Source code for picamera.array

# vim: set et sw=4 sts=4 fileencoding=utf-8:
#
# Python camera library for the Rasperry-Pi camera module
# Copyright (c) 2013-2015 Dave Jones <dave@waveform.org.uk>
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"""
The :mod:`picamera.array` module provides a set of classes which aid in
constructing n-dimensional `numpy`_ arrays from the camera output. In order to
avoid adding a hard dependency on numpy to picamera, the module is not
automatically imported by the main picamera package and must be explicitly
imported.

.. _numpy: http://www.numpy.org/

The following classes are defined in the module:


PiArrayOutput
=============

.. autoclass:: PiArrayOutput
    :members:


PiRGBArray
==========

.. autoclass:: PiRGBArray


PiYUVArray
==========

.. autoclass:: PiYUVArray


PiBayerArray
============

.. autoclass:: PiBayerArray


PiMotionArray
=============

.. autoclass:: PiMotionArray


PiAnalysisOutput
================

.. autoclass:: PiAnalysisOutput
    :members:


PiRGBAnalysis
=============

.. autoclass:: PiRGBAnalysis


PiYUVAnalysis
=============

.. autoclass:: PiYUVAnalysis


PiMotionAnalysis
================

.. autoclass:: PiMotionAnalysis
"""

from __future__ import (
    unicode_literals,
    print_function,
    division,
    absolute_import,
    )

# Make Py2's str and range equivalent to Py3's
native_str = str
str = type('')
try:
    range = xrange
except NameError:
    pass

import io
import warnings

import numpy as np
from numpy.lib.stride_tricks import as_strided

from .exc import PiCameraValueError, PiCameraDeprecated


motion_dtype = np.dtype([
    (native_str('x'),   np.int8),
    (native_str('y'),   np.int8),
    (native_str('sad'), np.uint16),
    ])


def raw_resolution(resolution):
    """
    Round a (width, height) tuple up to the nearest multiple of 32 horizontally
    and 16 vertically (as this is what the Pi's camera module does for
    unencoded output).
    """
    width, height = resolution
    fwidth = (width + 31) // 32 * 32
    fheight = (height + 15) // 16 * 16
    return fwidth, fheight


def bytes_to_yuv(data, resolution):
    """
    Converts a bytes object containing YUV data to a `numpy`_ array.
    """
    width, height = resolution
    fwidth, fheight = raw_resolution(resolution)
    y_len = fwidth * fheight
    uv_len = (fwidth // 2) * (fheight // 2)
    if len(data) != (y_len + 2 * uv_len):
        raise PiCameraValueError(
            'Incorrect buffer length for resolution %dx%d' % (width, height))
    # Separate out the Y, U, and V values from the array
    a = np.frombuffer(data, dtype=np.uint8)
    Y = a[:y_len]
    U = a[y_len:-uv_len]
    V = a[-uv_len:]
    # Reshape the values into two dimensions, and double the size of the
    # U and V values (which only have quarter resolution in YUV4:2:0)
    Y = Y.reshape((fheight, fwidth))
    U = U.reshape((fheight // 2, fwidth // 2)).repeat(2, axis=0).repeat(2, axis=1)
    V = V.reshape((fheight // 2, fwidth // 2)).repeat(2, axis=0).repeat(2, axis=1)
    # Stack the channels together and crop to the actual resolution
    return np.dstack((Y, U, V))[:height, :width]


def bytes_to_rgb(data, resolution):
    """
    Converts a bytes objects containing RGB/BGR data to a `numpy`_ array.
    """
    width, height = resolution
    fwidth, fheight = raw_resolution(resolution)
    if len(data) != (fwidth * fheight * 3):
        raise PiCameraValueError(
            'Incorrect buffer length for resolution %dx%d' % (width, height))
    # Crop to the actual resolution
    return np.frombuffer(data, dtype=np.uint8).\
            reshape((fheight, fwidth, 3))[:height, :width, :]


