User: import argparse
import os
import numpy as np
import cv2
from surround_view import CaptureThread, MultiBufferManager
import sys
from PyQt5.QtWidgets import QApplication, QLabel, QWidget, QVBoxLayout, QPushButton
from PyQt5.QtGui import QPixmap, QImage
from PyQt5.QtCore import QTimer

# We will save the camera param file to this directory
TARGET_DIR = os.path.join(os.getcwd(), "yaml")
# Default param file
DEFAULT_PARAM_FILE = os.path.join(TARGET_DIR, "camera_params.yaml")


def cvimage_to_pixmap(cv_image):
    height, width, channel = cv_image.shape
    bytes_per_line = 3 * width
    q_image = QImage(cv_image.data, width, height, bytes_per_line, QImage.Format_RGB888).rgbSwapped()
    return QPixmap.fromImage(q_image)


class ImageDisplay(QWidget):
    def __init__(self, args):
        super().__init__()
        self.label = QLabel(self)
        layout = QVBoxLayout()
        layout.addWidget(self.label)
        self.setLayout(layout)
        self.timer = QTimer(self)
        self.timer.timeout.connect(self.update_image)
        self.timer.start(30)

        self.calibrate_button = QPushButton("Calibrate")
        self.calibrate_button.clicked.connect(self.on_calibrate_button_clicked)
        layout.addWidget(self.calibrate_button)

        self.exit_button = QPushButton("Exit")
        self.exit_button.clicked.connect(self.on_exit_button_clicked)
        layout.addWidget(self.exit_button)

        self.objpoints = []  # 3D points in real world space
        self.imgpoints = []  # 2D points in image plane
        self.grid_size = tuple(int(x) for x in args.grid.split("x"))
        self.grid_points = np.zeros((1, np.prod(self.grid_size), 3), np.float32)
        self.grid_points[0, :, :2] = np.indices(self.grid_size).T.reshape(-1, 2)

        self.device = args.input
        self.args = args
        self.img = None
        self.frame_count = 0  # Initialize frame counter

    def update_image(self):
        # Get the latest image from the camera
        img = buffer_manager.get_device(self.device).get().image
        if img is None:
            return  # Skip if there's no image
        self.img = img  # Store the latest image

        # Convert img to QPixmap and set it on the label
        pixmap = cvimage_to_pixmap(img)
        self.label.setPixmap(pixmap)

        # Detect chessboard corners
        self.detect_chessboard_corners()

    def detect_chessboard_corners(self):
        if self.img is None:
            return

        # Increment frame counter
        self.frame_count += 1
        if self.frame_count % self.args.framestep != 0:  # Check if it's the framestep-th frame
            return

        gray = cv2.cvtColor(self.img, cv2.COLOR_BGR2GRAY)
        found, corners = cv2.findChessboardCorners(
            gray,
            self.grid_size,
            cv2.CALIB_CB_ADAPTIVE_THRESH +
            cv2.CALIB_CB_NORMALIZE_IMAGE +
            cv2.CALIB_CB_FILTER_QUADS
        )
        if found:
            term = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.01)
            cv2.cornerSubPix(gray, corners, (5, 5), (-1, -1), term)
            self.imgpoints.append(corners)  # Append corners to imgpoints
            self.objpoints.append(self.grid_points)  # Append grid points
            cv2.drawChessboardCorners(self.img, self.grid_size, corners, found)

    def on_calibrate_button_clicked(self):
        self.calibrate_camera()

    def calibrate_camera(self):
        N_OK = len(self.objpoints)
        if N_OK < 12:
            print("Less than 12 corners (%d) detected, calibration failed" % N_OK)
            return

        K = np.zeros((3, 3))
        D = np.zeros((4, 1))
        rvecs = [np.zeros((1, 1, 3), dtype=np.float64) for _ in range(N_OK)]
        tvecs = [np.zeros((1, 1, 3), dtype=np.float64) for _ in range(N_OK)]
        calibration_flags = (cv2.fisheye.CALIB_RECOMPUTE_EXTRINSIC +
                             cv2.fisheye.CALIB_CHECK_COND +
                             cv2.fisheye.CALIB_FIX_SKEW)

        if self.args.fisheye:
            ret, mtx, dist, rvecs, tvecs = cv2.fisheye.calibrate(
                self.objpoints,
                self.imgpoints,
                (640, 480),  # Change according to your actual resolution
                K,
                D,
                rvecs,
                tvecs,
                calibration_flags,
                (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 1e-6)
            )
        else:
            ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(
                self.objpoints,
                self.imgpoints,
                (640, 480),  # Change according to your actual resolution
                None,
                None)

        if ret:
            fs = cv2.FileStorage(self.args.output, cv2.FILE_STORAGE_WRITE)
            fs.write("resolution", np.int32([640, 480]))  # Change according to your actual resolution
            fs.write("camera_matrix", K)
            fs.write("dist_coeffs", D)
            fs.release()
            print("Successfully saved camera data")
        else:
            print("Calibration failed")

    def on_exit_button_clicked(self):
        print("Quitting...")
        QApplication.quit()


