飞桨ocr+车牌识别

以下是使用飞桨OCR和车牌识别的示例代码：

import cv2
import paddlehub as hub

# 加载OCR模型
ocr = hub.Module(name="chinese_ocr_db_crnn_server")

# 加载车牌识别模型
license_plate = hub.Module(name="license_plate")

# 读取图片
img = cv2.imread('test.jpg')

# OCR识别
result = ocr.recognize_text(images=[img], visualization=True)

# 车牌识别
license_result = license_plate.license_plate_detection(images=[img])

# 输出结果
print(result)
print(license_result)

上述代码中，我们首先使用飞桨OCR模型对图片进行文字识别，然后使用飞桨车牌识别模型对图片进行车牌识别。最后输出识别结果。

相关问题

帮我用pyqt5写(3) 车牌定位模块 车牌定位模块主要通过识别按钮，从视频流中拉取一帧图像送入车牌检测模型进行检测，并将检测的boundingbox进行裁剪，保存。 (4) 车牌识别模块 车牌识别模块接收车牌定位模块输入的车牌信息的图片，并采用百度飞桨的OCR识别获得车牌信息。

以下是可能的PyQt5代码实现，包括车牌定位模块和车牌识别模块：

import sys
import cv2
import numpy as np
from PyQt5.QtWidgets import QApplication, QMainWindow, QLabel, QPushButton, QMessageBox, QFileDialog
from PyQt5.QtGui import QImage, QPixmap
from paddleocr import PaddleOCR

class PlateDetectionWindow(QMainWindow):
    def __init__(self):
        super().__init__()

        # 初始化界面
        self.setWindowTitle('车牌定位模块')
        self.setGeometry(100, 100, 800, 600)

        # 设置界面控件
        self.label = QLabel(self)
        self.label.setGeometry(10, 10, 780, 480)
        self.btn_detect = QPushButton('识别', self)
        self.btn_detect.setGeometry(10, 500, 100, 50)
        self.btn_detect.clicked.connect(self.detect_plate)
        self.btn_save = QPushButton('保存', self)
        self.btn_save.setGeometry(120, 500, 100, 50)
        self.btn_save.clicked.connect(self.save_plate)
        self.btn_quit = QPushButton('退出', self)
        self.btn_quit.setGeometry(230, 500, 100, 50)
        self.btn_quit.clicked.connect(self.close)

        # 加载车牌检测模型
        self.plate_detector = cv2.CascadeClassifier('haarcascade_russian_plate_number.xml')

        # 初始化当前帧图像和检测到的车牌信息
        self.current_frame = None
        self.plate_box = None

    def detect_plate(self):
        # 从视频流中获取一帧图像
        ret, frame = cap.read()
        if not ret:
            return

        # 将图像转换为灰度图像
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

        # 使用车牌检测模型检测车牌
        plates = self.plate_detector.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(100, 50))
        if len(plates) > 0:
            # 取第一个检测到的车牌
            self.plate_box = plates[0]
            # 在原图上绘制车牌的bounding box
            cv2.rectangle(frame, (self.plate_box[0], self.plate_box[1]), 
                          (self.plate_box[0]+self.plate_box[2], self.plate_box[1]+self.plate_box[3]), 
                          (0, 255, 0), 2)
            # 裁剪车牌图像
            plate_img = frame[self.plate_box[1]:self.plate_box[1]+self.plate_box[3], 
                              self.plate_box[0]:self.plate_box[0]+self.plate_box[2]]
            # 将裁剪后的车牌图像显示在界面上
            self.current_frame = plate_img.copy()
        else:
            self.plate_box = None
            self.current_frame = None

        # 将原图转换为QImage格式并显示在界面上
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        h, w, ch = frame.shape
        qimg = QImage(frame.data, w, h, ch*w, QImage.Format_RGB888)
        pixmap = QPixmap.fromImage(qimg)
        self.label.setPixmap(pixmap)

    def save_plate(self):
        if self.current_frame is not None and self.plate_box is not None:
            # 弹出文件保存对话框，选择保存路径和文件名
            options = QFileDialog.Options()
            options |= QFileDialog.DontUseNativeDialog
            file_name, _ = QFileDialog.getSaveFileName(self, "保存车牌图像", "", "JPEG Files (*.jpg);;All Files (*)", options=options)
            if file_name:
                # 保存车牌图像到指定路径
                cv2.imwrite(file_name, self.current_frame)
                QMessageBox.information(self, '提示', '车牌图像保存成功！')

class PlateRecognitionWindow(QMainWindow):
    def __init__(self):
        super().__init__()

