def test_alpha_view():
qimg = QtGui.QImage(320, 240, QtGui.QImage.Format_ARGB32)
qimg.fill(23)
v = qimage2ndarray.alpha_view(qimg)
qimg.setPixel(12, 10, QtGui.qRgb(12,34,56))
assert_equal(v[10,12], 255)
assert_equal(v[10,11], 0)
def test_rgb_view():
qimg = QtGui.QImage(320, 240, QtGui.QImage.Format_RGB32)
qimg.fill(QtGui.qRgb(23,0,0))
v = qimage2ndarray.rgb_view(qimg)
qimg.setPixel(12, 10, QtGui.qRgb(12,34,56))
assert_equal(list(v[10,10]), [23,0,0])
assert_equal(list(v[10,12]), [12,34,56])
def test_RGBA8888():
qimg = QtGui.QImage(320, 240, QtGui.QImage.Format_RGBA8888)
qimg.fill(0)
v = _qimageview(qimg)
qimg.setPixel(12, 10, QtGui.qRgb(0x12, 0x34, 0x56))
assert_equal(v.shape, (240, 320))
assert_equal(v[10, 12],
0x123456ff if sys.byteorder == 'big' else 0xff563412)
assert_equal(v.nbytes, numBytes(qimg))
def test_ARGB32():
qimg = QtGui.QImage(320, 240, QtGui.QImage.Format_ARGB32)
qimg.fill(0)
v = _qimageview(qimg)
qimg.setPixel(12, 10, QtGui.qRgb(0x12, 0x34, 0x56))
assert_equal(v.shape, (240, 320))
assert_equal(v[10, 12],
0xff123456 if sys.byteorder == 'little' else 0x563412ff)
assert_equal(v.nbytes, numBytes(qimg))
def test_raw_rgba64():
qimg = QtGui.QImage(320, 240, QtGui.QImage.Format_RGBA64)
qimg.fill(0)
v = qimage2ndarray.raw_view(qimg)
qimg.fill(1)
qimg.setPixel(12, 10, QtGui.qRgb(0x12, 0x34, 0x56))
assert_equal(v.shape, (240, 320))
assert_equal(v[10, 10], 0x010100000000)
assert_equal(v[10, 12], 0xffff565634341212)
assert_equal(v.nbytes, numBytes(qimg))
def test_raw_grayscale8():
qimg = QtGui.QImage(320, 240, QtGui.QImage.Format_Grayscale8)
qimg.fill(0)
v = qimage2ndarray.raw_view(qimg)
qimg.fill(1)
qimg.setPixel(12, 10, QtGui.qRgb(42, 42, 42))
assert_equal(v.shape, (240, 320))
assert_equal(v[10, 10], 1)
assert_equal(v[10, 12], 42)
assert_equal(v.nbytes, numBytes(qimg))
def test_raw_rgb16():
qimg = QtGui.QImage(320, 240, QtGui.QImage.Format_RGB16)
qimg.fill(0)
v = qimage2ndarray.raw_view(qimg)
qimg.fill(23)
qimg.setPixel(12, 10, QtGui.qRgb(0, 0, 91))
assert_equal(v.shape, (240, 320))
assert_equal(v[10, 10], 23)
assert_equal(v[10, 12], 91 >> 3)
assert_equal(v.nbytes, numBytes(qimg))
def test_raw_rgb32():
qimg = QtGui.QImage(320, 240, QtGui.QImage.Format_RGB32)
qimg.fill(0)
v = qimage2ndarray.raw_view(qimg)
qimg.fill(23)
qimg.setPixel(12, 10, QtGui.qRgb(0, 0, 42))
assert_equal(v.shape, (240, 320))
assert_equal(v[10, 10], 23 | 0xff000000)
assert_equal(v[10, 12], 42 | 0xff000000)
assert_equal(v.nbytes, numBytes(qimg))
def test_recarray_view():
qimg = QtGui.QImage(320, 240, QtGui.QImage.Format_ARGB32)
qimg.fill(23)
v = qimage2ndarray.recarray_view(qimg)
qimg.setPixel(12, 10, QtGui.qRgb(12,34,56))
assert_equal(v["g"][10,12], 34)
assert_equal(v["g"].sum(), 34)
assert_equal(v["green"].sum(), 34)
assert_equal(v.g[10,12], 34)
# this worked in the past, but with NumPy 1.2.1, I get:
# TypeError: function takes at most 2 arguments (3 given)
assert_equal(v[10,12]["g"], 34)
def test_RGBX64():
qimg = QtGui.QImage(320, 240, QtGui.QImage.Format_RGBX64)
qimg.fill(QtGui.qRgb(0, 0, 0))
v = _qimageview(qimg)
qimg.setPixel(12, 10, QtGui.qRgb(0x12, 0x34, 0x56))
assert_equal(v.shape, (240, 320))
assert_equal(v[10, 10],
0xffff000000000000 if sys.byteorder == 'little' else 0xffff)
assert_equal(
v[10, 12], 0xffff565634341212
if sys.byteorder == 'little' else 0x121234345656ffff)
assert_equal(v.nbytes, numBytes(qimg))
def test_scalar2qimage():
a = numpy.zeros((240, 320), dtype = float)
a[12,10] = 42.42
a[13,10] = -10
qImg = qimage2ndarray.array2qimage(a)
assert not qImg.isNull()
assert_equal(qImg.width(), 320)
assert_equal(qImg.height(), 240)
assert_equal(qImg.format(), QtGui.QImage.Format_RGB32)
assert_equal(hex(qImg.pixel(10,12)), hex(QtGui.qRgb(42,42,42))) # max pixel
assert_equal(hex(qImg.pixel(10,14)), hex(QtGui.qRgb(0,0,0))) # zero pixel
assert_equal(hex(qImg.pixel(10,13)), hex(QtGui.qRgb(0,0,0))) # min pixel
def test_rgb2qimage():
a = numpy.zeros((240, 320, 3), dtype = float)
a[12,10] = (42.42, 20, 14)
a[13,10] = (-10, 0, -14)
qImg = qimage2ndarray.array2qimage(a)
assert not qImg.isNull()
assert_equal(qImg.width(), 320)
assert_equal(qImg.height(), 240)
assert_equal(qImg.format(), QtGui.QImage.Format_RGB32)
assert_equal(hex(qImg.pixel(10,12)), hex(QtGui.qRgb(42,20,14)))
assert_equal(hex(qImg.pixel(10,13)), hex(QtGui.qRgb(0,0,0)))
assert_equal(hex(qImg.pixel(10,14)), hex(QtGui.qRgb(0,0,0)))
def test_rgb2qimage():
a = numpy.zeros((240, 320, 3), dtype = float)
a[12,10] = (42.42, 20, 14)
a[13,10] = (-10, 0, -14)
qImg = qimage2ndarray.array2qimage(a)
assert not qImg.isNull()
assert_equal(qImg.width(), 320)
assert_equal(qImg.height(), 240)
assert_equal(qImg.format(), QtGui.QImage.Format_RGB32)
assert_equal(hex(qImg.pixel(10,12)), hex(QtGui.qRgb(42,20,14)))
assert_equal(hex(qImg.pixel(10,13)), hex(QtGui.qRgb(0,0,0)))
assert_equal(hex(qImg.pixel(10,14)), hex(QtGui.qRgb(0,0,0)))
def test_scalar2qimage():
a = numpy.zeros((240, 320), dtype = float)
a[12,10] = 42.42
a[13,10] = -10
qImg = qimage2ndarray.array2qimage(a)
assert not qImg.isNull()
assert_equal(qImg.width(), 320)
assert_equal(qImg.height(), 240)
assert_equal(qImg.format(), QtGui.QImage.Format_RGB32)
assert_equal(hex(qImg.pixel(10,12)), hex(QtGui.qRgb(42,42,42))) # max pixel
assert_equal(hex(qImg.pixel(10,14)), hex(QtGui.qRgb(0,0,0))) # zero pixel
assert_equal(hex(qImg.pixel(10,13)), hex(QtGui.qRgb(0,0,0))) # min pixel
def test_scalar2qimage_with_alpha():
a = numpy.zeros((240, 320, 2), dtype = float)
a[...,1] = 255
a[12,10] = (42.42, 128)
a[13,10] = (-10, 0)
qImg = qimage2ndarray.array2qimage(a)
assert not qImg.isNull()
assert_equal(qImg.width(), 320)
assert_equal(qImg.height(), 240)
assert_equal(qImg.format(), QtGui.QImage.Format_ARGB32)
assert_equal(hex(qImg.pixel(10,12)), hex(QtGui.qRgba(42,42,42,128))) # max pixel
assert_equal(hex(qImg.pixel(10,14)), hex(QtGui.qRgba(0,0,0,255))) # zero pixel
assert_equal(hex(qImg.pixel(10,13)), hex(QtGui.qRgba(0,0,0,0))) # min pixel
def test_scalar2qimage_with_alpha():
a = numpy.zeros((240, 320, 2), dtype = float)
a[...,1] = 255
a[12,10] = (42.42, 128)
a[13,10] = (-10, 0)
qImg = qimage2ndarray.array2qimage(a)
assert not qImg.isNull()
assert_equal(qImg.width(), 320)
assert_equal(qImg.height(), 240)
assert_equal(qImg.format(), QtGui.QImage.Format_ARGB32)
assert_equal(hex(qImg.pixel(10,12)), hex(QtGui.qRgba(42,42,42,128))) # max pixel
assert_equal(hex(qImg.pixel(10,14)), hex(QtGui.qRgba(0,0,0,255))) # zero pixel
assert_equal(hex(qImg.pixel(10,13)), hex(QtGui.qRgba(0,0,0,0))) # min pixel
def test_gray2qimage():
a = numpy.zeros((240, 320), dtype = float)
a[12,10] = 42.42
a[13,10] = -10
qImg = qimage2ndarray.gray2qimage(a)
assert not qImg.isNull()
assert_equal(qImg.width(), 320)
assert_equal(qImg.height(), 240)
assert_equal(qImg.format(), QtGui.QImage.Format_Indexed8)
assert_equal(a.nbytes, numBytes(qImg) * a.itemsize)
assert_equal(numColors(qImg), 256)
assert_equal(hex(qImg.pixel(10,12)), hex(QtGui.qRgb(42,42,42)))
assert_equal(hex(qImg.pixel(10,14)), hex(QtGui.qRgb(0,0,0)))
assert_equal(hex(qImg.pixel(10,13)), hex(QtGui.qRgb(0,0,0)))
def test_gray2qimage():
a = numpy.zeros((240, 320), dtype = float)
a[12,10] = 42.42
a[13,10] = -10
qImg = qimage2ndarray.gray2qimage(a)
assert not qImg.isNull()
assert_equal(qImg.width(), 320)
assert_equal(qImg.height(), 240)
assert_equal(qImg.format(), QtGui.QImage.Format_Indexed8)
assert_equal(a.nbytes, numBytes(qImg) * a.itemsize)
assert_equal(numColors(qImg), 256)
assert_equal(hex(qImg.pixel(10,12)), hex(QtGui.qRgb(42,42,42)))
assert_equal(hex(qImg.pixel(10,14)), hex(QtGui.qRgb(0,0,0)))
assert_equal(hex(qImg.pixel(10,13)), hex(QtGui.qRgb(0,0,0)))
def test_bool2qimage_normalize():
a = numpy.zeros((240, 320), dtype = bool)
a[12,10] = True
# normalization should scale to 0/255
# (not raise a numpy exception, see issue #17)
qImg = qimage2ndarray.gray2qimage(a, normalize = True)
assert not qImg.isNull()
assert_equal(qImg.width(), 320)
assert_equal(qImg.height(), 240)
assert_equal(hex(qImg.pixel(10,12)), hex(QtGui.qRgb(255,255,255)))
assert_equal(hex(qImg.pixel(0,0)), hex(QtGui.qRgb(0,0,0)))
a[:] = True
qImg = qimage2ndarray.gray2qimage(a, normalize = True)
# for boolean arrays, I would assume True should always map to 255
assert_equal(hex(qImg.pixel(0,0)), hex(QtGui.qRgb(255,255,255)))
def test_scalar2qimage_normalize_domain():
a = numpy.zeros((240, 320), dtype = float)
a[12,10] = 42.42
a[13,10] = -10
qImg = qimage2ndarray.array2qimage(a, normalize = (-100, 100))
assert not qImg.isNull()
assert_equal(qImg.width(), 320)
assert_equal(qImg.height(), 240)
assert_equal(qImg.format(), QtGui.QImage.Format_RGB32)
