【Opencv实战】一文看懂车牌识别系统全部内容，未来市场前景看好（很赞）
2024-04-25 00:02:34
前言
车辆号牌是车辆唯一身份证，它的特殊性与重要性决定车牌识别系统成为城市智能交通管理系统
中不可或缺的重要组成部分。
未来，随着我国城市化进程发展的提速，交通压力将更加严峻，因此智能化交通管理将是今后交通
发展的大方向。今天小编就来带大家探索车牌识别系统！
一、环境准备
Python3、 Pycharm 、PIL、cv2 、tkinter 一些车牌图片的素材大家可以自由选择这里就不展示，
等下会给大家随机找几组效果哈！
第三方库的安装：pip install +模块名 如安装出现问题可以直接找我私信即可哈二、效果展示
小程序界面：
效果展示：
三、代码展示
主要有部分源码。这里仅展示部分。需要的找我拿完整的哈！
主程序界面：
import tkinter as tk
from tkinter.filedialog import *
from tkinter import ttk;
import predict
import cv2
from PIL import Image,ImageTk
import threading
import timeclass Window(ttk.Frame):pic_path = ""viewHigh = 600viewWide = 600updataTime = 0thread = NonethreadRun = Falsecamera = NonecolorTransform = {"green":("绿","#55ff55"),"yello":("黄","#ffff00"),"blue":{"蓝","#6666ff"}}def __init__(self,win):ttk.Frame.__init__(self,win)frame_left = ttk.Frame(self)frame_right1 = ttk.Frame(self)frame_right2= ttk.Frame(self)win.title("车牌识别")win.state("normal")self.pack(fill=tk.BOTH,expand=tk.YES,padx="5",pady="5")frame_left.pack(side=LEFT,expand=1,fill=BOTH)frame_right1.pack(side=TOP, expand=1, fill=tk.Y)frame_right2.pack(side=RIGHT, expand=0)ttk.Label(frame_left, text='原图：').pack(anchor="nw")ttk.Label(frame_right1, text='车牌位置：').grid(column=0, row=0, sticky=tk.W)from_pic_ctl = ttk.Button(frame_right2, text="打开图片", width=20, command=self.from_pic)self.image_ctl = ttk.Label(frame_left)self.image_ctl.pack(anchor="nw")self.roi_ctl = ttk.Label(frame_right1)self.roi_ctl.grid(column=0, row=1, sticky=tk.W)ttk.Label(frame_right1, text='识别结果：').grid(column=0, row=2, sticky=tk.W)self.r_ctl = ttk.Label(frame_right1, text="")self.r_ctl.grid(column=0, row=3, sticky=tk.W)self.color_ctl = ttk.Label(frame_right1, text="", width="20")self.color_ctl.grid(column=0, row=4, sticky=tk.W)from_pic_ctl.pack(anchor="se", pady="5")self.predictor = predict.CardPredictor()self.predictor.train_svm()def from_pic(self):self.threadRun = Falseself.pic_path = askopenfilename(title="选择识别图片",filetypes=[("jpg图片","*.jpg")])if self.pic_path:img_bgr = predict.imreadex(self.pic_path)self.imgtk = self.get_imgtk(img_bgr)self.image_ctl.configure(image=self.imgtk)r,roi,color = self.predictor.predict(img_bgr)self.show_roi(r,roi,color)def show_roi(self,r,roi,color):if r :roi = cv2.cvtColor(roi,cv2.COLOR_BGR2RGB)roi = Image.fromarray(roi)self.imgtk_roi = ImageTk.PhotoImage(image=roi)self.roi_ctl.configure(image=self.imgtk_roi, state='enable')self.r_ctl.configure(text=str(r))self.update_time = time.time()try:c = self.colorTransform[color]self.color_ctl.configure(text=c[0], background=c[1], state='enable')except:self.color_ctl.configure(state='disabled')elif self.update_time + 8 < time.time():self.roi_ctl.configure(state='disabled')self.r_ctl.configure(text="")self.color_ctl.configure(state='disabled')def get_imgtk(self,img_bgr):img = cv2.cvtColor(img_bgr,cv2.COLOR_BGR2RGB)im = Image.fromarray(img)imgtk = ImageTk.PhotoImage(image=im)wide = imgtk.width()high = imgtk.height()if wide>self.viewWide or high > self.viewHigh:wide_factor = self.viewWide / widehigh_factor = self.viewHigh / highfactor = min(wide_factor,high_factor)wide = int(wide*factor)if wide <=0 : wide = 1high = int(high*factor)if high <= 0:high = 1im = im.resize((wide,high),Image.ANTIALIAS)imgtk = ImageTk.PhotoImage(image=im)return imgtkdef close_window():print("destroy")if window.threadRun :window.threadRun = Falsewindow.thread.join(2.0)win.destroy()if __name__ == '__main__':win = tk.Tk()window = Window(win)win.protocol('WM_DELETE_WINDOW', close_window)win.mainloop()模型训练：
import cv2
import numpy as np
from numpy.linalg import norm
import sys
import os
import json
