Charactor Input and Output (k&R)

本文介绍了C语言标准库中处理字符数据的基本方法。讲解了文本流的概念,并详细说明了getchar和putchar函数如何用于逐个字符地读写。此外,还提到了文件输入将在后续章节讨论。

这是K&R所写的<THE C PROGRAMMING LANGUAGE>中的一段讲字符串的,字符串原始设定是与INT有关,读一读原版的还是能体会不少作者的心思的。

We are now going to consider a family of related programs for processing character data . You will find that many programs are just expanded versions of the prototypes that we discuss here.

The model of input and output supported by the standard library is very simple . Text input or output , regardless of where it originates or where it goes to,is dealt with as streams of charccters. A text stream is a sequence of characters divided into lines ; each line consists of zero or more characters followed by a newline character. It isthe responsibility of the library to make each input or output stream conform to this model ; the C programmer using the library need not worry about how lines are represented outside the program.

The standard library provides several functions for reading or writing one character at a time, of which getchar and putchar are the simplest.Each time it is called ,getchar reads the next input character from a text stream and returns that as its value.That is after

Code:
  1. C=getchar()

the variable c contains the next character of input.The characters normally conme form the keyboard;input from files is discussed in Chapter 7.

The function putchar prints a character each time it is called:

Code:
  1. putchar(c)

prints the contents of the integer variable c as a character,usually on he screen.Calls to putchar and printf may be interleaved; the output willappear in the order in which the calls are made.