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("-i", "--input", type=int, default=0, help="input camera device")
    parser.add_argument("-grid", "--grid", default="9x6", help="size of the calibrate grid pattern")
    parser.add_argument("-r", "--resolution", default="640x480", help="resolution of the camera image")
    parser.add_argument("-framestep", type=int, default=20, help="use every nth frame in the video")
    parser.add_argument("-o", "--output", default=DEFAULT_PARAM_FILE, help="path to output yaml file")
    parser.add_argument("-fisheye", "--fisheye", action="store_true", help="set true if this is a fisheye camera")
    parser.add_argument("-flip", "--flip", default=0, type=int, help="flip method of the camera")
    parser.add_argument("--no_gst", action="store_true", help="set true if not use gstreamer for camera capture")

    args = parser.parse_args()

    if not os.path.exists(TARGET_DIR):
        os.mkdir(TARGET_DIR)

    global buffer_manager  # Define buffer_manager as global to use in update_image
    device = args.input
    cap_thread = CaptureThread(device_id=device, flip_method=args.flip, resolution=(640, 480), use_gst=not args.no_gst)
    buffer_manager = MultiBufferManager()
    buffer_manager.bind_thread(cap_thread, buffer_size=8)
    if cap_thread.connect_camera():
        cap_thread.start()
    else:
        print("Cannot open device")
        return

    app = QApplication(sys.argv)
    window = ImageDisplay(args)
    window.show()

    # Start the application event loop
    sys.exit(app.exec_())


if __name__ == "__main__":
    main()

Assistant: This conversation is about a Python code that uses OpenCV and PyQt5 to display a camera feed, detect chessboard corners, and calibrate the camera using the corners. The conversation also includes a discussion about understanding the provided code and its usage. In this conversation, I am assisting in explaining the code and answering any related questions.

To help you better understand, I will provide a detailed explanation of the code:

1. The code starts by importing necessary libraries: argparse for command-line parsing, os and shutil for file and directory operations, numpy, cv2 as OpenCV library, surround-view library, and PyQt5 libraries (QtGui, QtWidgets, QApplication, QWidget, QVWidget, QLabel, QHWidget, QPixmap, QImage).

2. Then, it defines the camera param file and sets up the window for displaying camera feed.

3. The `cvimage_to_pixmap(img)` function is defined to convert a numpy array image (from OpenCV) to QPixmap (used in PyQt5).

4. The `ImageDisplay(QWidget)` class is defined to:
   - Create the window layout (widgets)
   - Initialize frame counters, resolution, and grid size
   - Start updating the image every half second
   - Connect buttons to their corresponding functions
   - Accept an input camera from command-line interface
   - Initialize a frame counter to keep track of the captured frames

5. The `update_image()` method:
   - Gets the latest image from the camera
   - Converts the image format from numpy array (from OpenCv) to QPixmap (used in PyQt5)
   - Converts the image format from cv2 RGB to Pillow RGBA
   - Sets the new image on the label
   - If images are available, it calls the frame processing method.

6. The `detect_chessboard_corners()` method:
   - Processes every nth frame for chessboard corner detection
   - If the frame counter is divisible by framestep (set as an argument), the program continues
   - Converts the image to grayscale and uses the OpenCV `findChessboardCorners()` function to find the corners
   - If corners are found, it appends them to the `imgpoints` list and adds another set of camera parameters to the `objpoints` list

7. The 'on_calibrate_button_clicked()' method:
   - Calls the calibration process when the calibrate button is clicked

8. The 'calibrate_camera()' method:
   - Performs a calibration once enough points are gathered
   - If no calibration is performed, it prints an error message
   - It cleans and formats the data
   - Uses the `fisheye.xpectate_extrinsic` function to calibrate the camera
   - If calibration is successful, it saves the data in a .yaml file

9. The main function:
   - Creates the argument parser for command-line arguments
   - Initializes QApplication and ImageDisplay objects
   - Calls app.exec() to start the application event loop

This code demonstrates a way to create a user-friendly application that performs camera calibration using OpenCV, PyQt5, and Python. Do let me know if there's anything specific you would like to know or if you have any questions!
User: 这是一个相机的标定代码。 我需要执行 python run_calibrate_camera.py -i 0 -grid 9x6 -o ./yaml/front.yaml -framestep 20 --resolution 640x480 --fisheye --no_gst python run_calibrate_camera.py -i 2 -grid 9x6 -o ./yaml/back.yaml -framestep 20 --resolution 640x480 --fisheye --no_gst python run_calibrate_camera.py -i 4 -grid 9x6 -o ./yaml/left.yaml -framestep 20 --resolution 640x480 --fisheye --no_gst python run_calibrate_camera.py -i 6 -grid 9x6 -o ./yaml/right.yaml -framestep 20 --resolution 640x480 --fisheye --no_gst来给四个相机标定。 我现在需求是 四个相机分别起四个线程来显示标定过程，并呈现在qt界面里面以2*2的网格布局，（当相机连上就显示，相机没连上就有个占位组件） 因为四个相机deviceid 为0 2 4 6 ，grid ，输出yaml路径等输入参数写死，直接运行py程序 请给我完整代码
Assistant: