Python Lepton 3.5 How to Properly Colorize GRAY16_LE Raw Data from GStreamer V4L2 on Linux
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Phillip Class
Apr 18, 2019, 4:05:51 PM4/18/19
to Flir Lepton
I have a GroupGets PureThermal 2 fully radiometric Lepton and am programming on Linux ( Ubuntu 18.04 ). The uvc-radiometry.py script works great. Now I want to get the data using V4L2 into Python with GStreamer's backend for Python OpenCv. I understand the V4L2 GRAY16_LE format is the raw intensity sensor data for radiometry and palettization. It works and I can get the frames and display them and "see" a scene. However, all of my attempts at properly colorizing the frame with a GroupGets palette have turned up less than desirable. It doesn't look as good as if the Lepton sends its own Palettized frames with V4L2 as RGB ( instead of GRAY16_LE ), and it also looks palettized.
Here are some examples:
.jpeg?part=0.1&view=1)
Here is my code so far:
def launchPipelineAndSendFrames(self,scaledIRWidth=320,scaledIRHeight=240):
irPipeline = 'v4l2src device=%s ! video/x-raw, width=(int)160, height=(int)120,format=GRAY16_LE ! videoconvert ! appsink' % (self.videoDevice)
#irPipeline = 'v4l2src device=%s ! appsink' % (self.videoDevice)
self.debugPrint("IR Pipeline: %s"%(irPipeline))
irVideo = cv2.VideoCapture(irPipeline, cv2.CAP_GSTREAMER)
irWidth = irVideo.get(cv2.CAP_PROP_FRAME_WIDTH)
irHeight = irVideo.get(cv2.CAP_PROP_FRAME_HEIGHT)
irFps = irVideo.get(cv2.CAP_PROP_FPS)
if irVideo.isOpened():
self.numFramesCaptured = 0
colorMap = self.generateColourMap()
startCaptureTime = time.time()
didConfigureGPU = False
while True:
try:
irCheck, irFrame = irVideo.read()
if irCheck:
#print(irFrame.shape)
# Should be grayscale
#irFrame = cv2.cvtColor(irFrame, cv2.COLOR_BGR2GRAY)
# Change to rescale to frame size
#irFrame = cv2.resize(irFrame, (scaledIRWidth, scaledIRHeight))
minVal, maxVal, minLoc, maxLoc = cv2.minMaxLoc(irFrame)
#cv2.normalize(irFrame, irFrame, 0, 255, cv2.NORM_MINMAX, -1)
#np.right_shift(irFrame, 8, irFrame)
#irFrame = self.palettize_frame(irFrame)
#print(irFrame.shape)
### To grayscale and normalize
#mask_gray = cv2.cvtColor(irFrame, cv2.COLOR_BGR2GRAY)
irFrame = cv2.normalize(src=irFrame, dst=None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.COLOR_BGR2GRAY)
irFrame = cv2.LUT(irFrame, colorMap)
encodeParam = [int(cv2.IMWRITE_JPEG_QUALITY), 100]
encoded, buffer = cv2.imencode('.jpg', irFrame, encodeParam)
encodedJpeg = base64.b64encode(buffer)
self.senderSocket.send_json({"frame": encodedJpeg.decode("utf-8")})
else:
self.debugPrint("Frame not available")
self.debugPrint("%s" % (irVideo.isOpened()))
except Exception as err:
self.debugPrint("ERROR: %s"%(err))
continue
else:
self.debugPrint("Cannot open Camera!")
