In [1]:
from scipy.signal import fftconvolve
import numpy as np
import matplotlib.pyplot as plt
from skimage.transform import rescale, rotate
from skimage.filters import rank
from skimage.color import rgb2gray
from skimage.morphology import disk
from skimage.filters import rank
from skimage.transform import ProjectiveTransform, warp
from skimage.filters import gabor_kernel
from scipy.signal import fftconvolve
from skimage.filters import threshold_otsu
from skimage import measure
from skimage.transform import hough_line, hough_line_peaks
from skimage.morphology import skeletonize
from skimage.exposure import adjust_sigmoid, adjust_log
from skimage.util import img_as_ubyte
from skimage.transform import resize
import os
%matplotlib inline
def resize_im(im, max_side):
"""Resize an image so the its longer side is the specified length"""
ratio = float(max_side_length) / max(im.shape[:2])
new_h = int(round(ratio*im.shape[0]))
new_w = int(round(ratio*im.shape[1]))
return resize(im, (new_h, new_w))
def dist_angle_to_slope_interept(line):
"""Convert between line representations"""
angle, dist = line
slope = np.tan(angle - np.pi/2)
y_intercept = dist / np.sin(angle)
return slope, y_intercept
def line_intersection(line1, line2):
"""
Compute the point of intersection between two lines
(in dist-angle representation)
"""
if line1[0] == 0:
print('line 1 vertical')
if line2[0] == 0:
a, c = dist_angle_to_slope_interept(line1)
print('line 2 vertical')
print(line1, a, c)
a, c = dist_angle_to_slope_interept(line1)
b, d = dist_angle_to_slope_interept(line2)
if a == b: # lines are parallel or coincident
return None
x = (d - c) / (a - b)
y = (a*d - b*c) / (a - b)
return x, y
def line_invrot90(line, im_shape):
"""
Rotate a line in dist-angle representation in the same sense as np.rot90
"""
theta1, d1 = line
theta2 = np.pi/2 - theta1
if abs(theta2) == 0 :
return 0, im_shape[0] - d1
x1, y1 = d1 * np.cos(theta1), d1 * np.sin(theta1)
h1 = d1 / np.cos(theta2)
h2 = im_shape[0] - h1
d2 = h2 * np.cos(theta2)
return -theta2, d2
def edge_response(im, sigma, thetas=np.linspace(-np.pi/10, np.pi/10, 5)):
"""Compute the edge response max-pooled over a range of orientations"""
kernels = []
for theta in thetas:
kern = gabor_kernel(.1/sigma, theta=theta, sigma_x=sigma, sigma_y=2*sigma, n_stds=2).imag
kern = np.rot90(kern, 3)
kernels.append(np.fliplr(np.flipud(kern)))
# kernel responses, max pooled over orientations
resp_im = np.zeros_like(im)
for kern in kernels:
resp = fftconvolve(im, kern, mode='same')
resp_im = np.maximum(resp, resp_im)
return resp_im
def brightest_object_mask(gray):
"""
Threshold a grayscale response image and return a mask of the brightest object
"""
edges = gray > threshold_otsu(gray)
# create a mask containing the object with the strongest response
label_im = measure.label(edges)
regions = measure.regionprops(label_im)
if len(regions) == 0:
raise ValueError('mask must have at least one object')
max_resp = 0
for region in regions:
lbl = region.label
mask = label_im == lbl
region_resp = gray[mask].sum()
if region_resp > max_resp:
max_resp = region_resp
max_region = region
largest_object_mask = label_im == max_region.label
return largest_object_mask
def best_horizontal_line(im, theta_range=np.pi/10, n_theta=5):
"""Find the dominant horizontal (dark-above-bright) line in an image"""
# Compute horizontal edges, get biggest outline
resp_im = edge_response(im, sigma=5, thetas=np.linspace(-theta_range, theta_range, n_theta))
outline_mask = brightest_object_mask(resp_im)
# FIXME: if outline map overlaps the top of im, this means that the receipt
# is not centered correctly (receipt edge outside frame)
# Hough transform
h, theta, d = hough_line(skeletonize(outline_mask), theta=np.linspace(-np.pi/2, np.pi/2, 180))
