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# -*- coding: utf-8 -*-
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import os
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import cv2
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import numpy as np
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import debug
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import img_math
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import img_recognition
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import config
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SZ = 20 # 训练图片长宽
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MAX_WIDTH = 1000 # 原始图片最大宽度
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Min_Area = 2000 # 车牌区域允许最大面积
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PROVINCE_START = 1000
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class StatModel(object):
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def load(self, fn):
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self.model = self.model.load(fn)
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def save(self, fn):
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self.model.save(fn)
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class SVM(StatModel):
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def __init__(self, C=1, gamma=0.5):
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self.model = cv2.ml.SVM_create()
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self.model.setGamma(gamma)
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self.model.setC(C)
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self.model.setKernel(cv2.ml.SVM_RBF)
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self.model.setType(cv2.ml.SVM_C_SVC)
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# 训练svm
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def train(self, samples, responses):
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self.model.train(samples, cv2.ml.ROW_SAMPLE, responses)
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# 字符识别
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def predict(self, samples):
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r = self.model.predict(samples)
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return r[1].ravel()
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class CardPredictor:
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def __init__(self):
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pass
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def __del__(self):
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self.save_traindata()
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def train_svm(self):
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# 识别英文字母和数字
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self.model = SVM(C=1, gamma=0.5)
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# 识别中文
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self.modelchinese = SVM(C=1, gamma=0.5)
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if os.path.exists("svm.dat"):
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self.model.load("svm.dat")
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else:
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chars_train = []
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chars_label = []
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for root, dirs, files in os.walk("train\\chars2"):
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if len(os.path.basename(root)) > 1:
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continue
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root_int = ord(os.path.basename(root))
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for filename in files:
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filepath = os.path.join(root, filename)
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digit_img = cv2.imread(filepath)
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digit_img = cv2.cvtColor(digit_img, cv2.COLOR_BGR2GRAY)
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chars_train.append(digit_img)
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# chars_label.append(1)
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chars_label.append(root_int)
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chars_train = list(map(img_recognition.deskew, chars_train))
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chars_train = img_recognition.preprocess_hog(chars_train)
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# chars_train = chars_train.reshape(-1, 20, 20).astype(np.float32)
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chars_label = np.array(chars_label)
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print(chars_train.shape)
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self.model.train(chars_train, chars_label)
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if os.path.exists("svmchinese.dat"):
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self.modelchinese.load("svmchinese.dat")
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else:
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chars_train = []
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chars_label = []
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for root, dirs, files in os.walk("train\\charsChinese"):
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if not os.path.basename(root).startswith("zh_"):
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continue
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pinyin = os.path.basename(root)
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index = img_recognition.provinces.index(pinyin) + PROVINCE_START + 1 # 1是拼音对应的汉字
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for filename in files:
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filepath = os.path.join(root, filename)
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digit_img = cv2.imread(filepath)
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digit_img = cv2.cvtColor(digit_img, cv2.COLOR_BGR2GRAY)
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chars_train.append(digit_img)
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# chars_label.append(1)
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chars_label.append(index)
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chars_train = list(map(img_recognition.deskew, chars_train))
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chars_train = img_recognition.preprocess_hog(chars_train)
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# chars_train = chars_train.reshape(-1, 20, 20).astype(np.float32)
