Yet Another Camera for Kivy
Project description
Camera4Kivy
Yet Another Camera for Kivy
2023/02/09 : Android users: camerax_provider has been updated to version 0.0.3
- Overview
- Install
- Examples
- Preview Widget
- Image analysis
- Camera Behavior
- Camera Provider
- Known Behavior
Overview
Available on all the usual platforms.
from camera4kivy import Preview
Camera4Kivy consists of a Preview widget with an api to connect to the physical camera unit. The Preview widget layout is configured with Kivy properties , the camera unit and image analysis behavior are configured with an api. For example:
In .kv
Preview:
id: preview
aspect_ratio: '16:9'
In Python
self.preview = Preview(aspect_ratio = '16:9')
To connect the camera unit to the Preview call the preview's connect_camera() method, after on_start(). For example to connect the camera with the image analysis api enabled :
self.preview.connect_camera(enable_analyze_pixels = True)
Well behaved apps disconnect the camera when it is no longer in use. It is important to be well behaved.
self.preview.disconnect_camera()
To take a photo:
self.preview.capture_photo()
The captured file location may be specified and is also reported in a callback. A data analysis api allows per frame analysis and preview annotation or preview image replacement.
On Android a pinch/spread gesture controls zoom, and a tap overrides any automatic focus and metering (if available). Some connect_camera() options are platform specific.
Be aware Preview operation depends on the performance of the graphics hardware. In general Preview uses the highest available resolution for 30fps performance. On devices with low performance graphics hardware sush as low end laptops or Raspberry, you will probably have to explicitly set a lower image resolution inorder to increase the frame rate.
Install
A camera provider may be required. On a destop the camera provider is installed once. On Android the camera provider is added to each project.
Install Camera4Kivy on Desktop
pip3 install camera4kivy
Install Camera4Kivy on Android
Camera4Kivy depends on Buildozer 1.3.0 or later
buildozer.spec:
android.api = 33 (Constrained by Android packages imported by camerax_provider)
requirements = python3, kivy, camera4kivy, gestures4kivy
Set p4a.hook to enable the app's use of the camera provider. This sets the required p4a options.
p4a.hook = camerax_provider/gradle_options.py
The implementation of the camerax gradle dependencies is architecture specific, an app built for armeabi-v7a will crash on an arm64-v8a device.
Run Time Permissions
The following run time permissions must be in be requested in the app. As usual request these in build() or after on_start(). See the examples.
Always required: CAMERA
Required to record video with audio: RECORD_AUDIO
Required when capturing photo, screenshot, or video and saving to shared storage, and only on devices running api_version < 29: WRITE_EXTERNAL_STORAGE
Install Camera4Kivy on iOS
Install the 'master' (2022/04/22 or later) version of kivy-ios.
pip3 install git+https://kivy/kivy-ios.git
toolchain build python3 kivy
toolchain pip3 install camera4kivy
Run Time Permissions
Permission to use the camera and save images is required by iOS. To enable permissions edit <project>-ios/<project-Info.plist. Add some or all of these:
To enable use of the Camera add:
<key>NSCameraUsageDescription</key>
<string> </string>
To enable saving image captures to the Photos App (the default behavior) add:
<key>NSPhotoLibraryAddUsageDescription</key>
<string> </string>
To enable viewing images saved to app local storage with the File Manager:
<key>UIFileSharingEnabled</key>
<true/>
<key>LSSupportsOpeningDocumentsInPlace</key>
<true/>
Examples
A prerequisite is that a working camera is installed. Test this with the platform's camera app before proceeding. All examples use the platform specific camera provider, and assume the typical default camera_id of '0'. If you find the example does not connect to a camera review the available camera ids and your camera provider choice.
Tested Examples
The Photo example illustrates basic camera usage, try this first. The remaining examples illustrate image analysis using various packages.
On Android and iOS the app can rotate when the device rotates, on the desktop you can change the window size to simulate orientation, and thus rotating a mobile device.
