[英]Need to capture a still image during face detection with MLKit and Camera2
我正在使用Camera2和MLKit開發人臉檢測功能。
在開發人員指南的性能提示部分,他們說如果使用 Camera2 API,則以ImageFormat.YUV_420_888
格式捕獲圖像,這是我的情況。
然后,在人臉檢測器部分,他們建議使用尺寸至少為 480x360 像素的圖像進行實時人臉識別,這也是我的情況。
好的,讓我們 go,這是我的代碼,運行良好
private fun initializeCamera() = lifecycleScope.launch(Dispatchers.Main) {
// Open the selected camera
cameraDevice = openCamera(cameraManager, getCameraId(), cameraHandler)
val previewSize = if (isPortrait) {
Size(RECOMMANDED_CAPTURE_SIZE.width, RECOMMANDED_CAPTURE_SIZE.height)
} else {
Size(RECOMMANDED_CAPTURE_SIZE.height, RECOMMANDED_CAPTURE_SIZE.width)
}
// Initialize an image reader which will be used to display a preview
imageReader = ImageReader.newInstance(
previewSize.width, previewSize.height, ImageFormat.YUV_420_888, IMAGE_BUFFER_SIZE)
// Retrieve preview's frame and run detector
imageReader.setOnImageAvailableListener({ reader ->
lifecycleScope.launch(Dispatchers.Main) {
val image = reader.acquireNextImage()
logD { "Image available: ${image.timestamp}" }
faceDetector.runFaceDetection(image, getRotationCompensation())
image.close()
}
}, imageReaderHandler)
// Creates list of Surfaces where the camera will output frames
val targets = listOf(viewfinder.holder.surface, imageReader.surface)
// Start a capture session using our open camera and list of Surfaces where frames will go
session = createCaptureSession(cameraDevice, targets, cameraHandler)
val captureRequest = cameraDevice.createCaptureRequest(
CameraDevice.TEMPLATE_PREVIEW).apply {
addTarget(viewfinder.holder.surface)
addTarget(imageReader.surface)
}
// This will keep sending the capture request as frequently as possible until the
// session is torn down or session.stopRepeating() is called
session.setRepeatingRequest(captureRequest.build(), null, cameraHandler)
}
現在,我想捕捉靜止圖像......這是我的問題,因為理想情況下,我想要:
Camera2Basic 示例演示了如何捕獲圖像(視頻和慢動作的示例正在崩潰),而MLKit 示例使用了如此古老的相機 API,! 幸運的是,我成功地混合了這些樣本來開發我的功能,但我未能捕捉到具有不同分辨率的靜止圖像。
我想我必須停止預覽 session 才能重新創建一個用於圖像捕獲,但我不確定......
我所做的是以下,但它是在 480x360 中捕獲圖像:
session.stopRepeating()
// Unset the image reader listener
imageReader.setOnImageAvailableListener(null, null)
// Initialize an new image reader which will be used to capture still photos
// imageReader = ImageReader.newInstance(768, 1024, ImageFormat.JPEG, IMAGE_BUFFER_SIZE)
// Start a new image queue
val imageQueue = ArrayBlockingQueue<Image>(IMAGE_BUFFER_SIZE)
imageReader.setOnImageAvailableListener({ reader - >
val image = reader.acquireNextImage()
logD {"[Still] Image available in queue: ${image.timestamp}"}
if (imageQueue.size >= IMAGE_BUFFER_SIZE - 1) {
imageQueue.take().close()
}
imageQueue.add(image)
}, imageReaderHandler)
// Creates list of Surfaces where the camera will output frames
val targets = listOf(viewfinder.holder.surface, imageReader.surface)
val captureRequest = createStillCaptureRequest(cameraDevice, targets)
session.capture(captureRequest, object: CameraCaptureSession.CaptureCallback() {
override fun onCaptureCompleted(
session: CameraCaptureSession,
request: CaptureRequest,
