How to set QLabel using Base64 string or byte array
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1
down vote
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I have code that's up and running creating images from files. That code looks like this:
qlabel = QLabel()
qlabel.setText('')
w = QtGui.QPixmap(filename).width()
h = QtGui.QPixmap(filename).height()
qlabel.setGeometry(QtCore.QRect(0, 0, w, h))
qlabel.setPixmap(QtGui.QPixmap(filename))
qt_row_layout.addWidget(qlabel)
I've tried other techniques that I found on SO but none are working. Here is my latest attempt
qlabel = QLabel()
qlabel.setText('')
ba = QtCore.QByteArray.fromBase64(base64data)
image = QImage()
image.loadFromData(ba)
pixmap = QPixmap()
pixmap.fromImage(image)
w = image.width()
h = image.height()
qlabel.setGeometry(QtCore.QRect(0, 0, w, h))
qlabel.setPixmap(pixmap)
I am porting code that I have up and running on tkinter. It's tricky doing the translations.
EDIT - as requested, one of my graphics...
mac_red = b'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'
python image base64 pyside2
asked Nov 11 at 19:30
MikeyB
64349
add a comment |
up vote
1
down vote
favorite
I have code that's up and running creating images from files. That code looks like this:
qlabel = QLabel()
qlabel.setText('')
w = QtGui.QPixmap(filename).width()
h = QtGui.QPixmap(filename).height()
qlabel.setGeometry(QtCore.QRect(0, 0, w, h))
qlabel.setPixmap(QtGui.QPixmap(filename))
qt_row_layout.addWidget(qlabel)
I've tried other techniques that I found on SO but none are working. Here is my latest attempt
qlabel = QLabel()
qlabel.setText('')
ba = QtCore.QByteArray.fromBase64(base64data)
image = QImage()
image.loadFromData(ba)
pixmap = QPixmap()
pixmap.fromImage(image)
w = image.width()
h = image.height()
qlabel.setGeometry(QtCore.QRect(0, 0, w, h))
qlabel.setPixmap(pixmap)
I am porting code that I have up and running on tkinter. It's tricky doing the translations.
EDIT - as requested, one of my graphics...
mac_red = b'iVBORw0KGgoAAAANSUhEUgAAABgAAAAZCAYAAAArK+5dAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAGHRFWHRTb2Z0d2FyZQBwYWludC5uZXQgNC4xLjFjKpxLAAAGfklEQVR42o1W6VNTVxR/Kv4Htp1xZA0JhCWsAQmQAC4Yd0GtKBqXUUAREBdE8pYAWVhUotVWVOpGpzpVqI51pnas+sFtOnXUmXY6o10sErYASUAgybun5yUEoWOnfvjNOe/dc35nufe9cymO4ygBLMt6JMey01mansmaTJS5sVFRrdlsrpq/0LVNEk62RkTB5vBIvjBKRiqyFz0zlpQydUeOUFU6HcVoaT8fzwQXYgo5yzDTWGGhtpYyFO+u2afK7EBSt0Yk5ncEBUGJvz+UInYEBZMtoRKyPSaOr1i67EEDTS+r1usphqan+4jfBXhHPp3FTKppes6hJUvvbhWHQ1FgEDQEBpAboiB4mhQPr5Sp8EqVCk8T4+F6oD8cDphDivwDoCRBDrrtO3RCYsjjN6UC1tcWJGcrKz8pT1X+tkMkhkZRiPNhYABvkUoBtmkIGGsBmj/3os5ARlfnkI7AYHgSEuxuCPQfLcKEKtZvqNLp3wURIJDPoIWIWu3