picamera2 0.2.2

The libcamera-based Python interface to Raspberry Pi cameras, based on the original Picamera library

Picamera2

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WARNING! Picamera2 is currently available here as an alpha release. This means there may still be some issues, but we're hoping for feedback that can lead to further improvements. Consequently the API cannot be expected to be completely stable, but nor will we be making changes to it without due consideration.

Picamera2 is the libcamera-based replacement for Picamera which was a Python interface to the Raspberry Pi's legacy camera stack. Picamera2 also presents an easy to use Python API.

For the time being, the documentation here is mostly based on a number of supplied example programs. These are listed and discussed below.

Installation

These instructions are for a fresh 32-bit Bullseye image running on a Pi 4B but should work on other platforms too.

All the necessary packages can now be installed via apt and pip3, so the following should suffice. Please run sudo apt update and sudo apt upgrade first if you have not done so for some time. Then:

sudo apt install -y python3-libcamera python3-kms++
sudo apt install -y python3-pyqt5 python3-prctl libatlas-base-dev ffmpeg python3-pip
pip3 install numpy --upgrade
pip3 install picamera2

Or If you don't want the GUI dependencies:

sudo apt install -y python3-libcamera python3-kms++
sudo apt install -y python3-prctl libatlas-base-dev ffmpeg libopenjp2-7 python3-pip
pip3 install numpy --upgrade
NOGUI=1 pip3 install picamera2

Should you want to rebuild libcamera from source, please follow these instructions.

This README file and a set of numerous small examples can be found in the GitHub repository. Moreover you can run the GitHub version of Picamera2 by cloning it and adding /home/pi/picamera2 to your PYTHONPATH variable (assuming you cloned it to /home/pi).

Picamera2 on Pi 3 and ealier devices

On Pi 3 and earlier devices it will be necessary to enable Glamor if you want to use the preview window implementations that run under X-Windows. To enable Glamor, run sudo raspi-config, choose Advanced Options and enable Glamor graphic accleration.

You do not need to enable Glamor if you want to use Picamera2 without a preview window, or if you use the DRM/KMS preview implementation (which runs without X-Windows). In general, on lower powered Pi devices (especially 512MB devices), running without X-Windows will be beneficial to performance.

FFmpeg

We have suggested installing FFmpeg in order to use Picamera2. It is, however, only required to support certain complex file types, such as writing output to mp4 files. If you do not require this functionality then FFmpeg is not required, and you should avoid trying to use the FfmpegOutput class.

We note that it is installed by default in Raspberry Pi OS, but not in Raspberry Pi OS Lite.

OpenCV

OpenCV is not a requirement for Picamera2, though a number of the supplied examples use it. It can be installed from apt as follows. Normally this should avoid the very long build times that can sometimes be required by other methods.

sudo apt install -y python3-opencv
sudo apt install -y opencv-data

TensorFlow Lite

There are some examples that use TensorFlow Lite. This can be installed with

pip3 install tflite-runtime

Building libcamera from source

If you do need to build libcamera from source, please follow the standard instructions but with the following two differences:

1. Clone the picamera2 branch from the libcamera repo https://github.com/raspberrypi/libcamera.git (not from the usual location). You can use the command

git clone --branch picamera2 https://github.com/raspberrypi/libcamera.git

2. When configuring meson, add the option -Dpycamera=enabled.

Contributing

We are happy to receive pull requests that will fix bugs, add features and generally improve the code. Pull requests should be:

• Restricted to one change or feature each. Please try to avoid "drive-by fixes" especially in a larger set of changes, as it can make them harder to review.
• The commit history should consist of a number of commits that are as easy to review as possible. In particular this means:
  • Where one commit is fixing errors in an earlier commit in the set, please simply merge them.
  • Where a commit is reverting a commit from earlier in the set, please remove the commit entirely.
  • Please avoid adding merge commits or any other unnecessary commits.
  • The commit message should have a short single line at the top which is nonetheless as descriptive as possible. After that we encourage more lines explaining in a little more detail exactly what has been done.
  • In general, we don't need to see all the trials, errors and bug-fixes that went into this change, we only want to understand how it works now!
  • Try to ensure that the automated tests are working after all the commits in the set. This avoids other developers going back to an arbitrary earlier commit and finding that things don't work. There can be occasions when other problems cause test failures beyond our control, so we'll just have to remain alert to these and work around them as best we can.
• Where changes are likely to be more involved, or may change public APIs, authors should start a discussion with us first so that we can agree a good way forward.
• Before submitting a pull request, please ensure that all the automated tests are passing. They can be run using the tools/run_tests script. Please use tools/run_tests --help for more information.
• Any documentation should be updated accordingly. New examples and tests should be included wherever possible. Also consider making an entry in the change log.
• The author of the pull request needs to agree that they are donating the work to this project and to Raspberry Pi Ltd., so that we can continue to distribute it as open source to all our users. To indicate your agreement to this, we would ask that you finish commit messages with a blank line followed by Signed-off-by: Your Name <[e-mail]>.
• We'd like to conform to the common Python PEP 8 coding style wherever possible. To facilitate this we would recommend putting