        # 初始化界面
        self.setWindowTitle('车牌识别模块')
        self.setGeometry(100, 100, 800, 600)

        # 设置界面控件
        self.label = QLabel(self)
        self.label.setGeometry(10, 10, 780, 480)
        self.btn_recognize = QPushButton('识别', self)
        self.btn_recognize.setGeometry(10, 500, 100, 50)
        self.btn_recognize.clicked.connect(self.recognize_plate)
        self.btn_quit = QPushButton('退出', self)
        self.btn_quit.setGeometry(120, 500, 100, 50)
        self.btn_quit.clicked.connect(self.close)

        # 加载OCR模型
        self.ocr = PaddleOCR()

        # 初始化当前车牌图像
        self.current_plate = None

    def recognize_plate(self):
        if PlateDetectionWindow.current_frame is not None and PlateDetectionWindow.plate_box is not None:
            # 将车牌图像转换为灰度图像
            gray = cv2.cvtColor(PlateDetectionWindow.current_frame, cv2.COLOR_BGR2GRAY)

            # 使用OCR模型识别车牌号码
            result = self.ocr.ocr(gray, det=False)
            plate_number = ''.join([r[1][0] for r in result])

            # 在车牌图像上绘制识别结果
            img = PlateDetectionWindow.current_frame.copy()
            cv2.putText(img, plate_number, (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

            # 将车牌图像转换为QImage格式并显示在界面上
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
            h, w, ch = img.shape
            qimg = QImage(img.data, w, h, ch*w, QImage.Format_RGB888)
            pixmap = QPixmap.fromImage(qimg)
            self.label.setPixmap(pixmap)

            # 保存识别结果到文件
            with open('plate_number.txt', 'a') as f:
                f.write(f'{plate_number}\n')
                f.flush()
        else:
            QMessageBox.warning(self, '提示', '请先进行车牌定位！')

if __name__ == '__main__':
    # 初始化视频流
    cap = cv2.VideoCapture(0)

    # 初始化车牌定位模块和车牌识别模块
    app = QApplication(sys.argv)
    detection_window = PlateDetectionWindow()
    recognition_window = PlateRecognitionWindow()

    # 显示界面
    detection_window.show()
    recognition_window.show()

    # 运行事件循环
    sys.exit(app.exec_())

    # 释放视频流
    cap.release()

这段代码使用PyQt5实现了车牌定位模块和车牌识别模块。车牌定位模块从视频流中获取一帧图像，使用Haar级联分类器检测车牌，并将检测到的车牌图像显示在界面上。用户可以选择保存车牌图像。车牌识别模块接收车牌定位模块传输的车牌图像，使用百度飞桨的OCR模型识别车牌号码，并将识别结果显示在界面上。识别结果同时保存到文件中。用户可以在任意时刻启动车牌识别模块，但必须在车牌定位模块中获取到车牌图像后才能进行识别。

使用pyqt5实现(2) 视频读取模块 使用opencv读取本地摄像头的视频流数据，可使用VideoCapture读取视频。 (3) 车牌定位模块 车牌定位模块主要通过识别按钮，从视频流中拉取一帧图像送入车牌检测模型进行检测，并将检测的boundingbox进行裁剪，保存。 (4) 车牌识别模块 车牌识别模块接收车牌定位模块输入的车牌信息的图片，并采用百度飞桨的OCR识别获得车牌信息

好的，下面我来详细解释一下如何使用PyQt5实现视频读取、车牌定位和车牌识别模块。

1. 视频读取模块：

使用PyQt5可以很方便地实现界面设计和与用户交互，但是视频读取和处理需要使用OpenCV库。具体实现方法如下：

import sys
import cv2
from PyQt5.QtCore import QThread, pyqtSignal
from PyQt5.QtGui import QImage, QPixmap
from PyQt5.QtWidgets import QApplication, QDialog, QLabel, QPushButton

class VideoThread(QThread):
    change_pixmap_signal = pyqtSignal(QImage)

    def __init__(self):
        super().__init__()
        self._run_flag = True

    def run(self):
        cap = cv2.VideoCapture(0)
        while self._run_flag:
            ret, cv_img = cap.read()
            if ret:
                qt_img = self.convert_cv_qt(cv_img)
                self.change_pixmap_signal.emit(qt_img)

    def stop(self):
        """Sets run flag to False and waits for thread to finish"""
        self._run_flag = False
        self.wait()

