# -*- coding: utf-8 -*- import os import cv2 import numpy as np import debug import img_math import img_recognition import config 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 class StatModel(object): def load(self, fn): self.model = self.model.load(fn) def save(self, fn): self.model.save(fn) class SVM(StatModel): def __init__(self, C=1, gamma=0.5): self.model = cv2.ml.SVM_create() self.model.setGamma(gamma) self.model.setC(C) self.model.setKernel(cv2.ml.SVM_RBF) self.model.setType(cv2.ml.SVM_C_SVC) # 训练svm def train(self, samples, responses): self.model.train(samples, cv2.ml.ROW_SAMPLE, responses) # 字符识别 def predict(self, samples): r = self.model.predict(samples) return r[1].ravel() class CardPredictor: def __init__(self): pass def __del__(self): self.save_traindata() def train_svm(self): # 识别英文字母和数字 self.model = SVM(C=1, gamma=0.5) # 识别中文 self.modelchinese = SVM(C=1, gamma=0.5) if os.path.exists("svm.dat"): self.model.load("svm.dat") else: chars_train = [] chars_label = [] for root, dirs, files in os.walk("train\\chars2"): if len(os.path.basename(root)) > 1: continue root_int = ord(os.path.basename(root)) for filename in files: filepath = os.path.join(root, filename) digit_img = cv2.imread(filepath) digit_img = cv2.cvtColor(digit_img, cv2.COLOR_BGR2GRAY) chars_train.append(digit_img) # chars_label.append(1) chars_label.append(root_int) chars_train = list(map(img_recognition.deskew, chars_train)) chars_train = img_recognition.preprocess_hog(chars_train) # chars_train = chars_train.reshape(-1, 20, 20).astype(np.float32) chars_label = np.array(chars_label) print(chars_train.shape) self.model.train(chars_train, chars_label) if os.path.exists("svmchinese.dat"): self.modelchinese.load("svmchinese.dat") else: chars_train = [] chars_label = [] for root, dirs, files in os.walk("train\\charsChinese"): if not os.path.basename(root).startswith("zh_"): continue pinyin = os.path.basename(root) index = img_recognition.provinces.index(pinyin) + PROVINCE_START + 1 # 1是拼音对应的汉字 for filename in files: filepath = os.path.join(root, filename) digit_img = cv2.imread(filepath) digit_img = cv2.cvtColor(digit_img, cv2.COLOR_BGR2GRAY) chars_train.append(digit_img) # chars_label.append(1) chars_label.append(index) chars_train = list(map(img_recognition.deskew, chars_train)) chars_train = img_recognition.preprocess_hog(chars_train) # chars_train = chars_train.reshape(-1, 20, 20).astype(np.float32) chars_label = np.array(chars_label) print(chars_train.shape) self.modelchinese.train(chars_train, chars_label) def save_traindata(self): if not os.path.exists("svm.dat"): self.model.save("svm.dat") if not os.path.exists("svmchinese.dat"): self.modelchinese.save("svmchinese.dat") def img_first_pre(self, car_pic_file): """ :param car_pic_file: 图像文件 :return:已经处理好的图像文件 原图像文件 """ if type(car_pic_file) == type(""): img = img_math.img_read(car_pic_file) else: img = car_pic_file pic_hight, pic_width = img.shape[:2] if pic_width > MAX_WIDTH: resize_rate = MAX_WIDTH / pic_width img = cv2.resize(img, (MAX_WIDTH, int(pic_hight * resize_rate)), interpolation=cv2.INTER_AREA) # 缩小图片 blur = 5 img = cv2.GaussianBlur(img, (blur, blur), 0) oldimg = img img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 转化成灰度图像 Matrix = np.ones((20, 20), np.uint8) img_opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, Matrix) img_opening = cv2.addWeighted(img, 1, img_opening, -1, 0) # 创建20*20的元素为1的矩阵 开操作，并和img重合 ret, img_thresh = cv2.threshold(img_opening, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) img_edge = cv2.Canny(img_thresh, 100, 200) # Otsu’s二值化 找到图像边缘 Matrix = np.ones((4, 19), np.uint8) img_edge1 = cv2.morphologyEx(img_edge, cv2.MORPH_CLOSE, Matrix) img_edge2 = cv2.morphologyEx(img_edge1, cv2.MORPH_OPEN, Matrix) return img_edge2, oldimg def img_color_contours(self, img_contours, oldimg): """ :param img_contours: 预处理好的图像 :param oldimg: 原图像 :return: 已经定位好的车牌 """ if img_contours.any(): config.set_name(img_contours) pic_hight, pic_width = img_contours.shape[:2] card_contours = img_math.img_findContours(img_contours) card_imgs = img_math.img_Transform(card_contours, oldimg, pic_width, pic_hight) colors, car_imgs = img_math.img_color(card_imgs) predict_result = [] roi = None card_color = None for i, color in enumerate(colors): if color in ("blue", "yello", "green"): card_img = card_imgs[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) 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 = img_math.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 = img_math.find_waves(y_threshold, y_histogram) 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) point = wave_peaks[2] 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 = img_math.seperate_card(gray_img, wave_peaks) 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]) 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_only_color(self, filename, oldimg, img_contours): """ :param filename: 图像文件 :param oldimg: 原图像文件 :return: 已经定位好的车牌 """ pic_hight, pic_width = img_contours.shape[:2] lower_blue = np.array([100, 110, 110]) upper_blue = np.array([130, 255, 255]) lower_yellow = np.array([15, 55, 55]) upper_yellow = np.array([50, 255, 255]) lower_green = np.array([50, 50, 50]) upper_green = np.array([100, 255, 255]) hsv = cv2.cvtColor(filename, cv2.COLOR_BGR2HSV) mask_blue = cv2.inRange(hsv, lower_blue, upper_blue) mask_yellow = cv2.inRange(hsv, lower_yellow, upper_yellow) mask_green = cv2.inRange(hsv, lower_yellow, upper_green) output = cv2.bitwise_and(hsv, hsv, mask=mask_blue + mask_yellow + mask_green) # 根据阈值找到对应颜色 output = cv2.cvtColor(output, cv2.COLOR_BGR2GRAY) Matrix = np.ones((20, 20), np.uint8) img_edge1 = cv2.morphologyEx(output, cv2.MORPH_CLOSE, Matrix) img_edge2 = cv2.morphologyEx(img_edge1, cv2.MORPH_OPEN, Matrix) card_contours = img_math.img_findContours(img_edge2) card_imgs = img_math.img_Transform(card_contours, oldimg, pic_width, pic_hight) colors, car_imgs = img_math.img_color(card_imgs) predict_result = [] roi = None card_color = None for i, color in enumerate(colors): if color in ("blue", "yello", "green"): card_img = card_imgs[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) 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 = img_math.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 = img_math.find_waves(y_threshold, y_histogram) 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) point = wave_peaks[2] 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 = img_math.seperate_card(gray_img, wave_peaks) 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]) 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])