from matplotlib import pyplot as pltSZ = 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] = 0if 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 = Falseif histogram[0] > threshold:up_point = 0is_peak = Truewave_peaks = []for i, x in enumerate(histogram):if is_peak and x < threshold:if i - up_point > 2:is_peak = Falsewave_peaks.append((up_point, i))elif not is_peak and x >= threshold:is_peak = Trueup_point = iif 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 = 16bin = 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 kerneleps = 1e-7hist /= hist.sum() + epshist = np.sqrt(hist)hist /= norm(hist) + epssamples.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svmdef 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()breakelse: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:continueroot_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)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_"):continuepinyin = 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_numxr = 0yh = 0yl = 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 = 0for 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 += 1if count > col_num_limit:if yl > i:yl = iif yh < i:yh = ifor j in range(col_num):count = 0for 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 += 1if count > row_num - row_num_limit:if xl > j:xl = jif xr < j:xr = jreturn xl, xr, yh, yldef predict(self, car_pic):print("config: "+str(self.cfg))if type(car_pic) == type(""):img = imreadex(car_pic)else:img = car_picpic_hight, pic_width = img.shape[:2]if pic_width > MAX_WIDTH:resize_rate = MAX_WIDTH / pic_widthimg = cv2.resize(img, (MAX_WIDTH, int(pic_hight * resize_rate)), interpolation=cv2.INTER_AREA)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 = imgimg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# 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_widthwh_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.int0(box)# oldimg = cv2.drawConours(oldimg, [box], 0, (0, 0, 255), 2)# cv2.imshow("edge4", oldimg)# print(rect)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 = 1else: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)print("box_ :" +str(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 = pointif low_point[1] > point[1]:low_point = pointif heigth_point[1] < point[1]:heigth_point = pointif right_point[0] < point[0]:right_point = pointif 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])]print("card_img :" + str(card_img))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 = 0card_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:continuerow_num, col_num = card_img_hsv.shape[:2]card_img_count = row_num * col_numfor 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 += 1elif 35 < H <= 99 and S > 34: # 图片分辨率调整green += 1elif 99 < H <= 124 and S > 34: # 图片分辨率调整blue += 1if 0 < H < 180 and 0 < S < 255 and 0 < V < 46:black += 1elif 0 < H < 180 and 0 < S < 43 and 221 < V < 225:white += 1color = "no"limit1 = limit2 = 0if yello * 2 >= card_img_count:color = "yello"limit1 = 11limit2 = 34 # 有的图片有色偏偏绿elif green * 2 >= card_img_count:color = "green"limit1 = 35limit2 = 99elif blue * 2 >= card_img_count:color = "blue"limit1 = 100limit2 = 124 # 有的图片有色偏偏紫elif black + white >= card_img_count * 0.7: # TODOcolor = "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# 以上为车牌定位# 以下为识别车牌中的字符predict_result = []roi = Nonecard_color = Nonefor 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) / 2wave_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6if len(wave_peaks) <= 6:print("peak less 1:", len(wave_peaks))continuewave = 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 = 0for i, wave in enumerate(wave_peaks):if wave[1] - wave[0] + cur_dis > max_wave_dis * 0.6:breakelse: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))continuepart_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")continuepart_card_old = part_cardw = abs(part_card.shape[1] - SZ) // 2part_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 = 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])# 判断最后一个数是否是车牌边缘，假设车牌边缘被认为是1if 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continuepredict_result.append(charactor)roi = card_imgcard_color = colorbreakreturn predict_result, roi, card_color # 识别到的字符、定位的车牌图像、车牌颜色小结
好啦！文章内容到这里就正式结束~
车牌识别小程序就写到这里，之后会更新更多内容哦~记得关注我啦
所有文章完整的素材+源码都在
【Opencv实战】一文看懂车牌识别系统全部内容，未来市场前景看好（很赞）相关推荐
- 一文看懂人脸识别（4个特点+4个实现步骤+5个难点+算法发展轨迹）
2020-03-09 20:01:00 人脸识别是身份识别的一种方式,目的就是要判断图片和视频中人脸的身份时什么. 本文将详细介绍人脸识别的4个特点.4个步骤.5个难点及算法的发展轨迹. 什么是人脸识 ...