Here is my Palettize and LUT Function:
# Palettize the Received Lepton Frame
def palettize_frame(self, lepton_buf):
curPal_array = np.array(self.colormap_rainbow)
indexes = np.stack([3 * lepton_buf, 3 * lepton_buf + 1, 3 * lepton_buf + 2], axis=-1)
colors = curPal_array[indexes.ravel()]
palettizedFrame = colors.reshape(lepton_buf.shape[0], lepton_buf.shape[1], 3).astype(np.uint8)
return palettizedFrame
def generateColourMap(self):
"""
Conversion of the colour map from GetThermal to a numpy LUT:
https://github.com/groupgets/GetThermal/blob/bb467924750a686cc3930f7e3a253818b755a2c0/src/dataformatter.cpp#L6
"""
lut = np.zeros((256, 1, 3), dtype=np.uint8)
colormapIronBlack = [
255, 255, 255, 253, 253, 253, 251, 251, 251, 249, 249, 249, 247, 247,
247, 245, 245, 245, 243, 243, 243, 241, 241, 241, 239, 239, 239, 237,
237, 237, 235, 235, 235, 233, 233, 233, 231, 231, 231, 229, 229, 229,
227, 227, 227, 225, 225, 225, 223, 223, 223, 221, 221, 221, 219, 219,
219, 217, 217, 217, 215, 215, 215, 213, 213, 213, 211, 211, 211, 209,
209, 209, 207, 207, 207, 205, 205, 205, 203, 203, 203, 201, 201, 201,
199, 199, 199, 197, 197, 197, 195, 195, 195, 193, 193, 193, 191, 191,
191, 189, 189, 189, 187, 187, 187, 185, 185, 185, 183, 183, 183, 181,
181, 181, 179, 179, 179, 177, 177, 177, 175, 175, 175, 173, 173, 173,
171, 171, 171, 169, 169, 169, 167, 167, 167, 165, 165, 165, 163, 163,
163, 161, 161, 161, 159, 159, 159, 157, 157, 157, 155, 155, 155, 153,
153, 153, 151, 151, 151, 149, 149, 149, 147, 147, 147, 145, 145, 145,
143, 143, 143, 141, 141, 141, 139, 139, 139, 137, 137, 137, 135, 135,
135, 133, 133, 133, 131, 131, 131, 129, 129, 129, 126, 126, 126, 124,
124, 124, 122, 122, 122, 120, 120, 120, 118, 118, 118, 116, 116, 116,
114, 114, 114, 112, 112, 112, 110, 110, 110, 108, 108, 108, 106, 106,
106, 104, 104, 104, 102, 102, 102, 100, 100, 100, 98, 98, 98, 96, 96,
96, 94, 94, 94, 92, 92, 92, 90, 90, 90, 88, 88, 88, 86, 86, 86, 84, 84,
84, 82, 82, 82, 80, 80, 80, 78, 78, 78, 76, 76, 76, 74, 74, 74, 72, 72,
72, 70, 70, 70, 68, 68, 68, 66, 66, 66, 64, 64, 64, 62, 62, 62, 60, 60,
60, 58, 58, 58, 56, 56, 56, 54, 54, 54, 52, 52, 52, 50, 50, 50, 48, 48,
48, 46, 46, 46, 44, 44, 44, 42, 42, 42, 40, 40, 40, 38, 38, 38, 36, 36,
36, 34, 34, 34, 32, 32, 32, 30, 30, 30, 28, 28, 28, 26, 26, 26, 24, 24,
24, 22, 22, 22, 20, 20, 20, 18, 18, 18, 16, 16, 16, 14, 14, 14, 12, 12,
12, 10, 10, 10, 8, 8, 8, 6, 6, 6, 4, 4, 4, 2, 2, 2, 0, 0, 0, 0, 0, 9,
2, 0, 16, 4, 0, 24, 6, 0, 31, 8, 0, 38, 10, 0, 45, 12, 0, 53, 14, 0,
60, 17, 0, 67, 19, 0, 74, 21, 0, 82, 23, 0, 89, 25, 0, 96, 27, 0, 103,
29, 0, 111, 31, 0, 118, 36, 0, 120, 41, 0, 121, 46, 0, 122, 51, 0, 123,
56, 0, 124, 61, 0, 125, 66, 0, 126, 71, 0, 127, 76, 1, 128, 81, 1, 129,
