Recherche avancée

Sur d’autres sites (4332)

• Can't view and record graph at the same time using FFMpegWriter [closed]

  7 juillet 2024, par Barkın Özer

  So this code is used for graphing and logging sensor data coming from bluetooth ports. I wanted to add an function that will record the graph in mp4 format. In order to achieve this I used ffmpegWriter. The issue is while this code records the graph I can't view the graph at the same time.

  


  import serial
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation, FFMpegWriter
import openpyxl
from datetime import datetime

# Constants
GRAVITY = 9.81  # Standard gravity in m/s²

# Initialize serial connections to HC-06 devices
ser_x_accel = serial.Serial('COM4', 9600, timeout=1)  # X-axis acceleration data
ser_y_angle = serial.Serial('COM11', 9600, timeout=1)  # Y-axis angle data

# Initialize empty lists to store data
x_accel_data = []
y_angle_data = []
timestamps = []

# Initialize Excel workbook
wb = openpyxl.Workbook()
ws = wb.active
ws.title = "Sensor Data"
ws.append(["Timestamp", "X Acceleration (m/s²)", "Y Angle (degrees)"])

# Function to update the plot and log data
def update(frame):
    # Read data from serial connections
    line_x_accel = ser_x_accel.readline().decode('utf-8').strip()
    line_y_angle = ser_y_angle.readline().decode('utf-8').strip()
    
    try:
        # Parse and process X-axis acceleration data
        x_accel_g = float(line_x_accel)  # Acceleration in g read from serial
        x_accel_ms2 = x_accel_g * GRAVITY  # Convert from g to m/s²
        x_accel_data.append(x_accel_ms2)
        
        # Parse and process Y-axis angle data
        y_angle = float(line_y_angle)
        y_angle_data.append(y_angle)
        
        # Append timestamp
        timestamps.append(datetime.now())

        # Limit data points to show only the latest 100
        if len(x_accel_data) > 100:
            x_accel_data.pop(0)
            y_angle_data.pop(0)
            timestamps.pop(0)

        # Log data to Excel with timestamp
        timestamp_str = timestamps[-1].strftime("%H:%M:%S")
        ws.append([timestamp_str, x_accel_data[-1], y_angle_data[-1]])

        # Clear and update plots
        ax1.clear()
        ax1.plot(timestamps, x_accel_data, label='X Acceleration', color='b')
        ax1.legend(loc='upper left')
        ax1.set_ylim([-20, 20])  # Adjust based on expected acceleration range in m/s²
        ax1.set_title('Real-time X Acceleration Data')
        ax1.set_xlabel('Time')
        ax1.set_ylabel('Acceleration (m/s²)')
        ax1.grid(True)

        ax2.clear()
        ax2.plot(timestamps, y_angle_data, label='Y Angle', color='g')
        ax2.legend(loc='upper left')
        ax2.set_ylim([-180, 180])
        ax2.set_title('Real-time Y Angle Data')
        ax2.set_xlabel('Time')
        ax2.set_ylabel('Angle (degrees)')
        ax2.grid(True)

        # Update text boxes with latest values
        text_box.set_text(f'X Acceleration: {x_accel_data[-1]:.2f} m/s²')
        text_box2.set_text(f'Y Angle: {y_angle_data[-1]:.2f}°')
        
        # Save the workbook periodically (every 100 updates)
        if frame % 100 == 0:
            wb.save("sensor_data.xlsx")
        
    except ValueError:
        pass  # Ignore lines that are not properly formatted

# Setup the plots
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))
text_box = ax1.text(0.05, 0.95, '', transform=ax1.transAxes, fontsize=12, verticalalignment='top', bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
text_box2 = ax2.text(0.05, 0.95, '', transform=ax2.transAxes, fontsize=12, verticalalignment='top', bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))

# Animate the plots
ani = FuncAnimation(fig, update, interval=100)  # Update interval of 100ms

# Save the animation as a video file
writer = FFMpegWriter(fps=10, metadata=dict(artist='Me'), bitrate=1800)
ani.save("sensor_data.mp4", writer=writer)

plt.tight_layout()
plt.show()

# Save the workbook at the end of the session
wb.save("sensor_data.xlsx")



  


  I tried using OpenCV to record the graph but then I didn't even got any recording. I think solving this issue with my original code would be a better approach.

  


• Revision 36454 : uniformiser les inputs du formulaire de login

  19 mars 2010, par brunobergot@… — Log

  uniformiser les inputs du formulaire de login

• Compile FFmpeg with x264 for MacOS and Windows on Linux

  9 mars 2023, par RobinFrcd

  I successfully managed to compile a minimal standalone FFmpeg binary to create MP4 videos from JPG images encoded with x264. The binary is 100% functional and is 5.2MB.

  


  To do that, I used :

  


  ./configure \
--disable-everything \
--enable-decoder=mjpeg \
--enable-encoder=libx264 \
--enable-protocol=concat,file \
--enable-demuxer=image2 \
--enable-muxer=mp4 \
--enable-filter=scale \
--enable-gpl \
--enable-libx264 \
--extra-ldexeflags="-static" \
--pkg-config="pkg-config --static"


  


  I now would like to build the macOS and windows binaries directly from my Linux machine. I tried this repo and replaced the config args with mine, but the output exe is 30MB+. And I don't find anything about building for MacOS.

  


  Is there a solution to make this minimal build cross-platform compatible ?