result: TotalCaptureResult) {
super.onCaptureCompleted(session, request, result)
val resultTimestamp = result.get(CaptureResult.SENSOR_TIMESTAMP)
logD {"Capture result received: $resultTimestamp"}
// Set a timeout in case image captured is dropped from the pipeline
val exc = TimeoutException("Image dequeuing took too long")
val timeoutRunnable = Runnable {
continuation.resumeWithException(exc)
}
imageReaderHandler.postDelayed(timeoutRunnable, IMAGE_CAPTURE_TIMEOUT_MILLIS)
// Loop in the coroutine's context until an image with matching timestamp comes
// We need to launch the coroutine context again because the callback is done in
// the handler provided to the `capture` method, not in our coroutine context
@ Suppress("BlockingMethodInNonBlockingContext")
lifecycleScope.launch(continuation.context) {
while (true) {
// Dequeue images while timestamps don't match
val image = imageQueue.take()
if (image.timestamp != resultTimestamp)
continue
logD {"Matching image dequeued: ${image.timestamp}"}
// Unset the image reader listener
imageReaderHandler.removeCallbacks(timeoutRunnable)
imageReader.setOnImageAvailableListener(null, null)
// Clear the queue of images, if there are left
while (imageQueue.size > 0) {
imageQueue.take()
.close()
}
// Compute EXIF orientation metadata
val rotation = getRotationCompensation()
val mirrored = cameraFacing == CameraCharacteristics.LENS_FACING_FRONT
val exifOrientation = computeExifOrientation(rotation, mirrored)
logE {"captured image size (w/h): ${image.width} / ${image.height}"}
// Build the result and resume progress
continuation.resume(CombinedCaptureResult(
image, result, exifOrientation, imageReader.imageFormat))
// There is no need to break out of the loop, this coroutine will suspend
}
}
}
}, cameraHandler)
}
如果我取消注釋新的 ImageReader 實例,我有這個例外:
java.lang.IllegalArgumentException:CaptureRequest 包含未配置的輸入/輸出表面!
誰能幫我?
這個IllegalArgumentException
:
java.lang.IllegalArgumentException:CaptureRequest 包含未配置的輸入/輸出表面!
...顯然是指imageReader.surface
。
Meanhile(使用 CameraX)這工作方式不同,請參閱CameraFragment.kt ...
問題 #197: Firebase 人臉檢測 Api 問題,同時使用 cameraX API ;
可能很快就會有一個與您的用例匹配的示例應用程序。
ImageReader 對格式的選擇和/或使用標志的組合很敏感。 文檔指出某些格式組合可能不受支持。 對於某些 Android 設備(可能是一些較舊的手機型號),您可能會發現使用 JPEG 格式不會引發IllegalArgumentException
。 但這並沒有多大幫助——你想要多才多藝的東西。
我過去所做的是使用ImageFormat.YUV_420_888
格式(這將由硬件和 ImageReader 實現支持)。 此格式不包含阻止應用程序通過內部平面陣列訪問圖像的預優化。 我注意到您已經在您的initializeCamera()
方法中成功使用了它。
然后,您可以從您想要的幀中提取圖像數據
Image.Plane[] planes = img.getPlanes();
byte[] data = planes[0].getBuffer().array();
然后通過 Bitmap 使用 JPEG 壓縮、PNG 或您選擇的任何編碼創建靜止圖像。
ByteArrayOutputStream out = new ByteArrayOutputStream();
YuvImage yuvImage = new YuvImage(data, ImageFormat.NV21, width, height, null);
yuvImage.compressToJpeg(new Rect(0, 0, width, height), 100, out);
byte[] imageBytes = out.toByteArray();
Bitmap bitmap= BitmapFactory.decodeByteArray(imageBytes, 0, imageBytes.length);
ByteArrayOutputStream out2 = new ByteArrayOutputStream();
bitmap.compress(Bitmap.CompressFormat.JPEG, 75, out2);
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