x = int(255 * 142.42 / 200.0)
assert_equal(hex(qImg.pixel(10,12)), hex(QtGui.qRgb(x,x,x)))
x = int(255 * 90.0 / 200.0)
assert_equal(hex(qImg.pixel(10,13)), hex(QtGui.qRgb(x,x,x)))
x = int(255 * 100.0 / 200.0)
assert_equal(hex(qImg.pixel(10,14)), hex(QtGui.qRgb(x,x,x)))
def test_rgb2qimage_normalize():
a = numpy.zeros((240, 320, 3), dtype = float)
a[12,10] = (42.42, 20, 14)
a[13,10] = (-10, 20, 0)
qImg = qimage2ndarray.array2qimage(a, normalize = True)
assert not qImg.isNull()
assert_equal(qImg.width(), 320)
assert_equal(qImg.height(), 240)
assert_equal(qImg.format(), QtGui.QImage.Format_RGB32)
assert_equal(hex(qImg.pixel(10,12)),
hex(QtGui.qRgb(255,(255*30.0/52.42),(255*24/52.42))))
assert_equal(hex(qImg.pixel(10,13)),
hex(QtGui.qRgb(0,(255*30.0/52.42),(255*10/52.42))))
x = int(255 * 10.0 / 52.42)
assert_equal(hex(qImg.pixel(10,14)), hex(QtGui.qRgb(x,x,x))) # zero pixel
def test_rgb2qimage_normalize():
a = numpy.zeros((240, 320, 3), dtype = float)
a[12,10] = (42.42, 20, 14)
a[13,10] = (-10, 20, 0)
qImg = qimage2ndarray.array2qimage(a, normalize = True)
assert not qImg.isNull()
assert_equal(qImg.width(), 320)
assert_equal(qImg.height(), 240)
assert_equal(qImg.format(), QtGui.QImage.Format_RGB32)
assert_equal(hex(qImg.pixel(10,12)),
hex(QtGui.qRgb(255,(255*30.0/52.42),(255*24/52.42))))
assert_equal(hex(qImg.pixel(10,13)),
hex(QtGui.qRgb(0,(255*30.0/52.42),(255*10/52.42))))
x = int(255 * 10.0 / 52.42)
assert_equal(hex(qImg.pixel(10,14)), hex(QtGui.qRgb(x,x,x))) # zero pixel
def test_scalar2qimage_normalize_domain():
a = numpy.zeros((240, 320), dtype = float)
a[12,10] = 42.42
a[13,10] = -10
qImg = qimage2ndarray.array2qimage(a, normalize = (-100, 100))
assert not qImg.isNull()
assert_equal(qImg.width(), 320)
assert_equal(qImg.height(), 240)
assert_equal(qImg.format(), QtGui.QImage.Format_RGB32)
x = int(255 * 142.42 / 200.0)
assert_equal(hex(qImg.pixel(10,12)), hex(QtGui.qRgb(x,x,x)))
x = int(255 * 90.0 / 200.0)
assert_equal(hex(qImg.pixel(10,13)), hex(QtGui.qRgb(x,x,x)))
x = int(255 * 100.0 / 200.0)
assert_equal(hex(qImg.pixel(10,14)), hex(QtGui.qRgb(x,x,x)))
def test_gray2qimage_normalize_onlymax():
a = numpy.zeros((240, 320), dtype = float)
a[12,10] = 42.42
a[13,10] = -10
qImg = qimage2ndarray.gray2qimage(a, normalize = 80)
assert not qImg.isNull()
assert_equal(qImg.width(), 320)
assert_equal(qImg.height(), 240)
assert_equal(qImg.format(), QtGui.QImage.Format_Indexed8)
assert_equal(a.nbytes, qImg.numBytes() * a.itemsize)
assert_equal(qImg.numColors(), 256)
x = int(255 * 42.42 / 80.0)
assert_equal(hex(qImg.pixel(10,12)), hex(QtGui.qRgb(x,x,x)))
assert_equal(hex(qImg.pixel(10,13)), hex(QtGui.qRgb(0,0,0)))
assert_equal(hex(qImg.pixel(10,14)), hex(QtGui.qRgb(0,0,0)))
def test_alpha_view():
qimg = QtGui.QImage(320, 240, QtGui.QImage.Format_ARGB32)
qimg.fill(23)
v = qimage2ndarray.alpha_view(qimg)
qimg.setPixel(12, 10, QtGui.qRgb(12,34,56))
assert_equal(v[10,12], 255)
assert_equal(v[10,11], 0)
def test_scalar2qimage_masked():
a = numpy.zeros((240, 320), dtype = float)
a[12,10] = 42.42
a[13,10] = -10
a[:,160:] = 100
a = numpy.ma.masked_greater(a, 99)
qImg = qimage2ndarray.array2qimage(a, normalize = True)
assert not qImg.isNull()
assert_equal(qImg.width(), 320)
assert_equal(qImg.height(), 240)
assert_equal(qImg.format(), QtGui.QImage.Format_ARGB32)
assert_equal(hex(qImg.pixel(10,12)), hex(QtGui.qRgb(255,255,255)))
assert_equal(hex(qImg.pixel(10,13)), hex(QtGui.qRgb(0,0,0)))
x = int(255 * 10.0 / 52.42)
assert_equal(hex(qImg.pixel(10,14)), hex(QtGui.qRgb(x,x,x)))
assert_equal(QtGui.qAlpha(qImg.pixel(0,10)), 255)
assert_equal(QtGui.qAlpha(qImg.pixel(200,10)), 0)
def test_scalar2qimage_masked():
a = numpy.zeros((240, 320), dtype = float)
a[12,10] = 42.42
a[13,10] = -10
a[:,160:] = 100
a = numpy.ma.masked_greater(a, 99)
qImg = qimage2ndarray.array2qimage(a, normalize = True)
assert not qImg.isNull()
assert_equal(qImg.width(), 320)
assert_equal(qImg.height(), 240)
assert_equal(qImg.format(), QtGui.QImage.Format_ARGB32)
assert_equal(hex(qImg.pixel(10,12)), hex(QtGui.qRgb(255,255,255)))
assert_equal(hex(qImg.pixel(10,13)), hex(QtGui.qRgb(0,0,0)))
x = int(255 * 10.0 / 52.42)
assert_equal(hex(qImg.pixel(10,14)), hex(QtGui.qRgb(x,x,x)))
assert_equal(QtGui.qAlpha(qImg.pixel(0,10)), 255)
assert_equal(QtGui.qAlpha(qImg.pixel(200,10)), 0)
def test_ARGB32():
qimg = QtGui.QImage(320, 240, QtGui.QImage.Format_ARGB32)
qimg.fill(0)
v = _qimageview(qimg)
qimg.setPixel(12, 10, 42)
assert_equal(v.shape, (240, 320))
assert_equal(v[10, 12], 42)
assert_equal(v.nbytes, numBytes(qimg))
def test_viewcreation():
qimg = QtGui.QImage(320, 240, QtGui.QImage.Format_RGB32)
v = _qimageview(qimg)
assert_equal(v.shape, (240, 320))
assert v.base is qimg
del qimg
w, h = v.base.width(), v.base.height() # should not segfault
assert_equal((w, h), (320, 240))
def test_odd_size_32bit():
qimg = QtGui.QImage(321, 240, QtGui.QImage.Format_ARGB32)
qimg.fill(0)
v = _qimageview(qimg)
qimg.setPixel(12, 10, 42)
assert_equal(v.shape, (240, 321))
assert_equal(v[10, 12], 42)
assert_equal(v.strides[0], qimg.bytesPerLine())
def test_data_access():
qimg = QtGui.QImage(320, 240, QtGui.QImage.Format_Indexed8)
setNumColors(qimg, 256)
qimg.fill(42)
v = _qimageview(qimg)
assert_equal(v.shape, (240, 320))
assert_equal(v[10, 10], 42)
assert_equal(v.nbytes, numBytes(qimg))
def test_gray2qimage_normalize_domain():
a = numpy.zeros((240, 320), dtype = float)
a[12,10] = 42.42
a[13,10] = -10
qImg = qimage2ndarray.gray2qimage(a, normalize = (-100, 100))
assert not qImg.isNull()
assert_equal(qImg.width(), 320)
assert_equal(qImg.height(), 240)
assert_equal(qImg.format(), QtGui.QImage.Format_Indexed8)
assert_equal(a.nbytes, numBytes(qImg) * a.itemsize)
assert_equal(numColors(qImg), 256)
x = int(255 * 142.42 / 200.0)
assert_equal(hex(qImg.pixel(10,12)), hex(QtGui.qRgb(x,x,x)))
x = int(255 * 90.0 / 200.0)
assert_equal(hex(qImg.pixel(10,13)), hex(QtGui.qRgb(x,x,x)))
x = int(255 * 100.0 / 200.0)
assert_equal(hex(qImg.pixel(10,14)), hex(QtGui.qRgb(x,x,x)))
def test_byte_view_rgb32():
qimg = QtGui.QImage(320, 240, QtGui.QImage.Format_RGB32)
v = qimage2ndarray.byte_view(qimg)
qimg.fill(23)
qimg.setPixel(12, 10, 42)
assert_equal(v.shape, (240, 320, 4))
assert_equal(list(v[10,10]), [23, 0, 0, 0xff])
assert_equal(list(v[10,12]), [42, 0, 0, 0xff])
assert_equal(v.nbytes, numBytes(qimg))
def test_odd_size_8bit():
qimg = QtGui.QImage(321, 240, QtGui.QImage.Format_Indexed8)
setNumColors(qimg, 256)
qimg.fill(0)
v = _qimageview(qimg)
qimg.setPixel(12, 10, 42)
assert_equal(v.shape, (240, 321))
assert_equal(v[10, 12], 42)
assert_equal(v.strides[0], qimg.bytesPerLine())
def test_viewcreation():
qimg = QtGui.QImage(320, 240, QtGui.QImage.Format_RGB32)
v = _qimageview(qimg)
assert_equal(v.shape, (240, 320))
assert v.base is not None
del qimg
if hasattr(v.base, 'width'):
w, h = v.base.width(), v.base.height() # should not segfault
assert_equal((w, h), (320, 240))
v[239] = numpy.arange(320) # should not segfault
def test_byte_view_indexed():
qimg = QtGui.QImage(320, 240, QtGui.QImage.Format_Indexed8)
setNumColors(qimg, 256)
v = qimage2ndarray.byte_view(qimg)
qimg.fill(23)
qimg.setPixel(12, 10, 42)
assert_equal(v.shape, (240, 320, 1))
assert_equal(list(v[10,10]), [23])
assert_equal(list(v[10,12]), [42])
assert_equal(v.nbytes, numBytes(qimg))
def test_raw_indexed8():
qimg = QtGui.QImage(320, 240, QtGui.QImage.Format_Indexed8)
setNumColors(qimg, 256)
qimg.fill(0)
v = qimage2ndarray.raw_view(qimg)
qimg.fill(23)
qimg.setPixel(12, 10, 42)
assert_equal(v.shape, (240, 320))
assert_equal(v[10,10], 23)
assert_equal(v[10,12], 42)
assert_equal(v.nbytes, numBytes(qimg))
def test_coordinate_access():
qimg = QtGui.QImage(320, 240, QtGui.QImage.Format_Indexed8)
setNumColors(qimg, 256)
qimg.fill(0)
v = _qimageview(qimg)
qimg.fill(23)
qimg.setPixel(12, 10, 42)
assert_equal(v.shape, (240, 320))
assert_equal(v[10, 10], 23)
assert_equal(v[10, 12], 42)
assert_equal(v.nbytes, numBytes(qimg))
def test_empty2qimage():
a = numpy.ones((240, 320), dtype = float)
qImg = qimage2ndarray.gray2qimage(a, normalize = True)
assert_equal(hex(qImg.pixel(10,13)), hex(QtGui.qRgb(0,0,0)))
qImg = qimage2ndarray.array2qimage(a, normalize = True)
assert_equal(hex(qImg.pixel(10,13)), hex(QtGui.qRgb(0,0,0)))
def test_rgb_view():
qimg = QtGui.QImage(320, 240, QtGui.QImage.Format_RGB32)
qimg.fill(23)
v = qimage2ndarray.rgb_view(qimg)
qimg.setPixel(12, 10, QtGui.qRgb(12,34,56))
assert_equal(list(v[10,12]), [12,34,56])
def show_img(self):
global temp_t
success, self.img = self.camera.read()
if success:
