets...")
(data, labels) = load_eng_dataset(args["dataset"])
# pre-process the data and labels for training
print("[INFO] pre-processing datasets...")
data, labels, classWeight = process_dataset(data, labels)
# partition the data into training and testing splits using 80% of
# the data for training and the remaining 20% for testing
(train_images, test_images, train_labels, test_labels) = train_test_split(data,
labels, test_size=0.20, stratify=labels, random_state=42)
# show train data in plot
if args["show"]:
show_train_data(train_images, train_labels)
# initialize and compile our deep neural network
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print("[INFO] compiling model...")
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(62, activation='softmax'))
# Use categorical_crossentropy for one-hot coding labels
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
if args["show"]:
print(model.summary())
# train the network
print("[INFO] training model...")
history = model.fit(x=train_images,
y=train_labels,
validation_data=(test_images, test_labels),
batch_size=BS,
epochs=EPOCHS,
steps_per_epoch=len(train_images)//BS,
class_weight=classWeight)
# evaluate the network
print("[INFO] evaluating network...")
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, 1])
plt.legend(loc='lower right')