class FirstMainWin(QWidget):
def __init__(self):
super(QWidget, self).__init__()
self.draw = QColor()
self.draw.setBlue(53)
self.draw.setGreen(53)
self.draw.setRed(234)
self.draw.setAlpha(0)
self.initUI()
# 设置窗口的尺寸
self.setWindowTitle('显示图像')
def initUI(self):
self.resize(800, 300)
self.move(300, 200)
self.lbl = QLabel(self)
# img = plt.imread('G:\BrainSAR_Beta\SAR图片\show.jpg')
# print(img.shape)
self.pil_image = QImage('G:\BrainSAR_Beta\SAR图片\show.jpg')
self.pil_image.convertTo(QImage.Format_RGB888)
print(self.pil_image.size(), self.pil_image.height())
# self.pil_image = Image.open("G:\BrainSAR_Beta\SAR图片\show.jpg")
self.initshow()
# self.show()
self.timer1 = QtCore.QTimer(self) # 定义定时器，用于控制显示视频的帧率
self.timer1.timeout.connect(self.fcku)
self.timer1.start()
self.plotdots()
def fcku(self):
pil_image = self.m_resize(self.width(), self.height(), self.pil_image)
pixmap = QPixmap.fromImage(pil_image)
self.lbl.resize(pil_image.width(), pil_image.height())
self.lbl.setPixmap(pixmap)
def m_resize(self, w_box, h_box,
pil_image): # 参数是：要适应的窗口宽、高、Image.open后的图片
w, h = pil_image.width(), pil_image.height() # 获取图像的原始大小
f1 = 1.0 * w_box / w
f2 = 1.0 * h_box / h
factor = min([f1, f2])
width = int(w * factor)
height = int(h * factor)
return pil_image.scaled(width, height)
# 用来描点展示，输入原图对应的像素点，plot到pixmap中
def plotdots(self):
for show in range(1, 5):
x = random.randint(1500, 8000)
y = random.randint(1500, 9000)
for i in range(x - 1000, x + 1000): # 遍历所有长度的点
for j in range(y - 1000, y + 1000): # 遍历所有宽度的点
self.pil_image.setPixelColor(i, j, self.draw)
def initshow(self):
pil_image = self.m_resize(self.width(), self.height(), self.pil_image)
pixmap = QPixmap.fromImage(pil_image)
self.lbl.resize(pil_image.width(), pil_image.height())
self.lbl.setPixmap(pixmap)
def _convert_image(self, qimage: QImage) -> Optional[QDBusArgument]:
"""Convert a QImage to the structure DBus expects.
https://specifications.freedesktop.org/notification-spec/latest/ar01s05.html#icons-and-images-formats
"""
bits_per_color = 8
has_alpha = qimage.hasAlphaChannel()
if has_alpha:
image_format = QImage.Format_RGBA8888
channel_count = 4
else:
image_format = QImage.Format_RGB888
channel_count = 3
qimage.convertTo(image_format)
bytes_per_line = qimage.bytesPerLine()
width = qimage.width()
height = qimage.height()
image_data = QDBusArgument()
image_data.beginStructure()
image_data.add(width)
image_data.add(height)
image_data.add(bytes_per_line)
image_data.add(has_alpha)
image_data.add(bits_per_color)
image_data.add(channel_count)
try:
size = qimage.sizeInBytes()
except TypeError:
# WORKAROUND for
# https://www.riverbankcomputing.com/pipermail/pyqt/2020-May/042919.html
# byteCount() is obsolete, but sizeInBytes() is only available with
# SIP >= 5.3.0.
size = qimage.byteCount()
