热图像处理
Thermal Image Processing
我正在尝试使用“VideoCapture”和“cv2.imshow”脚本通过 Python OPENCV 流式传输 FLIR Lepton 3.5。然后，我会做一些检测和控制。这是我遇到的问题，我只能得到一个非常微弱的 black/gray 视频流，视频流底部似乎有几行坏点。这是预期的，因为输出应该是 16 位 RAW 图像数据。所以，
- 我正在尝试转换为 RGB888 图像数据，以便流具有“颜色”。
- 为什么流视频是静态模式，它不像普通嵌入式笔记本网络摄像头那样流视频？
我尝试了其他人共享的 codes/scripts，甚至尝试了 FLIR 应用说明中的示例代码，但没有用。感谢您的帮助。
环境：Windows10，Python3.7.6，PyCharm，OpenCV（最新），FLIR Lepton 3.5camera/PureThermal2
代码：
import cv2
import numpy as np
image_counter = 0
video = cv2.VideoCapture(0,cv2.CAP_DSHOW)
video.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter.fourcc('Y','1','6',' '))
video.set(cv2.CAP_PROP_CONVERT_RGB, 0)
if video.isOpened(): # try to get the first frame
rval, frame = video.read()
else:
rval = False
while rval:
normed = cv2.normalize(frame, None, 0, 65535, cv2.NORM_MINMAX)
nor=cv2.cvtColor(np.uint8(normed),cv2.COLOR_GRAY2BGR)
cv2.imshow("preview", cv2.resize(nor, dsize= (640, 480), interpolation = cv2.INTER_LINEAR))
key = cv2.waitKey(1)
if key == 27: # exit on ESC
break
没有相机就很难给出答案。
请注意，我无法验证我的解决方案。
我发现您的代码存在以下问题：
rval, frame = video.read() 必须在 while 循环内。
代码抓取下一帧。
如果要抓取多帧，应该循环执行。
normed = cv2.normalize(frame, None, 0, 65535, cv2.NORM_MINMAX)
Returns uint16 范围内的值 [0, 65535]。
通过 np.uint8(normed).
转换为 uint8 时出现溢出
我建议归一化到范围 [0, 255].
您还可以 select 结果的类型为 uint8:
normed = cv2.normalize(frame, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
这是完整的更新代码（未测试）：
import cv2
import numpy as np
image_counter = 0
video = cv2.VideoCapture(0,cv2.CAP_DSHOW)
video.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter.fourcc('Y','1','6',' '))
video.set(cv2.CAP_PROP_CONVERT_RGB, 0)
if video.isOpened(): # try to get the first frame
rval, frame = video.read()
else:
rval = False
# Create an object for executing CLAHE.
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
while rval:
# Get a Region of Interest slice - ignore the last 3 rows.
frame_roi = frame[:-3, :]
# Normalizing frame to range [0, 255], and get the result as type uint8.
normed = cv2.normalize(frame_roi, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
# Apply CLAHE - contrast enhancement.
# Note: apply the CLAHE on the uint8 image after normalize.
# CLAHE supposed to work with uint16 - you may try using it without using cv2.normalize
cl1 = clahe.apply(normed)
nor = cv2.cvtColor(cl1, cv2.COLOR_GRAY2BGR) # Convert gray-scale to BGR (no really needed).
cv2.imshow("preview", cv2.resize(nor, dsize=(640, 480), interpolation=cv2.INTER_LINEAR))
key = cv2.waitKey(1)
if key == 27: # exit on ESC
break
# Grab the next frame from the camera.
rval, frame = video.read()
着色：
https://groups.google.com/g/flir-lepton/c/Cm8lGQyspmk
结果：
这是带有颜色的代码示例（使用“铁黑”色图）：
import cv2
import numpy as np
# https://groups.google.com/g/flir-lepton/c/Cm8lGQyspmk
def generateColourMap():
"""
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
# Generate color map - used for colorizing the video frame.
colorMap = generateColourMap()
image_counter = 0
video = cv2.VideoCapture(0,cv2.CAP_DSHOW)
video.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter.fourcc('Y','1','6',' '))
video.set(cv2.CAP_PROP_CONVERT_RGB, 0)
if video.isOpened(): # try to get the first frame
rval, frame = video.read()
else:
rval = False
# Create an object for executing CLAHE.
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
while rval:
# Get a Region of Interest slice - ignore the last 3 rows.
frame_roi = frame[:-3, :]
# Normalizing frame to range [0, 255], and get the result as type uint8.
normed = cv2.normalize(frame_roi, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
# Apply CLAHE - contrast enhancement.
# Note: apply the CLAHE on the uint8 image after normalize.
# CLAHE supposed to work with uint16 - you may try using it without using cv2.normalize
cl1 = clahe.apply(normed)
nor = cv2.cvtColor(cl1, cv2.COLOR_GRAY2BGR) # Convert gray-scale to BGR (no really needed).
colorized_img = cv2.LUT(nor, colorMap) # Colorize the gray image with "false colors".
