# 4. Basic Recipes¶

The following recipes should be reasonably accessible to Python programmers of all skill levels. Please feel free to suggest enhancements or additional recipes.

## 4.1. Capturing to a file¶

Capturing an image to a file is as simple as specifying the name of the file as the output of whatever capture() method you require:

import time
import picamera

with picamera.PiCamera() as camera:
camera.resolution = (1024, 768)
camera.start_preview()
# Camera warm-up time
time.sleep(2)
camera.capture('foo.jpg')


Note that files opened by picamera (as in the case above) will be flushed and closed so that when the capture() method returns, the data should be accessible to other processes.

## 4.2. Capturing to a stream¶

Capturing an image to a file-like object (a socket(), a io.BytesIO stream, an existing open file object, etc.) is as simple as specifying that object as the output of whatever capture() method you’re using:

import io
import time
import picamera

# Create an in-memory stream
my_stream = io.BytesIO()
with picamera.PiCamera() as camera:
camera.start_preview()
# Camera warm-up time
time.sleep(2)
camera.capture(my_stream, 'jpeg')


Note that the format is explicitly specified in the case above. The BytesIO object has no filename, so the camera can’t automatically figure out what format to use.

One thing to bear in mind is that (unlike specifying a filename), the stream is not automatically closed after capture; picamera assumes that since it didn’t open the stream it can’t presume to close it either. In the case of file objects this can mean that the data doesn’t actually get written to the disk until the object is explicitly closed:

import time
import picamera

# Explicitly open a new file called my_image.jpg
my_file = open('my_image.jpg', 'wb')
with picamera.PiCamera() as camera:
camera.start_preview()
time.sleep(2)
camera.capture(my_file)
# Note that at this point the data is in the file cache, but may
# not actually have been written to disk yet
my_file.close()
# Now the file has been closed, other processes should be able to
# read the image successfully


Note that in the case above, we didn’t have to specify the format as the camera interrogated the my_file object for its filename (specifically, it looks for a name attribute on the provided object).

## 4.3. Capturing to a PIL Image¶

This is a variation on Capturing to a stream. First we’ll capture an image to a BytesIO stream (Python’s in-memory stream class), then we’ll rewind the position of the stream to the start, and read the stream into a PIL Image object:

import io
import time
import picamera
from PIL import Image

# Create the in-memory stream
stream = io.BytesIO()
with picamera.PiCamera() as camera:
camera.start_preview()
time.sleep(2)
camera.capture(stream, format='jpeg')
# "Rewind" the stream to the beginning so we can read its content
stream.seek(0)
image = Image.open(stream)


## 4.4. Capturing to an OpenCV object¶

This is another variation on Capturing to a stream. First we’ll capture an image to a BytesIO stream (Python’s in-memory stream class), then convert the stream to a numpy array and read the array with OpenCV:

import io
import time
import picamera
import cv2
import numpy as np

# Create the in-memory stream
stream = io.BytesIO()
with picamera.PiCamera() as camera:
camera.start_preview()
time.sleep(2)
camera.capture(stream, format='jpeg')
# Construct a numpy array from the stream
data = np.fromstring(stream.getvalue(), dtype=np.uint8)
# "Decode" the image from the array, preserving colour
image = cv2.imdecode(data, 1)
# OpenCV returns an array with data in BGR order. If you want RGB instead
# use the following...
image = image[:, :, ::-1]


## 4.5. Capturing timelapse sequences¶

The simplest way to capture long time-lapse sequences is with the capture_continuous() method. With this method, the camera captures images continually until you tell it to stop. Images are automatically given unique names and you can easily control the delay between captures. The following example shows how to capture images with a 5 minute delay between each shot:

import time
import picamera

with picamera.PiCamera() as camera:
camera.start_preview()
time.sleep(2)
for filename in camera.capture_continuous('img{counter:03d}.jpg'):
print('Captured %s' % filename)
time.sleep(300) # wait 5 minutes