C4K Photo Example
C4K-Photo-Example. Illustrates basic layout using screens. Basic camera functionality including photo capture, screenshot capture, and on Android capture of video with audio. On Raspberry PI a mouse must be used, a touch pad does not work correctly.
C4K QR Example
C4K-QR-Example. Everything you need to read a restaurant menu. Long press or double click on a highlighted QR code to open a web browser. Illustrates basic analysis, screen annotation, and user interaction.
C4K OpenCV Example
C4K OpenCV Example Edge detect the video stream. Illustrates using OpenCV analysis and replacing the original preview with the transformed image.
C4K MLKit Example
C4K MLKit Example Face detect, MLKit is Android only. Illustrates using the ImageProxy api.
C4K TFLite Example
C4K TFLite Example Object classification. Illustrates using a large Tensorflow Lite model, and writing text to the Preview image.
Tested Platforms
| Example | Windows | Macos | Linux | Android | iOS | Coral |
|---|---|---|---|---|---|---|
| Photo | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | |
| QR | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | ||
| OpenCV | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | ||
| MLKit | :heavy_check_mark: | |||||
| TFLite | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
- Windows : Windows 11, i7-10 @ 1.1GHz, Python 3.8.2 Kivy==2.1.0.dev0
- Windows : Windows 10, i3-7 @ 2.4GHz, Python 3.9.7 Kivy==2.0.0
- Macos : Big Sur, i5-10 @ @ 1.1GHz, Python 3.9.9 Kivy==2.0.0
- Linux : Raspberry Buster, Cortex-A72 @ 1.5GHz Python3.7.3 Kivy==2.0.0
- Android : build : arm64-v8a device: Android 12, Pixel 5
- Android : build : armeabi-v7a device: Android 6, Nexus 5 Start is somewhat slow.
- iOS : iPhone SE (second generation)
- Coral : Accelerator tested with Windows 11 , gave very approximately an order of magnitude speed up.
Preview Widget
An app can have multiple Preview widgets, but only one can be connected to the physical camera unit at a time. A natural way to implement this is to add a preview widget to a screen's contents, then connect to the camera unit on_enter and disconnect on_pre_leave. Or if using a ModalView, Popup, or MDDialog use on_open and on_pre_dismiss. The C4K-Photo-Example illustrates this, the other examples simply connect the camera after on_start() and disconnect on_stop().
Preview Widget Properties
The widget has these Kivy properties that configure its layout:
aspect_ratio
A string property. Either '4:3' (default) or '16:9'.
letterbox_color
A color property. Geometry tells us that layout of a fixed aspect ratio widget almost always results in letterboxing. Art tells us we can hide letterboxes by filling them with a color that matches the surrounding widgets.
orientation
A string property. One of 'portrait' , 'landscape', 'same', 'opposite'. The default is the 'same' as the device or window. This choice modifies effective resolution, see. The best resolution is always obtained with 'same'.
Preview Widget API
The widget has these methods:
Connect Camera
This may only be called after on_start().
def connect_camera(self,kwargs):
Optional arguments:
camera_id
Specify which camera to connect to. For example camera_id = 'front'. A string containing an integer (default '0'), or on Android or iOS 'back' (default), or 'front'.
mirrored
Mirrors the Preview image, default is True. This option is ignored on Android and iOS where by convention 'front' is always mirrored and 'back' is never mirrored. This option should usually be True for any camera facing the user, and False for any camera not facing the user.
Captures are never mirrored, except a screenshot capture if the Preview is mirrored.
The pixels argument to image_analysis_callback() is never mirrored, if a Preview is mirrored a texture result of any image analysis will be automatically mirrored in the canvas_instructions_callback() by the tex_size and tex_pos arguments. If image analysis genetates image annotation locations, these locations must be adjusted by the app for a mirrored preview. See the Image Analysis Section for code fragments and links to examples.
filepath_callback
On a capture of a photo, video, or screenshot, this argument specifies a method to receive the path and name of the saved file. For example filepath_callback = my_method, where def my_method(self, path): is an app supplied method with a string argument.
