版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/m_buddy/article/details/81463234
1. 前言
在进行文本图像处理的时候，会发现某些拍摄的图像并不是正的，存在着某些旋转的情况。为了修正该图片就需要检测文本图像旋转的角度，再根据这个角度将图像旋转回正确的位置上来。
本博客讲到的算法是本人在github上无意看到的，经过亲自测试之后发现效果不错，这里将其整理为一个demo与大家进行分享。
2. 实现代码
""" Calculates skew angle """
import os
import imghdr
import numpy as np
import matplotlib.pyplot as plt
from skimage import io
from skimage.feature import canny
from skimage.color import rgb2gray
from skimage.transform import hough_line, hough_line_peaks
from skimage.transform import rotate
class SkewDetect:
piby4 = np.pi / 4
def __init__(
self,
input_file=None,
batch_path=None,
output_file=None,
sigma=3.0,
display_output=None,
num_peaks=20,
plot_hough=None
):
self.sigma = sigma
self.input_file = input_file
self.batch_path = batch_path
self.output_file = output_file
self.display_output = display_output
self.num_peaks = num_peaks
self.plot_hough = plot_hough
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 display_hough(self, h, a, d):
plt.imshow(
np.log(1 + h),
extent=[np.rad2deg(a[-1]), np.rad2deg(a[0]), d[-1], d[0]],
cmap=plt.cm.gray,
aspect=1.0 / 90)
plt.show()
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(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.plot_hough:
if self.plot_hough.lower() == 'yes':
self.plot_hough = True
else:
self.plot_hough = False
if self.input_file is None:
if self.batch_path:
self.batch_process()
else:
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)
print(res)
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 batch_process(self):
wfile = None
if self.batch_path == '.':
self.batch_path = ''
abs_path = self.check_path(self.batch_path)
files = os.listdir(abs_path)
if self.output_file:
out_path = self.check_path(self.output_file)
wfile = open(file_path, 'w')
for f in files:
file_path = abs_path + '/' + f
if os.path.isdir(file_path):
continue
if imghdr.what(file_path):
res = self.determine_skew(file_path)
if wfile:
self.write_to_file(wfile, res)
if wfile:
wfile.close()
def determine_skew(self, img_file):
img = io.imread(img_file, as_grey=True)
edges = canny(img, sigma=self.sigma)
h, a, d = hough_line(edges)
_, ap, _ = hough_line_peaks(h, a, d, num_peaks=self.num_peaks)
if len(ap) == 0:
return {"Image File": img_file, "Message": "Bad Quality"}
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]
bin_0_45 = []
bin_45_90 = []
bin_0_45n = []
bin_45_90n = []
for ang in ap_deg:
deviation_sum = int(90 - ang + average_deviation)
if self.compare_sum(deviation_sum):
bin_45_90.append(ang)
continue
deviation_sum = int(ang + average_deviation)
if self.compare_sum(deviation_sum):
bin_0_45.append(ang)
continue
deviation_sum = int(-ang + average_deviation)
if self.compare_sum(deviation_sum):
bin_0_45n.append(ang)
continue
deviation_sum = int(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)):
l = len(angles[j])
if l > lmax:
lmax = l
maxi = j
if lmax:
ans_arr = self.get_max_freq_elem(angles[maxi])
ans_res = np.mean(ans_arr)
else:
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}
if self.display_output:
self.display(data)
if self.plot_hough:
self.display_hough(h, a, d)
return data
# main函数
if __name__ == '__main__':
input_file = 'test1.jpg'
# 定义文本旋转处理类对象
skew_obj = SkewDetect(input_file)
origin_img = io.imread(input_file) # 读取图像数据
# 显示原始的图像
plt.figure(0)
plt.imshow(origin_img)
res = skew_obj.process_single_file()
angle = res['Estimated Angle']
if (angle >= 0) and (angle <= 90):
rot_angle = angle - 90
if (angle >= -45) and (angle < 0):
rot_angle = angle - 90
if (angle >= -90) and (angle < -45):
rot_angle = 90 + angle
# 根据检测出来的旋转角度进行旋转操作
rotated = rotate(origin_img, rot_angle, resize=True)
# 显示自定义旋转之后的图像
plt.figure(1)
plt.imshow(rotated)
plt.show()3. 实现效果
输入的图像：
校正之后的图像：