# Despite the spec not mandating this, many notification daemons mandate that
# the last scanline does not have any padding bytes.
#
# Or in the words of dunst:
#
# The image is serialised rowwise pixel by pixel. The rows are aligned by a
# spacer full of garbage. The overall data length of data + garbage is
# called the rowstride.
#
# Mind the missing spacer at the last row.
#
# len: |<--------------rowstride---------------->|
# len: |<-width*pixelstride->|
# row 1: | data for row 1 | spacer of garbage |
# row 2: | data for row 2 | spacer of garbage |
# | . | spacer of garbage |
# | . | spacer of garbage |
# | . | spacer of garbage |
# row n-1: | data for row n-1 | spacer of garbage |
# row n: | data for row n |
#
# Source:
# https://github.com/dunst-project/dunst/blob/v1.6.1/src/icon.c#L292-L309
padding = bytes_per_line - width * channel_count
assert 0 <= padding <= 3, (padding, bytes_per_line, width,
channel_count)
size -= padding
if padding and self._quirks.no_padded_images:
return None
bits = qimage.constBits().asstring(size)
image_data.add(QByteArray(bits))
image_data.endStructure()
return image_data
def readBmp(self, file, len=None, off=0, silent=False, rotate=True):
""" Reads DOC-standard bat recordings in 8x row-compressed BMP format.
For similarity with readWav, accepts len and off args, in seconds.
rotate: if True, rotates to match setImage and other spectrograms (rows=time)
otherwise preserves normal orientation (cols=time)
"""
# !! Important to set these, as they are used in other functions
self.sampleRate = 176000
self.incr = 512
img = QImage(file, "BMP")
h = img.height()
w = img.width()
colc = img.colorCount()
if h == 0 or w == 0:
print("ERROR: image was not loaded")
return (1)
# Check color format and convert to grayscale
if not silent and (not img.allGray() or colc > 256):
print(
"Warning: image provided not in 8-bit grayscale, information will be lost"
)
img.convertTo(QImage.Format_Grayscale8)
# Convert to numpy
# (remember that pyqtgraph images are column-major)
ptr = img.constBits()
ptr.setsize(h * w * 1)
img2 = np.array(ptr).reshape(h, w)
# Determine if original image was rotated, based on expected num of freq bins and freq 0 being empty
# We also used to check if np.median(img2[-1,:])==0,
# but some files happen to have the bottom freq bin around 90, so we cannot rely on that.
if h == 64:
# standard DoC format
pass
elif w == 64:
# seems like DoC format, rotated at -90*
img2 = np.rot90(img2, 1, (1, 0))
w, h = h, w
else:
print("ERROR: image does not appear to be in DoC format!")
print("Format details:")
print(img2)
print(h, w)
print(min(img2[-1, :]), max(img2[-1, :]))
print(np.sum(img2[-1, :] > 0))
print(np.median(img2[-1, :]))
return (1)
# Could skip that for visual mode - maybe useful for establishing contrast?
img2[-1, :] = 254 # lowest freq bin is 0, flip that
img2 = 255 - img2 # reverse value having the black as the most intense
img2 = img2 / np.max(img2) # normalization
img2 = img2[:,
1:] # Cutting first time bin because it only contains the scale and cutting last columns
img2 = np.repeat(
img2, 8,
axis=0) # repeat freq bins 7 times to fit invertspectrogram
self.data = []
self.fileLength = (w - 2) * self.incr + self.window_width # in samples
# Alternatively:
# self.fileLength = self.convertSpectoAmpl(h-1)*self.sampleRate
# NOTE: conversions will use self.sampleRate and self.incr, so ensure those are already set!
# trim to specified offset and length:
if off > 0 or len is not None:
# Convert offset from seconds to pixels
off = int(self.convertAmpltoSpec(off))
if len is None:
img2 = img2[:, off:]
else:
# Convert length from seconds to pixels:
len = int(self.convertAmpltoSpec(len))
img2 = img2[:, off:(off + len)]
if rotate:
# rotate for display, b/c required spectrogram dimensions are:
# t increasing over rows, f increasing over cols
# This will be enough if the original image was spectrogram-shape.
img2 = np.rot90(img2, 1, (1, 0))
self.sg = img2
if QtMM:
self.audioFormat.setChannelCount(0)
self.audioFormat.setSampleSize(0)
self.audioFormat.setSampleRate(self.sampleRate)
#else:
#self.audioFormat['channelCount'] = 0
#self.audioFormat['sampleSize'] = 0
#self.audioFormat['sampleRate'] = self.sampleRate
self.minFreq = 0
self.maxFreq = self.sampleRate // 2
self.minFreqShow = max(self.minFreq, self.minFreqShow)
self.maxFreqShow = min(self.maxFreq, self.maxFreqShow)
if not silent:
print("Detected BMP format: %d x %d px, %d colours" % (w, h, colc))
return (0)