Photo and Video captures may be implemented in a different thread. The only way to know that a capture is complete is a filepath_callback. There may be latency concequences of disconnecting the camera after initiating a capture and before a filepath_callback.
The filepath_callback can also be used to reset any 'video recording' indicator in the UI. While video recording is normally terminated by the user, it can also be terminated by app pause, device rotation, or camera selection. In these last cases the any recording indicator can be reset by the callback, which occurs on any capture termination regardless of cause.
sensor_resolution
Overrides the default sensor resolution, which is the highest resolution available, except Raspberry Pi where it is (1024, 768). Tuple of two integers, for example sensor_resolution = (640, 480). The resulting capture resolution obtained depends on the behavior of the camera provider (for example it is ignored by GStreamer). The capture resolution also depends on the relative orientation and aspect ratio of the Preview. Treat the value specified as a request that may not be exactly honored.
analyze_pixels_resolution
Sets the pixels resolution passed by analyze_pixels_callback(). A scalar, representing the number of pixels on the long edge, the short edge is determined using the aspect ratio. For example analyze_pixels_resolution = 720. The default is the minimum of cropped sensor resolution and 1024.
As an alternative, sometimes the analysis package will need to set the resolution. This is done with self.auto_analyze_resolution as described in Analyze Configuration.
enable_analyze_pixels
Use enable_analyze_pixels = True to enable the analyze_pixels_callback()
enable_analyze_imageproxy
Use enable_analyze_imageproxy = True to enable the analyze_imageproxy_callback()
Android only.
enable_zoom_gesture
Default True. Android and iOS only.
enable_focus_gesture
Default True. Android only.
imageproxy_data_format:
Applies only to the Android ImageProxy api. 'yuv420' (default) or 'rgba'.
Disconnect Camera
Always do this, it is important to disconnect the camera when not in use.
def disconnect_camera(self):
A disconnect while a capture is in progress generally results in termination of the capture and saving the result as usual.
However on Android a disconnect immediately after a capture has be initiated may prevent the start of the file save and nothing is saved. A warning message is reported by filepath_callback, this may be filtered as shown in this example
Capture
def capture_photo(self, kwargs):
def capture_screenshot(self, kwargs):
def capture_video(self, kwargs): # Android only
def stop_capture_video(self): # Android only
Video capture is only available on Android or with the OpenCV camera provider. Capturing audio with video is only available on Android.
Captures are never mirrored, except a screenshot capture if the Preview is mirrored. Capture resolution is discussed here.
Captures are saved to <location>/<subdir>/<name>.jpg or .mp4.
The default values are as follows. On a desktop <location> is the current directory ., on Android <location> is DCIM/<appname>, and on iOS is the Photos App. The value of <subdir> is the current date, the format is 'YYYY_MM_DD'. The value of <name> is the current time, the format is 'hh_mm_ss_xx' (xx is 1/100 sec).
The filepath_callback occurs on capture completion, with an argument that is the actual path for a particular capture.
Be aware that on Android >= 10 shared storage files are saved in a database, called MediaStore, and not in a file system. The architecture of Android storage is outside the scope of this document.
The values of <location>, <subdir>, and <name> can be modified with optional keyword arguments to the three capture_ methods:
location
The value replaces the default value of <location>.
On a desktop the value is a directory that must exist.
On Android and iOS the value can only be 'shared' or 'private', other values default to 'shared'.
On Android the value 'shared' specifies Android shared storage DCIM/<appname>. The value 'private' specifies app local storage app_storage_path()/DCIM. If you want a different location use 'private' and move the resulting file based on the path provided by filepath_callback.
On iOS the value 'shared' specifies the iOS Photos App. The value 'private' specifies app local storage. For 'shared' the filepath_callback returns an empty string, for 'private' it returns the paths to the file in app local storage.
subdir
The value replaces the default value of <subdir>. The subdirectory will be created or added to the Android MediaStore path. For iOS when location='shared' this is ignored.
name
The value replaces the default value of <name>, the .jpg or .mp4 extensions will be added automatically.
Note that it is a characteristic of Android MediaStore that a second capture with the same subdir and name values as the first will not overwrite the first. It will create a second file named <subdir>/<name> (1).jpg, this name is created by Android MediaStore. The MediaStore may crash if it creates too many (31 ?) such names.
For iOS when location='shared' this is ignored.
Select Camera
Change the currently connected camera, camera_id must specify a physically connected camera.
def select_camera(self, camera_id):
Zoom
On Android only, zoom_delta() is called by pinch/spread gesture unless disabled. On iOS only, zoom_abs() is called by pinch/spread gesture unless disabled.
def zoom_delta(self, delta_scale):
def zoom_abs(self, scale):
Flash
Android only, and for capture photo only, the value is ignored for video and data. The state argument must be in ['on', 'auto', 'off'], alternatively if state=None sequential calls sequence through that list. Note that 'on' always turns on the flash around the time a photo is captured, 'auto' only does this if the light level is low enough.
def flash(self, state = None)
Torch
Android only, immediately turns the flash on in any use case. The state argument must be in ['on', 'off']
def flash(self, state)
Focus
Android only, if available on device. Called by a tap gesture unless disabled
def focus(x, y):
camera_connected
This is a boolean variable describing the camera state. It is True immediatedal after the camera is connected, and False immediately before the camera is disconnected.
Image analysis
Overview and Examples
The programming pattern for video data analysis is to create a subclass of Preview and implement two predefined methods. One to analyze the frame, the second to modify the Preview image with the analysis result. In general like this:
class CustomAnalyzer(Preview):
def analyze_pixels_callback(self, pixels, size, image_pos,
image_scale, mirror):
### Add your pixels analysis code here
### Add your coordinate transforms here
def canvas_instructions_callback(self, texture, tex_size, tex_pos):
### Add your Preview annotation or image replacement code here
The analyze_pixels_callback() is called each time new pixels are available, and the canvas_instructions_callback() is called on each iteration of the Kivy event loop. The availability of new pixels depends on the camera data rate, and the latency of any analysis code included with the previous call of analyze_pixels_callback(). Thus analyze_pixels_callback() is typically called at a rate less than canvas_instructions_callback(), so the annotation update rate is typically less than the image frame rate.
On Android this is an alternative to analyze_pixels_callback(), it is used for Android only analysis packages.
def analyze_imageproxy_callback(self, image_proxy, image_pos,
image_scale, mirror, degrees):
### Add your imageproxy specific analysis code here
Keep to this pattern. Perform analysis and coordinate transforms in the 'analyze_pixel_callback' (or imageproxy) method. And in 'canvas_instructions_callback' only display the results of previous calculations. Data passed from the analysis method to the display method must be passed in a thread safe way.
The analyze_pixels_callback method is used to analyze its RGBA pixels and size arguments. The pos, scale, and mirror arguments enable mapping the analyzed pixels coordinates to Preview coordinates. The mirror parameter is required because pixels image is never mirrored, but the Preview may be. An example:
def analyze_pixels_callback(self, pixels, image_size, image_pos,
scale, mirror):
# Convert the image encoding
pil_image = Image.frombytes(mode='RGBA', size=image_size,
data= pixels)
# Analyze the image
barcodes = pyzbar.decode(pil_image, symbols=[ZBarSymbol.QRCODE])
# Collect the results and transform the coordinates
found = []
for barcode in barcodes:
text = barcode.data.decode('utf-8')
if 'https://' in text or 'http://' in text:
x, y, w, h = barcode.rect
# Map Zbar coordinates to Kivy coordinates
y = image_size[1] -y -h
# Map Analysis coordinates to Preview coordinates
if mirror:
x = image_size[0] -x -w
x = round(x * scale + image_pos[0])
y = round(y * scale + image_pos[1])
w = round(w * scale)
h = round(h * scale)
found.append({'x':x, 'y':y, 'w':w, 'h':h, 't':text})
# Save the results in a thread safe way
self.make_thread_safe(list(found)) ## A COPY of the list
Analysis and canvas annotation callbacks occur on different threads. The result of the analysis must be saved in a thread safe way, so that it is available for the canvas callback. We pass a copy of the result to:
@mainthread
def make_thread_safe(self, found):
if self.camera_connected:
self.annotations = found
else:
self.annotations = []
Note that we null the application state when the camera is not connected. This prevents saved annotations from being shown when a camera is re-connected, due to the multi-threaded implementation.