H5WnKX4pDxXAlVDTWKZGABdswuGwZcTc1grPtKrifPPLA9e01cNYboTNeTrok4dApCSPtIcFju0NEsD9v/QEdtktot6cCbVXVTKPROKsmd83z3WIJ3BaLXD3SCOjAjXwtkcLQVg3wF88B/9MTICMjHgg6f74F+ubPh9fiMNIRKYPeiEhyJzTEWYYclRpNuQ7bhXviR9EGPVVfVsaUR8mgTSIe60PjjugY8kYWAx1hUrCvWwv8hRZwP3oIZKAfeAFCJWeboSctHTqkkfAG7f+OjgFrVDRpw9YeTEyCOi2diZ2ZTh0xmRIPZas7T4QE813RMt4Sm0A6ZbFgiY2HTnTqmZsCTqYKyDeXgdy/C/y9H4FcvQKOokLoxKQsMXFeW1ksQV+wREW7zKIQol3z6S0WW0XpC4qauNg4eC4Nhz48DZa4BOiKT/TAIkh07sUg9o35MHLoIIxUHYTB9XnQHY92k2y78Bl9iTVBzt8Xi3itUvXaVFc3m+Jy1wx8KQ3jrXHx0C1PJt1YXo882YtxvRsDd2Om3UjUgxD0CZtJEHz7kubCXzKZ67AsGuh9+6TUfiS+FxUBtpRU6MZMe1MUU9CH7/sUiNQ06EXZ69Px/b9thXb2pKSS/uRk/hxW0cTpzJQ+Jpq8iI2BAUUaLiq8ZON4F0QxQewL5LHxrU+yFzhsqN+QhEKLlgXqs8hw+D0pEWyqDOhPV0K/UuWFoOO7wQULYDA7GwbVarAtXjwB4Xlw4UIYmDcPrJP8+hBDGZnkVkQYmItLXNTRSKn7ZbIcHJmZSKiCgYwMGEDpIczJAVturgf298C3ZluxAgYxkOBnRf9h5PouXAJnOQ6oRkUKPEtKIMP40fRnZZEBXLTlrALH5s1g27QJ7AjHuJwCjcYjbRs3gh1t7fn5nor6szLJcNY8cgMPTuuRo72UYX3+D3cSYmF4vFzb8uVgLyoCe2GhBw5B/x/YBNtduzxBbQsWglWV7vpakQwGjlNStfsrdp5PTXFZM1XEplYTzIo4DhwAe3k5OPbu/SAItnaUtj17yFBODv9nstx9Mjvbom9omEXp6utmNK7Lu/04IY68VatdtoICcHAcsdM0OBjmw+C1JTaUb1evdt7FU2koKGDp6mr82XEsZaKZeedxc96kK9wjBYXEXl8PQwYDDBmNHwSHwUDsJiOM1NTwHco0d8uiRf26mtqPWIaeSQnjkaupoYy7issvyxPcg4vVo6NGI3GcOEGGjh4lw2YzDB879p8YamoijqYmGGludg9szHdez1CCWVddSnvnjN/EqGQwyKmS0kc38Mh2r1ox5jx5gn/b2gqOlhYyfPo0vAdk6MwZMnzxIjhbW139xTvh+0wVmLX0floYXiwzg500MqcJ/26TyTT78K5i/Vcpc+FFlgo3rtzlPHPWPXbtGhlpayOjbe3gwbU2MtbeDs7LV9x2g8H568rlcCkr4w8TTS/iqms843f8AjE+9McfGIbBPeGo45WHmLOrVva1yxPhUUY6vNyQ5+7aWei2Vh4gVm0l6dm7x/1yi8b1eIkarmMyp/LWPahmOZHgyzHMjMkXiYnhzHrlNKFvQol6nS7gWFlZ48k1a38+hx/fJSS6kJwE5xGCfhG/m9Mb8p9+wenqaGHYe5OcQj4lADc+pH2Ggq7FY8YZDFQ9w8h1FQfjb5qPPb9pPv6cQ/1wba2cw7tTlUCGSSGm+Tox+dryD68sSIU4MRj4AAAAAElFTkSuQmCC'
python image base64 pyside2
asked Nov 11 at 19:30
MikeyB
64349
add a comment |
up vote
1
down vote
favorite
up vote
1
down vote
favorite
I have code that's up and running creating images from files. That code looks like this:
qlabel = QLabel()
qlabel.setText('')
w = QtGui.QPixmap(filename).width()
h = QtGui.QPixmap(filename).height()
qlabel.setGeometry(QtCore.QRect(0, 0, w, h))
qlabel.setPixmap(QtGui.QPixmap(filename))
qt_row_layout.addWidget(qlabel)
I've tried other techniques that I found on SO but none are working. Here is my latest attempt
qlabel = QLabel()
qlabel.setText('')
ba = QtCore.QByteArray.fromBase64(base64data)
image = QImage()
image.loadFromData(ba)
pixmap = QPixmap()
pixmap.fromImage(image)
w = image.width()
h = image.height()
qlabel.setGeometry(QtCore.QRect(0, 0, w, h))
qlabel.setPixmap(pixmap)
I am porting code that I have up and running on tkinter. It's tricky doing the translations.