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chars_label = np.array(chars_label)
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print(chars_train.shape)
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self.modelchinese.train(chars_train, chars_label)
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def save_traindata(self):
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if not os.path.exists("svm.dat"):
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self.model.save("svm.dat")
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if not os.path.exists("svmchinese.dat"):
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self.modelchinese.save("svmchinese.dat")
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def img_first_pre(self, car_pic_file):
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"""
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:param car_pic_file: 图像文件
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:return:已经处理好的图像文件 原图像文件
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"""
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if type(car_pic_file) == type(""):
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img = img_math.img_read(car_pic_file)
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else:
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img = car_pic_file
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pic_hight, pic_width = img.shape[:2]
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if pic_width > MAX_WIDTH:
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resize_rate = MAX_WIDTH / pic_width
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img = cv2.resize(img, (MAX_WIDTH, int(pic_hight * resize_rate)), interpolation=cv2.INTER_AREA)
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# 缩小图片
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blur = 5
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img = cv2.GaussianBlur(img, (blur, blur), 0)
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oldimg = img
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# 转化成灰度图像
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Matrix = np.ones((20, 20), np.uint8)
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img_opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, Matrix)
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img_opening = cv2.addWeighted(img, 1, img_opening, -1, 0)
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# 创建20*20的元素为1的矩阵 开操作，并和img重合
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ret, img_thresh = cv2.threshold(img_opening, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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img_edge = cv2.Canny(img_thresh, 100, 200)
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# Otsu’s二值化 找到图像边缘
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Matrix = np.ones((4, 19), np.uint8)
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img_edge1 = cv2.morphologyEx(img_edge, cv2.MORPH_CLOSE, Matrix)
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img_edge2 = cv2.morphologyEx(img_edge1, cv2.MORPH_OPEN, Matrix)
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return img_edge2, oldimg
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def img_color_contours(self, img_contours, oldimg):
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"""
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:param img_contours: 预处理好的图像
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:param oldimg: 原图像
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:return: 已经定位好的车牌
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"""
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if img_contours.any():
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config.set_name(img_contours)
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pic_hight, pic_width = img_contours.shape[:2]
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card_contours = img_math.img_findContours(img_contours)
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card_imgs = img_math.img_Transform(card_contours, oldimg, pic_width, pic_hight)
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colors, car_imgs = img_math.img_color(card_imgs)
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predict_result = []
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roi = None
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card_color = None
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for i, color in enumerate(colors):
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if color in ("blue", "yello", "green"):
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card_img = card_imgs[i]
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gray_img = cv2.cvtColor(card_img, cv2.COLOR_BGR2GRAY)
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# 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
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if color == "green" or color == "yello":
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gray_img = cv2.bitwise_not(gray_img)
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ret, gray_img = cv2.threshold(gray_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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x_histogram = np.sum(gray_img, axis=1)
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x_min = np.min(x_histogram)
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x_average = np.sum(x_histogram) / x_histogram.shape[0]
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x_threshold = (x_min + x_average) / 2
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wave_peaks = img_math.find_waves(x_threshold, x_histogram)
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if len(wave_peaks) == 0:
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print("peak less 0:")
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continue
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# 认为水平方向，最大的波峰为车牌区域
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wave = max(wave_peaks, key=lambda x: x[1] - x[0])
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gray_img = gray_img[wave[0]:wave[1]]
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# 查找垂直直方图波峰
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row_num, col_num = gray_img.shape[:2]
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# 去掉车牌上下边缘1个像素，避免白边影响阈值判断
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gray_img = gray_img[1:row_num - 1]
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y_histogram = np.sum(gray_img, axis=0)
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y_min = np.min(y_histogram)
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y_average = np.sum(y_histogram) / y_histogram.shape[0]
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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
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wave_peaks = img_math.find_waves(y_threshold, y_histogram)
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if len(wave_peaks) <= 6:
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print("peak less 1:", len(wave_peaks))
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continue
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wave = max(wave_peaks, key=lambda x: x[1] - x[0])
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max_wave_dis = wave[1] - wave[0]
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# 判断是否是左侧车牌边缘
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if wave_peaks[0][1] - wave_peaks[0][0] < max_wave_dis / 3 and wave_peaks[0][0] == 0:
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wave_peaks.pop(0)
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# 组合分离汉字
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cur_dis = 0
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for i, wave in enumerate(wave_peaks):
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if wave[1] - wave[0] + cur_dis > max_wave_dis * 0.6:
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break
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else:
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cur_dis += wave[1] - wave[0]
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if i > 0:
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wave = (wave_peaks[0][0], wave_peaks[i][1])
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wave_peaks = wave_peaks[i + 1:]
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wave_peaks.insert(0, wave)
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point = wave_peaks[2]
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point_img = gray_img[:, point[0]:point[1]]
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if np.mean(point_img) < 255 / 5:
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wave_peaks.pop(2)
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if len(wave_peaks) <= 6:
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print("peak less 2:", len(wave_peaks))
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continue
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part_cards = img_math.seperate_card(gray_img, wave_peaks)
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for i, part_card in enumerate(part_cards):
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# 可能是固定车牌的铆钉
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if np.mean(part_card) < 255 / 5:
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print("a point")
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continue
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part_card_old = part_card
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w = abs(part_card.shape[1] - SZ) // 2
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part_card = cv2.copyMakeBorder(part_card, 0, 0, w, w, cv2.BORDER_CONSTANT, value=[0, 0, 0])
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part_card = cv2.resize(part_card, (SZ, SZ), interpolation=cv2.INTER_AREA)
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part_card = img_recognition.preprocess_hog([part_card])
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if i == 0:
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resp = self.modelchinese.predict(part_card)
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charactor = img_recognition.provinces[int(resp[0]) - PROVINCE_START]
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else:
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resp = self.model.predict(part_card)
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charactor = chr(resp[0])
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# 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
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if charactor == "1" and i == len(part_cards) - 1:
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if part_card_old.shape[0] / part_card_old.shape[1] >= 7: # 1太细，认为是边缘
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continue
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predict_result.append(charactor)
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roi = card_img
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card_color = color
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break
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return predict_result, roi, card_color # 识别到的字符、定位的车牌图像、车牌颜色
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def img_only_color(self, filename, oldimg, img_contours):
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"""
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:param filename: 图像文件
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:param oldimg: 原图像文件
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:return: 已经定位好的车牌
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"""
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pic_hight, pic_width = img_contours.shape[:2]
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lower_blue = np.array([100, 110, 110])
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upper_blue = np.array([130, 255, 255])
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lower_yellow = np.array([15, 55, 55])
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upper_yellow = np.array([50, 255, 255])
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lower_green = np.array([50, 50, 50])
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upper_green = np.array([100, 255, 255])
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hsv = cv2.cvtColor(filename, cv2.COLOR_BGR2HSV)
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mask_blue = cv2.inRange(hsv, lower_blue, upper_blue)
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mask_yellow = cv2.inRange(hsv, lower_yellow, upper_yellow)
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mask_green = cv2.inRange(hsv, lower_yellow, upper_green)
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output = cv2.bitwise_and(hsv, hsv, mask=mask_blue + mask_yellow + mask_green)
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# 根据阈值找到对应颜色
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output = cv2.cvtColor(output, cv2.COLOR_BGR2GRAY)
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Matrix = np.ones((20, 20), np.uint8)
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img_edge1 = cv2.morphologyEx(output, cv2.MORPH_CLOSE, Matrix)
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img_edge2 = cv2.morphologyEx(img_edge1, cv2.MORPH_OPEN, Matrix)
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card_contours = img_math.img_findContours(img_edge2)
