# -*- coding: utf-8 -*-
__author__ = 'd1bysj'
import pymysql
db = t(host = '', # 远程主机的ip地址，
ur = '', # MySQL用户名
db = '', # databa名
passwd = '', # 数据库密码
port = 3306, #数据库监听端口，默认3306
chart = "utf8") #指定utf8编码的连接
cur= db.cursor()
sql="lect * from x"
try:
ute(sql)
re=cur.fetchall()
for it in re:
name = it[0]
num = it[1]
print(name,num)
except Exception as e:
鄙视你的英文
rai e
finally:
db.clo()
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 = Fal
if histogram[0] > threshold:
up_point = 0
is_peak = True
叟猴 wave_peaks = []
for i, x in enumerate(histogram):
if is_peak and x < threshold:
if i - up_point > 2:
is_peak = Fal
wave_peaks.append((up_point, i))
elif not is_peak and x >= threshold:
is_peak = True
more information
up_point = i
if is_peak and up_point != -1 and i - up_point > 4:
科学的英语 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 perate_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 = s(img)
南回归线英文
if abs(m['mu02']) < 1e-2:
py()
skew = m['mu11'] / m['mu02']在线词源
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)
中国英文怎么写
gy = cv2.Sobel(img, cv2.CV_32F, 0, 1)
mag, ang = cv2.cartToPolar(gx, gy)
cashmere coat
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",
t off "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(lf, fn):
lf.model = lf.model.load(fn)
def save(lf, fn):
lf.model.save(fn)
class SVM(StatModel):
def __init__(lf, C=1, gamma=0.5):
lf.model = cv2.ml.SVM_create()
lf.model.tGamma(gamma)
lf.model.tC(C)
lf.model.tKernel(cv2.ml.SVM_RBF)
lf.model.tType(cv2.ml.SVM_C_SVC)
# 训练svm
def train(lf, samples, respons):
ain(samples, cv2.ml.ROW_SAMPLE, respons)
# 字符识别瘦骨嶙峋
def predict(lf, samples):
r = lf.model.predict(samples)
return r[1].ravel()
class CardPredictor:
def __init__(lf):
# 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"]:
print(c)