#!/bin/bash

exec git diff --cached | ./tools/checkstyle.py --staged

into your .git/hooks/pre-commit file. We note that there are some occasions when other formatting is actually better in which case please use that in spite of the style checker, but do note this in your pull request so that we understand.

Thank you!

How Picamera2 Works

Readers are recommended to refer to the supplied examples in conjunction with the descriptions below.

Opening the Camera

The camera system should be opened as shown.

from picamera2 import Picamera2

picam2 = Picamera2()

Multi-camera support will be added in a future release.

Configuring the Camera

The imaging hardware on the Pi is capable of supplying up to 3 image streams to an application.

1. There is always a main stream.
2. Optionally there may be a second low resolution stream (referred to as the lores stream). Both dimensions of this stream must be no larger than the main stream. The lores stream must always have a YUV (not RGB) type of pixel format - this is a hardware limitation.
3. Optionally an application may request a raw stream, consisting of the raw Bayer data received from the sensor before processing by the ISP.

After opening the camera, Picamera2 must be configured with the Picamera2.configure method. It provides three methods for generating suitable configurations:

• Picamera2.preview_configuration generates configurations normally suitable for camera preview.
• Picamera2.still_configuration generates configurations normally suitable for still image capture.
• Picamera2.video_configuration generates configurations for video recording.

In all cases, these functions can generate configurations with no user arguments at all. Otherwise the user may supply the following parameters:

• main - description for the main stream. These are supplied as dictionaries. The most common entries are for the image size ("size") and pixel format ("format"). A main stream is always generated, whether or not this parameter is supplied.
• lores - request a low resolution stream. This argument has the same format as main. If not specified, no low resolution stream will be generated.
• raw - request a raw stream. Again this argument has the same format, though the pixel format ("format") will be supplied for you. If not specified, no raw stream will be available.

All three methods optionally also allow the following to be specified. They apply to all the configured streams.

• transform - the images may be horizontally or vertically flipped.
• colour_space - the colour space of the returned images (ignored for any raw stream).
• buffer_count - the number of buffers to allocate. The camera system always requires buffers to be available when the sensor starts to deliver a new frame, so having too few buffers can cause frames to be dropped.

These methods return the camera configuration. This may be further amended by the application before passing it to the Picamera2.configure function.

Examples:

Configure a default resolution preview.

config = picam2.preview_configuration()
picam2.configure(config)

Configure for a full resolution capture.

config = picam2.still_configuration()
picam2.configure(config)

Here we configure both a VGA preview (main) stream, and a QVGA low resolution (lores) stream.

config = picam2.preview_configuration(main={"size": (640, 480)},
                                      lores={"size": (320, 240), "format": "YUV420"})
picam2.configure(config)

Ask for an additional raw stream at the sensor resolution. This will force the camera to run at the maximum resolution, probably at a reduced framerate. Without the size parameter (just raw={}) the raw frame would be the one that libcamera would naturally choose for the resolution of the main stream (probably much smaller).

config = picam2.preview_configuration(raw={"size": picam2.sensor_resolution})
picam2.configure(config)

Rotate all the images by 180 degrees.

config = picam2.preview_configuration(transform=libcamera.Transform(hflip=1, vflip=1))
picam2.configure(config)

Still image capture normally configures only a single buffer, as this is all you need. But if you're doing some form of burst capture, increasing the buffer count may enable the application to receive images more quickly.

config = picam2.still_configuration(buffer_count=3)
picam2.configure(config)

Driving the Camera

The Picamera2 class implements most of the camera functionality, however, it does not run an event loop which feeds image buffers in and out of libcamera. The application has a number of choices:

• Use the QtPreview or QtGlPreview class. This starts a preview window implemented using PyQt and the Qt event loop drives the camera.
• Use the DrmPreview class. This should be used to render a preview window using DRM/KMS (i.e. when not running X Windows).
• Use the NullPreview class. This class actually generates no preview window at all and merely supplies an event loop that drives the camera.
• In a Qt application, the QPicamer2 or QGlPicamera2 widgets are provided and automatically use the Qt event loop to drive the camera.