- 硬核科普：一文看懂人脸识别技术流程
小编是个天生懒惰的人,同时又是个急性子,这样的人最享受被科技服务的乐趣. 举个例子,十多年前大家还在普遍用现金的时代,小编在商店买东西排队结账,每当看到收银员找零时手忙脚乱的样子就会心急如焚,只恨不能 ...
- 一文看懂人脸识别技术发展脉络
来源:InfoQ 人脸识别可以说是人工智能领域中成熟较早.落地较广的技术之一,从机场.火车站的安检闸机,到平常用户手机中的"刷脸"支付,人脸识别技术已经深入到我们的生活当中. 为了 ...
- 数仓实战|一文看懂数据中台接口数据采集
作者简介:王春波,<高效使用Greenplum:入门.进阶和数据中台>作者,"数据中台研习社"号主,十年数据开发从业者,资深零售数仓项目实施专家. 以下内容摘自< ...
- 一文看懂互联网支付系统架构
一.支付系统的简介 什么是支付系统?自古以来,所有的商业活动都会伴随着经济的收款与付款行为.随着时代的发展,记录收付款行为的方式不断迭代:古代的钱庄通过手工(算盘)记账,工业社会通过收银机机械记账-- ...
- 一文读懂视频监控系统全过程内容
视频监控系统由实时控制系统.监视系统及管理信息系统组成.视频监控系统是安全防范系统的组成部分,它是一种防范能力较强的综合系统.视频监控以其直观.方便.信息内容丰富而广泛应用于许多场合.接下来就由飞畅科 ...
- 移动网流量用户身份识别系统的源代码_安定门车牌识别系统厂家哪家好
产品品牌 北京同兴宏业建筑装饰 产品型号 齐全 生产城市 北京 发货城市 北京 供货总量 10000 最小起订 1 产品单价 1 计量单位 个 安定门车牌识别系统厂家哪家好 北京同兴宏业建筑装饰有限公 ...
- 一文看懂“声纹识别VPR” | AI产品经理需要了解的AI技术概念_团员分享_@cony
前言:声纹识别是AI领域中一个看似很小.但其实有机会在近期落地,且比较有意思的细分方向:本文作者是"AI产品经理大本营"团员@cony ,她总结了AI产品经理"最必要& ...
- OpenCV计算机视觉实战（Python）| 10、项目实战：文档扫描OCR识别
文章目录 简介 总结 1. 介绍 2. 流程 3. 程序 4. 知识点总结 简介 本节为<OpenCV计算机视觉实战(Python)>版第10讲,项目实战:文档扫描OCR识别,的总结. 总 ...
最新文章
- “面试不败计划”：集合、日期、异常、序列化、jvm、其他
- 如何使用idea生成javaDoc文档
- 360安全路由器v2处理器_您的路由器有多不安全？
- 云服务器装teamviewer
- Zephyr学习（一）Zephyr介绍
- duilib加载xml以及资源文件的路径问题
- c语言程序调试时无法启动程序,编译没有异常，调试时报错: 无法启动程序“C:Projects\Driver\mydriver_check\HelloDDk.sys...
- PostgreSQL12.2_cn.chm 中文手册 最新版
- 钉钉小程序-打开外部链接（文件链接）
- 详解自监督发展趋势! 何恺明连获三年CVPR最高引用的秘诀是?
- 机器学习零基础？手把手教你用TensorFlow搭建图像识别系统
- 违法占耕辅助处理软件
- Windows安全检查脚本 bat 批处理
- centos 7.5 内核升级
- RichTextBox 增加行间距
- 拆机详解2：比Macintosh还早？苹果Lisa拆解
- Vue引用原生高德地图标注
- NRF24L01+双向通信
- C语言实现贪吃蛇（easyx）
- 尚硅谷springboot 2核心技术学习笔记
热门文章