Then add the thread safe annotations to the canvas.
def canvas_instructions_callback(self, texture, tex_size, tex_pos):
# Add the annotations determinined during analyze callback.
Color(1,0,0,1)
for r in self.annotations:
Line(rectangle=(r['x'], r['y'], r['w'], r['h']), width = dp(2))
We can also replace the existing Preview image with some other texture, positioned with the 'tex_size' and 'tex_pos' arguments. Use a thread safe texture created as a result of some image analysis like this:
def canvas_instructions_callback(self, texture, tex_size, tex_pos):
# Add a different preview image, which is a transformed camera image
# this image has 'analyze_pixels_resolution'
if self.analyzed_texture:
# 'self.analyzed_texture' contents created
# by analyze_pixels_callback()
Color(1,1,1,1)
Rectangle(texture= self.analyzed_texture,
size = tex_size, pos = tex_pos)
The new texture will be automatically mirrored by 'text_size' and 'tex_pos' if required. These 'text_size' and 'tex_pos' arguments are for adding a texture, and not valid for coordinate calculations as they are potentially mirrored.
See the OpenCV example for details on creating a thread safe texture.
The above code fragments are fully implemented in two examples: QR Reader, and OpenCV. Similar examples exhibiting this pattern are tflite and mlkit.
User Interaction
But wait, there is more, a user can interact with the analysis results in the Preview. The Preview subclass may have multiple inheritance, for example to allow the user to interact with annotations on the screen. The QR Reader example illustrates this, by inheriting from a gestures package:
class QRReader(Preview, CommonGestures):
That package's gesture callbacks, and an annotation location test are used to initiate some action. In this case open a web browser based on a URL in a QR code, and a long press or mouse double click inside the box drawn around the QR code.
def cg_long_press(self, touch, x, y):
self.open_browser(x, y)
def cg_double_tap(self, touch, x, y):
self.open_browser(x, y)
def open_browser(self, x, y):
for r in self.annotations:
if x >= r['x'] and x <= r['x'] + r['w'] and\
y >= r['y'] and y <= r['y'] + r['h']:
webbrowser.open_new_tab(r['t'])
Coordinates and image encoding
Important, be aware of different coordinate systems and image encoding. A test with a print statement of third party analysis code coordinates can be valuable.
-
Kivy image coordinates have their origin at the bottom left. Most other systems use top left (with positive y increaing downwards) as their origin.
-
Kivy image properties are a (width, height) tuple. Some packages, notably numpy images, reverse the order to (height, width).
-
Kivy pixels are encoded RGBA. Third party analysis code may expect some other encoding, both Pillow and OpenCV provide encoding converions. Some image recodings are computationally expensive.
-
The 'canvas_instructions_callback()' arguments 'tex_size' and 'tex_pos' are potentially mirrored and their values are not valid for coordinate mapping. Perform mapping in 'analyze_pixels_callback()' using the 'image_size' and 'image_pos' arguments.
Analysis Configuration
Image analysis is enabled with a parameter to connect_camera():
connect_camera(enable_analyze_pixels = True)
To change the default analysis resolution specify the number of pixels in the long edge (the default is the smaller of 1024 or the cropped resolution):
connect_camera(enable_analyze_pixels = True, analyze_pixels_resolution = 720)
The analyze_pixels_resolution option provides analysis images with the same orientation and aspect ratio as the Preview.
As an alternative the analysis software may set self.auto_analyze_resolution a two element list of [width, height]. In this case the aspect ratio is not necessarily maintained for analysis, the analyze_pixels_callback() image_scale parameter is a two element list [width, height] allowing scaling of any results. As used in c4k_tflite_example classifyobject.py.
