def setMode(self, modeID, report=False):
self.curAudioScreenMode = modeID
modeInfo = self.audioScreenModes[modeID]
if self.imagePlayer:
imagePlayer = self.imagePlayer
self.imagePlayer = None
imagePlayer.terminate()
self.screenBitmap = None
if modeInfo[1] is None:
if report: ui.message(_("AudioScreen off"))
else:
modeConf = {
k: v
for k, v in config.conf["audioScreen_%s" %
modeInfo[1].__name__].iteritems()
}
self.imagePlayer = modeInfo[1](**modeConf)
self.screenBitmap = screenBitmap.ScreenBitmap(
self.imagePlayer.width, self.imagePlayer.height)
if report:
inputType = _("touch input") if touchHandler.handler else _(
"mouse input")
ui.message(
_("AudioScreen mode {mode}, {inputType}").format(
mode=modeInfo[0], inputType=inputType))
def recognizeNavigatorObject(recognizer):
"""User interface function to recognize content in the navigator object.
This should be called from a script or in response to a GUI action.
@param recognizer: The content recognizer to use.
@type recognizer: L{contentRecog.ContentRecognizer}
"""
global _activeRecog
if isinstance(api.getFocusObject(), RecogResultNVDAObject):
# Translators: Reported when content recognition (e.g. OCR) is attempted,
# but the user is already reading a content recognition result.
ui.message(_("Already in a content recognition result"))
return
nav = api.getNavigatorObject()
# Translators: Reported when content recognition (e.g. OCR) is attempted,
# but the content is not visible.
notVisibleMsg = _("Content is not visible")
try:
left, top, width, height = nav.location
except TypeError:
log.debugWarning("Object returned location %r" % nav.location)
ui.message(notVisibleMsg)
return
try:
imgInfo = RecogImageInfo.createFromRecognizer(left, top, width, height, recognizer)
except ValueError:
ui.message(notVisibleMsg)
return
if _activeRecog:
_activeRecog.cancel()
# Translators: Reporting when content recognition (e.g. OCR) begins.
ui.message(_("Recognizing"))
sb = screenBitmap.ScreenBitmap(imgInfo.recogWidth, imgInfo.recogHeight)
pixels = sb.captureImage(left, top, width, height)
_activeRecog = recognizer
recognizer.recognize(pixels, imgInfo, _recogOnResult)
def initialize():
global curMousePos, scrBmpObj
scrBmpObj=screenBitmap.ScreenBitmap(1,1)
(x,y)=winUser.getCursorPos()
desktopObject=api.getDesktopObject()
try:
mouseObject=desktopObject.objectFromPoint(x,y)
except:
log.exception("Error retrieving initial mouse object")
mouseObject=None
if not mouseObject:
mouseObject=api.getDesktopObject()
api.setMouseObject(mouseObject)
curMousePos=(x,y)
winInputHook.initialize()
winInputHook.setCallbacks(mouse=internal_mouseEvent)
def recognizeNavigatorObject(recognizer, filterNonGraphic=True):
"""User interface function to recognize content in the navigator object.
This should be called from a script or in response to a GUI action.
@param recognizer: The content recognizer to use.
@type recognizer: L{contentRecog.ContentRecognizer}
"""
if isinstance(api.getFocusObject(), RecogResultNVDAObject):
# Translators: Reported when content recognition is attempted, but the user is already reading a
# content recognition result.
ui.message(_("Already in a content recognition result"))
return
# Get the object that currently has system focus
obj = api.getFocusObject()
# treeInterceptor may be None is some cases. If so, use the navigator object instead.
if obj.treeInterceptor:
isFocusModeEnabled = obj.treeInterceptor.passThrough
# if Focus mode is enabled we must check if any child of the focus object is graphic because it
# itself cannot be graphic
if isFocusModeEnabled:
recognizer.checkChildren = True
# if focus mode is disabled, use the navigator object
else:
obj = api.getNavigatorObject()
else:
obj = api.getNavigatorObject()
# if filterNonGraphic True, validate the object. If invalid end the recognition process
if filterNonGraphic and not recognizer.validateObject(obj):
return
# Translators: Reported when content recognition is attempted, but the content is not visible.
notVisibleMsg = _("Content is not visible")
try:
left, top, width, height = obj.location
except TypeError:
log.debugWarning("Object returned location %r" % obj.location)
ui.message(notVisibleMsg)
return
# If the object bounds are not valid, end the recognition process.
if not recognizer.validateBounds(obj.location):
return
try:
imgInfo = RecogImageInfo.createFromRecognizer(left, top, width, height,
recognizer)
except ValueError:
ui.message(notVisibleMsg)
return
global _activeRecog
if _activeRecog:
# If a recognition process is already occurring and a new one is started after more than 3 seconds,
# warn the user and block the new recognition process. If the delay is less than 3 seconds, the
# user probably pressed the gesture multiple times so cancel the old process and let the new one
# continue. Cancelling the old process results in errors that cause both recognition processes to
# fail so we just update the resultHandlerClass of the previous recognition process instead of
# cancelling it and end the new recognition process here.
if ((time.time() - _activeRecog.timeCreated) <=
3) and (_activeRecog.resultHandlerClass != BrowseableResult):
_activeRecog.resultHandlerClass = BrowseableResult
# Translators: Reporting when content recognition begins.
ui.message(_("Recognizing"))
else:
ui.message(
"Already running an image captioning process. Please try again later."
)
return
# capture object pixels
sb = screenBitmap.ScreenBitmap(imgInfo.recogWidth, imgInfo.recogHeight)
pixels = sb.captureImage(left, top, width, height)
# calculate L{imageHash} using the inbuilt hash function. Only one channel is used to calculate the
# hash to save time but all pixels in that channel must be used since using only part of the image may
# cause false cache hits for images with padding.
rowHashes = []
for i in range(imgInfo.recogWidth):
row = []
for j in range(imgInfo.recogHeight):
row.append(pixels[j][i].rgbRed) # column major order
rowHashes.append(hash(str(row)))
imageHash = hash(str(rowHashes))
global _cachedResults
# check if the hash of the current object matches that of any previous result
for result in _cachedResults:
# if a match is found, call the recognizer's I{getResultHandler} method with the cached result and
# end the current recognition process here.
if result[0] == imageHash:
handler = recognizer.getResultHandler(result)
return
# Translators: Reporting when content recognition begins.
ui.message(_("Recognizing"))
# Store a copy of the recognizer before image captioning really starts. This can also be used to check
# recognition process is active
_activeRecog = recognizer
recognizer.recognize(imageHash, pixels, imgInfo, _recogOnResult)