However, you may wish to capture images at a particular time, say at the start of every hour. This simply requires a refinement of the delay in the loop (the datetime module is slightly easier to use for calculating dates and times; this example also demonstrates the timestamp template in the captured filenames):

import time
import picamera
from datetime import datetime, timedelta

def wait():
# Calculate the delay to the start of the next hour
next_hour = (datetime.now() + timedelta(hour=1)).replace(
minute=0, second=0, microsecond=0)
delay = (next_hour - datetime.now()).seconds
time.sleep(delay)

with picamera.PiCamera() as camera:
camera.start_preview()
wait()
for filename in camera.capture_continuous('img{timestamp:%Y-%m-%d-%H-%M}.jpg'):
print('Captured %s' % filename)
wait()


## 4.6. Capturing to a network stream¶

This is a variation of Capturing timelapse sequences. Here we have two scripts: a server (presumably on a fast machine) which listens for a connection from the Raspberry Pi, and a client which runs on the Raspberry Pi and sends a continual stream of images to the server. Firstly the server script (which relies on PIL for reading JPEGs, but you could replace this with any other suitable graphics library, e.g. OpenCV or GraphicsMagick):

import io
import socket
import struct
from PIL import Image

# Start a socket listening for connections on 0.0.0.0:8000 (0.0.0.0 means
# all interfaces)
server_socket = socket.socket()
server_socket.bind(('0.0.0.0', 8000))
server_socket.listen(0)

# Accept a single connection and make a file-like object out of it
connection = server_socket.accept()[0].makefile('rb')
try:
while True:
# Read the length of the image as a 32-bit unsigned int. If the
# length is zero, quit the loop
image_len = struct.unpack('<L', connection.read(4))[0]
if not image_len:
break
# Construct a stream to hold the image data and read the image
# data from the connection
image_stream = io.BytesIO()
image_stream.write(connection.read(image_len))
# Rewind the stream, open it as an image with PIL and do some
# processing on it
image_stream.seek(0)
image = Image.open(image_stream)
print('Image is %dx%d' % image.size)
image.verify()
print('Image is verified')
finally:
connection.close()
server_socket.close()


Now for the client side of things, on the Raspberry Pi:

import io
import socket
import struct
import time
import picamera

# Connect a client socket to my_server:8000 (change my_server to the
# hostname of your server)
client_socket = socket.socket()
client_socket.connect(('my_server', 8000))

# Make a file-like object out of the connection
connection = client_socket.makefile('wb')
try:
with picamera.PiCamera() as camera:
camera.resolution = (640, 480)
# Start a preview and let the camera warm up for 2 seconds
camera.start_preview()
time.sleep(2)

# Note the start time and construct a stream to hold image data
# temporarily (we could write it directly to connection but in this
# case we want to find out the size of each capture first to keep
# our protocol simple)
start = time.time()
stream = io.BytesIO()
for foo in camera.capture_continuous(stream, 'jpeg'):
# Write the length of the capture to the stream and flush to
# ensure it actually gets sent
connection.write(struct.pack('<L', stream.tell()))
connection.flush()
# Rewind the stream and send the image data over the wire
stream.seek(0)
connection.write(stream.read())
# If we've been capturing for more than 30 seconds, quit
if time.time() - start > 30:
break
# Reset the stream for the next capture
stream.seek(0)
stream.truncate()
# Write a length of zero to the stream to signal we're done
connection.write(struct.pack('<L', 0))
finally:
connection.close()
client_socket.close()


The server script should be run first to ensure there’s a listening socket ready to accept a connection from the client script.

