树莓派tensorflow lite摄像头识别数据导出并用pyqt界面显示的代码

抱歉，我是一名语言模型AI，我不能提供实时的代码编写。但是，我可以提供一些参考：

1. 安装TensorFlow Lite和OpenCV库

pip install tensorflow-lite
pip install opencv-python

2. 摄像头识别代码

import cv2
import numpy as np
import tensorflow.lite as tflite

# Load the TFLite model and allocate tensors.
interpreter = tflite.Interpreter(model_path='model.tflite')
interpreter.allocate_tensors()

# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# Open the camera.
cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    # Preprocess the image.
    img = cv2.resize(frame, (224, 224))
    img = img / 255.
    img = np.expand_dims(img, axis=0).astype(np.float32)

    # Run inference.
    interpreter.set_tensor(input_details[0]['index'], img)
    interpreter.invoke()
    output_data = interpreter.get_tensor(output_details[0]['index'])
    output = np.argmax(output_data)

    # Display the result.
    cv2.putText(frame, str(output), (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
    cv2.imshow('frame', frame)

    if cv2.waitKey(1) == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

3. 导出识别数据

import csv

with open('data.csv', 'w', newline='') as csvfile:
    writer = csv.writer(csvfile)
    writer.writerow(['image', 'label'])

    # Load the TFLite model and allocate tensors.
    interpreter = tflite.Interpreter(model_path='model.tflite')
    interpreter.allocate_tensors()

    # Get input and output tensors.
    input_details = interpreter.get_input_details()
    output_details = interpreter.get_output_details()

    # Loop through the images.
    for i in range(10):
        img = cv2.imread(f'image_{i}.jpg')
        img = cv2.resize(img, (224, 224))
        img = img / 255.
        img = np.expand_dims(img, axis=0).astype(np.float32)

        # Run inference.
        interpreter.set_tensor(input_details[0]['index'], img)
        interpreter.invoke()
        output_data = interpreter.get_tensor(output_details[0]['index'])
        output = np.argmax(output_data)

        # Write the data to the CSV file.
        writer.writerow([f'image_{i}.jpg', output])

4. PyQt界面显示代码

from PyQt5.QtCore import Qt
from PyQt5.QtGui import QPixmap
from PyQt5.QtWidgets import QApplication, QLabel, QVBoxLayout, QWidget

# Create the window.
app = QApplication([])
window = QWidget()

# Load the image and the label.
img = QPixmap('image.jpg')
label = QLabel()
label.setPixmap(img)

# Set the layout.
layout = QVBoxLayout()
layout.addWidget(label)
window.setLayout(layout)

# Show the window.
window.show()
app.exec_()

请根据您的需求自行修改代码。