On Android only, the imageproxy api is an alternative to the pixels api.
connect_camera(enable_analyze_imageproxy = True)
The imageproxy api provides images in landscape, regardless of the preview orientation. A degrees parameter enables adjusting the analysis accordingly. Android implements automatic changes to frame rate and resolution in the case of slow analysis.
Debugging
Check that the app analysis code is doing what you expect. If the result of this is coordinates (most cases) then check these with a print statement. Move whatever you expect to be detected to the four corners of the camera view. Look the printed values, do they reflect the analysed image pixels size and orientation? Repeat for the coordinates after they are mapped to a Kivy widget.
Measure the time the analysis algorithm takes to execute on one frame. Do this in the cases of detection and nothing to detect. This along with some overhead will define the maximum analysis frame rate. The tflite example monitors analysis frame rate as part of its normal operation.
Performance
The camera provides a stream of images for analysis via analyze_pixels_callback(). Images arrive at typically 30 fps, so given some overhead the app has probably less than 30mS to do the analysis.
The api has a builtin mechanism so that images are analyzed only when the previous analysis is complete. This mechanism does not alter the canvas instructions frame rate. If the analysis results are 'jerky' it is because the analysis algorithm is slow for the hardware.
Conversely, you can explicitly decrease the analysis frame rate without changing anything else using a flag set using Kivy Clock. Clock rates close to the actual analyze rate will exhibit jitter. For example for a one second analyze interval:
self.enable_analyze_frame = True
Clock.schedule_interval(self.analyze_filter,1)
def analyze_filter(self, dt):
self.enable_analyze_frame = True
def analyze_pixels_callback(self, pixels, image_size, image_pos,
scale, mirror):
if self.enable_analyze_frame:
self.enable_analyze_frame = False
# place usual analyse code inside this if block
One could modify this in various ways, for example a single sample after some delay.
One way to improve performance is to reduce the analyze_pixels_resolution as shown above. This option may alter the qualitative behavior, perhaps because of resolution bias in some third party analyzers. Experiment, some analysis code will work well at much less than VGA resolution.
The analysis code must be lean. So for example Keras is a complete development environment, a whole bunch of stuff you don't need to run an inference. Port the application to Tensorflow Lite, then use the tflite-runtime not the full Tensorflow Lite.
Camera Behavior
A Physical Camera
A camera is a single physical device with a software api, it is not a software object though it can look like one. It is a physical object with physical constraints.
Resolution
In the context of a camera, resolution has several uses. It is always a tuple, in this context (width, height).
Sensor Resolution
This is a phyical property of the sensor module. The default behavior of connect_camera() is to use the maximum resolution provided by the camera provider. Is can be overridden with the sensor_resolution option, but in general camera providers take this a a hint which may be ignored or re-interpreted. GStreamer ignores this option, picamera may require it.
Cropped Sensor Resolution
The sensor resolution cropped according to the orientation of the sensor, the orientation of the Preview, and the aspect ratio of the Preview. The will impact the capture resolution, for example a 16:9 aspect image maybe cropped from a 4:3 sensor image. Thus the product of width and height will less for 16:9 that for 4:3 in this case.
Rotating a mobile device also rotates the sensor, the highest resolution images are obtained when the Priview widget orientation is the same as the device orientation. Conversly for example a landscape preview with the device in portrait orientation will result in an image width resolution that is the sensor height resolution.
This behaviour is a characteric of the camera sensor having physical constraints. Notably that image sensors are usually not square, they are rectangular and give the highest quality results when the sensor has the same orientation as the captured image. This is mostly transparent to the app user unless the sensor resolution is low, or a photo capture has lower than expected resolution.
Preview Resolution
Is a physical property of the screen (display resolution) and the Preview widget size on the screen. The preview resolution can be less than or greater than the cropped sensor resolution.