## 4.7. Preview vs Still resolution¶

One thing you may have noted while experimenting with the camera’s preview mode is that captured images typically show more than the preview. The reason for this is that the camera does not (usually) use the full sensor area for preview or video captures, but does for image captures. Specifically, the camera’s sensor has a resolution of 2592x1944 pixels (approximately 5 mega-pixels in area), but only the 1920x1080 pixels in the center of the sensor are used for previews or video:

When still images are captured, the full sensor area is used and the resulting image is scaled to the requested resolution. This usually results in a considerably larger field of view being observed in the final image than was present in the preview shown before the capture. The following image shows the preview area for the 1920x1080 resolution, and the resulting capture area (which is scaled to 1920x1080 during capture):

The main method of mitigating this effect is to force the preview to use the full sensor area. This can be done by setting resolution to 2592x1944:

import time
import picamera

with picamera.PiCamera() as camera:
camera.resolution = (2592, 1944)
# The following is equivalent
#camera.resolution = camera.MAX_IMAGE_RESOLUTION
camera.start_preview()
time.sleep(2)
camera.capture('foo.jpg')


When the preview runs at full resolution, you may notice that the frame-rate is a little lower (specifically it is set to 15fps), however captures will show the same content as the preview before hand. The main downside to this method is that captured images are obviously full resolution. If you want something smaller than full resolution, you will need to use the resize parameter for whatever capture method you choose:

import time
import picamera

with picamera.PiCamera() as camera:
camera.resolution = (2592, 1944)
camera.start_preview()
time.sleep(2)
camera.capture('foo.jpg', resize=(1024, 768))


Bear in mind that the full resolution of the sensor has an aspect ratio of 4:3 (i.e. not wide-screen), so if you specify a resize area with a different aspect ratio, the result will appear stretched.

Changed in version 1.0: The resize parameter was first added in 1.0

## 4.8. Recording video to a file¶

Recording a video to a file is simple:

import picamera

with picamera.PiCamera() as camera:
camera.resolution = (640, 480)
camera.start_recording('my_video.h264')
camera.wait_recording(60)
camera.stop_recording()


Note that we use wait_recording() in the example above instead of time.sleep() which we’ve been using in the image capture recipes above. The wait_recording() method is similar in that it will pause for the number of seconds specified, but unlike time.sleep() it will continually check for recording errors (e.g. an out of disk space condition) while it is waiting. If we had used time.sleep() instead, such errors would only be raised by the stop_recording() call (which could be long after the error actually occurred).

## 4.9. Recording video to a stream¶

This is very similar to Recording video to a file:

import io
import picamera

stream = io.BytesIO()
with picamera.PiCamera() as camera:
camera.resolution = (640, 480)
camera.start_recording(stream, quantization=23)
camera.wait_recording(15)
camera.stop_recording()


Here, we’ve set the quantization parameter which will cause the video encoder to use VBR (variable bit-rate) encoding. This can be considerably more efficient especially in mostly static scenes (which can be important when recording to memory, as in the example above). Quantization values (for the H.264 format) can be between 0 and 40, where 0 represents the highest possible quality, and 40 the lowest. Typically, a value in the range of 20-25 provides reasonable quality for reasonable bandwidth.

## 4.10. Recording over multiple files¶

If you wish split your recording over multiple files, you can use the split_recording() method to accomplish this:

import picamera

with picamera.PiCamera() as camera:
camera.resolution = (640, 480)
camera.start_recording('1.h264')
camera.wait_recording(5)
for i in range(2, 11):
camera.split_recording('%d.h264' % i)
camera.wait_recording(5)
camera.stop_recording()


This should produce 10 video files named 1.h264, 2.h264, etc. each of which is approximately 5 seconds long (approximately because the split_recording() method will only split files at a key-frame).

New in version 0.8.

## 4.11. Recording full-resolution video¶

As noted in the Preview vs Still resolution section above, video recording typically only uses the center 1920x1080 pixels of the camera’s sensor. However, it is possible to record video using the full area of the camera’s sensor although due to GPU limitations the output must be down-scaled prior to encoding and the frame-rate will be limited to 15fps. To achieve this, simply specify the down-scaled resolution as the resize parameter to the start_recording() method, after setting the camera’s resolution:

import picamera

with picamera.PiCamera() as camera:
camera.resolution = (2592, 1944)
camera.start_recording('full_res.h264', resize=(1024, 768))
camera.wait_recording(60)
camera.stop_recording()


New in version 1.0.

