1.确定特定字体类型；
2.收集合适的图片作为背景
3.在背景图上填写特定字体的字符内容
1）字体无法确认时怎么办？
方法一：可以将文本行裁剪出来去网站上确认，网站链接：字体识别-在线扫一扫图片找字体-搜字体!
方法二:将文字输入到文档文件中，更换不同的字体，看是否与字体目标匹配；
字体可以去网上下载，也可以在本机查找；本机的字体所在位置：
个人用户字体文件：~/.local/share/fonts
系统字体文件：/usr/share/fonts
字体配置文件：/etc/fonts/
下面是我处理的代码，仅供参考：
def check_dir1(path):
if not os.path.exists(path):
os.mkdir(path)
else:
files = os.listdir(path)
for file in files:
file_path = os.path.join(path, file)
os.remove(file_path)'''
制作一些文本行数据
'''
from PIL import ImageFont, ImageDraw
import PIL.Image as PImage
import random
import os
import numpy as np
import cv2
from rec.temporary_boundary.line_process import cut_line3_1
from result_process.preprocess import check_dir1
if __name__=='__main__':
cha_list = ['A','B','C','D','E','F','G','H','I','J','K',\
'L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z']
save_dir = '/home/fuxueping/4tdisk/data/certificate_reader/北京现场测试数据/20240614针对识别问题/SAU_name'
check_dir1(save_dir)
txt_parh = '/home/fuxueping/4tdisk/data/certificate_reader/北京现场测试数据/20240614针对识别问题/SAU_name.txt'
bg_img_dir = '/home/fuxueping/4tdisk/data/certificate_reader/北京现场测试数据/20240614针对识别问题/bg'
bg_imgs = os.listdir(bg_img_dir)
f_save = open(txt_parh, 'w', encoding='utf-8')
check_dir1(save_dir)
num = 50
while num:
all_num = 0
bg_img = random.choice(bg_imgs)
num1=random.choice([2, 3])
chr_str = ''
all_num += num1
while num1:
chr_ = random.choice(cha_list)
chr_str += chr_
num1 -=1
char_med = ''
for i in range(3):
num2=random.choice([5,6,7,8])
chr_str2=''
all_num += num2
while num2:
chr_ = random.choice(cha_list)
chr_str2 += chr_
num2 -= 1
if i == 0:
char_med += chr_str2+', '
elif i == 1:
char_med += chr_str2 + ' '
elif i == 2:
char_med += chr_str2 + ' '
chr_1 = random.choice(cha_list)
result_str = chr_str+' '+char_med+chr_1
all_num += 1
im = PImage.open(os.path.join(bg_img_dir, bg_img))
w, h = im.size
font_size = 24
w_len = int(0 + all_num * (font_size-3) + 4)
if w_len > w:
num -= 1
continue
name_font = ImageFont.truetype('/home/fuxueping/4tdisk/data/certificate_reader/北京现场测试数据/20240614针对识别问题/fonts/n019003l.pfb', font_size)
draw = ImageDraw.Draw(im)
y_len = random.randint(0, h-font_size-5)
color = tuple([random.randint(0, 20) for _ in range(3)])
draw.text((2, y_len), result_str, fill=color, font=name_font)
box = (0, y_len, w_len, y_len+font_size+5)
rect_img = im.crop(box)
image_array = np.array(rect_img)
cv2_image = cv2.cvtColor(image_array, cv2.COLOR_RGB2BGR)
result, _ = cut_line3_1(cv2_image)
if len(result):
region_rec = cv2_image[result[1]:result[3], result[0]:min(w, result[2]+2)] # 裁剪出待识别的区域
image_array = cv2.cvtColor(region_rec, cv2.COLOR_BGR2RGB)
rect_img = PImage.fromarray(image_array)
# image_array = cv2.cvtColor(cv2_image, cv2.COLOR_BGR2RGB)
# rect_img = PImage.fromarray(image_array)
save_path = os.path.join(save_dir, str(num)+'_'+result_str+'.jpg')
