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card_seg.py
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card_seg.py
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import cv2
import numpy as np
from numpy.linalg import norm
import sys
import os
import json
from car_id_detect import *
from svm_train import *
SZ = 20 #训练图片长宽
MAX_WIDTH = 1000 #原始图片最大宽度
Min_Area = 2000 #车牌区域允许最大面积
PROVINCE_START = 1000
svm_model = SVM(C=1, gamma=0.5)
model_1,model_2 = svm_model.train_svm()
def find_waves(threshold, histogram):
'''
根据设定的阈值和图片直方图，找出波峰，用于分隔字符
'''
up_point = -1 #上升点
is_peak = False
if histogram[0] > threshold:
up_point = 0
is_peak = True
wave_peaks = []
for i,x in enumerate(histogram):
if is_peak and x < threshold:
if i - up_point > 2:
is_peak = False
wave_peaks.append((up_point, i))
elif not is_peak and x >= threshold:
is_peak = True
up_point = i
if is_peak and up_point != -1 and i - up_point > 4:
wave_peaks.append((up_point, i))
return wave_peaks
def seperate_card(img, waves):
'''
根据找出的波峰，分隔图片，从而得到逐个字符图片
'''
part_cards = []
for wave in waves:
part_cards.append(img[:, wave[0]:wave[1]])
return part_cards
def Cardseg(rois,colors,save_path):
'''
把一个roi列表和color列表，对应的每个车牌分割成一个一个的字
然后做预测分类
当然也可以考虑OCR的办法，这里使用的是传统的分类问题解决的！！！！
'''
seg_dic = {}
old_seg_dic = {}
for i, color in enumerate(colors):
if color in ("blue", "yello", "green"):
card_img = rois[i]
gray_img = cv2.cvtColor(card_img, cv2.COLOR_BGR2GRAY)
#黄、绿车牌字符比背景暗、与蓝车牌刚好相反，所以黄、绿车牌需要反向
if color == "green" or color == "yello":
gray_img = cv2.bitwise_not(gray_img)
ret, gray_img = cv2.threshold(gray_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
#查找水平直方图波峰
x_histogram = np.sum(gray_img, axis=1)
x_min = np.min(x_histogram)
x_average = np.sum(x_histogram)/x_histogram.shape[0]
x_threshold = (x_min + x_average)/2
wave_peaks = find_waves(x_threshold, x_histogram)
if len(wave_peaks) == 0:
# print("peak less 0:")
continue
#认为水平方向，最大的波峰为车牌区域
wave = max(wave_peaks, key=lambda x:x[1]-x[0])
gray_img = gray_img[wave[0]:wave[1]]
#查找垂直直方图波峰
row_num, col_num= gray_img.shape[:2]
#去掉车牌上下边缘1个像素，避免白边影响阈值判断
gray_img = gray_img[1:row_num-1]
y_histogram = np.sum(gray_img, axis=0)
y_min = np.min(y_histogram)
y_average = np.sum(y_histogram)/y_histogram.shape[0]
y_threshold = (y_min + y_average)/5 #U和0要求阈值偏小，否则U和0会被分成两半
wave_peaks = find_waves(y_threshold, y_histogram)
#for wave in wave_peaks:
# cv2.line(card_img, pt1=(wave[0], 5), pt2=(wave[1], 5), color=(0, 0, 255), thickness=2)
#车牌字符数应大于6
if len(wave_peaks) <= 6:
# print("peak less 1:", len(wave_peaks))
continue
wave = max(wave_peaks, key=lambda x:x[1]-x[0])
max_wave_dis = wave[1] - wave[0]
#判断是否是左侧车牌边缘
if wave_peaks[0][1] - wave_peaks[0][0] < max_wave_dis/3 and wave_peaks[0][0] == 0:
wave_peaks.pop(0)
#组合分离汉字
cur_dis = 0
for i,wave in enumerate(wave_peaks):
if wave[1] - wave[0] + cur_dis > max_wave_dis * 0.6:
break
else:
cur_dis += wave[1] - wave[0]
if i > 0:
wave = (wave_peaks[0][0], wave_peaks[i][1])
wave_peaks = wave_peaks[i+1:]
wave_peaks.insert(0, wave)
#去除车牌上的分隔点
point = wave_peaks[2]
if point[1] - point[0] < max_wave_dis/3:
point_img = gray_img[:,point[0]:point[1]]
if np.mean(point_img) < 255/5:
wave_peaks.pop(2)
if len(wave_peaks) <= 6:
# print("peak less 2:", len(wave_peaks))
continue
part_cards = seperate_card(gray_img, wave_peaks)
predict_result = []
for i, part_card in enumerate(part_cards):
#可能是固定车牌的铆钉
if np.mean(part_card) < 255/5:
# print("a point")
continue
part_card_old = part_card
w = abs(part_card.shape[1] - SZ)//2
part_card = cv2.copyMakeBorder(part_card, 0, 0, w, w, cv2.BORDER_CONSTANT, value = [0,0,0]) #用来给图片添加边框
part_card = cv2.resize(part_card, (SZ, SZ), interpolation=cv2.INTER_AREA)
#part_card = deskew(part_card)
part_card = preprocess_hog([part_card])
if i == 0:
resp = model_2.predict(part_card)
charactor = provinces[int(resp[0]) - PROVINCE_START]
else:
resp = model_1.predict(part_card)
charactor = chr(resp[0])
#判断最后一个数是否是车牌边缘，假设车牌边缘被认为是1
if charactor == "1" and i == len(part_cards)-1:
if part_card_old.shape[0]/part_card_old.shape[1] >= 7:#1太细，认为是边缘
continue
predict_result.append(charactor)
# # 保存图片
# cv2.imwrite(os.path.join(save_path,str(i)+".jpg"),part_card)
seg_dic[i] = part_cards
old_seg_dic[i] = part_card_old
return seg_dic, old_seg_dic, predict_result
if __name__ == "__main__":
for pic_file in os.listdir("./test_img"):
roi, label, color = CaridDetect(os.path.join("./test_img",pic_file))
save_path = "./result_seg/"+pic_file.split(".")[0]
if not os.path.exists(save_path):
os.makedirs(save_path)
seg_dict, _ , pre= Cardseg([roi],[color],save_path)
print(pre)