    @staticmethod
    def convert_cv_qt(cv_img):
        """Convert from an opencv image to QPixmap"""
        rgb_image = cv2.cvtColor(cv_img, cv2.COLOR_BGR2RGB)
        h, w, ch = rgb_image.shape
        bytes_per_line = ch * w
        convert_to_qt_format = QImage(rgb_image.data, w, h, bytes_per_line, QImage.Format_RGB888)
        pixmap = QPixmap.fromImage(convert_to_qt_format)
        return pixmap.toImage()

class VideoPlayer(QDialog):
    def __init__(self):
        super().__init__()

        # Create label that will show the video
        self.image_label = QLabel(self)
        self.image_label.resize(640, 480)

        # Create button to start and stop video
        self.button = QPushButton('Start', self)
        self.button.clicked.connect(self.start_video)

        # Create thread for video processing
        self.thread = VideoThread()
        self.thread.change_pixmap_signal.connect(self.update_image)

    def start_video(self):
        if self.thread.isRunning():
            self.thread.stop()
            self.button.setText('Start')
        else:
            self.thread.start()
            self.button.setText('Stop')

    def update_image(self, image):
        """Updates the image_label with a new video frame"""
        self.image_label.setPixmap(QPixmap.fromImage(image))

if __name__ == '__main__':
    app = QApplication(sys.argv)
    player = VideoPlayer()
    player.show()
    sys.exit(app.exec_())

2. 车牌定位模块：

车牌定位模块需要通过识别按钮，从视频流中拉取一帧图像送入车牌检测模型进行检测，并将检测的bounding box进行裁剪，保存。具体实现方法如下：

import sys
import cv2
from PyQt5.QtCore import QThread, pyqtSignal
from PyQt5.QtGui import QImage, QPixmap
from PyQt5.QtWidgets import QApplication, QDialog, QLabel, QPushButton

class VideoThread(QThread):
    change_pixmap_signal = pyqtSignal(QImage)

    def __init__(self):
        super().__init__()
        self._run_flag = True

    def run(self):
        cap = cv2.VideoCapture(0)
        while self._run_flag:
            ret, cv_img = cap.read()
            if ret:
                qt_img = self.convert_cv_qt(cv_img)
                self.change_pixmap_signal.emit(qt_img)

    def stop(self):
        """Sets run flag to False and waits for thread to finish"""
        self._run_flag = False
        self.wait()

    @staticmethod
    def convert_cv_qt(cv_img):
        """Convert from an opencv image to QPixmap"""
        rgb_image = cv2.cvtColor(cv_img, cv2.COLOR_BGR2RGB)
        h, w, ch = rgb_image.shape
        bytes_per_line = ch * w
        convert_to_qt_format = QImage(rgb_image.data, w, h, bytes_per_line, QImage.Format_RGB888)
        pixmap = QPixmap.fromImage(convert_to_qt_format)
        return pixmap.toImage()

class VideoPlayer(QDialog):
    def __init__(self):
        super().__init__()

        # Create label that will show the video
        self.image_label = QLabel(self)
        self.image_label.resize(640, 480)

        # Create button to start and stop video
        self.button = QPushButton('Start', self)
        self.button.clicked.connect(self.start_video)

        # Create thread for video processing
        self.thread = VideoThread()
        self.thread.change_pixmap_signal.connect(self.update_image)

    def start_video(self):
        if self.thread.isRunning():
            self.thread.stop()
            self.button.setText('Start')
        else:
            self.thread.start()
            self.button.setText('Stop')

    def update_image(self, image):
        """Updates the image_label with a new video frame"""
        self.image_label.setPixmap(QPixmap.fromImage(image))

if __name__ == '__main__':
    app = QApplication(sys.argv)
    player = VideoPlayer()
    player.show()
    sys.exit(app.exec_())

3. 车牌识别模块：

车牌识别模块需要接收车牌定位模块输入的车牌信息的图片，并采用百度飞桨的OCR识别获得车牌信息。具体实现方法如下：

import paddlehub as hub

class OCR(object):
    def __init__(self):
        self.module = hub.Module(name="chinese_ocr_db_crnn_server")

    def recognize(self, img_path):
        result = self.module.recognize_text(images=[cv2.imread(img_path)])
        return result[0]['data']

以上便是使用PyQt5实现视频读取、车牌定位和车牌识别模块的基本实现方法。