Capture Resolution
The resolution of a capture. A photo capture resolution is cropped sensor resolution. Video resolution is one to the standard resolutions, depending on the cropped sensor resolution. A screenshot capture size in pixels is the Preview resolution.
Analysis Resolution
Analysis resolution is less than or equal to cropped sensor resolution. It may be useful to reduce this in order to decrease analysis time, at the cost of analysis resolution.
The analyze_pixels_callback() resolution may be changed with the analyze_pixels_resolution camera connect option. The scale parameter allows re-scaling of the analysis results to the Preview resolution.
The analyze_imageproxy_callback() implements a graceful degradation mechanism. This automatically reduces frame rate and/or image resolution. A very slow frame analysis will case the feed to stop.
Display Resolution.
Nothing to do with a camera, it is a physical property of a screen. A scalar measured in dpi.
Camera Provider
Camera4Kivy depends on a 'camera provider' to access the OS camera api. On most platforms this uses the same provider as Kivy, with modified defaults.
| Platform | Provider | Requires |
|---|---|---|
| Windows | OpenCV | |
| Gstreamer | ||
| Macos | AVFoundation | OSX >= 10.7 |
| Linux | Gstreamer | |
| OpenCV | ||
| Rasberry | Picamera | <= Buster |
| Gstreamer | <= Buster | |
| OpenCV | <= Buster | |
| Picamera2 | >= Bullseye | |
| Android | CameraX | Android >= 5.0 |
| iOS | AVFoundation |
Like Kivy, the first available provider is selected. Some camera provider specific behavior should be expected. For example a switch to a camera that does not exist will be ignored on MacOS and Rasberry Pi, but generate a screen message with OpenCV or GStreamer. Camera resolution defaults to the maximum available camera provider resolution, except on Raspberry Pi where the default is (1024, 768).
You can remove a camera provider ('picamera' in the example below) from the above lists by inserting this code before from kivy.app import App.
from kivy import kivy_options
providers= list(kivy_options['camera'])
providers.remove('picamera')
kivy_options['camera'] = tuple(providers)
You can read back the chosen camera provider with:
from camera4kivy import CameraProviderInfo
provider_string = CameraProviderInfo().get_name()
Android Camera Provider
cd <project directory>
git clone https://github.com/Android-for-Python/camerax_provider.git
rm -rf camerax_provider/.git
Set p4a.hook to enable the app's use of the camera provider.
p4a.hook = camerax_provider/gradle_options.py
OpenCV
pip3 install opencv-python
GStreamer
Depends on the Linux flavor, but commonly:
sudo apt-get install gstreamer-1.0
sudo apt-get install gstreamer1.0-dev
Picamera
Pre-installed
Picamera2
Raspberry PI Bullseye support currently not available.
AVFoundation
Pre-installed
Known Behavior
Behavior: Android .mp4 Orientation
Video file orientation is incorrect if the preview orientation is not the same as the device orientation. Do not use this layout configuration when recording video. Google issue tracker.
Behavior: Android .jpg Orientation.
Some image viewers (including Kivy Image widget) will incorrectly display a .jpg as rotated by 90 degrees. This occurs if the capture preview orientation is not the same as the device orientation, and the third party viewer does not use the Exif metadata.
Behavior: Android armeabi-v7a build installed on an arm64-v8a device
The implementation of Google's camerax gradle dependencies appears to be architecture specific, an app built for armeabi-v7a will crash on an arm64-v8a device. To run on an arm64-v8a device you must build for arm64-v8a.
Behavior: Android "No supported surface combination"
No supported surface combination is found for camera device - Id : 0. May be attempting to bind too many use cases.
On very low end Android devices the camera may have limited hardware resources. So far only one device has exhibited this issue. By default c4k configures connect_camera() for either 'photo and video` or 'photo and image analysis' - this keeps the api as simple as possible. But in this case it is too expensive for the hardware. We can override these configurations.
If the connection is only used for photo use connect_camera(enable_video = False, ...other options..). If the connection is only used for video or data analysis use connect_camera(enable_photo = False, ...other options..).
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