86, 1, 130, 91, 1, 131, 96, 1, 132, 101, 1, 133, 106, 1, 134, 111, 1,
135, 116, 1, 136, 121, 1, 136, 125, 2, 137, 130, 2, 137, 135, 3, 137,
139, 3, 138, 144, 3, 138, 149, 4, 138, 153, 4, 139, 158, 5, 139, 163,
5, 139, 167, 5, 140, 172, 6, 140, 177, 6, 140, 181, 7, 141, 186, 7,
141, 189, 10, 137, 191, 13, 132, 194, 16, 127, 196, 19, 121, 198, 22,
116, 200, 25, 111, 203, 28, 106, 205, 31, 101, 207, 34, 95, 209, 37,
90, 212, 40, 85, 214, 43, 80, 216, 46, 75, 218, 49, 69, 221, 52, 64,
223, 55, 59, 224, 57, 49, 225, 60, 47, 226, 64, 44, 227, 67, 42, 228,
71, 39, 229, 74, 37, 230, 78, 34, 231, 81, 32, 231, 85, 29, 232, 88,
27, 233, 92, 24, 234, 95, 22, 235, 99, 19, 236, 102, 17, 237, 106, 14,
238, 109, 12, 239, 112, 12, 240, 116, 12, 240, 119, 12, 241, 123, 12,
241, 127, 12, 242, 130, 12, 242, 134, 12, 243, 138, 12, 243, 141, 13,
244, 145, 13, 244, 149, 13, 245, 152, 13, 245, 156, 13, 246, 160, 13,
246, 163, 13, 247, 167, 13, 247, 171, 13, 248, 175, 14, 248, 178, 15,
249, 182, 16, 249, 185, 18, 250, 189, 19, 250, 192, 20, 251, 196, 21,
251, 199, 22, 252, 203, 23, 252, 206, 24, 253, 210, 25, 253, 213, 27,
254, 217, 28, 254, 220, 29, 255, 224, 30, 255, 227, 39, 255, 229, 53,
255, 231, 67, 255, 233, 81, 255, 234, 95, 255, 236, 109, 255, 238, 123,
255, 240, 137, 255, 242, 151, 255, 244, 165, 255, 246, 179, 255, 248,
193, 255, 249, 207, 255, 251, 221, 255, 253, 235, 255, 255, 24]
def colormapChunk(ulist, step):
return map(lambda i: ulist[i: i + step], range(0, len(ulist), step))
chunks = colormapChunk(colormapIronBlack, 3)
red = []
green = []
blue = []
for chunk in chunks:
red.append(chunk[0])
green.append(chunk[1])
blue.append(chunk[2])
lut[:, 0, 0] = blue
lut[:, 0, 1] = green
lut[:, 0, 2] = red
return lut
# Include palettes
self.colormap_rainbow = [1, 3, 74, 0, 3, 74, 0, 3, 75, 0, 3, 75, 0, 3, 76, 0, 3, 76, 0, 3, 77, 0, 3, 79, 0, 3,
82, 0,
5, 85, 0, 7, 88, 0, 10, 91, 0, 14, 94, 0, 19, 98, 0, 22, 100, 0, 25, 103, 0, 28, 106,
0, 32,
109, 0, 35, 112, 0, 38, 116, 0, 40, 119, 0, 42, 123, 0, 45, 128, 0, 49, 133, 0, 50,
134, 0,
51, 136, 0, 52, 137, 0, 53, 139, 0, 54, 142, 0, 55, 144, 0, 56, 145, 0, 58, 149, 0, 61,
154,
0, 63, 156, 0, 65, 159, 0, 66, 161, 0, 68, 164, 0, 69, 167, 0, 71, 170, 0, 73, 174, 0,
75,
179, 0, 76, 181, 0, 78, 184, 0, 79, 187, 0, 80, 188, 0, 81, 190, 0, 84, 194, 0, 87,
198, 0,
88, 200, 0, 90, 203, 0, 92, 205, 0, 94, 207, 0, 94, 208, 0, 95, 209, 0, 96, 210, 0, 97,
211,
0, 99, 214, 0, 102, 217, 0, 103, 218, 0, 104, 219, 0, 105, 220, 0, 107, 221, 0, 109,
223, 0,
111, 223, 0, 113, 223, 0, 115, 222, 0, 117, 221, 0, 118, 220, 1, 120, 219, 1, 122, 217,
2,
124, 216, 2, 126, 214, 3, 129, 212, 3, 131, 207, 4, 132, 205, 4, 133, 202, 4, 134, 197,
5,