self.Image_num += 1
if self.Image_num % 10 == 9:
frame_rate = 10 / (time.clock() - self.timelb)
self.FmRateLCD.display(frame_rate)
self.timelb = time.clock()
if self.case == 0:
showImg = cv2.cvtColor(self.img, cv2.COLOR_BGR2RGB)
showImg = qimage2ndarray.array2qimage(showImg)
self.Camera_2.setPixmap(QPixmap(showImg)) # 展示图片
self.Camera_2.show()
if self.case == 1:
bounding_boxes, landmarks = detect_faces(self.img)
self.img = show_bboxes(self.img, bounding_boxes, landmarks)
showImg = cv2.cvtColor(self.img, cv2.COLOR_BGR2RGB)
showImg = qimage2ndarray.array2qimage(showImg)
self.Camera_2.setPixmap(QPixmap(showImg)) # 展示图片
self.Camera_2.show()
if self.case == 2:
img_copy = self.img.copy()
frag_gray = False
self.time_ing = time.time()
# point=[100,0,540,480]
if self.frag_cap:
bounding_boxes, landmarks = detect_faces(self.img)
print('正在定位······')
if len(bounding_boxes) == 1:
self.point.clear()
for b in bounding_boxes:
b = [int(round(value)) for value in b]
for i in b:
self.point.append(i)
self.frag_cap = False
# print(point)
# cv2.rectangle(draw, (b[0], b[1]), (b[2], b[3]), (0, 255, 0), 2)
# 裁剪坐标为[y0:y1, x0:x1]
if not self.frag_cap:
if self.point[0] < 540:
self.img = self.img[self.point[1] - 10:479,
self.point[0] - 100:self.point[2] +
100]
else:
self.img = self.img[self.point[1] - 10:479,
self.point[0] - 100:639]
else:
self.img = self.img[1:479, 1:640]
if int(self.time_ing - self.time_first) % 60 == 0:
self.frag_cap = True
else:
self.frag_cap = False
bounding_boxes, landmarks = detect_faces(self.img)
#通过MTCNN人脸框判断，当检测不到人脸时判断低头or瞌睡
if len(bounding_boxes) == 0:
self.nod_fps += 1
if self.nod_fps >= 3:
self.Head_state.setText('点头')
self.nod_count += 1
if len(bounding_boxes) > 0:
self.nod_fps = 0
#通过头部姿态欧拉角角度变化判断是否摇头
if len(bounding_boxes) > 0:
Head_Y_X_Z = get_head_pose(landmarks)
print('pitch:{}, yaw:{}, roll:{}'.format(
Head_Y_X_Z[1], Head_Y_X_Z[2], Head_Y_X_Z[3]))
if (Head_Y_X_Z[2] < -0.75):
self.shake_fps_l += 1
if (Head_Y_X_Z[2] >= -0.75):
self.shake_fps_l = 0
if self.shake_fps_l >= 5:
self.shake_count += 1
self.Head_state.setText('摇头')
if Head_Y_X_Z[3] >= 0.30:
self.shake_fps_r += 1
if self.shake_fps_r >= 5:
self.shake_count += 1
self.Head_state.setText('摇头')
if Head_Y_X_Z[3] < 0.30:
self.shake_fps_r = 0
# print(Head_Y_X_Z[1])
# print(Head_Y_X_Z[2])
# print(Head_Y_X_Z[3])
if time.time() - self.nod_start > 3:
self.Head_state.setText('')
if time.time() - self.shake_start > 3:
self.Head_state.setText('')
# 计算低头频率 每10s计算一次
if time.time() - self.nod_start > 10:
times = time.time() - self.nod_start
self.nod_freq = self.nod_count / times
self.nod_start = time.time()
self.Nod_LCD.display(self.nod_freq)
# 计算摇头频率
if time.time() - self.shake_start > 10:
times = time.time() - self.shake_start
self.shake_freq = self.shake_count / times
self.shake_start = time.time()
self.shake_LCD.display(self.shake_freq)
if len(bounding_boxes) > 0:
Emotions = get_emotion(
get_face_expression(self.img, bounding_boxes))
self.Emotion.setText(Emotions[1])
self.Emotion_pred.display(float(Emotions[0]))
# print(Emotions)
canvas = cv2.imread('img_resource/label_pred.jpg',
flags=cv2.IMREAD_UNCHANGED)
for (i,
(emotion,
prob)) in enumerate(zip(self.EMOTIONS, Emotions[2])):
# text = "{}: {:.2f}%".format(emotion, prob * 100)
text = "{:.2f}%".format(prob * 100)
# 绘制表情类和对应概率的条形图
w = int(prob * 180)
# print(text)
# canvas = 255 * np.ones((250, 300, 3), dtype="uint8")
cv2.rectangle(canvas, (0, (i * 44) + 25),
(w, (i * 43) + 40), (100, 200, 130), -1)
cv2.putText(canvas, text, (170, (i * 43) + 40),
cv2.FONT_HERSHEY_DUPLEX, 0.6, (0, 0, 0), 1)
show = cv2.cvtColor(canvas, cv2.COLOR_BGR2RGB)
showImage = QtGui.QImage(show.data, show.shape[1],
show.shape[0],
QtGui.QImage.Format_RGB888)
# cv2.imshow('test', showImage)
# showImg=QPixmap(showImage)
self.label_pred_img.setPixmap(
QtGui.QPixmap.fromImage(showImage))
# # print('test')
# print('Head_Y_X_Z')
# print(Head_Y_X_Z)
x = cv2.resize(self.img, (300, 300)).astype(np.float32)
flag_B = True # 是否闭眼的flag
flag_Y = False
num_rec = 0 # 检测到的眼睛的数量
# 分界线
x -= self.img_mean
x = x.astype(np.float32)
x = x[:, :, ::-1].copy()
x = torch.from_numpy(x).permute(2, 0, 1)
xx = Variable(x.unsqueeze(0))
# if torch.cuda.is_available():
# xx = xx.cuda()
xx = xx.cuda()
y = self.net(xx)
softmax = nn.Softmax(dim=-1)