In the final case (running inside a Qt application), start_preview should not be called at all as the Qt exec() function supplies the event loop.

In all other cases, the user should call start_preview before starting Picamera2 with the Picamera2.start method. The call to start_preview may be omitted in which case Picamera2 will automatically start the "null" preview (with no preview window) when Picamera2.start is called.

Example:

from picamera2 import Picamera2, Preview

picam2 = Picamera2()
picam2.start_preview(Preview.QTGL)

config = picam2.preview_configuration()
picam2.configure(config)

picam2.start()

Note that

from picamera2.previews import QtGlPreview
picam2.start_preview(QtGlPreview())

is equivalent to picam2.start_preview(Preview.QTGL).

To use the DRM preview window, use picam2.start_preview(Preview.DRM) instead.

To use the Qt (non-GL) preview window, use picam2.start_preview(Preview.QT) instead.

For no preview window at all, use picam2.start_preview() or picam2.start_preview(Preview.NULL). Alternatively the call to start_preview may be omitted entirely.

Preview windows can be be assigned a particular location on the screen (picam2.start_preview(Preview.QTGL, x=100, y=200)).

Please refer to the supplied examples for more information.

Overlays

All the preview window implementations support simply overlays, which allows images with an alpha channel to be blended on top of the camera image. The facility is intended for adding simple graphics over the camera image rather than for complex high frame rate animations.

An overlay must be a numpy array of shape (height, width, 4). Every pixel consists of 4 values, in the order RGBA, so the final value of the four is the alpha channel, and must also have the datatype numpy.uint8.

Once the preview has been started using the start_preview method, an overlay may be applied using the Picamera2.set_overlay method. The overlay is copied internally and so the application may continue to update the overlay as soon as the set_overlay call has returned. Note that the overlay on the display is only re-drawn when the next camera frame arrives.

Overlays will always be stetched to cover the complete camera image. For example:

from picamera2 import Picamera2, Preview
import numpy as np

picam2 = Picamera2()
picam2.configure(picam2.preview_configuration({"size": (640, 480)}))
picam2.start_preview(Preview.QTGL)
overlay = np.zeros((200, 200, 4), dtype=np.uint8)
overlay[:100, 100:] = (255, 0, 0, 64)  # red
overlay[100:, :100] = (0, 255, 0, 64)  # green
overlay[100:, 100:] = (0, 0, 255, 64)  # blue
picam2.set_overlay(overlay)
picam2.start()

Requests and Capturing

libcamera works by receiving requests from an application and returning them once they have been completed. Most simply, a request contains a buffer for each of the configured streams and completing the request means filling each of the buffers with an image from the camera. All the images in a request are created from a single raw frame from the sensor.

In the Picamera2 framework most of this hidden from the application, and users can concentrate simply on capturing images. But if they want, applications can capture an entire completed request. From this it can extract images for all the configured streams as well as the metadata, namely the parameters of the system (exposure time, gain, and so on) used to capture those images.

When an application captures a request, that request can no longer be re-used by the camera system until the application returns it using the request's release method. If requests are repeatedly captured and not returned, the risk is that camera frames will start to be dropped and ultimately it can stall completely.

A limitation of capturing requests is that they only remain valid until the camera is re-configured (by another call to Picamera.configure), or the Picamera2 object is deleted.

To capture a PIL image:

image = picam2.capture_image()
# The image is a copy and belongs to the application here.

To capture an image as a numpy array:

np_array = picam2.capture_array()
# Also a copy.

If the lores stream is configured for YUV420 images, these can also be returned as 2-d arrays. In this case the height of the array is 50% greater so as to accommodate the U and V planes. For OpenCV users, there is an efficient function to convert this into RGB format:

yuv420 = picam2.capture_array("lores")
rgb = cv2.cvtColor(yuv420, cv2.COLOR_YUV420p2RGB)

Normally, any end-of-row padding is removed from the result returned by make_array, though in the case of YUV420 images it is left in situ. Conversion to RGB (for example, for feeding to other OpenCV functions or to TensorFlow) is quite a common use case, and padding may be more conveniently removed once in RGB format.

