diff --git a/img_function.py b/img_function.py new file mode 100644 index 0000000..9baf137 --- /dev/null +++ b/img_function.py @@ -0,0 +1,408 @@ +# -*- 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])