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""" Calculates skew angle """
"""
This code is based on the following file:
https://github.com/kakul/Alyn/blob/master/alyn/skew_detect.py
"""
import os
import optparse
import numpy as np
# import matplotlib.pyplot as plt
from skimage import io
from skimage.feature import canny
from skimage.transform import hough_line, hough_line_peaks
import cv2
class SkewDetect:
piby4 = np.pi / 4
def __init__(
self,
input_file=None,
output_file=None,
sigma=0.50,
display_output=None,
num_peaks=20,
skew_max=4.0,
acc_deg=0.5,
roi_w=1.0,
roi_h=1.0,
):
self.sigma = sigma
self.input_file = input_file
self.output_file = output_file
self.display_output = display_output
self.num_peaks = num_peaks
self.skew_max = skew_max
self.acc_deg = acc_deg
self.roi_w = roi_w
self.roi_h = roi_h
def write_to_file(self, wfile, data):
for d in data:
wfile.write(d + ': ' + str(data[d]) + '\n')
wfile.write('\n')
def get_max_freq_elem(self, arr):
max_arr = []
freqs = {}
for i in arr:
if i in freqs:
freqs[i] += 1
else:
freqs[i] = 1
sorted_keys = sorted(freqs, key=freqs.get, reverse=True)
max_freq = freqs[sorted_keys[0]]
for k in sorted_keys:
if freqs[k] == max_freq:
max_arr.append(k)
return max_arr
def compare_sum(self, value):
if value >= 44 and value <= 46:
return True
else:
return False
def display(self, data):
for i in data:
print(str(i) + ": " + str(data[i]))
def calculate_deviation(self, angle):
angle_in_degrees = np.abs(angle)
deviation = np.abs(SkewDetect.piby4 - angle_in_degrees)
return deviation
def run(self):
if self.display_output:
if self.display_output.lower() == 'yes':
self.display_output = True
else:
self.display_output = False
if self.input_file is None:
print("Invalid input, nothing to process.")
else:
self.process_single_file()
def check_path(self, path):
if os.path.isabs(path):
full_path = path
else:
full_path = os.getcwd() + '/' + str(path)
return full_path
def process_single_file(self):
file_path = self.check_path(self.input_file)
res = self.determine_skew(file_path)
if self.output_file:
output_path = self.check_path(self.output_file)
wfile = open(output_path, 'w')
self.write_to_file(wfile, res)
wfile.close()
return res
def determine_skew(self, img_file):
img_ori = io.imread(img_file, as_gray=True)
height, width = img_ori.shape
img = img_ori[int(height*(0.5-self.roi_h/2.0)):int(height*(0.5+self.roi_h/2.0)),
int(width * (0.5-self.roi_w/2.0)):int(width * (0.5+self.roi_w/2.0))]
img = cv2.resize(img, (img.shape[1]//4, img.shape[0]//4))
edges = canny(img, sigma=self.sigma)
range_rad = np.arange(-np.pi/2, -np.pi/2+np.deg2rad(self.skew_max),
step=np.deg2rad(self.acc_deg))
range_rad = np.concatenate(
[range_rad,
np.arange(-np.deg2rad(self.skew_max), np.deg2rad(self.skew_max),
step=np.deg2rad(self.acc_deg))],
axis=0)
range_rad = np.concatenate(
[range_rad,
np.arange(np.pi/2-np.deg2rad(self.skew_max), np.pi/2,
step=np.deg2rad(self.acc_deg))],
axis=0)
h, a, d = hough_line(edges, theta=range_rad)
th = 0.2 * h.max()
_, ap, _ = hough_line_peaks(
h, a, d, threshold=th, num_peaks=self.num_peaks)
if len(ap) == 0:
data = {
"Image File": img_file,
"Average Deviation from pi/4": 0.0,
"Estimated Angle": 0.0,
"Angle bins": [[], [], [], []],
"Message": "Bad Quality"}
return data
absolute_deviations = [self.calculate_deviation(k) for k in ap]
average_deviation = np.mean(np.rad2deg(absolute_deviations))
ap_deg = [np.rad2deg(x) for x in ap]
for i in range(len(ap_deg)):
if ap_deg[i] >= 45.0:
ap_deg[i] -= 90.0
elif ap_deg[i] <= -45.0:
ap_deg[i] += 90.0
bin_0_45 = []
bin_45_90 = []
bin_0_45n = []
bin_45_90n = []
for ang in ap_deg:
deviation_sum = (90 - ang + average_deviation)
if self.compare_sum(deviation_sum):
bin_45_90.append(ang)
continue
deviation_sum = (ang + average_deviation)
if self.compare_sum(deviation_sum):
bin_0_45.append(ang)
continue
deviation_sum = (-ang + average_deviation)
if self.compare_sum(deviation_sum):
bin_0_45n.append(ang)
continue
deviation_sum = (90 + ang + average_deviation)
if self.compare_sum(deviation_sum):
bin_45_90n.append(ang)
angles = [bin_0_45, bin_45_90, bin_0_45n, bin_45_90n]
lmax = 0
for j in range(len(angles)):
tmp_l = len(angles[j])
if tmp_l > lmax:
lmax = tmp_l
maxi = j
if lmax:
ans_arr = self.get_max_freq_elem(angles[maxi]) # 最多頻度の角度array
ans_res = np.mean(ans_arr) # 同数最多が複数あるかもしれないのでavg