EDIT - as requested, one of my graphics...
mac_red = b'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'
python image base64 pyside2
asked Nov 11 at 19:30
MikeyB
64349
I have code that's up and running creating images from files. That code looks like this:
qlabel = QLabel()
qlabel.setText('')
w = QtGui.QPixmap(filename).width()
h = QtGui.QPixmap(filename).height()
qlabel.setGeometry(QtCore.QRect(0, 0, w, h))
qlabel.setPixmap(QtGui.QPixmap(filename))
qt_row_layout.addWidget(qlabel)
I've tried other techniques that I found on SO but none are working. Here is my latest attempt
qlabel = QLabel()
qlabel.setText('')
ba = QtCore.QByteArray.fromBase64(base64data)
image = QImage()
image.loadFromData(ba)
pixmap = QPixmap()
pixmap.fromImage(image)
w = image.width()
h = image.height()
qlabel.setGeometry(QtCore.QRect(0, 0, w, h))
qlabel.setPixmap(pixmap)
I am porting code that I have up and running on tkinter. It's tricky doing the translations.
EDIT - as requested, one of my graphics...
mac_red = b'iVBORw0KGgoAAAANSUhEUgAAABgAAAAZCAYAAAArK+5dAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAGHRFWHRTb2Z0d2FyZQBwYWludC5uZXQgNC4xLjFjKpxLAAAGfklEQVR42o1W6VNTVxR/Kv4Htp1xZA0JhCWsAQmQAC4Yd0GtKBqXUUAREBdE8pYAWVhUotVWVOpGpzpVqI51pnas+sFtOnXUmXY6o10sErYASUAgybun5yUEoWOnfvjNOe/dc35nufe9cymO4ygBLMt6JMey01mansmaTJS5sVFRrdlsrpq/0LVNEk62RkTB5vBIvjBKRiqyFz0zlpQydUeOUFU6HcVoaT8fzwQXYgo5yzDTWGGhtpYyFO+u2afK7EBSt0Yk5ncEBUGJvz+UInYEBZMtoRKyPSaOr1i67EEDTS+r1usphqan+4jfBXhHPp3FTKppes6hJUvvbhWHQ1FgEDQEBpAboiB4mhQPr5Sp8EqVCk8T4+F6oD8cDphDivwDoCRBDrrtO3RCYsjjN6UC1tcWJGcrKz8pT1X+tkMkhkZRiPNhYABvkUoBtmkIGGsBmj/3os5ARlfnkI7AYHgSEuxuCPQfLcKEKtZvqNLp3wURIJDPoIWIWu3H5WnKX4pDxXAlVDTWKZGABdswuGwZcTc1grPtKrifPPLA9e01cNYboTNeTrok4dApCSPtIcFju0NEsD9v/QEdtktot6cCbVXVTKPROKsmd83z3WIJ3BaLXD3SCOjAjXwtkcLQVg3wF88B/9MTICMjHgg6f74F+ubPh9fiMNIRKYPeiEhyJzTEWYYclRpNuQ7bhXviR9EGPVVfVsaUR8mgTSIe60PjjugY8kYWAx1hUrCvWwv8hRZwP3oIZKAfeAFCJWeboSctHTqkkfAG7f+OjgFrVDRpw9YeTEyCOi2diZ2ZTh0xmRIPZas7T4QE813RMt4Sm0A6ZbFgiY2HTnTqmZsCTqYKyDeXgdy/C/y9H4FcvQKOokLoxKQsMXFeW1ksQV+wREW7zKIQol3z6S0WW0XpC4qauNg4eC4Nhz48DZa4BOiKT/TAIkh07sUg9o35MHLoIIxUHYTB9XnQHY92k2y78Bl9iTVBzt8Xi3itUvXaVFc3m+Jy1wx8KQ3jrXHx0C1PJt1YXo882YtxvRsDd2Om3UjUgxD0CZtJEHz7kubCXzKZ67AsGuh9+6TUfiS+FxUBtpRU6MZMe1MUU9CH7/sUiNQ06EXZ69Px/b9thXb2pKSS/uRk/hxW0cTpzJQ+Jpq8iI2BAUUaLiq8ZON4F0QxQewL5LHxrU+yFzhsqN+QhEKLlgXqs8hw+D0pEWyqDOhPV0K/UuWFoOO7wQULYDA7GwbVarAtXjwB4Xlw4UIYmDcPrJP8+hBDGZnkVkQYmItLXNTRSKn7ZbIcHJmZSKiCgYwMGEDpIczJAVturgf298C3ZluxAgYxkOBnRf9h5PouXAJnOQ6oRkUKPEtKIMP40fRnZZEBXLTlrALH5s1g27QJ7AjHuJwCjcYjbRs3gh1t7fn5nor6szLJcNY8cgMPTuuRo72UYX3+D3cSYmF4vFzb8uVgLyoCe2GhBw5B/x/YBNtduzxBbQsWglWV7vpakQwGjlNStfsrdp5PTXFZM1XEplYTzIo4DhwAe3k5OPbu/SAItnaUtj17yFBODv9nstx9Mjvbom9omEXp6utmNK7Lu/04IY68VatdtoICcHAcsdM0OBjmw+C1JTaUb1evdt7FU2koKGDp6mr82XEsZaKZeedxc96kK9wjBYXEXl8PQwYDDBmNHwSHwUDsJiOM1NTwHco0d8uiRf26mtqPWIaeSQnjkaupoYy7issvyxPcg4vVo6NGI3GcOEGGjh4lw2YzDB879p8YamoijqYmGGludg9szHdez1CCWVddSnvnjN/EqGQwyKmS0kc38Mh2r1ox5jx5gn/b2gqOlhYyfPo0vAdk6MwZMnzxIjhbW139xTvh+0wVmLX0floYXiwzg500MqcJ/26TyTT78K5i/Vcpc+FFlgo3rtzlPHPWPXbtGhlpayOjbe3gwbU2MtbeDs7LV9x2g8H568rlcCkr4w8TTS/iqms843f8AjE+9McfGIbBPeGo45WHmLOrVva1yxPhUUY6vNyQ5+7aWei2Vh4gVm0l6dm7x/1yi8b1eIkarmMyp/LWPahmOZHgyzHMjMkXiYnhzHrlNKFvQol6nS7gWFlZ48k1a38+hx/fJSS6kJwE5xGCfhG/m9Mb8p9+wenqaGHYe5OcQj4lADc+pH2Ggq7FY8YZDFQ9w8h1FQfjb5qPPb9pPv6cQ/1wba2cw7tTlUCGSSGm+Tox+dryD68sSIU4MRj4AAAAAElFTkSuQmCC'
python image base64 pyside2
python image base64 pyside2
asked Nov 11 at 19:30
MikeyB
64349
asked Nov 11 at 19:30
MikeyB
64349
edited Nov 12 at 0:59
asked Nov 11 at 19:30
MikeyB
64349
asked Nov 11 at 19:30
MikeyB
64349
asked Nov 11 at 19:30
MikeyB
64349
64349
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1 Answer
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Qt does not require the header data:image/png;base64,, so you must remove it.
from PySide2 import QtCore, QtGui, QtWidgets
if __name__ == '__main__':
import sys
app = QtWidgets.QApplication(sys.argv)
base64data = b'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'
qlabel = QtWidgets.QLabel(alignment=QtCore.Qt.AlignCenter)
ba = QtCore.QByteArray.fromBase64(base64data)
pixmap = QtGui.QPixmap()
pixmap.loadFromData(ba)
qlabel.setPixmap(pixmap)
qlabel.show()
sys.exit(app.exec_())
answered Nov 11 at 20:43
eyllanesc
70.7k93052
You NAILED it! I was close-ish. Must ask questions sooner!
– MikeyB
Nov 12 at 1:00
@MikeyB What questions do you have?
