Python車牌識(shí)別6是一種基于Python編程語(yǔ)言實(shí)現(xiàn)的自動(dòng)車牌識(shí)別系統(tǒng)。該系統(tǒng)使用圖像處理算法對(duì)車輛上的車牌進(jìn)行快速、準(zhǔn)確的識(shí)別，從而提高交通管理效率和公共安全。

代碼實(shí)現(xiàn)如下：

# 導(dǎo)入必要的庫(kù)和模塊
import cv2
import numpy as np
import pytesseract
# 定義車牌識(shí)別函數(shù)
def license_plate_recognition(image_path):
# 讀取車牌圖片
img = cv2.imread(image_path)
# 對(duì)圖像進(jìn)行預(yù)處理
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5, 5), 0)
canny = cv2.Canny(blur, 50, 150)
# 查找輪廓
contours, hierarchy = cv2.findContours(canny, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# 儲(chǔ)存矩形框坐標(biāo)
rects = []
for cnt in contours:
x, y, w, h = cv2.boundingRect(cnt)
rects.append([x, y, x + w, y + h])
# 去除包含在其他矩形框內(nèi)的矩形框
indexes = []
for i in range(len(rects)):
for j in range(len(rects)):
if i != j and rects[i] != [-1, -1, -1, -1]:
if rects[j][0]< rects[i][0] and rects[j][1]< rects[i][1] and rects[j][2] >rects[i][2] and rects[j][3] >rects[i][3]:
indexes.append(i)
break
indexes = set(indexes)
final_rects = [rects[i] for i in range(len(rects)) if i not in indexes]
# 對(duì)矩形框進(jìn)行排序
final_rects.sort(key=lambda x: x[0])
# 判斷車牌號(hào)是否存在
if len(final_rects) == 0:
return None
# 從矩形框中提取車牌號(hào)圖像
x1, y1, x2, y2 = final_rects[0]
plate_img = img[y1:y2, x1:x2]
# 對(duì)車牌號(hào)圖像進(jìn)行二值化處理
gray_plate = cv2.cvtColor(plate_img, cv2.COLOR_BGR2GRAY)
binary_plate = cv2.threshold(gray_plate, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
# 利用pytesseract進(jìn)行車牌號(hào)識(shí)別
plate_text = pytesseract.image_to_string(binary_plate, lang='eng', config='--psm 6')
# 返回車牌號(hào)
return plate_text

通過(guò)這段Python代碼，我們可以實(shí)現(xiàn)對(duì)車輛上的車牌進(jìn)行自動(dòng)識(shí)別，提高了對(duì)交通管理和公共安全的保障效果。同時(shí)，Python車牌識(shí)別6也有著廣泛的應(yīng)用前景，可以應(yīng)用在停車場(chǎng)管理、機(jī)場(chǎng)安檢、物聯(lián)網(wǎng)等領(lǐng)域。