import cv2 import numpy as np from numpy.linalg import norm import sys import os import json 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 #读取图片文件 def imreadex(filename): return cv2.imdecode(np.fromfile(filename, dtype=np.uint8), cv2.IMREAD_COLOR) def point_limit(point): if point[0] < 0: point[0] = 0 if point[1] < 0: point[1] = 0 #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(threshold, histogram): 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 #根据找出的波峰,分隔图片,从而得到逐个字符图片 def seperate_card(img, waves): part_cards = [] for wave in waves: part_cards.append(img[:, wave[0]:wave[1]]) return part_cards #来自opencv的sample,用于svm训练 def deskew(img): m = cv2.moments(img) if abs(m['mu02']) < 1e-2: return img.copy() skew = m['mu11']/m['mu02'] M = np.float32([[1, skew, -0.5*SZ*skew], [0, 1, 0]]) img = cv2.warpAffine(img, M, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR) return img #来自opencv的sample,用于svm训练 def preprocess_hog(digits): samples = [] for img in digits: gx = cv2.Sobel(img, cv2.CV_32F, 1, 0) gy = cv2.Sobel(img, cv2.CV_32F, 0, 1) mag, ang = cv2.cartToPolar(gx, gy) bin_n = 16 bin = np.int32(bin_n*ang/(2*np.pi)) bin_cells = bin[:10,:10], bin[10:,:10], bin[:10,10:], bin[10:,10:] mag_cells = mag[:10,:10], mag[10:,:10], mag[:10,10:], mag[10:,10:] hists = [np.bincount(b.ravel(), m.ravel(), bin_n) for b, m in zip(bin_cells, mag_cells)] hist = np.hstack(hists) # transform to Hellinger kernel eps = 1e-7 hist /= hist.sum() + eps hist = np.sqrt(hist) hist /= norm(hist) + eps samples.append(hist) return np.float32(samples) #不能保证包括所有省份 provinces = [ "zh_cuan", "川", "zh_e", "鄂", "zh_gan", "赣", "zh_gan1", "甘", "zh_gui", "贵", "zh_gui1", "桂", "zh_hei", "黑", "zh_hu", "沪", "zh_ji", "冀", "zh_jin", "津", "zh_jing", "京", "zh_jl", "吉", "zh_liao", "辽", "zh_lu", "鲁", "zh_meng", "蒙", "zh_min", "闽", "zh_ning", "宁", "zh_qing", "靑", "zh_qiong", "琼", "zh_shan", "陕", "zh_su", "苏", "zh_sx", "晋", "zh_wan", "皖", "zh_xiang", "湘", "zh_xin", "新", "zh_yu", "豫", "zh_yu1", "渝", "zh_yue", "粤", "zh_yun", "云", "zh_zang", "藏", "zh_zhe", "浙" ] 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): #车牌识别的部分参数保存在js中,便于根据图片分辨率做调整 f = open('config.js') j = json.load(f) for c in j["config"]: if c["open"]: self.cfg = c.copy() break else: raise RuntimeError('没有设置有效配置参数') 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(deskew, chars_train)) chars_train = preprocess_hog(chars_train) #chars_train = chars_train.reshape(-1, 20, 20).astype(np.float32) chars_label = np.array(chars_label) 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 = 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(deskew, chars_train)) chars_train = 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 accurate_place(self, card_img_hsv, limit1, limit2, color): row_num, col_num = card_img_hsv.shape[:2] xl = col_num xr = 0 yh = 0 yl = row_num #col_num_limit = self.cfg["col_num_limit"] row_num_limit = self.cfg["row_num_limit"] col_num_limit = col_num * 0.8 if color != "green" else col_num * 0.5#绿色有渐变 for i in range(row_num): count = 0 for j in range(col_num): H = card_img_hsv.item(i, j, 0) S = card_img_hsv.item(i, j, 1) V = card_img_hsv.item(i, j, 2) if limit1 < H <= limit2 and 34 < S and 46 < V: count += 1 if count > col_num_limit: if yl > i: yl = i if yh < i: yh = i for j in range(col_num): count = 0 for i in range(row_num): H = card_img_hsv.item(i, j, 0) S = card_img_hsv.item(i, j, 1) V = card_img_hsv.item(i, j, 2) if limit1 < H <= limit2 and 34 < S and 46 < V: count += 1 if count > row_num - row_num_limit: if xl > j: xl = j if xr < j: xr = j return xl, xr, yh, yl def predict(self, car_pic, resize_rate=1): if