[docs]class PiArrayOutput(io.BytesIO): """ Base class for capture arrays. This class extends :class:`io.BytesIO` with a `numpy`_ array which is intended to be filled when :meth:`~io.IOBase.flush` is called (i.e. at the end of capture). .. attribute:: array After :meth:`~io.IOBase.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)``. """ def __init__(self, camera, size=None): super(PiArrayOutput, self).__init__() self.camera = camera self.size = size self.array = None def close(self): super(PiArrayOutput, self).close() self.array = None
[docs] def truncate(self, size=None): """ 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 :meth:`~io.IOBase.seek` and :meth:`truncate` as one would with regular :class:`~io.BytesIO` instances. """ if size is not None: warnings.warn( PiCameraDeprecated( 'This method changes the position of the stream to the ' 'truncated length; this is deprecated functionality and ' 'you should not rely on it (seek before or after truncate ' 'to ensure position is consistent)')) super(PiArrayOutput, self).truncate(size) if size is not None: self.seek(size)
[docs]class PiRGBArray(PiArrayOutput): """ 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 :attr:`~PiArrayOutput.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 :meth:`~picamera.camera.PiCamera.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])) """ def flush(self): super(PiRGBArray, self).flush() self.array = bytes_to_rgb(self.getvalue(), self.size or self.camera.resolution)
[docs]class PiYUVArray(PiArrayOutput): """ 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 :attr:`~PiArrayOutput.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 :attr:`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 :meth:`~picamera.camera.PiCamera.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])) .. _ITU-R BT.601: http://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion """ def __init__(self, camera, size=None): super(PiYUVArray, self).__init__(camera, size) self._rgb = None def flush(self): super(PiYUVArray, self).flush() self.array = bytes_to_yuv(self.getvalue(), self.size or self.camera.resolution) @property def rgb_array(self): if self._rgb is None: # Apply the standard biases YUV = self.array.copy() YUV[:, :, 0] = YUV[:, :, 0] - 16 # Offset Y by 16 YUV[:, :, 1:] = YUV[:, :, 1:] - 128 # Offset UV by 128 # YUV conversion matrix from ITU-R BT.601 version (SDTV) # Y U V M = np.array([[1.164, 0.000, 1.596], # R [1.164, -0.392, -0.813], # G [1.164, 2.017, 0.000]]) # B # Calculate the dot product with the matrix to produce RGB output, # clamp the results to byte range and convert to bytes self._rgb = YUV.dot(M.T).clip(0, 255).astype(np.uint8) return self._rgb
[docs]class PiBayerArray(PiArrayOutput): """ Produces a 3-dimensional RGB array from raw Bayer data. This custom output class is intended to be used with the :meth:`~picamera.camera.PiCamera.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 :attr:`~PiArrayOutput.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); 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 :meth:`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). .. _de-mosaicing: http://en.wikipedia.org/wiki/Demosaicing .. _Bayer pattern: http://en.wikipedia.org/wiki/Bayer_filter """ def __init__(self, camera): super(PiBayerArray, self).__init__(camera, size=None) self._demo = None def flush(self): super(PiBayerArray, self).flush() self._demo = None data = self.getvalue()[-6404096:] if data[:4] != b'BRCM': raise PiCameraValueError('Unable to locate Bayer data at end of buffer') # Strip header data = data[32768:] # Reshape into 2D pixel values data = np.frombuffer(data, dtype=np.uint8).\ reshape((1952, 3264))[:1944, :3240] # Unpack 10-bit values; every 5 bytes contains the high 8-bits of 4 # values followed by the low 2-bits of 4 values packed into the fifth # byte data = data.astype(np.uint16) << 2 for byte in range(4): data[:, byte::5] |= ((data[:, 4::5] >> ((4 - byte) * 2)) & 3) data = np.delete(data, np.s_[4::5], 1) # XXX Should test camera's vflip and hflip settings here and adjust self.array = np.zeros(data.shape + (3,), dtype=data.dtype) self.array[1::2, 0::2, 0] = data[1::2, 0::2] # Red self.array[0::2, 0::2, 1] = data[0::2, 0::2] # Green self.array[1::2, 1::2, 1] = data[1::2, 1::2] # Green self.array[0::2, 1::2, 2] = data[0::2, 1::2] # Blue def demosaic(self): if self._demo is None: # XXX Again, should take into account camera's vflip and hflip here # Construct representation of the bayer pattern bayer = np.zeros(self.array.shape, dtype=np.uint8) bayer[1::2, 0::2, 0] = 1 # Red bayer[0::2, 0::2, 1] = 1 # Green bayer[1::2, 1::2, 1] = 1 # Green bayer[0::2, 1::2, 2] = 1 # Blue # Allocate output array with same shape as data and set up some # constants to represent the weighted average window window = (3, 3) borders = (window[0] - 1, window[1] - 1) border = (borders[0] // 2, borders[1] // 2) # Pad out the data and the bayer pattern (np.pad is faster but # unavailable on the version of numpy shipped with Raspbian at the # time of writing) rgb = np.zeros(( self.array.shape[0] + borders[0], self.array.shape[1] + borders[1], self.array.shape[2]), dtype=self.array.dtype) rgb[ border[0]:rgb.shape[0] - border[0], border[1]:rgb.shape[1] - border[1], :] = self.array bayer_pad = np.zeros(( self.array.shape[0] + borders[0], self.array.shape[1] + borders[1], self.array.shape[2]), dtype=bayer.dtype) bayer_pad[ border[0]:bayer_pad.shape[0] - border[0], border[1]:bayer_pad.shape[1] - border[1], :] = bayer bayer = bayer_pad # For each plane in the RGB data, construct a view over the plane # of 3x3 matrices. Then do the same for the bayer array and use # Einstein summation to get the weighted average self._demo = np.empty(self.array.shape, dtype=self.array.dtype) for plane in range(3): p = rgb[..., plane] b = bayer[..., plane] pview = as_strided(p, shape=( p.shape[0] - borders[0], p.shape[1] - borders[1]) + window, strides=p.strides * 2) bview = as_strided(b, shape=( b.shape[0] - borders[0], b.shape[1] - borders[1]) + window, strides=b.strides * 2) psum = np.einsum('ijkl->ij', pview) bsum = np.einsum('ijkl->ij', bview) self._demo[..., plane] = psum // bsum return self._demo