detect = Detect(config.class_num, 0, 200, 0.01, 0.45)
priors = utils.default_prior_box()
loc, conf = y
loc = torch.cat([o.view(o.size(0), -1) for o in loc], 1)
conf = torch.cat([o.view(o.size(0), -1) for o in conf], 1)
detections = detect(
loc.view(loc.size(0), -1, 4),
softmax(conf.view(conf.size(0), -1, config.class_num)),
torch.cat([o.view(-1, 4) for o in priors], 0)).data
labels = VOC_CLASSES
# 将检测结果放置于图片上
scale = torch.Tensor(self.img.shape[1::-1]).repeat(2)
self.img = show_bboxes(self.img, bounding_boxes, landmarks)
for i in range(detections.size(1)):
j = 0
while detections[0, i, j, 0] >= 0.4:
score = detections[0, i, j, 0]
label_name = labels[i - 1]
if label_name == 'calling' and score > 0.8:
self.Danger_state.setText('打电话')
self.danger_count += 1
frag_gray = True
if label_name == 'smoke' and score > 0.8:
self.Danger_state.setText('吸烟')
self.danger_count += 1
frag_gray = True
if label_name != 'smoke' and label_name != 'calling':
self.danger_t += 1
if self.danger_t >= 20:
self.Danger_state.setText('')
self.danger_t = 0
if label_name == 'open_eye':
self.open_t += 1
if self.open_t >= 20:
self.Eyes_state.setText('')
self.open_t = 0
if label_name == 'closed_mouth':
self.Mouth_state.setText(' ')
if label_name == 'closed_eye':
flag_B = False
frag_gray = True
if label_name == 'open_mouth':
flag_Y = True
display_txt = '%s:%.2f' % (label_name, score)
pt = (detections[0, i, j, 1:] * scale).cpu().numpy()
self.coords = (
pt[0], pt[1]), pt[2] - pt[0] + 1, pt[3] - pt[1] + 1
color = self.colors_tableau[i]
cv2.rectangle(self.img, (pt[0], pt[1]), (pt[2], pt[3]),
color, 2)
cv2.putText(self.img, display_txt,
(int(pt[0]), int(pt[1]) + 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.4,
(255, 255, 255), 1, 8)
j += 1
num_rec += 1
# cv2.imshow('test', self.img)
if num_rec > 0:
if flag_B:
# print(' 1:eye-open')
self.list_B = np.append(self.list_B, 1) # 睁眼为‘1’
self.list_blink = np.append(self.list_blink, 1)
else:
# print(' 0:eye-closed')
self.list_B = np.append(self.list_B, 0) # 闭眼为‘0’
self.list_blink = np.append(self.list_blink, 0)
self.list_blink = np.delete(self.list_blink, 0)
self.list_B = np.delete(self.list_B, 0)
if flag_Y:
self.list_Y = np.append(self.list_Y, 1)
else:
self.list_Y = np.append(self.list_Y, 0)
self.list_Y = np.delete(self.list_Y, 0)
else:
self.Msg.clear()
self.Msg.setPlainText('Nothing detected.')
# print(list)
# 实时计算PERCLOS
self.perclos = 1 - np.average(self.list_blink)
# print('perclos={:f}'.format(perclos))
self.PERCLOS.display(self.perclos)
if self.list_B[8] == 1 and self.list_B[9] == 0:
# 如果上一帧为’1‘，此帧为’0‘则判定为眨眼
self.Eyes_state.setText('眨眼')
self.blink_count += 1
frag_gray = True
str = datetime.datetime.now().strftime("%H:%M:%S")
self.State_record.append(str + '：眨眼')
# img_copy=cv2.cvtColor(img_copy,cv2.COLOR_RGB2GRAY)
blink_T = time.time() - self.blink_start
if blink_T > 30:
# 每30秒计算一次眨眼频率
blink_freq = self.blink_count / blink_T
self.blink_start = time.time()
self.blink_count = 0
print('blink_freq={:f}'.format(blink_freq))
self.Blink_freq.display(blink_freq * 2)
# 检测打哈欠
# if Yawn(list_Y,list_Y1):
if (self.list_Y[len(self.list_Y) -
len(self.list_Y1):] == self.list_Y1).all():
# print('----------------------打哈欠----------------------')
self.Mouth_state.setText('打哈欠')
self.yawn_count += 1
frag_gray = True
str = datetime.datetime.now().strftime("%H:%M:%S")
self.State_record.append(str + '：打哈欠')
self.list_Y = np.zeros(50)
# 计算打哈欠频率
yawn_T = time.time() - self.yawn_start
if yawn_T > 60:
yawn_freq = self.yawn_count / yawn_T
self.yawn_start = time.time()
self.yawn_count = 0
print('yawn_freq={:f}'.format(yawn_freq))
self.Yawn_freq.display(yawn_freq)
# 计算危险行为频率
DangerAct_T = time.time() - self.danger_start
if DangerAct_T > 60:
danger_freq = self.danger_count / DangerAct_T
self.danger_start = time.time()
self.danger_count = 0
print('danger_freq={:f}'.format(danger_freq))
self.Danger_LCD.display(danger_freq)
if (self.perclos > 0.4):
# print('疲劳')
self.State.setText('疲劳')
elif (self.blink_freq > 0.3):
# print('疲劳')
self.State.setText('疲劳')
self.blink_freq = 0 # 如果因为眨眼频率判断疲劳，则初始化眨眼频率
elif (self.yawn_freq > 5.0 / 60):
# print("疲劳")
self.State.setText('疲劳')
self.yawn_freq = 0 # 初始化，同上
else:
self.State.setText('清醒')
if not frag_gray:
showImg = cv2.cvtColor(img_copy, cv2.COLOR_BGR2RGB)
else:
if self.isRecordImg:
