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# -*- coding: utf-8 -*-
"""
Include functions for normalizing images of words and letters
Main functions: word_normalization, letter_normalization, image_standardization
"""
import numpy as np
import cv2
import math
from .helpers import *
def image_standardization(image):
"""Image standardization should result in same output
as tf.image.per_image_standardization.
"""
return (image - np.mean(image)) / max(np.std(image), 1.0/math.sqrt(image.size))
def _crop_add_border(img, height, threshold=50, border=True, border_size=15):
"""Crop and add border to word image of letter segmentation."""
# Clear small values
ret, img = cv2.threshold(img, threshold, 255, cv2.THRESH_TOZERO)
x0 = 0
y0 = 0
x1 = img.shape[1]
y1 = img.shape[0]
for i in range(img.shape[0]):
if np.count_nonzero(img[i, :]) > 1:
y0 = i
break
for i in reversed(range(img.shape[0])):
if np.count_nonzero(img[i, :]) > 1:
y1 = i+1
break
for i in range(img.shape[1]):
if np.count_nonzero(img[:, i]) > 1:
x0 = i
break
for i in reversed(range(img.shape[1])):
if np.count_nonzero(img[:, i]) > 1:
x1 = i+1
break
if height != 0:
img = resize(img[y0:y1, x0:x1], height, True)
else:
img = img[y0:y1, x0:x1]
if border:
return cv2.copyMakeBorder(img, 0, 0, border_size, border_size,
cv2.BORDER_CONSTANT,
value=[0, 0, 0])
return img
def _word_tilt(img, height, border=True, border_size=15):
"""Detect the angle and tilt the image."""
edges = cv2.Canny(img, 50, 150, apertureSize = 3)
lines = cv2.HoughLines(edges, 1, np.pi/180, 30)
if lines is not None:
meanAngle = 0
# Set min number of valid lines (try higher)
numLines = np.sum(1 for l in lines if l[0][1] < 0.7 or l[0][1] > 2.6)
if numLines > 1:
meanAngle = np.mean([l[0][1] for l in lines if l[0][1] < 0.7 or l[0][1] > 2.6])
# Look for angle with correct value
if meanAngle != 0 and (meanAngle < 0.7 or meanAngle > 2.6):
img = _tilt_by_angle(img, meanAngle, height)
return _crop_add_border(img, height, 50, border, border_size)
def _tilt_by_angle(img, angle, height):
"""Tilt the image by given angle."""
dist = np.tan(angle) * height
width = len(img[0])
sPoints = np.float32([[0,0], [0,height], [width,height], [width,0]])
# Dist is positive for angle < 0.7; negative for angle > 2.6
# Image must be shifed to right
if dist > 0:
tPoints = np.float32([[0,0],
[dist,height],
[width+dist,height],
[width,0]])
else:
tPoints = np.float32([[-dist,0],
[0,height],
[width,height],
[width-dist,0]])
M = cv2.getPerspectiveTransform(sPoints, tPoints)
return cv2.warpPerspective(img, M, (int(width+abs(dist)), height))
def _sobel_detect(channel):
"""The Sobel Operator."""
sobelX = cv2.Sobel(channel, cv2.CV_16S, 1, 0)
sobelY = cv2.Sobel(channel, cv2.CV_16S, 0, 1)
# Combine x, y gradient magnitudes sqrt(x^2 + y^2)
sobel = np.hypot(sobelX, sobelY)
sobel[sobel > 255] = 255
return np.uint8(sobel)
class HysterThresh:
def __init__(self, img):
img = 255 - img
img = (img - np.min(img)) / (np.max(img) - np.min(img)) * 255
hist, bins = np.histogram(img.ravel(), 256, [0,256])
self.high = np.argmax(hist) + 65
self.low = np.argmax(hist) + 45
self.diff = 255 - self.high
self.img = img
self.im = np.zeros(img.shape, dtype=img.dtype)
def get_image(self):
self._hyster()
return np.uint8(self.im)
def _hyster_rec(self, r, c):
h, w = self.img.shape
for ri in range(r-1, r+2):
for ci in range(c-1, c+2):
if (h > ri >= 0
and w > ci >= 0
and self.im[ri, ci] == 0
and self.high > self.img[ri, ci] >= self.low):
self.im[ri, ci] = self.img[ri, ci] + self.diff
self._hyster_rec(ri, ci)
def _hyster(self):
r, c = self.img.shape
for ri in range(r):
for ci in range(c):
if (self.img[ri, ci] >= self.high):
self.im[ri, ci] = 255
self.img[ri, ci] = 255
self._hyster_rec(ri, ci)
def _hyst_word_norm(image):
"""Word normalization using hystheresis thresholding."""
gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# img = cv2.bilateralFilter(gray, 0, 10, 30)
img = cv2.bilateralFilter(gray, 10, 10, 30)
return HysterThresh(img).get_image()
def word_normalization(image, height, border=True, tilt=True, border_size=15, hyst_norm=False):
""" Preprocess a word - resize, binarize, tilt world."""
image = resize(image, height, True)
if hyst_norm:
th = _hyst_word_norm(image)
else:
img = cv2.bilateralFilter(image, 10, 30, 30)
gray = 255 - cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
norm = cv2.normalize(gray, None, 0, 255, cv2.NORM_MINMAX)
ret,th = cv2.threshold(norm, 50, 255, cv2.THRESH_TOZERO)
if tilt:
return _word_tilt(th, height, border, border_size)
return _crop_add_border(th, height=height, border=border, border_size=border_size)
def _resize_letter(img, size = 56):
"""Resize bigger side of the image to given size."""
if (img.shape[0] > img.shape[1]):
rat = size / img.shape[0]
return cv2.resize(img, (int(rat * img.shape[1]), size))
else:
rat = size / img.shape[1]
return cv2.resize(img, (size, int(rat * img.shape[0])))
return img
def letter_normalization(image, is_thresh=True, dim=False):
"""Preprocess a letter - crop, resize"""
if is_thresh and image.shape[0] > 0 and image.shape[1] > 0:
image = _crop_add_border(image, height=0, threshold=80, border=False)
resized = image
if image.shape[0] > 1 and image.shape[1] > 1:
resized = _resize_letter(image)
result = np.zeros((64, 64), np.uint8)
offset = [0, 0]
# Calculate offset for smaller size
if image.shape[0] > image.shape[1]:
offset = [int((result.shape[1] - resized.shape[1])/2), 4]
else:
offset = [4, int((result.shape[0] - resized.shape[0])/2)]
# Replace zeros by image
result[offset[1]:offset[1] + resized.shape[0],
offset[0]:offset[0] + resized.shape[1]] = resized
if dim:
return result, image.shape
return result
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