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Version: v24.05

Use Multiple Cameras

In this tutorial, we learn how to access 2 cameras and obtain their images via ion-kit based on the previous tutorial

Prerequisite​

  • ionpy
  • numpy
  • OpenCV
pip3 install -U pip
pip3 install opencv-python
pip3 install numpy
pip3 install ion-python==1.8.10

Tutorial​

Get Device Information​

To display images with ionpy, we need to get the following information of the device.

  • Width
  • Height
  • PixelFormat

The previous tutorial or arv-tool-0.8 will help to get these values.

Build a pipeline​

Import the modules, and set up the pipeline are the same as tutorial that display 1-camera image.

Allow BB to access 2 cameras​

While the structurte of BB is the same as the tutorial that display 1-camera image, we need some small changes to access two devices.

We need to set Param of BB image_io_u3v_cameraN_u<bit-depth>x<dimension> called num_devices to 2 so that it will try to detect 2 cameras connected to the host machine.

num_device = 2
num_devices = Param('num_devices', str(num_device))
node = builder.add(bb_name)\
    .set_param([num_devices, frame_sync, realtime_display_mode, ])

Since this is the only one BB in our pipeline, output port of the node can be the output port of the pipeline, and we name is output_p.

Now, the BB is ready to access and control 2 cameras.

Similarly, output requires 2 Buffers to store two camera images that BB obtains. Therefore, the number of Buffer to append the output List is 2 as follws.

outputs = []
output_datas = []
output_size = (height, width, )
if pixelformat == "RGB8":
    output_size += (3,)
for i in range(num_device):
    output_datas.append(np.full(output_size, fill_value=0, dtype=data_type))
    outputs.append(Buffer(array= output_datas[i]))
# set I/O ports
for i in range(num_device):
    output_p[i].bind(outputs[i])

Execute the pipeline​

The pipeline is now designed for 2 cameras, so builder.run() can be executed same as the signle-camera tutorial.

Display with OpenCV​

outputs is the List having 2 buffers. Each buffer is stored into numpy array, and we can access it by index (output_datas[i]).

while(user_input == -1):
    # running the builder
    builder.run()
    for i in range(num_device):
        output_datas[i] *= coef
        cv2.imshow("img" + str(i), output_datas[i])
    user_input = cv2.waitKeyEx(1)

Do not forget to destroy windows that displayed the image after for loop.

cv2.destroyAllWindows()

Complete code​

Complete code used in the tutorial is here.