_, angles, dists = hough_line_peaks(h, theta, d, threshold=0.1 * h.max(), num_peaks=10)
# Compute gradient strength along each Hough line
line_strength_dict = {}
for angle, dist in zip(angles, dists):
y0 = (dist - 0 * np.cos(angle)) / np.sin(angle)
y1 = (dist - outline_mask.shape[1] * np.cos(angle)) / np.sin(angle)
y0 = min([y0, outline_mask.shape[0]])
y1 = min([y1, outline_mask.shape[0]])
pt1 = (y0, 0)
pt2 = (y1, outline_mask.shape[1])
line_profile = measure.profile_line(resp_im, pt1, pt2)
line_strength_dict[(angle, dist)] = line_profile.mean()
horizontal_lines = [line for line in line_strength_dict if np.pi/2 - abs(line[0]) < theta_range]
try:
best_line = max(horizontal_lines, key=lambda x: line_strength_dict[x])
except ValueError:
return None
return best_line
def get_receipt_edges(gray):
"""The (straight-line) edges of a centered 4-sided white object"""
# TODO: disk/selem size should be fraction of input size
median = rank.median(gray, disk(11))
# Divide into halves
top_im = median[:int(im.shape[0]/2), :]
bottom_im = median[int(im.shape[0]/2):, :]
left_im = median[:, :int(im.shape[1]/2)]
right_im = median[:, int(im.shape[1]/2):]
# Rotate so center is down, detect best horizontal line
top_line = best_horizontal_line(top_im)
left_line = best_horizontal_line(np.rot90(left_im,3))
right_line = best_horizontal_line(np.rot90(right_im))
bottom_line = best_horizontal_line(np.rot90(bottom_im,2))
# Rotate back to original orientation
right_line = line_invrot90(right_line, np.rot90(right_im).shape)
bottom_line = line_invrot90(line_invrot90(bottom_line, np.rot90(bottom_im,2).shape), np.rot90(bottom_im).shape)
left_line = line_invrot90(line_invrot90(line_invrot90(left_line, np.rot90(left_im).shape), np.rot90(left_im,2).shape), np.rot90(left_im,3).shape)
# Correct for offset/cropping
right_line = right_line[0], right_line[1] + np.cos(right_line[0])*gray.shape[1]/2
bottom_line = bottom_line[0], + bottom_line[1] + np.sin(bottom_line[0]) * gray.shape[0]/2
return top_line, right_line, bottom_line, left_line
In [22]:
max_side_length = 1024
in_fn = 'data/receipt7.jpg'
im = plt.imread(in_fn)
im = resize_im(im, max_side_length)
gray = rgb2gray(im)
# Get the edges of the receipt
top_line, right_line, bottom_line, left_line = get_receipt_edges(gray)
# print(top_line, right_line, bottom_line, left_line)
In [23]:
# Intersect to get corners
TR = line_intersection(top_line, right_line)
TL = line_intersection(top_line, left_line)
BR = line_intersection(bottom_line, right_line)
BL = line_intersection(bottom_line, left_line)
# Warp so receipt corners are image corners
transform = ProjectiveTransform()
height = max([BL[1] - TL[1], BR[1] - TR[1]])
width = max([TR[0] - TL[0], BR[1] - BL[1]])
src_pts = np.array([TL, TR, BL, BR])
dest_pts = np.array([[0, 0],
[width, 0],
[0, height],
[width, height]
])
success = transform.estimate(src_pts, dest_pts)
warped_im = warp(gray, transform.inverse)[:int(height), :int(width)]
warped_gray = rgb2gray(warped_im)
enhanced_gray = img_as_ubyte(adjust_log(warped_gray))
fname, ext = os.path.splitext(in_fn)
out_fn = fname + '_preprocessed' + ext
plt.imsave(out_fn, enhanced_gray)
In [24]:
plt.figure(figsize=(12,12))
plt.imshow(gray, cmap='gray')
plt.plot(TR[0], TR[1], 'ro')
plt.plot(TL[0], TL[1], 'ro')
plt.plot(BR[0], BR[1], 'ro')
plt.plot(BL[0], BL[1], 'ro')
plt.figure(figsize=(14,14))
plt.subplot(121)
plt.imshow(img_as_ubyte(warped_gray), cmap='gray')
plt.axis('off')
plt.subplot(122)
plt.imshow(enhanced_gray, cmap='gray')
plt.axis('off')
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