import tensorflow as tf
from tensorflow import keras
import numpy as np
import pandas as pd
print(tf.__version__)
2.6.0
mnist = keras.datasets.mnist
(train_images_mnist,train_labels_mnist),(test_images_mnist,test_labels_mnist) = mnist.load_data()
# images are reshaped to be used by the flow method of a keras ImageGenerator
train_images_mnist = np.reshape(train_images_mnist,(train_images_mnist.shape[0],28,28,1))
test_images_mnist = np.reshape(test_images_mnist,(test_images_mnist.shape[0],28,28,1))
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
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az_data_path = '/datasets/handwritingdataset'
AZ_data = pd.read_csv(az_data_path +'/AZ_Handwritten_Data.csv',header = None)
# the first column contains label values, while the remaining are the flattened array of 28 x 28 image pixels
AZ_labels = AZ_data.values[:,0]
AZ_images = AZ_data.values[:,1:]
# images are reshaped to be used by the flow method of a keras ImageGenerator
AZ_images = np.reshape(AZ_images,(AZ_images.shape[0],28,28,1))
# join datasets
# split AZ data in train and test
from sklearn.model_selection import train_test_split
test_size = float(len(test_labels_mnist))/len(train_labels_mnist)
print(f'test set size: {test_size}')
train_images_AZ, test_images_AZ, train_labels_AZ, test_labels_AZ = train_test_split(AZ_images,AZ_labels, test_size=test_size)
#shift mnist labels
train_labels_mnist = train_labels_mnist + max(AZ_labels)+1
test_labels_mnist = test_labels_mnist + max(AZ_labels)+1
# concatenate datasets
train_images = np.concatenate((train_images_AZ,train_images_mnist),axis=0)
train_labels = np.concatenate((train_labels_AZ,train_labels_mnist))
test_images = np.concatenate((test_images_AZ,test_images_mnist),axis=0)
test_labels = np.concatenate((test_labels_AZ,test_labels_mnist))
print('Data ready')
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import tensorflow as tf
from tensorflow.keras.optimizers import RMSprop
model = tf.keras.models.Sequential([
# Note the input shape is the desired size of the image 150x150 with 3 bytes color
tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28, 28, 1)),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(32, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
# Flatten the results to feed into a DNN
tf.keras.layers.Flatten(),
# 512 neuron hidden layer
tf.keras.layers.Dense(512, activation='relu'),
# Only 1 output neuron. It will contain a value from 0-1 where 0 for 1 class ('cats') and 1 for the other ('dogs')
tf.keras.layers.Dense(len(np.unique(train_labels)), activation='softmax')
])
model.compile(optimizer=RMSprop(learning_rate=1e-4),
loss='sparse_categorical_crossentropy',
metrics = ['accuracy'])
model.summary()
from tensorflow.keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(
rescale=1./255,
rotation_range=15,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.1,
zoom_range=0.2,
horizontal_flip=False,
fill_mode='nearest')
test_datagen = ImageDataGenerator(rescale=1./255)
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# Flow training images in batches using generator
train_generator = train_datagen.flow(train_images, train_labels, batch_size=50, shuffle=True)
validation_generator = test_datagen.flow(test_images, test_labels, batch_size=50, shuffle=True)
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history = model.fit(
train_generator,
steps_per_epoch=500,
epochs=100,
validation_data=validation_generator,
validation_steps=50,
verbose=2)
model.save('model_v2')
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from tensorflow.keras.models import load_model
import tensorflow as tf
import cv2
import matplotlib.pyplot as plt
import imutils
from imutils.contours import sort_contours
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# loads the model with the keras load_model function
model_path = 'model_v2'
print("Loading NN model...")
model = load_model(model_path)
print("Done")
# loads the input image
image_path = 'handwriting_example1.png'
image = cv2.imread(image_path)
#from google.colab import files
#image = files.upload()
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cropped = gray[120:,:]
blurred = cv2.GaussianBlur(cropped, (5, 5), 0)
%matplotlib inline
from matplotlib import cm
fig = plt.figure(figsize=(16,4))
ax = plt.subplot(1,4,1)
ax.imshow(image)
ax.set_title('original image');
ax = plt.subplot(1,4,2)
ax.imshow(gray,cmap=cm.binary_r)
ax.set_axis_off()
ax.set_title('grayscale image');
ax = plt.subplot(1,4,3)
ax.imshow(cropped,cmap=cm.binary_r)
ax.set_axis_off()
ax.set_title('cropped image');
ax = plt.subplot(1,4,4)
ax.imshow(blurred,cmap=cm.binary_r)
ax.set_axis_off()
ax.set_title('blurred image');
#plt.imshow(gray,cmap=cm.binary_r)
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# perform edge detection, find contours in the edge map, and sort the
# resulting contours from left-to-right
edged = cv2.Canny(blurred, 30, 250) #low_threshold, high_threshold
cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
cnts = sort_contours(cnts, method="left-to-right")[0]
figure = plt.figure(figsize=(7,7))
plt.axis('off');
plt.imshow(edged,cmap=cm.binary_r);
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chars = []
# loop over the contours
for c in cnts:
# compute the bounding box of the contour and isolate ROI
(x, y, w, h) = cv2.boundingRect(c)
roi = cropped[y:y + h, x:x + w]
#binarize image, finds threshold with OTSU method
thresh = cv2.threshold(roi, 0, 255,cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
# resize largest dimension to input size
(tH, tW) = thresh.shape
if tW > tH:
thresh = imutils.resize(thresh, width=28)
# otherwise, resize along the height
else:
thresh = imutils.resize(thresh, height=28)
# find how much is needed to pad
(tH, tW) = thresh.shape
dX = int(max(0, 28 - tW) / 2.0)
dY = int(max(0, 28 - tH) / 2.0)
# pad the image and force 28 x 28 dimensions
padded = cv2.copyMakeBorder(thresh, top=dY, bottom=dY,
left=dX, right=dX, borderType=cv2.BORDER_CONSTANT,
value=(0, 0, 0))
padded = cv2.resize(padded, (28, 28))
# reshape and rescale padded image for the model
padded = padded.astype("float32") / 255.0
padded = np.expand_dims(padded, axis=-1)
# append image and bounding box data in char list
chars.append((padded, (x, y, w, h)))
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# plot isolated characters
n_cols = 10
n_rows = np.floor(len(chars)/ n_cols)+1
fig = plt.figure(figsize=(1.5*n_cols,1.5*n_rows))
for i,char in enumerate(chars):
ax = plt.subplot(n_rows,n_cols,i+1)
ax.imshow(char[0][:,:,0],cmap=cm.binary,aspect='auto')
#plt.axis('off')
plt.tight_layout()
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boxes = [b[1] for b in chars]
chars = np.array([c[0] for c in chars], dtype="float32")
# OCR the characters using our handwriting recognition model
preds = model.predict(chars)
# define the list of label names
labelNames = "ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789"
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image = cv2.imread(image_path)
cropped = image[120:,:]
for (pred, (x, y, w, h)) in zip(preds, boxes):
# find the index of the label with the largest corresponding
# probability, then extract the probability and label
i = np.argmax(pred)
prob = pred[i]
label = labelNames[i]
# draw the prediction on the image and it's probability
label_text = f"{label},{prob * 100:.1f}%"
cv2.rectangle(cropped, (x, y), (x + w, y + h), (0,255 , 0), 2)
cv2.putText(cropped, label_text, (x - 10, y - 10),cv2.FONT_HERSHEY_SIMPLEX,0.5, (0,255, 0), 1)
# show the image
plt.figure(figsize=(15,10))
plt.imshow(cropped)
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