type(car_pic) == type(""): img = imreadex(car_pic) else: img = car_pic pic_hight, pic_width = img.shape[:2] if pic_width > MAX_WIDTH: pic_rate = MAX_WIDTH / pic_width img = cv2.resize(img, (MAX_WIDTH, int(pic_hight*pic_rate)), interpolation=cv2.INTER_LANCZOS4) if resize_rate != 1: img = cv2.resize(img, (int(pic_width*resize_rate), int(pic_hight*resize_rate)), interpolation=cv2.INTER_LANCZOS4) pic_hight, pic_width = img.shape[:2] print("h,w:", pic_hight, pic_width) blur = self.cfg["blur"] #高斯去噪 if blur > 0: img = cv2.GaussianBlur(img, (blur, blur), 0)#图片分辨率调整 oldimg = img img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #equ = cv2.equalizeHist(img) #img = np.hstack((img, equ)) #去掉图像中不会是车牌的区域 kernel = np.ones((20, 20), np.uint8) img_opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel) img_opening = cv2.addWeighted(img, 1, img_opening, -1, 0); #找到图像边缘 ret, img_thresh = cv2.threshold(img_opening, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) img_edge = cv2.Canny(img_thresh, 100, 200) #使用开运算和闭运算让图像边缘成为一个整体 kernel = np.ones((self.cfg["morphologyr"], self.cfg["morphologyc"]), np.uint8) img_edge1 = cv2.morphologyEx(img_edge, cv2.MORPH_CLOSE, kernel) img_edge2 = cv2.morphologyEx(img_edge1, cv2.MORPH_OPEN, kernel) #查找图像边缘整体形成的矩形区域,可能有很多,车牌就在其中一个矩形区域中 try: contours, hierarchy = cv2.findContours(img_edge2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) except ValueError: image, contours, hierarchy = cv2.findContours(img_edge2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours = [cnt for cnt in contours if cv2.contourArea(cnt) > Min_Area] print('len(contours)', len(contours)) #一一排除不是车牌的矩形区域 car_contours = [] for cnt in contours: rect = cv2.minAreaRect(cnt) area_width, area_height = rect[1] if area_width < area_height: area_width, area_height = area_height, area_width wh_ratio = area_width / area_height #print(wh_ratio) #要求矩形区域长宽比在2到5.5之间,2到5.5是车牌的长宽比,其余的矩形排除 if wh_ratio > 2 and wh_ratio < 5.5: car_contours.append(rect) box = cv2.boxPoints(rect) box = np.intp(box) #oldimg = cv2.drawContours(oldimg, [box], 0, (0, 0, 255), 2) #cv2.imshow("edge4", oldimg) #cv2.waitKey(0) print(len(car_contours)) print("精确定位") card_imgs = [] #矩形区域可能是倾斜的矩形,需要矫正,以便使用颜色定位 for rect in car_contours: if rect[2] > -1 and rect[2] < 1:#创造角度,使得左、高、右、低拿到正确的值 angle = 1 else: angle = rect[2] rect = (rect[0], (rect[1][0]+5, rect[1][1]+5), angle)#扩大范围,避免车牌边缘被排除 box = cv2.boxPoints(rect) heigth_point = right_point = [0, 0] left_point = low_point = [pic_width, pic_hight] for point in box: if left_point[0] > point[0]: left_point = point if low_point[1] > point[1]: low_point = point if heigth_point[1] < point[1]: heigth_point = point if right_point[0] < point[0]: right_point = point if left_point[1] <= right_point[1]:#正角度 new_right_point = [right_point[0], heigth_point[1]] pts2 = np.float32([left_point, heigth_point, new_right_point])#字符只是高度需要改变 pts1 = np.float32([left_point, heigth_point, right_point]) M = cv2.getAffineTransform(pts1, pts2) dst = cv2.warpAffine(oldimg, M, (pic_width, pic_hight)) point_limit(new_right_point) point_limit(heigth_point) point_limit(left_point) card_img = dst[int(left_point[1]):int(heigth_point[1]), int(left_point[0]):int(new_right_point[0])] card_imgs.append(card_img) #cv2.imshow("card", card_img) #cv2.waitKey(0) elif left_point[1] > right_point[1]:#负角度 new_left_point = [left_point[0], heigth_point[1]] pts2 = np.float32([new_left_point, heigth_point, right_point])#字符只是高度需要改变 pts1 = np.float32([left_point, heigth_point, right_point]) M = cv2.getAffineTransform(pts1, pts2) dst = cv2.warpAffine(oldimg, M, (pic_width, pic_hight)) point_limit(right_point) point_limit(heigth_point) point_limit(new_left_point) card_img = dst[int(right_point[1]):int(heigth_point[1]), int(new_left_point[0]):int(right_point[0])] card_imgs.append(card_img) #cv2.imshow("card", card_img) #cv2.waitKey(0) #开始使用颜色定位,排除不是车牌的矩形,目前只识别蓝、绿、黄车牌 colors = [] for card_index,card_img in enumerate(card_imgs): green = yello = blue = black = white = 0 card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV) #有转换失败的可能,原因来自于上面矫正矩形出错 if card_img_hsv is None: continue row_num, col_num= card_img_hsv.shape[:2] card_img_count = row_num * col_num for i in range(row_num): for j in range(col_num): H = card_img_hsv.item(i, j, 0) S = card_img_hsv.item(i, j, 1) V = card_img_hsv.item(i, j, 2) if 11 < H <= 34 and S > 34:#图片分辨率调整 yello += 1 elif 35 < H <= 99 and S > 34:#图片分辨率调整 green += 1 elif 99 < H <= 124 and S > 34:#图片分辨率调整 blue += 1 if 0 < H <180 and 0 < S < 255 and 0 < V < 46: black += 1 elif 0 < H <180 and 0 < S < 43 and 221 < V < 225: white += 1 color = "no" limit1 = limit2 = 0 if yello*2 >= card_img_count: color = "yello" limit1 = 11 limit2 = 34#有的图片有色偏偏绿 elif green*2 >= card_img_count: color = "green" limit1 = 35 limit2 = 99 elif blue*2 >= card_img_count: color = "blue" limit1 = 100 limit2 = 124#有的图片有色偏偏紫 elif black + white >= card_img_count*0.7:#TODO color = "bw" print(color) colors.append(color) print(blue, green, yello, black, white, card_img_count) #cv2.imshow("color", card_img) #cv2.waitKey(0) if limit1 == 0: continue #以上为确定车牌颜色 #以下为根据车牌颜色再定位,缩小边缘非车牌边界 xl, xr, yh, yl = self.accurate_place(card_img_hsv, limit1, limit2, color) if yl == yh and xl == xr: continue need_accurate = False if yl >= yh: yl = 0 yh = row_num need_accurate = True if xl >= xr: xl = 0 xr = col_num need_accurate = True card_imgs[card_index] = card_img[yl:yh, xl:xr] if color != "green" or yl < (yh-yl)//4 else card_img[yl-(yh-yl)//4:yh, xl:xr] if need_accurate:#可能x或y方向未缩小,需要再试一次 card_img = card_imgs[card_index] card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV) xl, xr, yh, yl = self.accurate_place(card_img_hsv, limit1, limit2, color) if yl == yh and xl == xr: continue if yl >= yh: yl = 0 yh = row_num if xl >= xr: xl = 0 xr = col_num card_imgs[card_index] = card_img[yl:yh, xl:xr] if color != "green" or yl < (yh-yl)//4 else card_img[yl-(yh-yl)//4:yh, xl:xr] #以上为车牌定位 #以下为识别车牌中的字符 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) #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) 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 w = part_card.shape[1] // 3 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) #cv2.imshow("part", part_card_old) #cv2.waitKey(0) #cv2.imwrite("u.jpg", part_card) #part_card = deskew(part_card) part_card = preprocess_hog([part_card]) if i == 0: resp = self.modelchinese.predict(part_card) charactor = provinces[int(resp[0]) - PROVINCE_START] else: resp = self.model.predict(part_card) # charactor = chr(resp[0]) charactor = chr(int(resp[0])) # 显式转换为int #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] >= 8:#1太细,认为是边缘 print(part_card_old.shape) 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 #实现单图多车牌识别 def separate_card(self, gray_img, wave_peaks): """字符分割函数""" part_cards = [] for wave in wave_peaks: part_card = gray_img[:, wave[0]:wave[1]] if part_card.shape[1] > 0: part_cards.append(part_card) return part_cards def predict_multi(self, car_pic, resize_rate=1): if type(car_pic) == type(""): img = imreadex(car_pic) else: img = car_pic pic_hight, pic_width = img.shape[:2] if pic_width > MAX_WIDTH: pic_rate = MAX_WIDTH / pic_width img = cv2.resize(img, (MAX_WIDTH, int(pic_hight * pic_rate)), interpolation=cv2.INTER_LANCZOS4) if resize_rate != 1: img = cv2.resize(img, (int(pic_width * resize_rate), int(pic_hight * resize_rate)), interpolation=cv2.INTER_LANCZOS4) # 初始化参数 blur = self.cfg["blur"] if blur > 0: img = cv2.GaussianBlur(img, (blur, blur), 0) img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) kernel = np.ones((20, 20), np.uint8) img_opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel) img_opening = cv2.addWeighted(img, 1, img_opening, -1, 0) ret, img_thresh = cv2.threshold(img_opening, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) img_edge = cv2.Canny(img_thresh, 100, 200) kernel = np.ones((self.cfg["morphologyr"], self.cfg["morphologyc"]), np.uint8) img_edge1 = cv2.morphologyEx(img_edge, cv2.MORPH_CLOSE, kernel) img_edge2 = cv2.morphologyEx(img_edge1, cv2.MORPH_OPEN, kernel) contours, hierarchy = cv2.findContours(img_edge2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours = [cnt for cnt in contours if cv2.contourArea(cnt) > Min_Area] car_contours = [] for cnt in contours: rect = cv2.minAreaRect(cnt) area_width, area_height = rect[1] if area_width < area_height: area_width, area_height = area_height, area_width wh_ratio = area_width / area_height if wh_ratio > 2 and wh_ratio < 5.5: car_contours.append(rect) results = [] for rect in car_contours: # 矫正车牌区域 if rect[2] > -1 and rect[2] < 1: angle = 1 else: angle = rect[2] rect = (rect[0], (rect[1][0] + 5, rect[1][1] + 5), angle) box = cv2.boxPoints(rect) box = np.intp(box) heigth_point = right_point = [0, 0] left_point = low_point = [pic_width, pic_hight] for point in box: if left_point[0] > point[0]: left_point = point if low_point[1] > point[1]: low_point = point if heigth_point[1] < point[1]: heigth_point = point if right_point[0] < point[0]: right_point = point if left_point[1] <= right_point[1]: # 正角度 new_right_point = [right_point[0], heigth_point[1]] pts2 = np.float32([left_point, heigth_point, new_right_point]) pts1 = np.float32([left_point, heigth_point, right_point]) M = cv2.getAffineTransform(pts1, pts2) dst = cv2.warpAffine(img, M, (pic_width, pic_hight)) point_limit(new_right_point) point_limit(heigth_point) point_limit(left_point) card_img = dst[int(left_point[1]):int(heigth_point[1]), int(left_point[0]):int(new_right_point[0])] elif left_point[1] > right_point[1]: # 负角度 new_left_point = [left_point[0], heigth_point[1]] pts2 = np.float32([new_left_point, heigth_point, right_point]) pts1 = np.float32([left_point, heigth_point, right_point]) M = cv2.getAffineTransform(pts1, pts2) dst = cv2.warpAffine(img, M, (pic_width, pic_hight)) point_limit(right_point) point_limit(heigth_point) point_limit(new_left_point) card_img = dst[int(right_point[1]):int(heigth_point[1]), int(new_left_point[0]):int(right_point[0])] # 颜色识别(蓝/绿/黄) if len(card_img.shape) == 2: # 灰度图检测 card_img = cv2.cvtColor(card_img, cv2.COLOR_GRAY2BGR) card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV) # 必须的转换步骤 color = self.detect_color(card_img_hsv) # 字符分割与识别 gray_img = cv2.cvtColor(card_img, cv2.COLOR_BGR2GRAY) if color in ["green", "yello"]: gray_img = cv2.bitwise_not(gray_img) ret, gray_img = cv2.threshold(gray_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) # 水平投影分割 horizontal_sum = np.sum(gray_img, axis=1) wave_peaks = self.find_peaks(horizontal_sum) if len(wave_peaks) > 0: gray_img = gray_img[wave_peaks[0][0]:wave_peaks[-1][1], :] # 垂直投影分割 vertical_sum = np.sum(gray_img, axis=0) char_peaks = self.find_peaks(vertical_sum) # 字符识别 plate_chars = [] for i, (start, end) in enumerate(char_peaks): char_img = gray_img[:, start:end] char_img = cv2.resize(char_img, (20, 20)) if i == 0: # 第一个字符为中文 char_img = preprocess_hog([char_img]) resp = self.modelchinese.predict(char_img) plate_chars.append(provinces[int(resp[0]) - PROVINCE_START]) else: # 其他字符为字母/数字 char_img = preprocess_hog([char_img]) resp = self.model.predict(char_img) plate_chars.append(chr(int(resp[0]))) results.append({ "plate": "".join(plate_chars), "color": color, "roi": card_img }) return results def detect_color(self, hsv_img): """识别车牌颜色""" # 颜色阈值定义 blue_lower = np.array([100, 50, 50]) blue_upper = np.array([124, 255, 255]) green_lower = np.array([35, 50, 50]) green_upper = np.array([99, 255, 255]) yellow_lower = np.array([11, 50, 50]) yellow_upper = np.array([34, 255, 255]) # 计算各颜色像素占比 blue_mask = cv2.inRange(hsv_img, blue_lower, blue_upper) green_mask = cv2.inRange(hsv_img, green_lower, green_upper) yellow_mask = cv2.inRange(hsv_img, yellow_lower, yellow_upper) total_pixels = hsv_img.shape[0] * hsv_img.shape[1] blue_ratio = np.count_nonzero(blue_mask) / total_pixels green_ratio = np.count_nonzero(green_mask) / total_pixels yellow_ratio = np.count_nonzero(yellow_mask) / total_pixels # 确定主要颜色 if blue_ratio > 0.3: return "blue" elif green_ratio > 0.3: return "green" elif yellow_ratio > 0.3: return "yello" else: return "unknown" def find_peaks(self, histogram, min_width=2): """寻找直方图波峰""" peaks = [] start = 0 rising = False for i, value in enumerate(histogram): if value > 0 and not rising: start = i rising = True elif value == 0 and rising: if i - start >= min_width: peaks.append((start, i)) rising = False if rising and len(histogram) - start >= min_width: peaks.append((start, len(histogram))) return peaks def process_video(self, source=0, output_path=None, show=True): cap = cv2.VideoCapture(source) if not cap.isOpened(): print("无法打开摄像头") return while True: ret, frame = cap.read() if not ret: print("无法接收帧 (stream end?). Exiting ...") break r, roi, color = self.predict(frame) if roi is not None and show: cv2.imshow("识别的车牌", roi) if cv2.waitKey(1) & 0xFF == 27: # 按ESC键退出 break cap.release() cv2.destroyAllWindows() # if __name__ == '__main__': # c = CardPredictor() # c.train_svm() # r, roi, color = c.predict("10.jpg") # print(r) if __name__ == '__main__': c = CardPredictor() c.train_svm() # 用户交互菜单 while True: print("\n===== 车牌识别系统 =====") print("1. 单图片单车牌识别") print("2. 单图片多车牌识别") print("3. 摄像头实时识别") print("4. 视频文件识别") print("5. 退出") choice = input("请选择功能 (1-5): ") if choice == "1": # 单图片单车牌识别(原功能) img_path = input("请输入图片路径: ") r, roi, color = c.predict(img_path) print(f"识别结果: {''.join(r)}, 车牌颜色: {color}") # 显示识别的车牌 if roi is not None: cv2.imshow("识别的车牌", roi) cv2.waitKey(0) cv2.destroyAllWindows() elif choice == "2": # 单图片多车牌识别 img_path = input("请输入图片路径: ") results = c.predict_multi(img_path) if results: print(f"共识别到 {len(results)} 个车牌:") for i, result in enumerate(results, 1): try: # 安全获取字段并设置默认值 plate_num = ''.join(result.get("plate_number", ["识别失败"])) plate_color = result.get("plate_color", "未知颜色") roi_img = result.get("roi", None) print(f"车牌 {i}: {plate_num}, 颜色: {plate_color}") # 仅当存在ROI图像时显示 if roi_img is not None: cv2.imshow(f"车牌 {i}", roi_img) cv2.waitKey(1) # 实时刷新显示 else: print(f"警告: 车牌 {i} 无ROI图像") except Exception as e: print(f"处理车牌 {i} 时出错: {str(e)}") continue cv2.waitKey(0) cv2.destroyAllWindows() else: print("未识别到车牌") elif choice == "3": # 摄像头实时识别 print("按ESC键退出...") c.process_video(0, show=True) elif choice == "4": # 视频文件识别 video_path = input("请输入视频路径: ") output_path = input("请输入输出路径 (留空则不保存): ") output_path = output_path if output_path.strip() else None print("按ESC键退出...") c.process_video(video_path, output_path) elif choice == "5": print("程序已退出") break else: print("无效选择,请重新输入") 基于这个代码修改,让它可以识别出车牌号和车牌颜色以及有几个车牌
06-11
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