For the raw image:

raw_np_array = picam2.capture_array("raw")

To capture a complete request:

request = picam2.capture_request()
# This request has been taken by the application and can now be used, for example
request.save("main", "test.jpg")
# Once done, the request must be returned.
request.release()

When switching to a full resolution capture, it's common to re-start the camera in preview mode afterwards. When capturing a whole request, however, you can't re-start the camera until you're finished with that request:

capture_config = picam2.still_configuration()
request = picam2.switch_mode_capture_request_and_stop(capture_config)
# The camera is stopped but we can use the request. Then return the request:
request.release()
# And the camera could be re-configured and restarted.
preview_config = picam2.preview_configuration()
picam2.configure(preview_configuration)
picam2.start()

DNG Support

Picamera2 supports saving DNG files through the PiDNG library. When capturing DNGs, you will need to specify that you want a raw stream. For example, if the camera is running a normal preview, the following snippet would switch to full resolution mode (with a raw stream) and capture a DNG file.

capture_config = picam2.still_configuration(raw={})
picam2.switch_mode_and_capture_file(capture_config, "full-res.dng", name="raw")

The Tuning File

Being Python-based, Picamera2 is a good environment for inspecting and altering the tuning files that Raspberry Pi ships for all its supported cameras.

A tuning file lists all the parameters needed for a specific camera to produce images of acceptable quality, configuring both hardware (specifically of the ISP, or Image Signal Processor) and also of the 3A and other real-time algorithms that run to control the behaviour of the camera system.

This is a more advanced topic, so readers are referred to the Raspberry Pi Camera Algorithm and Tuning Guide, and to chapter 5 in particular, for a more detailed explanation. We also provide an example tuning_file.py which illustrates one simple way to use this feature.

Examples

A number of small example programs are provided in the examples folder. Users who are new to Picamera2 should probably start with the following:

• preview.py - how to start a preview window. Also check out the other preview_*.py examples.
• capture_jpeg.py - how to capture a JPEG image.
• capture_full_res.py - how to capture a full resolution JPEG image. Also check out the other capture_*.py examples.

Once you've looked at these, you might like to investigate some further topics:

• capture_image_full_res.py - you can capture PIL images or numpy arrays, not just JPEG or PNG files.
• metadata.py - how to find out the camera's current parameters. Also look at metadata_with_image.py to see how to capture the exact parameters that apply to a specific image.
• controls.py and exposure_fixed.py for how to set the camera controls for yourself, either while it's running or when it starts.
• capture_video.py shows you how to capture an h.264 video file.

Finally, for more advanced use cases:

• If you're interested in using OpenCV with Python cv2, please look at, for example, opencv_face_detect.py.
• If you'd like to embed camera functionality in a PyQt application, please look at app_capture.py.
• To find out how and why you might want to use a low resolution ("lores") stream, please look at opencv_face_detect_2.py.
• To capture raw camera buffers, please see raw.py.
• For an example of how you might capture and stream h.264 video over the network, please check capture_stream.py.
• To capture a DNG and JPEG file concurrently (that is, the JPEG is made from the same raw data is the DNG), please look at capture_dng_and_jpeg.py.
• Users wishing to capture mp4 files (rather than raw h.264 streams) should check out mp4_capture.py. There's also an example showing how to record an audio stream.

capture_dng_and_jpeg.py

This example switches to full resolution capture mode after a short delay and captures a request. Note how we have to specify that we want a raw stream (raw={}), otherwise no raw image buffers would be available to us. All the images in the request are made from the same raw camera data, so we're now free to save both of them to disk.

app_capture.py

This is a very simple PyQt application. It creates a camera preview widget that uses OpenGL hardware acceleration (the QGlPicamera2 widget). This widget drives the camera processing. A button is hooked up to do a full resolution JPEG capture - the implementation of this is all hidden within the Picamera2 object which has processing requests forwarded to it by the QGlPicamera2 widget.

We also use the Picamera2 object's request callback, which is called whenever an image is about to be displayed in the preview pane. Here we just fetch the camera's current parameters (the metadata) and write all of the information to a text pane.

There is a second version of this app_capture2.py that is very similar but uses the Qt signal/slot mechanism to regain control when the capture operation is complete. Camera operations may not block the main Qt thread because that is the same thread that actually handles camera activity, so this would result in a deadlock.

capture_full_res.py

This application starts a preview window and then performs a full resolution JPEG capture. The preview normally uses one of the camera's faster readout modes, so we have to pick a separate capture mode, and switch to it, for the final capture.