[docs]class PiMotionArray(PiArrayOutput): """ 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 :meth:`~picamera.camera.PiCamera.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 :attr:`~PiArrayOutput.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 :class:`PiMotionAnalysis` class instead. .. _macro-blocks: http://en.wikipedia.org/wiki/Macroblock .. _sum of absolute differences: http://en.wikipedia.org/wiki/Sum_of_absolute_differences """ def flush(self): super(PiMotionArray, self).flush() width, height = self.size or self.camera.resolution cols = ((width + 15) // 16) + 1 rows = (height + 15) // 16 b = self.getvalue() frames = len(b) // (cols * rows * motion_dtype.itemsize) self.array = np.frombuffer(b, dtype=motion_dtype).reshape((frames, rows, cols))
[docs]class PiAnalysisOutput(io.IOBase): """ Base class for analysis outputs. This class extends :class:`io.IOBase` with a stub :meth:`analyse` method which will be called for each frame output. In this base implementation the method simply raises :exc:`NotImplementedError`. """ def __init__(self, camera, size=None): super(PiAnalysisOutput, self).__init__() self.camera = camera self.size = size def writeable(self): return True def write(self, b): return len(b)
[docs] def analyse(self, array): """ Stub method for users to override. """ raise NotImplementedError
[docs]class PiRGBAnalysis(PiAnalysisOutput): """ Provides a basis for per-frame RGB analysis classes. This custom output class is intended to be used with the :meth:`~picamera.camera.PiCamera.start_recording` method when it is called with *format* set to ``'rgb'`` or ``'bgr'``. While recording is in progress, the :meth:`~PiAnalysisOutput.write` method converts incoming frame data into a numpy array and calls the stub :meth:`~PiAnalysisOutput.analyse` method with the resulting array (this deliberately raises :exc:`NotImplementedError` in this class; you must override it in your descendent class). .. warning:: Because the :meth:`~PiAnalysisOutput.analyse` method will be running within the encoder's callback, it must be **fast**. Specifically, it needs to return before the next frame is produced. Therefore, if the camera is running at 30fps, analyse cannot take more than 1/30s or 33ms to execute (and should take considerably less given that this doesn't take into account encoding overhead). You may wish to adjust the framerate of the camera accordingly. The array passed to :meth:`~PiAnalysisOutput.analyse` 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'``). """ def write(self, b): result = super(PiRGBAnalysis, self).write(b) self.analyse(bytes_to_rgb(b, self.size or self.camera.resolution)) return result
[docs]class PiYUVAnalysis(PiAnalysisOutput): """ Provides a basis for per-frame YUV analysis classes. This custom output class is intended to be used with the :meth:`~picamera.camera.PiCamera.start_recording` method when it is called with *format* set to ``'yuv'``. While recording is in progress, the :meth:`~PiAnalysisOutput.write` method converts incoming frame data into a numpy array and calls the stub :meth:`~PiAnalysisOutput.analyse` method with the resulting array (this deliberately raises :exc:`NotImplementedError` in this class; you must override it in your descendent class). .. warning:: Because the :meth:`~PiAnalysisOutput.analyse` method will be running within the encoder's callback, it must be **fast**. Specifically, it needs to return before the next frame is produced. Therefore, if the camera is running at 30fps, analyse cannot take more than 1/30s or 33ms to execute (and should take considerably less given that this doesn't take into account encoding overhead). You may wish to adjust the framerate of the camera accordingly. The array passed to :meth:`~PiAnalysisOutput.analyse` 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. """ def write(self, b): result = super(PiYUVAnalysis, self).write(b) self.analyse(bytes_to_yuv(b, self.size or self.camera.resolution)) return result
[docs]class PiMotionAnalysis(PiAnalysisOutput): """ 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 :meth:`~picamera.camera.PiCamera.start_recording` method. While recording is in progress, the write method converts incoming motion data into numpy arrays and calls the stub :meth:`~PiAnalysisOutput.analyse` method with the resulting array (which deliberately raises :exc:`NotImplementedError` in this class). .. warning:: Because the :meth:`~PiAnalysisOutput.analyse` method will be running within the encoder's callback, it must be **fast**. Specifically, it needs to return before the next frame is produced. Therefore, if the camera is running at 30fps, analyse cannot take more than 1/30s or 33ms to execute (and should take considerably less given that this doesn't take into account encoding overhead). You may wish to adjust the framerate of the camera accordingly. The array passed to :meth:`~PiAnalysisOutput.analyse` 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 analyse(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 :meth:`~picamera.camera.PiCamera.start_recording`. """ def __init__(self, camera, size=None): super(PiMotionAnalysis, self).__init__(camera, size) self.cols = None self.rows = None def write(self, b): result = super(PiMotionAnalysis, self).write(b) if self.cols is None: width, height = self.size or self.camera.resolution self.cols = ((width + 15) // 16) + 1 self.rows = (height + 15) // 16 self.analyse( np.frombuffer(b, dtype=motion_dtype).\ reshape((self.rows, self.cols))) return result