opencv如何读取pixmap图像

要使用OpenCV读取pixmap图像，需要先将pixmap转换为OpenCV能够处理的格式，例如numpy数组或Mat对象。以下是一个示例代码：

from PyQt5.QtGui import QPixmap
import numpy as np
import cv2

# 读取pixmap图像
pixmap = QPixmap("image.png")

# 将pixmap转换为numpy数组
qimage = pixmap.toImage()
width, height = qimage.width(), qimage.height()
buffer = qimage.bits().asstring(qimage.byteCount())
arr = np.frombuffer(buffer, dtype=np.uint8).reshape((height, width, 4))

# 将numpy数组转换为OpenCV的Mat对象
mat = cv2.cvtColor(arr, cv2.COLOR_RGBA2BGR)

在此示例中，我们首先使用PyQt5的QPixmap读取pixmap图像，然后将其转换为QImage对象，并将其数据存储在numpy数组中。最后，我们将numpy数组转换为OpenCV的Mat对象，以便可以使用OpenCV对图像进行处理。

相关问题

opencv 二值图像转qgraphicsitem

在OpenCV中处理图像后转换成QGraphicsItem通常涉及到两个步骤：首先，你需要将图像数据从OpenCV格式转换为Qt可以理解的格式，然后创建一个QGraphicsPixmapItem来显示这个图像。

1. **图像转换**:
- 使用`cv::Mat`读取OpenCV的二值图像（通常是灰度图像通过阈值处理得到）。例如：

     cv::Mat binaryImage = cv::imread("binary_image.png", cv::IMREAD_GRAYSCALE);
     cv::threshold(binaryImage, binaryImage, 128, 255, cv::THRESH_BINARY); // 对图像进行二值化处理

2. **转换到 QPixmap**:
- 将`cv::Mat`转换为Qt的`QImage`，因为`QGraphicsPixmapItem`需要`QPixmap`作为其内容：

     QImage qimage(binaryImage.data, binaryImage.cols, binaryImage.rows, QImage::Format_Indexed8);
     qimage.setColorTable(cv::Mat(brightnessRange).col(0)); // 或者使用自定义颜色表

- 然后创建`QPixmap`：

     QPixmap pixmap(qimage);

3. **创建QGraphicsPixmapItem**:
- 最后，你可以创建一个`QGraphicsPixmapItem`并将其添加到`QGraphicsScene`中：

     QGraphicsPixmapItem* pixmapItem = new QGraphicsPixmapItem(pixmap);
     scene.addItem(pixmapItem);

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。