line = save_path+'\t'+result_str+'\n'
f_save.write(line)
rect_img.save(save_path)
num -= 1
f_save.close()
# 根据设定的阈值和图片直方图，找出波峰，用于分隔字符
def find_waves_row(threshold, histogram):#行数是59
# up_point = -1 # 上升点
# is_peak = False
# if histogram[0] >= threshold:
up_point = 0 #起始位置
is_peak = True
wave_peaks = []
top_cut = []
for i, x in enumerate(histogram): #x是对应的像素和，i是行
if is_peak and x >= threshold:
if i - up_point >=2 :
# top_cut.append((up_point, i)) #加这一行，相当于裁减掉多于的空行
up_point = i-1
else:
up_point = i
is_peak = False
elif not is_peak and x < threshold:#随后找到字符消失的位置
is_peak = True
if 1 < i < histogram.shape[0]-1:#行数不是在开头也不在结尾
wave_peaks.append((up_point, i+1))
else:
wave_peaks.append((up_point, i))
up_point = i
# if is_peak and up_point != -1 and i - up_point > 4:
# wave_peaks.append((up_point, i))
if not is_peak and x >= threshold:#虽然数据已经结束，但是没有出现小于阈值的情况
wave_peaks.append((up_point, i))
return wave_peaks
def cut_line3_1(rgb_img, kernel_size = 3, y_len = 5, row_threshold=255 * 1, col_thresh = 255*1):
'''
切割出每一行，只保留高度满足条件的一行内容，然后切除掉每一行的前端后尾端的空白
'''
rgb_img = method_9(rgb_img) #高斯滤波
# 使用sauvola进行二值化
h, w = rgb_img.shape[:2]
sau_bin = sauvola_bin(rgb_img) #sauvola二值化
# cv2.imwrite('./../temp/sauvola_bin.jpg', sau_bin)
# sau_bin = get_charcter_region(rgb_img) # 局部区域算阈值二值化
# cv2.imwrite('./../temp/sau_bin1.jpg', sau_bin)
sau_bin_inv = 255 - sau_bin
# cv2.imwrite('./../temp/sau_bin_inv1.jpg', sau_bin_inv)
if kernel_size != 0:
sau_bin_inv = cv2.medianBlur(sau_bin_inv, kernel_size)
# cv2.imwrite('./../temp/sau_bin_inv_dinose1.jpg', sau_bin_inv)
col_histogram = np.sum(sau_bin_inv, axis=1)
wave_peaks = find_waves_row(col_thresh, col_histogram)
result = []
#找出高度最大的区域,只保留一行内容
max_y = 0
result_y = []
if not len(wave_peaks):
return [], sau_bin_inv
for i, wave_peak in enumerate(wave_peaks):
y1 = wave_peak[0]
y2 = wave_peak[1]
if y2 - y1 < y_len: #20之前是这个阈值 ，将高度不满足>=5的字符区域去掉
continue
if max_y < y2 - y1:
max_y = y2 - y1
result_y = [y1, y2]
if len(result_y): #有时候裁剪的图片可能是没有字符，这种情况多出现在证件类别错误的情况
y1 = result_y[0]
y2 = result_y[1]
else:
return [], sau_bin_inv
line_img = sau_bin_inv[y1:y2, :]
# line_img_bgr = rgb_img[wave_peak[0]:wave_peak[1], :]
# save_other = os.path.join(save_path, file + '_'+str(i)+'.jpg')
# cv2.imwrite(save_other, line_img)
row_histogram = np.sum(line_img, axis=0) # 数组的每一列求和
# row_max = np.max(row_histogram)
# row_threshold = row_max - 255*1
wave_peaks_line = find_waves_col(row_threshold, row_histogram)
# cv2.imwrite('./../temp/line_img.jpg', line_img)
x1 = 0
x2 = w
result_ = []
for wave_ in wave_peaks_line:
len_x = wave_[1] - wave_[0]
if len_x > 5:
result_.append(wave_)
if len(result_): # 有时候朝水平投影内容消失了，就用【0，w】代替
x1 = result_[0][0]
x2 = result_[-1][1]
return [x1, y1, x2, y2], sau_bin_inv