class SimpleMnistDataVisualizer:
def __init__(self, X_train, y_train, mapp):
self.X_train = X_train
self.y_train = y_train
self.mapp = mapp
self.plotter = Plotter()
def plot_specified_digit(self, code):
"""
Plot a digit or character
:param code:
"""
for i in range(self.y_train.shape[0]):
if (self.y_train[i, code] == 1):
self.plotter.plot_image(self.X_train[i].reshape(28,28))
break
def plot_first_digit(self, ):
"""
Plot first element of the training set
"""
self.plotter.plot_image(self.X_train[0].reshape(28, 28))
def plot_range(self):
"""
Plot characters from training set using a range
"""
for i in range(100, 109):
plt.subplot(330 + (i + 1))
plt.imshow(self.X_train[i].reshape(28, 28), cmap=plt.get_cmap('gray'))
plt.title(chr(self.mapp[np.argmax(self.y_train[i])]))
plt.show()
def plotgraph(self, epochs, acc, val_acc):
"""
Plot a graph
:param epochs:
:param acc:
:param val_acc:
"""
self.plotter.plotgraph(epochs, acc, val_acc)
def plot_loss_acc(self):
"""
Plot loss based on parameters of each epoch
"""
history_path = './experiments/2019-12-15/conv_emnist_from_config/history_params/parameters.csv'
data_frame = pd.read_csv(history_path, delimiter=',')
self.plotter.plotgraph(data_frame['epoch'].values, data_frame['acc'].values, data_frame['val_acc'])
self.plotter.plotgraph(data_frame['epoch'].values, data_frame['loss'].values, data_frame['val_loss'])
class ImagePreProcessor():
def __init__(self):
self.plotter = Plotter()
def execute(self, image, readImage=True):
"""
Pre process image to make it look as close as possible to emnist data set
:param image:
:param readImage:
:return:
"""
# create an array where we can store our picture
images = np.zeros((1, 784))
# and the correct values
correct_vals = np.zeros((1, 10))
# read the image and transform to black and white
gray = 0
if readImage:
gray = cv2.imread(image, 0)
else:
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
(threshi, img_bw) = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
self.plotter.plot_image(gray) # Plot image in gray scale
gray = self.cut(gray) # Cut image
if len(gray) > 0:
# resize the images and invert it (black background)
gray = cv2.resize(255 - gray, (28, 28))
self.plotter.plot_image(gray) # Plot cut image
cv2.imwrite("test_images/image_1.png", gray) # Save image for testing purposes
# better black and white version
(thresh, gray) = cv2.threshold(gray, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
self.plotter.plot_image(gray)
"""
We want to fit the images into this 20x20 pixel box.
Therefore we need to remove every row and column at the sides
of the image which are completely black.
"""
while np.sum(gray[0]) == 0:
gray = gray[1:]
while np.sum(gray[:, 0]) == 0:
gray = np.delete(gray, 0, 1)
while np.sum(gray[-1]) == 0:
gray = gray[:-1]
while np.sum(gray[:, -1]) == 0:
gray = np.delete(gray, -1, 1)
rows, cols = gray.shape
"""
Now we want to resize our outer box to fit it into a 20x20 box.
We need a resize factor for this.
"""
if rows > cols:
factor = 20.0 / rows
rows = 20
cols = int(round(cols * factor))
# first cols than rows
gray = cv2.resize(gray, (cols, rows))
else:
factor = 20.0 / cols
cols = 20
rows = int(round(rows * factor))
# first cols than rows
gray = cv2.resize(gray, (cols, rows))
"""
But at the end we need a 28x28 pixel image so we add the missing
black rows and columns using the np.lib.pad function which adds 0s
to the sides.
"""
colsPadding = (int(math.ceil((28 - cols) / 2.0)), int(math.floor((28 - cols) / 2.0)))
rowsPadding = (int(math.ceil((28 - rows) / 2.0)), int(math.floor((28 - rows) / 2.0)))
gray = np.lib.pad(gray, (rowsPadding, colsPadding), 'constant')
self.plotter.plot_image(gray)
shiftx, shifty = self.get_best_shift(gray)
shifted = self.shift(gray, shiftx, shifty)
gray = shifted
cv2.imwrite("test_images/image_2.png", gray)
"""
all images in the training set have an range from 0-1
and not from 0-255 so we divide our flatten images
(a one dimensional vector with our 784 pixels)
to use the same 0-1 based range
"""
# im = gray / 255
im = gray
return im
def cut(self, img):
"""
Cut image
:param img:
:return:
"""
left = img.shape[1]
top = img.shape[0]
right, bottom = 0, 0
for i in range(img.shape[0]):
for j in range(img.shape[1]):
if img[i, j] != 255:
left = min(left, j)
right = max(right, j)
top = min(top, i)
bottom = max(bottom, i)
length = bottom - top
width = right - left
left = max(0, left - int(length / 2))
right = min(right + int(length / 2), img.shape[1])
top = max(0, top - int(width / 2))
bottom = min(bottom + int(width / 2), img.shape[0])
print(str(left) + "," + str(right) + "," + str(top) + "," + str(bottom))
img = img[top:bottom, left:right]
return img
def get_best_shift(self, img):
cy, cx = ndimage.measurements.center_of_mass(img)
rows, cols = img.shape
shiftx = np.round(cols / 2.0 - cx).astype(int)
shifty = np.round(rows / 2.0 - cy).astype(int)
return shiftx, shifty
def shift(self, img, sx, sy):
rows, cols = img.shape
M = np.float32([[1, 0, sx], [0, 1, sy]])
shifted = cv2.warpAffine(img, M, (cols, rows))
return shifted
def rotate(self, image):
"""
Rotate image and flip it
:param image:
:return:
"""
image = image.reshape([HEIGHT, WIDTH])
image = np.fliplr(image)
image = np.rot90(image)
return image
def split_letters(self, img):
"""
Method that takes an images and splits all the letters in the image, it does this by using contours
:param image:
:return:
"""
images = []
image = cv2.imread(img)
height, width, depth = image.shape
# resizing the image to find spaces better
image = cv2.resize(image, dsize=(width * 5, height * 4), interpolation=cv2.INTER_CUBIC)
# grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# binary
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV)
# dilation
kernel = np.ones((5, 5), np.uint8)
img_dilation = cv2.dilate(thresh, kernel, iterations=1)
# adding GaussianBlur
gsblur = cv2.GaussianBlur(img_dilation, (5, 5), 0)
# find contours
ctrs, hier = cv2.findContours(gsblur.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
m = list()
# sort contours
sorted_ctrs = sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[0])
pchl = list()
dp = image.copy()
for i, ctr in enumerate(sorted_ctrs):
# Get bounding box
x, y, w, h = cv2.boundingRect(ctr)
cv2.rectangle(dp, (x - 10, y - 10), (x + w + 10, y + h + 10), (90, 0, 255), 9)
crop_img = image[y:y + h, x:x + w]
bordersize = 700
border = cv2.copyMakeBorder(
crop_img,
top=bordersize,
bottom=bordersize,
left=bordersize,
right=bordersize,
borderType=cv2.BORDER_CONSTANT,
value=[255, 255, 255]
)
images.append(border)
# border = cv2.resize(border, dsize=(28,28), interpolation=cv2.INTER_CUBIC)
cv2.imwrite("cropped{0}.jpg".format(i), border)
# roi = cv2.cvtColor(roi,cv2.COLOR_BGR2GRAY)
# roi = np.array(roi)
plt.imshow(border)
plt.show()
cv2.imwrite("boxes.jpg", dp)
plt.imshow(dp)
plt.show()
return images