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card_imgs = img_math.img_Transform(card_contours, oldimg, pic_width, pic_hight)
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colors, car_imgs = img_math.img_color(card_imgs)
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predict_result = []
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roi = None
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card_color = None
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for i, color in enumerate(colors):
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if color in ("blue", "yello", "green"):
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card_img = card_imgs[i]
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gray_img = cv2.cvtColor(card_img, cv2.COLOR_BGR2GRAY)
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# 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
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if color == "green" or color == "yello":
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gray_img = cv2.bitwise_not(gray_img)
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ret, gray_img = cv2.threshold(gray_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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x_histogram = np.sum(gray_img, axis=1)
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x_min = np.min(x_histogram)
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x_average = np.sum(x_histogram) / x_histogram.shape[0]
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x_threshold = (x_min + x_average) / 2
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wave_peaks = img_math.find_waves(x_threshold, x_histogram)
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if len(wave_peaks) == 0:
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print("peak less 0:")
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continue
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# 认为水平方向，最大的波峰为车牌区域
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wave = max(wave_peaks, key=lambda x: x[1] - x[0])
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gray_img = gray_img[wave[0]:wave[1]]
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# 查找垂直直方图波峰
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row_num, col_num = gray_img.shape[:2]
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# 去掉车牌上下边缘1个像素，避免白边影响阈值判断
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gray_img = gray_img[1:row_num - 1]
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y_histogram = np.sum(gray_img, axis=0)
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y_min = np.min(y_histogram)
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y_average = np.sum(y_histogram) / y_histogram.shape[0]
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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
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wave_peaks = img_math.find_waves(y_threshold, y_histogram)
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if len(wave_peaks) < 6:
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print("peak less 1:", len(wave_peaks))
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continue
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wave = max(wave_peaks, key=lambda x: x[1] - x[0])
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max_wave_dis = wave[1] - wave[0]
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# 判断是否是左侧车牌边缘
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if wave_peaks[0][1] - wave_peaks[0][0] < max_wave_dis / 3 and wave_peaks[0][0] == 0:
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wave_peaks.pop(0)
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# 组合分离汉字
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cur_dis = 0
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for i, wave in enumerate(wave_peaks):
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if wave[1] - wave[0] + cur_dis > max_wave_dis * 0.6:
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break
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else:
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cur_dis += wave[1] - wave[0]
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if i > 0:
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wave = (wave_peaks[0][0], wave_peaks[i][1])
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wave_peaks = wave_peaks[i + 1:]
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wave_peaks.insert(0, wave)
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point = wave_peaks[2]
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point_img = gray_img[:, point[0]:point[1]]
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if np.mean(point_img) < 255 / 5:
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wave_peaks.pop(2)
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if len(wave_peaks) <= 6:
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print("peak less 2:", len(wave_peaks))
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continue
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part_cards = img_math.seperate_card(gray_img, wave_peaks)
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for i, part_card in enumerate(part_cards):
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# 可能是固定车牌的铆钉
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|
|
|
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])
|
|
|
part_card = cv2.resize(part_card, (SZ, SZ), interpolation=cv2.INTER_AREA)
|
|
|
part_card = img_recognition.preprocess_hog([part_card])
|
|
|
if i == 0:
|
|
|
resp = self.modelchinese.predict(part_card)
|
|
|
charactor = img_recognition.provinces[int(resp[0]) - PROVINCE_START]
|
|
|
else:
|
|
|
resp = self.model.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)
|
|
|
|
|
|
roi = card_img
|
|
|
card_color = color
|
|
|
break
|
|
|
return predict_result, roi, card_color # 识别到的字符、定位的车牌图像、车牌颜色
|
|
|
|
|
|
def img_mser(self, filename):
|
|
|
if type(filename) == type(""):
|
|
|
img = img_math.img_read(filename)
|
|
|
else:
|
|
|
img = filename
|
|
|
oldimg = img
|
|
|
mser = cv2.MSER_create(_min_area=600)
|
|
|
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
|
|
|
regions, boxes = mser.detectRegions(gray)
|
|
|
colors_img = []
|
|
|
for box in boxes:
|
|
|
x, y, w, h = box
|
|
|
width, height = w, h
|
|
|
if width < height:
|
|
|
width, height = height, width
|
|
|
ration = width / height
|
|
|
|
|
|
if w * h > 1500 and 3 < ration < 4 and w > h:
|
|
|
cropimg = img[y:y + h, x:x + w]
|
|
|
colors_img.append(cropimg)
|
|
|
|
|
|
debug.img_show(img)
|
|
|
colors, car_imgs = img_math.img_color(colors_img)
|
|
|
for i, color in enumerate(colors):
|
|
|
if color != "no":
|
|
|
print(color)
|
|
|
debug.img_show(car_imgs[i])
|