– eyllanesc
Nov 12 at 1:04
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1 Answer
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Qt does not require the header data:image/png;base64,, so you must remove it.
from PySide2 import QtCore, QtGui, QtWidgets
if __name__ == '__main__':
import sys
app = QtWidgets.QApplication(sys.argv)
base64data = 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'
qlabel = QtWidgets.QLabel(alignment=QtCore.Qt.AlignCenter)
ba = QtCore.QByteArray.fromBase64(base64data)
pixmap = QtGui.QPixmap()
pixmap.loadFromData(ba)
qlabel.setPixmap(pixmap)
qlabel.show()
sys.exit(app.exec_())
answered Nov 11 at 20:43
eyllanesc
70.7k93052
You NAILED it! I was close-ish. Must ask questions sooner!
– MikeyB
Nov 12 at 1:00
@MikeyB What questions do you have?
– eyllanesc
Nov 12 at 1:04
add a comment |
up vote
1
down vote
accepted
Qt does not require the header data:image/png;base64,, so you must remove it.
from PySide2 import QtCore, QtGui, QtWidgets
if __name__ == '__main__':
import sys
app = QtWidgets.QApplication(sys.argv)
base64data = 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'
qlabel = QtWidgets.QLabel(alignment=QtCore.Qt.AlignCenter)
ba = QtCore.QByteArray.fromBase64(base64data)
pixmap = QtGui.QPixmap()
pixmap.loadFromData(ba)
qlabel.setPixmap(pixmap)
qlabel.show()
sys.exit(app.exec_())
answered Nov 11 at 20:43
eyllanesc
70.7k93052
You NAILED it! I was close-ish. Must ask questions sooner!
– MikeyB
Nov 12 at 1:00
@MikeyB What questions do you have?
– eyllanesc
Nov 12 at 1:04
add a comment |
up vote
1
down vote
accepted
up vote
1
down vote
accepted
Qt does not require the header data:image/png;base64,, so you must remove it.
from PySide2 import QtCore, QtGui, QtWidgets
if __name__ == '__main__':
import sys
app = QtWidgets.QApplication(sys.argv)
base64data = 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'
qlabel = QtWidgets.QLabel(alignment=QtCore.Qt.AlignCenter)
ba = QtCore.QByteArray.fromBase64(base64data)
pixmap = QtGui.QPixmap()
pixmap.loadFromData(ba)
qlabel.setPixmap(pixmap)
qlabel.show()
sys.exit(app.exec_())
answered Nov 11 at 20:43
eyllanesc
70.7k93052
Qt does not require the header data:image/png;base64,, so you must remove it.
from PySide2 import QtCore, QtGui, QtWidgets
if __name__ == '__main__':
import sys
app = QtWidgets.QApplication(sys.argv)
base64data = 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'
qlabel = QtWidgets.QLabel(alignment=QtCore.Qt.AlignCenter)
ba = QtCore.QByteArray.fromBase64(base64data)
pixmap = QtGui.QPixmap()
pixmap.loadFromData(ba)
qlabel.setPixmap(pixmap)
qlabel.show()
sys.exit(app.exec_())
answered Nov 11 at 20:43
eyllanesc
70.7k93052
answered Nov 11 at 20:43
eyllanesc
70.7k93052
answered Nov 11 at 20:43
eyllanesc
70.7k93052
answered Nov 11 at 20:43
eyllanesc
70.7k93052
70.7k93052
You NAILED it! I was close-ish. Must ask questions sooner!
– MikeyB
Nov 12 at 1:00
@MikeyB What questions do you have?
– eyllanesc
Nov 12 at 1:04
add a comment |
You NAILED it! I was close-ish. Must ask questions sooner!
– MikeyB
Nov 12 at 1:00
@MikeyB What questions do you have?
– eyllanesc
Nov 12 at 1:04
You NAILED it! I was close-ish. Must ask questions sooner!
– MikeyB
Nov 12 at 1:00
You NAILED it! I was close-ish. Must ask questions sooner!
– MikeyB
Nov 12 at 1:00
@MikeyB What questions do you have?
– eyllanesc
Nov 12 at 1:04
@MikeyB What questions do you have?
– eyllanesc
Nov 12 at 1:04
add a comment |
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