str = datetime.datetime.now().strftime(
"%Y_%m_%d_%H_%M_%S")
temp = 'ImgRecord/' + str + '.jpg'
cv2.imwrite(temp, img_copy)
showImg = cv2.cvtColor(img_copy, cv2.COLOR_RGB2GRAY)
showImg = qimage2ndarray.array2qimage(showImg)
self.Camera_2.setPixmap(QPixmap(showImg)) # 展示图片
self.Camera_2.show()
if self.case == 3:
img_copy = self.img.copy()
frag_gray = False
self.time_ing = time.time()
# point=[100,0,540,480]
if self.frag_cap:
bounding_boxes, landmarks = detect_faces(self.img)
print('正在定位······')
if len(bounding_boxes) == 1:
self.point.clear()
for b in bounding_boxes:
b = [int(round(value)) for value in b]
for i in b:
self.point.append(i)
self.frag_cap = False
# print(point)
# cv2.rectangle(draw, (b[0], b[1]), (b[2], b[3]), (0, 255, 0), 2)
# 裁剪坐标为[y0:y1, x0:x1]
if not self.frag_cap:
if self.point[0] < 540:
self.img = self.img[self.point[1] - 10:479,
self.point[0] - 100:self.point[2] +
100]
else:
self.img = self.img[self.point[1] - 10:479,
self.point[0] - 100:639]
else:
self.img = self.img[1:479, 1:640]
if int(self.time_ing - self.time_first) % 60 == 0:
self.frag_cap = True
else:
self.frag_cap = False
bounding_boxes, landmarks = detect_faces(self.img)
# 通过MTCNN人脸框判断，当检测不到人脸时判断低头or瞌睡
if len(bounding_boxes) == 0:
self.nod_fps += 1
if self.nod_fps >= 3:
self.Head_state.setText('点头')
self.nod_count += 1
if len(bounding_boxes) > 0:
self.nod_fps = 0
# 通过头部姿态欧拉角角度变化判断是否摇头
if len(bounding_boxes) > 0:
Head_Y_X_Z = get_head_pose(landmarks)
print('pitch:{}, yaw:{}, roll:{}'.format(
Head_Y_X_Z[1], Head_Y_X_Z[2], Head_Y_X_Z[3]))
if (Head_Y_X_Z[2] < -0.75):
self.shake_fps_l += 1
if (Head_Y_X_Z[2] >= -0.75):
self.shake_fps_l = 0
if self.shake_fps_l >= 5:
self.shake_count += 1
self.Head_state.setText('摇头')
if Head_Y_X_Z[3] >= 0.30:
self.shake_fps_r += 1
if self.shake_fps_r >= 5:
self.shake_count += 1
self.Head_state.setText('摇头')
if Head_Y_X_Z[3] < 0.30:
self.shake_fps_r = 0
# print(Head_Y_X_Z[1])
# print(Head_Y_X_Z[2])
# print(Head_Y_X_Z[3])
if time.time() - self.nod_start > 3:
self.Head_state.setText('')
if time.time() - self.shake_start > 3:
self.Head_state.setText('')
# 计算低头频率 每10s计算一次
if time.time() - self.nod_start > 10:
times = time.time() - self.nod_start
self.nod_freq = self.nod_count / times
self.nod_start = time.time()
self.Nod_LCD.display(self.nod_freq)
# 计算摇头频率
if time.time() - self.shake_start > 10:
times = time.time() - self.shake_start
self.shake_freq = self.shake_count / times
self.shake_start = time.time()
self.shake_LCD.display(self.shake_freq)
if len(bounding_boxes) > 0:
Emotions = get_emotion(
get_face_expression(self.img, bounding_boxes))
self.Emotion.setText(Emotions[1])
self.Emotion_pred.display(float(Emotions[0]))
# print(Emotions)
canvas = cv2.imread('img_resource/label_pred.jpg',
flags=cv2.IMREAD_UNCHANGED)
for (i,
(emotion,
prob)) in enumerate(zip(self.EMOTIONS, Emotions[2])):
# text = "{}: {:.2f}%".format(emotion, prob * 100)
text = "{:.2f}%".format(prob * 100)
# 绘制表情类和对应概率的条形图
w = int(prob * 180)
# print(text)
# canvas = 255 * np.ones((250, 300, 3), dtype="uint8")
cv2.rectangle(canvas, (0, (i * 44) + 25),
(w, (i * 43) + 40), (100, 200, 130), -1)
cv2.putText(canvas, text, (170, (i * 43) + 40),
cv2.FONT_HERSHEY_DUPLEX, 0.6, (0, 0, 0), 1)
show = cv2.cvtColor(canvas, cv2.COLOR_BGR2RGB)
showImage = QtGui.QImage(show.data, show.shape[1],
show.shape[0],
QtGui.QImage.Format_RGB888)
# cv2.imshow('test', showImage)
# showImg=QPixmap(showImage)
self.label_pred_img.setPixmap(
QtGui.QPixmap.fromImage(showImage))
# # print('test')
# print('Head_Y_X_Z')
# print(Head_Y_X_Z)
x = cv2.resize(self.img, (300, 300)).astype(np.float32)
flag_B = True # 是否闭眼的flag
flag_Y = False
num_rec = 0 # 检测到的眼睛的数量
# 分界线
x -= self.img_mean
x = x.astype(np.float32)
x = x[:, :, ::-1].copy()
x = torch.from_numpy(x).permute(2, 0, 1)
xx = Variable(x.unsqueeze(0))
# if torch.cuda.is_available():
# xx = xx.cuda()
xx = xx.cuda()
y = self.net(xx)
softmax = nn.Softmax(dim=-1)
detect = Detect(config.class_num, 0, 200, 0.01, 0.45)
priors = utils.default_prior_box()
loc, conf = y
loc = torch.cat([o.view(o.size(0), -1) for o in loc], 1)
conf = torch.cat([o.view(o.size(0), -1) for o in conf], 1)
detections = detect(
loc.view(loc.size(0), -1, 4),
softmax(conf.view(conf.size(0), -1, config.class_num)),
torch.cat([o.view(-1, 4) for o in priors], 0)).data
