How to Fix OpenCV "Error open VIDEOIO(V4L2:/dev/video0): can't open camera by index" Raspberry Pi

This article delves into a common problem encountered by Raspberry Pi users: capturing images from a webcam using Python. We'll examine a script that works flawlessly on a Windows computer but fails on the Raspberry Pi after the first successful capture.

The script's purpose is simple: to capture images from a webcam on key press and save them in a specified folder. The code is based on a common structure, incorporating the OpenCV library for image processing and threading for efficient camera handling.

Understanding the Issue: A Closer Look at the Error

The error message "open VIDEOIO(V4L2:/dev/video0): can't open camera by index" indicates a problem accessing the webcam. The error occurs after the initial successful image capture, suggesting that the issue lies in the way the webcam object is being handled.

The Code: A Breakdown of the Script

Let's break down the code to identify the root of the problem.

1. Imports and Initial Setup:

from datetime import datetime
import cv2
import time
import queue
import threading

intpath = "/home/pi/Python Images/"
file_Prefix = 'IMG100'
file_Extension = '.png'
    

This section imports necessary libraries and defines path variables for storing captured images.

2. The VideoCapture Class:

class VideoCapture:

    def __init__(self, name):
        self.cap = cv2.VideoCapture(name)
        self.q = queue.Queue()
        t = threading.Thread(target=self._reader)
        t.daemon = True
        t.start()

    def _reader(self):
        while True:
            ret, frame = self.cap.read()
            if not ret:
                break
            if not self.q.empty():
                try:
                    self.q.get_nowait()
                except queue.Empty:
                    pass
            self.q.put(frame)

    def read(self):
        return self.q.get()
    

The VideoCapture class uses threading to read frames from the webcam in a separate thread, ensuring the main thread remains responsive. This is crucial for smooth operation, especially on a resource-constrained device like the Raspberry Pi.

3. The main Function:

  def main():
    while True:
        try:
            windowName = "Live Video Feed"
            cv2.namedWindow(windowName)

            if cv2.waitKey(1) == ord("c"):

                time.sleep(1)
                now = datetime.now()
                formatted_time = now.strftime('%Y-%m-%d %H-%M-%S.%f')[:-3]
                cam = VideoCapture(0)
                frame1 = cam.read()
                cv2.imshow(windowName,frame1)
                cv2.imwrite(intpath + file_Prefix + formatted_time + file_Extension, frame1)
                print(formatted_time)
            else:
                continue
        except:
            pass
    

The main function is the heart of the script. It enters an infinite loop, continuously waiting for a user input (ord("c")). Upon receiving this input, it captures a frame, displays it in a window, saves it to a file, and prints the capture time.

The Root of the Issue: A Case of Improper Resource Management

The culprit is the placement of the cam = VideoCapture(0) line within the while True loop of the main function. Each time the loop iterates, a new VideoCapture object is created. On Windows, this doesn't seem to cause issues, but on the Raspberry Pi, this leads to the error.

Here's why:

• Resource Contention: Creating a new VideoCapture object for each capture attempt results in resource contention. The operating system might not be able to release the previous webcam resource quickly enough before the next instance is created, leading to the error.

• Multithreading: The use of threading in the VideoCapture class makes this problem more pronounced. Each instance of VideoCapture creates a new thread, further straining resources.

The Solution: A Single Instance for Long-Term Access

The solution is simple: Move the creation of the VideoCapture object outside the loop. This ensures a single instance of the object is used throughout the script, eliminating resource contention and allowing the camera to be accessed consistently.

Modified Code:

from datetime import datetime
import cv2
import time
import queue
import threading

intpath = "/home/pi/Python Images/"
file_Prefix = 'IMG100'
file_Extension = '.png'

# Initialize the webcam outside the loop
cam = VideoCapture(0)

class VideoCapture:

    # ... (rest of the code remains the same) ...

def main():
    while True:
        try:
            windowName = "Live Video Feed"
            cv2.namedWindow(windowName)

            if cv2.waitKey(1) == ord("c"):

                time.sleep(1)
                now = datetime.now()
                formatted_time = now.strftime('%Y-%m-%d %H-%M-%S.%f')[:-3]

                # Use the existing cam object
                frame1 = cam.read()
                cv2.imshow(windowName,frame1)
                cv2.imwrite(intpath + file_Prefix + formatted_time + file_Extension, frame1)
                print(formatted_time)
            else:
                continue
        except:
            pass

if __name__ == "__main__":
    main()
    

Conclusion

This case study highlights the importance of managing resources effectively, especially when dealing with hardware components like webcams. By understanding the potential for resource contention and using proper object instantiation techniques, we can create robust and reliable scripts that work seamlessly across different operating systems.

Source: Stackoverflow