The Picamera2.preview_configuration method is best for selecting camera modes suitable for previewing, and Picamera2.still_configuration is best for selecting high resolution still capture modes.

capture_image_full_res.py

This is very similar to capture_full_res.py except that we capture a PIL ("Python Image Library") image object rather than a JPEG.

capture_jpeg.py

This is the simplest way to capture a JPEG. It starts a preview window and then captures a JPEG, whilst still running the camera in its preview mode. It does not switch over to the full resolution capture mode (for which see capture_full_res.py).

capture_png.py

Like capture_jpeg.py, only it saves a PNG file rather than a JPEG.

capture_stream.py

Only slightly more complicated than capturing video to a file, we can route the output of the hardware video encoder to a network socket instead.

capture_video.py

This is pretty much the simplest way to capture and encode the images into an h.264 file.

controls.py

Camera control values can be updated while the camera is running. In this example we fix the camera's exposure time, gain and white balance values so that, after the controls have been set, the camera will no longer adapt these parameters to the operating environment.

controls_2.py

This is an easier way to fix the gains and white balance - the AGC/AEC and AWB algorithms can simply be turned off.

exposure_fixed.py

Camera control values can be updated while the camera is running, but they can also be set when the camera starts, as we see here. Setting the exposure time and gain when it starts is particularly useful, because it means that the very first frame we capture will have these values. (Once the camera is running, it normally takes "a few frames" for such values to take effect.)

Note that the exposure time control takes an integer and not a floating point value. The analogue gain, however, will accept either.

metadata.py

While the camera is running, we can query its current settings, known as the metadata.

metadata_with_image.py

In metadata.py we see how to get the current state of the camera. But in actual fact, these values are obtained from a particular image. If we then ask to capture an image we're not guaranteed that this new image will have exactly the same values. We can solve this problem by capturing a request instead.

Libcamera works by having applications submit requests, and it returns them to the application once it has filled all the image buffers in the request. All the images in a request come from the same camera image. Picamera2 lets us capture one of these requests, so this contains all the images for the streams that we configured, and the metadata for that capture.

In this example, we capture a whole request. This request now belongs to us and libcamera cannot refill it until we return that request. So we now extract images and the associated metadata, knowing that they belong together. Once we're done, we must return the request to libcamera, using the release method. If we keep capturing requests without returning them, the camera system may start to drop frames and will ultimately stall completely if it has no buffers left in which to put camera images.

mjpeg_server.py

This is a simple MJPEG web server, derived from one of the old Picamera examples. You will need to install simplejpeg (pip3 install simplejpeg) as the existing JPEG encoders in PIL and OpenCV are not particularly convenient.

At some point in the future Picamera2 may include built-in support for JPEG encoding, so this example is certainly liable to change.

To try it, just start the server on your Pi and then, on a different computer open a web browser and visit http://<your-Pi's-IP-address>:8000.

mp4_capture.py

We can use FFmpeg to save videos to mp4 format files, rather than as raw h.264 bitstreams, as shown in this example. FFmpeg also allows us to record an audio stream with the video, as shown here.

Using FFmpeg like this leaves the audio/video sync somewhat in the hands of the video encoder and FFmpeg itself, so you may in general find you need to tweak it slightly depending on your precise use case and configuration. The FfmpegOutput class has an audio_sync parameter that allows you to do this.

opencv_face_detect.py

Face detection is very easy using OpenCV (the Python cv2 module). Note how, in this example, we use the NullPreview. This drives the camera system but without displaying a preview window, because we're going to use OpenCV to display the images too.

The image has to be converted to greyscale for face detection, and then we draw face boxes on the original colour images that we display.

opencv_face_detect_2.py

This is a more sophisticated version of opencv_face_detect.py. There are two critical differences.

Firstly, we define a second lower resolution ("lores") image stream. A constraint in the Pi's hardware is that such a stream has to be encoded with YUV colour values rather than RGB. But this is quite useful because we can turn the Y plane of this image into the greyscale image that OpenCV's face detector wants.

Secondly, we're using Picamera2's hardware accelerated preview window, which renders frames at the full camera rate. Luckily, Picamera2 gives us a request callback which is called upon every request before it is shown in the preview window, and runs in the camera thread also at the camera framerate. The callback here has been defined to draw the face boxes directly into the camera's image buffer.