else: # angls が空のとき
ans_arr = self.get_max_freq_elem(ap_deg)
ans_res = np.mean(ans_arr)
data = {
"Image File": img_file,
"Average Deviation from pi/4": average_deviation,
"Estimated Angle": ans_res,
"Angle bins": angles,
"Message": "Successfully detected lines"}
if self.display_output:
self.display(data)
return data
def determine_skew_on_memory(self, img_data):
img_ori = cv2.cvtColor(img_data, cv2.COLOR_BGR2GRAY)
height, width = img_ori.shape
img = img_ori[int(height*(0.5-self.roi_h/2.0)):int(height*(0.5+self.roi_h/2.0)),
int(width * (0.5-self.roi_w/2.0)):int(width * (0.5+self.roi_w/2.0))]
img = cv2.resize(img, (img.shape[1]//4, img.shape[0]//4))
edges = canny(img, sigma=self.sigma)
range_rad = np.arange(-np.pi/2, -np.pi/2+np.deg2rad(self.skew_max),
step=np.deg2rad(self.acc_deg))
range_rad = np.concatenate([range_rad,
np.arange(-np.deg2rad(self.skew_max),
np.deg2rad(self.skew_max),
step=np.deg2rad(self.acc_deg))],
axis=0)
range_rad = np.concatenate([range_rad,
np.arange(np.pi/2-np.deg2rad(self.skew_max),
np.pi/2,
step=np.deg2rad(self.acc_deg))],
axis=0)
h, a, d = hough_line(edges, theta=range_rad)
th = 0.2 * h.max()
_, ap, _ = hough_line_peaks(
h, a, d, threshold=th, num_peaks=self.num_peaks)
if len(ap) == 0:
data = {
"Average Deviation from pi/4": 0.0,
"Estimated Angle": 0.0,
"Angle bins": [[], [], [], []],
"Message": "Bad Quality"}
return data
absolute_deviations = [self.calculate_deviation(k) for k in ap]
average_deviation = np.mean(np.rad2deg(absolute_deviations))
ap_deg = [np.rad2deg(x) for x in ap]
for i in range(len(ap_deg)):
if ap_deg[i] >= 45.0:
ap_deg[i] -= 90.0
elif ap_deg[i] <= -45.0:
ap_deg[i] += 90.0
bin_0_45 = []
bin_45_90 = []
bin_0_45n = []
bin_45_90n = []
for ang in ap_deg:
deviation_sum = (90 - ang + average_deviation)
if self.compare_sum(deviation_sum):
bin_45_90.append(ang)
continue
deviation_sum = (ang + average_deviation)
if self.compare_sum(deviation_sum):
bin_0_45.append(ang)
continue
deviation_sum = (-ang + average_deviation)
if self.compare_sum(deviation_sum):
bin_0_45n.append(ang)
continue
deviation_sum = (90 + ang + average_deviation)
if self.compare_sum(deviation_sum):
bin_45_90n.append(ang)
angles = [bin_0_45, bin_45_90, bin_0_45n, bin_45_90n]
lmax = 0
for j in range(len(angles)):
tmp_l = len(angles[j])
if tmp_l > lmax:
lmax = tmp_l
maxi = j
if lmax:
ans_arr = self.get_max_freq_elem(angles[maxi]) # 最多頻度の角度array
ans_res = np.mean(ans_arr) # 同数最多が複数あるかもしれないのでavg
else: # angls が空のとき
ans_arr = self.get_max_freq_elem(ap_deg)
ans_res = np.mean(ans_arr)
data = {
"Average Deviation from pi/4": average_deviation,
"Estimated Angle": ans_res,
"Angle bins": angles,
"Message": "Successfully detected lines"}
return data
def determine_line(self, img_file):
img_ori = io.imread(img_file, as_gray=True)
height, width = img_ori.shape
img = img_ori[int(height*(0.5-self.roi_h/2.0)):int(height*(0.5+self.roi_h/2.0)),
int(width * (0.5-self.roi_w/2.0)):int(width * (0.5+self.roi_w/2.0))]
edges = canny(img, sigma=self.sigma)
range_rad = np.arange(-np.pi/2, -np.pi/2+np.deg2rad(self.skew_max),
step=np.deg2rad(self.acc_deg))
range_rad = np.concatenate([range_rad,
np.arange(-np.deg2rad(self.skew_max),
np.deg2rad(self.skew_max),
step=np.deg2rad(self.acc_deg))],
axis=0)
range_rad = np.concatenate([range_rad,
np.arange(np.pi/2-np.deg2rad(self.skew_max), np.pi/2,
step=np.deg2rad(self.acc_deg))],
axis=0)
h, a, d = hough_line(edges, theta=range_rad)
th = 0.2 * h.max()
ac, ap, d = hough_line_peaks(
h, a, d, threshold=th, num_peaks=self.num_peaks)
return ac, ap, d
if __name__ == '__main__':
parser = optparse.OptionParser()
parser.add_option(
'-d', '--display',
default=None,
dest='display_output',
help='Display logs')
parser.add_option(
'-i', '--input',
default=None,
dest='input_file',
help='Input file name')
parser.add_option(
'-o', '--output',
default=None,
dest='output_file',
help='Output file name')
parser.add_option(
'-p', '--plot',
default=None,
dest='plot_hough',
help='Plot the Hough Transform')
parser.add_option(
'-s', '--sigma',
default=3.0,
dest='sigma',
help='Sigma for Canny Edge Detection',
type=float)
options, args = parser.parse_args()
skew_obj = SkewDetect(
options.input_file,
options.output_file,
options.sigma,
options.display_output,
options.num_peaks,
options.plot_hough)
skew_obj.run()
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