labels = VOC_CLASSES
# 将检测结果放置于图片上
scale = torch.Tensor(self.img.shape[1::-1]).repeat(2)
self.img = show_bboxes(self.img, bounding_boxes, landmarks)
for i in range(detections.size(1)):
j = 0
while detections[0, i, j, 0] >= 0.4:
score = detections[0, i, j, 0]
label_name = labels[i - 1]
if label_name == 'calling' and score > 0.8:
self.Danger_state.setText('打电话')
self.danger_count += 1
frag_gray = True
if label_name == 'smoke' and score > 0.8:
self.Danger_state.setText('吸烟')
self.danger_count += 1
frag_gray = True
if label_name != 'smoke' and label_name != 'calling':
self.danger_t += 1
if self.danger_t >= 20:
self.Danger_state.setText('')
self.danger_t = 0
if label_name == 'open_eye':
self.open_t += 1
if self.open_t >= 20:
self.Eyes_state.setText('')
self.open_t = 0
if label_name == 'closed_mouth':
self.Mouth_state.setText(' ')
if label_name == 'closed_eye':
flag_B = False
frag_gray = True
if label_name == 'open_mouth':
flag_Y = True
display_txt = '%s:%.2f' % (label_name, score)
pt = (detections[0, i, j, 1:] * scale).cpu().numpy()
self.coords = (
pt[0], pt[1]), pt[2] - pt[0] + 1, pt[3] - pt[1] + 1
color = self.colors_tableau[i]
cv2.rectangle(self.img, (pt[0], pt[1]), (pt[2], pt[3]),
color, 2)
cv2.putText(self.img, display_txt,
(int(pt[0]), int(pt[1]) + 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.4,
(255, 255, 255), 1, 8)
j += 1
num_rec += 1
# cv2.imshow('test', self.img)
if num_rec > 0:
if flag_B:
# print(' 1:eye-open')
self.list_B = np.append(self.list_B, 1) # 睁眼为‘1’
self.list_blink = np.append(self.list_blink, 1)
else:
# print(' 0:eye-closed')
self.list_B = np.append(self.list_B, 0) # 闭眼为‘0’
self.list_blink = np.append(self.list_blink, 0)
self.list_blink = np.delete(self.list_blink, 0)
self.list_B = np.delete(self.list_B, 0)
if flag_Y:
self.list_Y = np.append(self.list_Y, 1)
else:
self.list_Y = np.append(self.list_Y, 0)
self.list_Y = np.delete(self.list_Y, 0)
else:
self.Msg.clear()
self.Msg.setPlainText('Nothing detected.')
# print(list)
# 实时计算PERCLOS
self.perclos = 1 - np.average(self.list_blink)
# print('perclos={:f}'.format(perclos))
self.PERCLOS.display(self.perclos)
if self.list_B[8] == 1 and self.list_B[9] == 0:
# 如果上一帧为’1‘，此帧为’0‘则判定为眨眼
self.Eyes_state.setText('眨眼')
self.blink_count += 1
frag_gray = True
str = datetime.datetime.now().strftime("%H:%M:%S")
self.State_record.append(str + '：眨眼')
# img_copy=cv2.cvtColor(img_copy,cv2.COLOR_RGB2GRAY)
blink_T = time.time() - self.blink_start
if blink_T > 30:
# 每30秒计算一次眨眼频率
blink_freq = self.blink_count / blink_T
self.blink_start = time.time()
self.blink_count = 0
print('blink_freq={:f}'.format(blink_freq))
self.Blink_freq.display(blink_freq * 2)
# 检测打哈欠
# if Yawn(list_Y,list_Y1):
if (self.list_Y[len(self.list_Y) -
len(self.list_Y1):] == self.list_Y1).all():
# print('----------------------打哈欠----------------------')
self.Mouth_state.setText('打哈欠')
self.yawn_count += 1
frag_gray = True
str = datetime.datetime.now().strftime("%H:%M:%S")
self.State_record.append(str + '：打哈欠')
self.list_Y = np.zeros(50)
# 计算打哈欠频率
yawn_T = time.time() - self.yawn_start
if yawn_T > 60:
yawn_freq = self.yawn_count / yawn_T
self.yawn_start = time.time()
self.yawn_count = 0
print('yawn_freq={:f}'.format(yawn_freq))
self.Yawn_freq.display(yawn_freq)
# 计算危险行为频率
DangerAct_T = time.time() - self.danger_start
if DangerAct_T > 60:
danger_freq = self.danger_count / DangerAct_T
self.danger_start = time.time()
self.danger_count = 0
print('danger_freq={:f}'.format(danger_freq))
self.Danger_LCD.display(danger_freq)
if (self.perclos > 0.4):
# print('疲劳')
self.State.setText('疲劳')
elif (self.blink_freq > 0.3):
# print('疲劳')
self.State.setText('疲劳')
self.blink_freq = 0 # 如果因为眨眼频率判断疲劳，则初始化眨眼频率
elif (self.yawn_freq > 5.0 / 60):
# print("疲劳")
self.State.setText('疲劳')
self.yawn_freq = 0 # 初始化，同上
else:
self.State.setText('清醒')
if not frag_gray:
showImg = cv2.cvtColor(img_copy, cv2.COLOR_BGR2RGB)
else:
if self.isRecordImg:
str = datetime.datetime.now().strftime(
"%Y_%m_%d_%H_%M_%S")
temp = 'ImgRecord/' + str + '.jpg'
cv2.imwrite(temp, img_copy)
showImg = cv2.cvtColor(img_copy, cv2.COLOR_RGB2GRAY)
self.State_record.moveCursor(QTextCursor.End)
showImg = qimage2ndarray.array2qimage(showImg)
self.Camera_2.setPixmap(QPixmap(showImg)) # 展示图片
self.Camera_2.show()