cv2.imshow("preview", cv2.resize(colorized_img, dsize=(640, 480), interpolation=cv2.INTER_LINEAR))
key = cv2.waitKey(1)
if key == 27: # exit on ESC
break
# Grab the next frame from the camera.
rval, frame = video.read()
注：
红外传感器不是彩色传感器。
为框架上色使用“假色”- 上色可用于美化目的。
“假色”没有物理意义。
红外图像着色的方法有很多种，没有“标准着色”方法。
我正在尝试使用“VideoCapture”和“cv2.imshow”脚本通过 Python OPENCV 流式传输 FLIR Lepton 3.5。然后，我会做一些检测和控制。这是我遇到的问题，我只能得到一个非常微弱的 black/gray 视频流，视频流底部似乎有几行坏点。这是预期的，因为输出应该是 16 位 RAW 图像数据。所以，
- 我正在尝试转换为 RGB888 图像数据，以便流具有“颜色”。
- 为什么流视频是静态模式，它不像普通嵌入式笔记本网络摄像头那样流视频？
我尝试了其他人共享的 codes/scripts，甚至尝试了 FLIR 应用说明中的示例代码，但没有用。感谢您的帮助。
环境：Windows10，Python3.7.6，PyCharm，OpenCV（最新），FLIR Lepton 3.5camera/PureThermal2
代码：
import cv2
import numpy as np
image_counter = 0
video = cv2.VideoCapture(0,cv2.CAP_DSHOW)
video.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter.fourcc('Y','1','6',' '))
video.set(cv2.CAP_PROP_CONVERT_RGB, 0)
if video.isOpened(): # try to get the first frame
rval, frame = video.read()
else:
rval = False
while rval:
normed = cv2.normalize(frame, None, 0, 65535, cv2.NORM_MINMAX)
nor=cv2.cvtColor(np.uint8(normed),cv2.COLOR_GRAY2BGR)
cv2.imshow("preview", cv2.resize(nor, dsize= (640, 480), interpolation = cv2.INTER_LINEAR))
key = cv2.waitKey(1)
if key == 27: # exit on ESC
break
没有相机就很难给出答案。
请注意，我无法验证我的解决方案。
我发现您的代码存在以下问题：
rval, frame = video.read()必须在while循环内。
代码抓取下一帧。
如果要抓取多帧，应该循环执行。normed = cv2.normalize(frame, None, 0, 65535, cv2.NORM_MINMAX)
Returnsuint16范围内的值 [0, 65535]。
通过np.uint8(normed).
转换为uint8时出现溢出 我建议归一化到范围 [0, 255].
您还可以 select 结果的类型为uint8:normed = cv2.normalize(frame, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
这是完整的更新代码（未测试）：
import cv2
import numpy as np
image_counter = 0
video = cv2.VideoCapture(0,cv2.CAP_DSHOW)
video.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter.fourcc('Y','1','6',' '))
video.set(cv2.CAP_PROP_CONVERT_RGB, 0)
if video.isOpened(): # try to get the first frame
rval, frame = video.read()
else:
rval = False
# Create an object for executing CLAHE.
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
while rval:
# Get a Region of Interest slice - ignore the last 3 rows.
frame_roi = frame[:-3, :]
# Normalizing frame to range [0, 255], and get the result as type uint8.
normed = cv2.normalize(frame_roi, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
# Apply CLAHE - contrast enhancement.
# Note: apply the CLAHE on the uint8 image after normalize.
# CLAHE supposed to work with uint16 - you may try using it without using cv2.normalize
cl1 = clahe.apply(normed)
nor = cv2.cvtColor(cl1, cv2.COLOR_GRAY2BGR) # Convert gray-scale to BGR (no really needed).
cv2.imshow("preview", cv2.resize(nor, dsize=(640, 480), interpolation=cv2.INTER_LINEAR))
key = cv2.waitKey(1)
if key == 27: # exit on ESC
break
# Grab the next frame from the camera.
rval, frame = video.read()
着色：
https://groups.google.com/g/flir-lepton/c/Cm8lGQyspmk
结果：
这是带有颜色的代码示例（使用“铁黑”色图）：
import cv2
import numpy as np
# https://groups.google.com/g/flir-lepton/c/Cm8lGQyspmk
def generateColourMap():
"""
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
# Generate color map - used for colorizing the video frame.
colorMap = generateColourMap()
image_counter = 0
video = cv2.VideoCapture(0,cv2.CAP_DSHOW)
video.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter.fourcc('Y','1','6',' '))
video.set(cv2.CAP_PROP_CONVERT_RGB, 0)
if video.isOpened(): # try to get the first frame
rval, frame = video.read()
else:
rval = False
# Create an object for executing CLAHE.
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
while rval:
# Get a Region of Interest slice - ignore the last 3 rows.
frame_roi = frame[:-3, :]
# Normalizing frame to range [0, 255], and get the result as type uint8.
normed = cv2.normalize(frame_roi, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
# Apply CLAHE - contrast enhancement.
# Note: apply the CLAHE on the uint8 image after normalize.
# CLAHE supposed to work with uint16 - you may try using it without using cv2.normalize
cl1 = clahe.apply(normed)
nor = cv2.cvtColor(cl1, cv2.COLOR_GRAY2BGR) # Convert gray-scale to BGR (no really needed).
colorized_img = cv2.LUT(nor, colorMap) # Colorize the gray image with "false colors".
cv2.imshow("preview", cv2.resize(colorized_img, dsize=(640, 480), interpolation=cv2.INTER_LINEAR))
key = cv2.waitKey(1)
if key == 27: # exit on ESC
break
# Grab the next frame from the camera.
rval, frame = video.read()
注：
红外传感器不是彩色传感器。
为框架上色使用“假色”- 上色可用于美化目的。
“假色”没有物理意义。
红外图像着色的方法有很多种，没有“标准着色”方法。