## 4.12. Recording to a network stream¶

This is similar to Recording video to a stream but instead of an in-memory stream like BytesIO, we will use a file-like object created from a socket(). Unlike the example in Capturing to a network stream we don’t need to complicate our network protocol by writing things like the length of images. This time we’re sending a continual stream of video frames (which necessarily incorporates such information, albeit in a much more efficient form), so we can simply dump the recording straight to the network socket.

Firstly, the server side script which will simply read the video stream and pipe it to VLC for display:

import socket
import subprocess

# Start a socket listening for connections on 0.0.0.0:8000 (0.0.0.0 means
# all interfaces)
server_socket = socket.socket()
server_socket.bind(('0.0.0.0', 8000))
server_socket.listen(0)

# Accept a single connection and make a file-like object out of it
connection = server_socket.accept()[0].makefile('rb')
try:
# Run VLC with the appropriately selected demuxer (as we're not giving
# it a filename which would allow it to guess correctly)
vlc = subprocess.Popen(
['vlc', '--demux', 'h264', '-'],
stdin=subprocess.PIPE)
while True:
# Repeatedly read 1k of data from the connection and write it to
# VLC's stdin
data = connection.read(1024)
if not data:
break
vlc.stdin.write(data)
finally:
connection.close()
server_socket.close()
vlc.terminate()


Note

If you run this script on Windows you will probably need to provide a complete path to the VLC executable.

Now for the client side script which simply starts a recording over a file-like object created from the network socket:

import socket
import time
import picamera

# Connect a client socket to my_server:8000 (change my_server to the
# hostname of your server)
client_socket = socket.socket()
client_socket.connect(('my_server', 8000))

# Make a file-like object out of the connection
connection = client_socket.makefile('wb')
try:
with picamera.PiCamera() as camera:
camera.resolution = (640, 480)
# Start a preview and let the camera warm up for 2 seconds
camera.start_preview()
time.sleep(2)
# Start recording, sending the output to the connection for 60
# seconds, then stop
camera.start_recording(connection, format='h264')
camera.wait_recording(60)
camera.stop_recording()
finally:
connection.close()
client_socket.close()


You will probably notice several seconds of latency with this setup. This is normal and is because VLC buffers several seconds to guard against unreliable network streams. Low latency video streaming requires rather more effort (the x264dev blog provides some insight into the complexity involved)!

It should also be noted that the effect of the above is much more easily achieved (at least on Linux) with a combination of netcat and the raspivid executable. For example:

server-side: nc -l 8000 | vlc --demux h264 -
client-side: raspivid -w 640 -h 480 -t 60000 -o - | nc my_server 8000

However, this recipe does serve as a starting point for video streaming applications. For example, it shouldn’t be terribly difficult to extend the recipe above to permit the server to control some aspects of the client’s video stream.

## 4.13. Controlling the LED¶

In certain circumstances, you may find the camera module’s red LED a hindrance. For example, in the case of automated close-up wild-life photography, the LED may scare off animals. It can also cause unwanted reflected red glare with close-up subjects.

One trivial way to deal with this is simply to place some opaque covering on the LED (e.g. blue-tack or electricians tape). However, provided you have the RPi.GPIO package installed, and provided your Python process is running with sufficient privileges (typically this means running as root with sudo python), you can also control the LED via the led attribute:

import picamera

with picamera.PiCamera() as camera:
# Turn the camera's LED off
camera.led = False
# Take a picture while the LED remains off
camera.capture('foo.jpg')


Warning

Be aware when you first use the LED property it will set the GPIO library to Broadcom (BCM) mode with GPIO.setmode(GPIO.BCM) and disable warnings with GPIO.setwarnings(False). The LED cannot be controlled when the library is in BOARD mode.