def test_mat_construct(self):
data = np.random.random([10, 10, 3])
#print(np.ndarray.__dictoffset__) # 0
#print(cv.Mat.__dictoffset__) # 88 (> 0)
#print(cv.Mat) # <class cv2.Mat>
#print(cv.Mat.__base__) # <class 'numpy.ndarray'>
mat_data0 = cv.Mat(data)
assert isinstance(mat_data0, cv.Mat)
assert isinstance(mat_data0, np.ndarray)
self.assertEqual(mat_data0.wrap_channels, False)
res0 = cv.utils.dumpInputArray(mat_data0)
self.assertEqual(res0, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=300 dims(-1)=3 size(-1)=[10 10 3] type(-1)=CV_64FC1")
mat_data1 = cv.Mat(data, wrap_channels=True)
assert isinstance(mat_data1, cv.Mat)
assert isinstance(mat_data1, np.ndarray)
self.assertEqual(mat_data1.wrap_channels, True)
res1 = cv.utils.dumpInputArray(mat_data1)
self.assertEqual(res1, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=100 dims(-1)=2 size(-1)=10x10 type(-1)=CV_64FC3")
mat_data2 = cv.Mat(mat_data1)
assert isinstance(mat_data2, cv.Mat)
assert isinstance(mat_data2, np.ndarray)
self.assertEqual(mat_data2.wrap_channels, True) # fail if __array_finalize__ doesn't work
res2 = cv.utils.dumpInputArray(mat_data2)
self.assertEqual(res2, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=100 dims(-1)=2 size(-1)=10x10 type(-1)=CV_64FC3")
def qimage2CvMat(inImage, inCloneImageData=True):
if inImage.format() == QImage.Format_ARGB32 or inImage.format(
) == QImage.Format_ARGB32_Premultiplied:
m = cv2.Mat(inImage.height(), inImage.width(), cv2.CV_8UC4,
inImage.bits(), inImage.bytesPerLine())
return (m.clone() if inCloneImageData else m)
elif inImage.format() == QImage.Format_RGB32 or inImage.format(
) == QImage.Format_RGB888:
if inCloneImageData == False:
qWarning(
"QImageToCvMat() - Conversion requires cloning because we use a temporary QImage"
)
swapped = inImage
if inImage.format() == QImage.Format_RGB32:
swapped = swapped.convertToFormat(QImage.Format_RGB888)
swapped = swapped.rgbSwapped()
return cv2.Mat(swapped.height, swapped.width(), cv2.CV_8UC3,
swapped.bits(), swapped.bytePerLine()).clone()
elif inImage.format() == QImage.Format_Indexed8:
mat = cv2.Mat(inImage.height(), inImage.width(), cv2.CV_8UCl,
inImage.bits(), inImage.bytePerLine())
return (mat.clone() if inCloneImageData else mat)
else:
qWarning("QImageToCvMat() - QImage format not handled in switch:" +
inImage.format())
return cv2.Mat()
def test_comparison(self):
# Undefined behavior, do NOT use that.
# Behavior may be changed in the future
data = np.ones((10, 10, 3))
mat_wrapped = cv.Mat(data, wrap_channels=True)
mat_simple = cv.Mat(data)
np.testing.assert_equal(mat_wrapped, mat_simple) # ???: wrap_channels is not checked for now
np.testing.assert_equal(data, mat_simple)
np.testing.assert_equal(data, mat_wrapped)
def test_mat_wrap_channels_fail(self):
data = np.random.random([2, 3, 4, 520])
mat_data0 = cv.Mat(data)
assert isinstance(mat_data0, cv.Mat)
assert isinstance(mat_data0, np.ndarray)
self.assertEqual(mat_data0.wrap_channels, False)
res0 = cv.utils.dumpInputArray(mat_data0)
self.assertEqual(res0, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=12480 dims(-1)=4 size(-1)=[2 3 4 520] type(-1)=CV_64FC1")
with self.assertRaises(cv.error):
mat_data1 = cv.Mat(data, wrap_channels=True) # argument unable to wrap channels, too high (520 > CV_CN_MAX=512)
res1 = cv.utils.dumpInputArray(mat_data1)
print(mat_data1.__dict__)
print(res1)
def read32FC1Mat(data, scale):
if len(data) == 0:
return []
bias16U = cv2.imdecode(asMat(data), cv2.IMREAD_ANYDEPTH)
bias32F = bias16U.astype(np.float32)
mat32F = bias32F
mat32F[:, :] -= Encode32FBias
if scale == 0:
return cv2.Mat()
else:
return mat32F / scale
def test_mat_construct_4d(self):
data = np.random.random([5, 10, 10, 3])
mat_data0 = cv.Mat(data)
assert isinstance(mat_data0, cv.Mat)
assert isinstance(mat_data0, np.ndarray)
self.assertEqual(mat_data0.wrap_channels, False)
res0 = cv.utils.dumpInputArray(mat_data0)
self.assertEqual(res0, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=1500 dims(-1)=4 size(-1)=[5 10 10 3] type(-1)=CV_64FC1")
mat_data1 = cv.Mat(data, wrap_channels=True)
assert isinstance(mat_data1, cv.Mat)
assert isinstance(mat_data1, np.ndarray)
self.assertEqual(mat_data1.wrap_channels, True)
res1 = cv.utils.dumpInputArray(mat_data1)
self.assertEqual(res1, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=500 dims(-1)=3 size(-1)=[5 10 10] type(-1)=CV_64FC3")
mat_data2 = cv.Mat(mat_data1)
assert isinstance(mat_data2, cv.Mat)
assert isinstance(mat_data2, np.ndarray)
self.assertEqual(mat_data2.wrap_channels, True) # __array_finalize__ doesn't work
res2 = cv.utils.dumpInputArray(mat_data2)
self.assertEqual(res2, "InputArray: empty()=false kind=0x00010000 flags=0x01010000 total(-1)=500 dims(-1)=3 size(-1)=[5 10 10] type(-1)=CV_64FC3")
def displayImg():
while (not stopFlag.isSet()):
try:
print('before displayImg read ')
rawImg = process.stdout.read(100)
print('-----stdout byts------: ', rawImg)
process.stdout.flush()
return
if len(rawImg) != WIDTH * HEIGHT * 3:
continue
img = np.fromstring(rawImg, dtype='uint8')
img = img.reshape((HEIGHT, WIDTH, 3))
mat = cv2.Mat(img)
cv2.imshow('drone', mat)
cv2.waitKey(0.01)
except Exception as e:
print("displayImg error", e)
def fit(proj_skeleton, mask):
return cv2.Mat()
def test_ufuncs(self):
data = np.arange(10)
mat_data = cv.Mat(data)
mat_data2 = 2 * mat_data
self.assertEqual(type(mat_data2), cv.Mat)
np.testing.assert_equal(2 * data, 2 * mat_data)