136, 192, 6, 138, 185, 7, 141, 178, 8, 142, 172, 10, 144, 166, 10, 144, 162, 11, 145,
158,
12, 146, 153, 13, 147, 149, 15, 149, 140, 17, 151, 132, 22, 153, 120, 25, 154, 115, 28,
156,
109, 34, 158, 101, 40, 160, 94, 45, 162, 86, 51, 164, 79, 59, 167, 69, 67, 171, 60, 72,
173,
54, 78, 175, 48, 83, 177, 43, 89, 179, 39, 93, 181, 35, 98, 183, 31, 105, 185, 26, 109,
187,
23, 113, 188, 21, 118, 189, 19, 123, 191, 17, 128, 193, 14, 134, 195, 12, 138, 196, 10,
142,
197, 8, 146, 198, 6, 151, 200, 5, 155, 201, 4, 160, 203, 3, 164, 204, 2, 169, 205, 2,
173,
206, 1, 175, 207, 1, 178, 207, 1, 184, 208, 0, 190, 210, 0, 193, 211, 0, 196, 212, 0,
199,
212, 0, 202, 213, 1, 207, 214, 2, 212, 215, 3, 215, 214, 3, 218, 214, 3, 220, 213, 3,
222,
213, 4, 224, 212, 4, 225, 212, 5, 226, 212, 5, 229, 211, 5, 232, 211, 6, 232, 211, 6,
233,
211, 6, 234, 210, 6, 235, 210, 7, 236, 209, 7, 237, 208, 8, 239, 206, 8, 241, 204, 9,
242,
203, 9, 244, 202, 10, 244, 201, 10, 245, 200, 10, 245, 199, 11, 246, 198, 11, 247, 197,
12,
248, 194, 13, 249, 191, 14, 250, 189, 14, 251, 187, 15, 251, 185, 16, 252, 183, 17,
252,
178, 18, 253, 174, 19, 253, 171, 19, 254, 168, 20, 254, 165, 21, 254, 164, 21, 255,
163, 22,
255, 161, 22, 255, 159, 23, 255, 157, 23, 255, 155, 24, 255, 149, 25, 255, 143, 27,
255,
139, 28, 255, 135, 30, 255, 131, 31, 255, 127, 32, 255, 118, 34, 255, 110, 36, 255,
104, 37,
255, 101, 38, 255, 99, 39, 255, 93, 40, 255, 88, 42, 254, 82, 43, 254, 77, 45, 254, 69,
47,
254, 62, 49, 253, 57, 50, 253, 53, 52, 252, 49, 53, 252, 45, 55, 251, 39, 57, 251, 33,
59,
251, 32, 60, 251, 31, 60, 251, 30, 61, 251, 29, 61, 251, 28, 62, 250, 27, 63, 250, 27,
65,
249, 26, 66, 249, 26, 68, 248, 25, 70, 248, 24, 73, 247, 24, 75, 247, 25, 77, 247, 25,
79,
247, 26, 81, 247, 32, 83, 247, 35, 85, 247, 38, 86, 247, 42, 88, 247, 46, 90, 247, 50,
92,
248, 55, 94, 248, 59, 96, 248, 64, 98, 248, 72, 101, 249, 81, 104, 249, 87, 106, 250,
93,
108, 250, 95, 109, 250, 98, 110, 250, 100, 111, 251, 101, 112, 251, 102, 113, 251, 109,
117,
252, 116, 121, 252, 121, 123, 253, 126, 126, 253, 130, 128, 254, 135, 131, 254, 139,
133,
254, 144, 136, 254, 151, 140, 255, 158, 144, 255, 163, 146, 255, 168, 149, 255, 173,
152,
255, 176, 153, 255, 178, 155, 255, 184, 160, 255, 191, 165, 255, 195, 168, 255, 199,
172,
255, 203, 175, 255, 207, 179, 255, 211, 182, 255, 216, 185, 255, 218, 190, 255, 220,
196,
255, 222, 200, 255, 225, 202, 255, 227, 204, 255, 230, 206, 255, 233, 208]
# Colorize Palette
self.flip = []
counter = 0
for p in range(0, len(self.colormap_rainbow), 3):
a = self.colormap_rainbow[p]
b = self.colormap_rainbow[p + 1]
c = self.colormap_rainbow[p + 2]
self.flip.append(c)
self.flip.append(b)
self.flip.append(a)
self.colormap_rainbow = self.flip
How can I properly palettize? Thanks!