The net effect is to run the preview, with face locations, at the full camera rate, although the actual locations of these face boxes only update at the rate that OpenCV can manage. We can also, as in this example, run the face detection on a different resolution from the preview image.

But please note that we generally advise against doing too much processing within the request callback function.

opencv_mertens_merge.py

In this example we use the NullPreview so as to drive the camera system without a preview window, and supply controls to the start method (as in exposure_fixed.py) so as to get pre-determined exposure values. Finally we use OpenCV's Mertens merge image fusion method to get HDR-like images.

overlay_drm.py

This example, and the other similar overlay examples, show very trivially how to superimpose an alpha-blended overlay over the camera image. All the preview window implementations share the same set_overlay interface.

preview.py

Starts a camera preview window. In this case we use the QtGlPreview to display the images. This implementation uses GPU hardware acceleration which is therefore normally the most efficient way display them (through X Windows).

preview_drm.py

Displays a preview window when running without X Windows, that is, in console mode (or you can usually suspend X Windows with Ctrl+Alt+F1). It uses Linux DRM/KMS to render the images. On a lower powered device such as a Pi Zero, this would be the recommended way to show camera images with good performance.

preview_x_forwarding.py

Whilst we normally recommend the use of the QtGlPreview, the GPU hardware acceleration doesn't work with X forwarding. This would typically include users who are logged into their Pi via ssh and wish to display the preview back on their local machine. In these cases the QtPreview should be used instead. It uses software rendering and is therefore more taxing on the Pi's CPU cores.

raw.py

This example requests the raw stream alongside the main one, and shows how you would capture an image.

rotation.py

Picamera2 allows horizontal or vertical flips to be applied to camera images, or both together to give a 180 degree rotation.

still_during_video.py

Here we show how to record a lower resolution video whilst simultaneously capturing a higher resolution still image. The video encoder can be instructed to capture the low resolution (lores) stream instead of the main stream, which in this case is set to be half the sensor's maximum resolution.

The reason for this choice is that, at this resolution, the camera can still deliver us frames at 30fps. If the Pi has enough memory we could technically request the full resolution, giving us very high resolution JPEGs, however the camera would then be restricted to run at a lower framerate.

switch_mode.py

Example of how to switch between one camera mode and another. In this case we reduce the number of very large buffers (for the "other" configuration) down to 3 to save some memory.

switch_mode_2.py

Alternative example of how to switch camera modes (like switch_mode.py). Under the hood both methods are in fact doing exactly the same thing.

tuning_file.py

The camera tuning file is actually a JSON file, so it can be readily loaded into a Python application. We provide a function load_tuning_file to do exactly this, and then the tuning file object, a Python dictionary, is easily manipulated.

When the Picamera2 instance is created, it can optionally be passed either the file name of a custom camera tuning file, or a Python tuning file object. In this example we substitute the default exposure profile for one that prefers longer exposure times, and only ramps the analogue gain as a last resort. For more information on the algorithms and parameters permitted in the tuning file, please consult the Raspberry Pi Camera Algorithm and Tuning Guide.

yuv_to_rgb.py

A limitation of the second "lores" output stream is that it has to be in a YUV format. Sometimes it might be convenient to have a reasonably sized preview window, but a much smaller RGB version (for example for passing to a neural network). This example shows how to convert a YUV420 image to the interleaved RGB form that OpenCV and TensorFlow Lite normally prefer.

You could use the YUV420_to_RGB function supplied with Picamera2 which converts the low resolution image into a half-size RGB image (that is, matching the resolution of the U and V planes). However, if you have OpenCV there are some more efficient alternatives, providing you are happy with its treatment of colour primaries and YCbCr encoding matrices. In this example, we call cv2.cvtColor with the cv2.COLOR_YUV420p2RGB parameter. When using OpenCV in this way, there may be padding on the end of the rows of the YUV420 image which you would have to trim off after the conversion.

zoom.py

Digital zoom is achieved using the ScalerCrop control, which takes a rectangle in the form (offset_x, offset_y, width, height) to specify which part of the full image we see in the output. The coordinates of this rectangle always correspond to the sensor at full resolution, which can be obtained using the Picamera2.sensor_resolution property.

The current value of the ScalerScrop can be read in the normal way from the camera's metadata. A value of all zeroes should be taken as meaning the full sensor resolution.

Notice how this example synchronises ScalerCrop updates with frames from the camera. Otherwise all the controls would be set at once, with the final value overwriting all the others - so the zoom, rather than occurring gradually, would happen in a single large jump.