The picamera library provides a set of classes designed to aid in construction of n-dimensional numpy arrays from camera output. In order to avoid adding a hard dependency on numpy to picamera, this module (picamera.array) is not automatically imported by the main picamera package and must be explicitly imported, e.g.:
import picamera
import picamera.array
Base class for capture arrays.
This class extends io.BytesIO with a numpy array which is intended to be filled when flush() is called (i.e. at the end of capture).
After flush() is called, this attribute contains the frame’s data as a multi-dimensional numpy array. This is typically organized with the dimensions (rows, columns, plane). Hence, an RGB image with dimensions x and y would produce an array with shape (y, x, 3).
Resize the stream to the given size in bytes (or the current position if size is not specified). This resizing can extend or reduce the current file size. The new file size is returned.
In prior versions of picamera, truncation also changed the position of the stream (because prior versions of these stream classes were non-seekable). This functionality is now deprecated; scripts should use seek() and truncate() as one would with regular BytesIO instances.
Produces a 3-dimensional RGB array from an RGB capture.
This custom output class can be used to easily obtain a 3-dimensional numpy array, organized (rows, columns, colors), from an unencoded RGB capture. The array is accessed via the array attribute. For example:
import picamera
import picamera.array
with picamera.PiCamera() as camera:
with picamera.array.PiRGBArray(camera) as output:
camera.capture(output, 'rgb')
print('Captured %dx%d image' % (
output.array.shape[1], output.array.shape[0]))
You can re-use the output to produce multiple arrays by emptying it with truncate(0) between captures:
import picamera
import picamera.array
with picamera.PiCamera() as camera:
with picamera.array.PiRGBArray(camera) as output:
camera.resolution = (1280, 720)
camera.capture(output, 'rgb')
print('Captured %dx%d image' % (
output.array.shape[1], output.array.shape[0]))
output.truncate(0)
camera.resolution = (640, 480)
camera.capture(output, 'rgb')
print('Captured %dx%d image' % (
output.array.shape[1], output.array.shape[0]))
If you are using the GPU resizer when capturing (with the resize parameter of the various capture() methods), specify the resized resolution as the optional size parameter when constructing the array output:
import picamera
import picamera.array
with picamera.PiCamera() as camera:
camera.resolution = (1280, 720)
with picamera.array.PiRGBArray(camera, size=(640, 360)) as output:
camera.capture(output, 'rgb', resize=(640, 360))
print('Captured %dx%d image' % (
output.array.shape[1], output.array.shape[0]))
Produces 3-dimensional YUV & RGB arrays from a YUV capture.
This custom output class can be used to easily obtain a 3-dimensional numpy array, organized (rows, columns, channel), from an unencoded YUV capture. The array is accessed via the array attribute. For example:
import picamera
import picamera.array
with picamera.PiCamera() as camera:
with picamera.array.PiYUVArray(camera) as output:
camera.capture(output, 'yuv')
print('Captured %dx%d image' % (
output.array.shape[1], output.array.shape[0]))
The rgb_array attribute can be queried for the equivalent RGB array (conversion is performed using the ITU-R BT.601 matrix):
import picamera
import picamera.array
with picamera.PiCamera() as camera:
with picamera.array.PiYUVArray(camera) as output:
camera.resolution = (1280, 720)
camera.capture(output, 'yuv')
print(output.array.shape)
print(output.rgb_array.shape)
If you are using the GPU resizer when capturing (with the resize parameter of the various capture() methods), specify the resized resolution as the optional size parameter when constructing the array output:
import picamera
import picamera.array
with picamera.PiCamera() as camera:
camera.resolution = (1280, 720)
with picamera.array.PiYUVArray(camera, size=(640, 360)) as output:
camera.capture(output, 'yuv', resize=(640, 360))
print('Captured %dx%d image' % (
output.array.shape[1], output.array.shape[0]))
Produces a 3-dimensional RGB array from raw Bayer data.
This custom output class is intended to be used with the capture() method, with the bayer parameter set to True, to include raw Bayer data in the JPEG output. The class strips out the raw data, constructing a 3-dimensional numpy array organized as (rows, columns, colors). The resulting data is accessed via the array attribute:
import picamera
import picamera.array
with picamera.PiCamera() as camera:
with picamera.array.PiBayerArray(camera) as output:
camera.capture(output, 'jpeg', bayer=True)
print(output.array.shape)
Note that Bayer data is always full resolution, so the resulting array always has the shape (1944, 2592, 3) with the V1 module, or (2464, 3280, 3) with the V2 module; this also implies that the optional size parameter (for specifying a resizer resolution) is not available with this array class. As the sensor records 10-bit values, the array uses the unsigned 16-bit integer data type.
By default, de-mosaicing is not performed; if the resulting array is viewed it will therefore appear dark and too green (due to the green bias in the Bayer pattern). A trivial weighted-average demosaicing algorithm is provided in the demosaic() method:
import picamera
import picamera.array
with picamera.PiCamera() as camera:
with picamera.array.PiBayerArray(camera) as output:
camera.capture(output, 'jpeg', bayer=True)
print(output.demosaic().shape)
Viewing the result of the de-mosaiced data will look more normal but still considerably worse quality than the regular camera output (as none of the other usual post-processing steps like auto-exposure, white-balance, vignette compensation, and smoothing have been performed).
