旋转文本图片校正算法

迈不过友情╰ 2022-05-17 04:53 253阅读 0赞

# 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(out_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：  
![这里写图片描述][70]  
校正之后的图像：  
![这里写图片描述][70 1]

[70]: /images/20220517/237a5a12d9844c2db54461e089c25ca2.png
[70 1]: /images/20220517/89064666224c4defbbcccfbfc70629fc.png