Kurt Kiefer
Apr 18, 2019, 5:30:27 PM4/18/19
to Flir Lepton
At first glance I think this has something to do with the way you're normalizing your image -- as if you've spread 8 bits out across a 16 bit range and then thrown away half the bits. You should normalize over the whole 16 bit range before truncating.
Message has been deleted
Phillip Class
Apr 19, 2019, 10:59:46 AM4/19/19
to Flir Lepton
Thanks for the message. I had to patch OpenCV to allow GRAY16_LE appsink and then my code works as follows:
irPipeline = 'v4l2src device=%s ! video/x-raw, width=(int)160, height=(int)120,format=GRAY16_LE ! appsink' % (self.videoDevice)
self.debugPrint("IR Pipeline: %s"%(irPipeline))
irVideo = cv2.VideoCapture(irPipeline, cv2.CAP_GSTREAMER)
irWidth = int(irVideo.get(cv2.CAP_PROP_FRAME_WIDTH))
irHeight = int(irVideo.get(cv2.CAP_PROP_FRAME_HEIGHT))
irFps = int(irVideo.get(cv2.CAP_PROP_FPS))
if irVideo.isOpened():
self.numFramesCaptured = 0
colorMap = self.generateColourMap()
startCaptureTime = time.time()
didConfigureGPU = False
while True:
try:
irCheck, irFrame = irVideo.read()
if irCheck:
# Increment number of frames captured
self.numFramesCaptured += 1
# Create empty frame to populate with 16-bit values
emptyFrame = np.zeros(shape=(irHeight, irWidth), dtype=np.uint16)
# Make 2 8-bit channels 1 16-bit channel
irFrame = irFrame.astype(np.uint16)
emptyFrame[:,:] = ( irFrame[:,:,1] << 8 ) | irFrame[:,:,0]
# New IR FRame is empty frame populated with 16-bit values
irFrame = emptyFrame
# Change to rescale to frame size
irFrame = cv2.resize(irFrame, (scaledIRWidth, scaledIRHeight))
# Get Min/Max values and locations
minVal, maxVal, minLoc, maxLoc = cv2.minMaxLoc(irFrame)
# Normalize for contrast from min to max
cv2.normalize(irFrame, irFrame, 0, 65535, cv2.NORM_MINMAX) # extend contrast
# Convert to 8-bit
np.right_shift(irFrame, 8, irFrame)
irFrame = irFrame.astype(np.uint8)
# To RGB repeated values for LUT
irFrame = cv2.cvtColor(irFrame, cv2.COLOR_GRAY2BGR)
# Colorize
irFrame = cv2.LUT(irFrame, colorMap)
#irFrame = cv2.applyColorMap(irFrame, cv2.COLORMAP_JET)
# irFrame = self.palettize_frame(irFrame)
# Blue the image a bit
# irFrame = cv2.blur(irFrame, (10, 10))
else:
print("Frame not available")
print("%s" % (irVideo.isOpened()))
except Exception as err:
print("ERROR: %s" % (err))
continue
else:
self.debugPrint("Cannot open Camera!")
Sangpil Kim
May 5, 2019, 3:52:00 PM5/5/19
to Flir Lepton
I am using PureThermal 2 with Lepton3.5.
Whenever, I ran the uvc-radiometry.py I got following error:
uvc_open error
Have you got this error before?
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