Produces a 3-dimensional array of motion vectors from the H.264 encoder.
This custom output class is intended to be used with the motion_output parameter of the start_recording() method. Once recording has finished, the class generates a 3-dimensional numpy array organized as (frames, rows, columns) where rows and columns are the number of rows and columns of macro-blocks (16x16 pixel blocks) in the original frames. There is always one extra column of macro-blocks present in motion vector data.
The data-type of the array is an (x, y, sad) structure where x and y are signed 1-byte values, and sad is an unsigned 2-byte value representing the sum of absolute differences of the block. For example:
import picamera
import picamera.array
with picamera.PiCamera() as camera:
with picamera.array.PiMotionArray(camera) as output:
camera.resolution = (640, 480)
camera.start_recording(
'/dev/null', format='h264', motion_output=output)
camera.wait_recording(30)
camera.stop_recording()
print('Captured %d frames' % output.array.shape[0])
print('Frames are %dx%d blocks big' % (
output.array.shape[2], output.array.shape[1]))
If you are using the GPU resizer with your recording, use the optional size parameter to specify the resizer’s output resolution when constructing the array:
import picamera
import picamera.array
with picamera.PiCamera() as camera:
camera.resolution = (640, 480)
with picamera.array.PiMotionArray(camera, size=(320, 240)) as output:
camera.start_recording(
'/dev/null', format='h264', motion_output=output,
resize=(320, 240))
camera.wait_recording(30)
camera.stop_recording()
print('Captured %d frames' % output.array.shape[0])
print('Frames are %dx%d blocks big' % (
output.array.shape[2], output.array.shape[1]))
Note
This class is not suitable for real-time analysis of motion vector data. See the PiMotionAnalysis class instead.
Base class for analysis outputs.
This class extends io.IOBase with a stub analyze() method which will be called for each frame output. In this base implementation the method simply raises NotImplementedError.
Provides a basis for per-frame RGB analysis classes.
This custom output class is intended to be used with the start_recording() method when it is called with format set to 'rgb' or 'bgr'. While recording is in progress, the write() method converts incoming frame data into a numpy array and calls the stub analyze() method with the resulting array (this deliberately raises NotImplementedError in this class; you must override it in your descendent class).
Note
If your overridden analyze() method runs slower than the required framerate (e.g. 33.333ms when framerate is 30fps) then the camera’s effective framerate will be reduced. Furthermore, this doesn’t take into account the overhead of picamera itself so in practice your method needs to be a bit faster still.
The array passed to analyze() is organized as (rows, columns, channel) where the channels 0, 1, and 2 are R, G, and B respectively (or B, G, R if format is 'bgr').
Provides a basis for per-frame YUV analysis classes.
This custom output class is intended to be used with the start_recording() method when it is called with format set to 'yuv'. While recording is in progress, the write() method converts incoming frame data into a numpy array and calls the stub analyze() method with the resulting array (this deliberately raises NotImplementedError in this class; you must override it in your descendent class).
Note
If your overridden analyze() method runs slower than the required framerate (e.g. 33.333ms when framerate is 30fps) then the camera’s effective framerate will be reduced. Furthermore, this doesn’t take into account the overhead of picamera itself so in practice your method needs to be a bit faster still.
The array passed to analyze() is organized as (rows, columns, channel) where the channel 0 is Y (luminance), while 1 and 2 are U and V (chrominance) respectively. The chrominance values normally have quarter resolution of the luminance values but this class makes all channels equal resolution for ease of use.
Provides a basis for real-time motion analysis classes.
This custom output class is intended to be used with the motion_output parameter of the start_recording() method. While recording is in progress, the write method converts incoming motion data into numpy arrays and calls the stub analyze() method with the resulting array (which deliberately raises NotImplementedError in this class).
Note
If your overridden analyze() method runs slower than the required framerate (e.g. 33.333ms when framerate is 30fps) then the camera’s effective framerate will be reduced. Furthermore, this doesn’t take into account the overhead of picamera itself so in practice your method needs to be a bit faster still.
The array passed to analyze() is organized as (rows, columns) where rows and columns are the number of rows and columns of macro-blocks (16x16 pixel blocks) in the original frames. There is always one extra column of macro-blocks present in motion vector data.
The data-type of the array is an (x, y, sad) structure where x and y are signed 1-byte values, and sad is an unsigned 2-byte value representing the sum of absolute differences of the block.
An example of a crude motion detector is given below:
import numpy as np
import picamera
import picamera.array
class DetectMotion(picamera.array.PiMotionAnalysis):
def analyze(self, a):
a = np.sqrt(
np.square(a['x'].astype(np.float)) +
np.square(a['y'].astype(np.float))
).clip(0, 255).astype(np.uint8)
# If there're more than 10 vectors with a magnitude greater
# than 60, then say we've detected motion
if (a > 60).sum() > 10:
print('Motion detected!')
with picamera.PiCamera() as camera:
with DetectMotion(camera) as output:
camera.resolution = (640, 480)
camera.start_recording(
'/dev/null', format='h264', motion_output=output)
camera.wait_recording(30)
camera.stop_recording()
You can use the optional size parameter to specify the output resolution of the GPU resizer, if you are using the resize parameter of start_recording().