热图像处理

问题描述

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黑色/灰色视频流，在流的底部似乎有几行坏点。这是意料之中的，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，它不像普通的嵌入式笔记本网络摄像头那样流视频？

我已经尝试了其他人共享的代码/脚本，甚至是 FLIR 应用笔记中的示例代码，但没有奏效。感谢您的帮助。

环境：Windows 10、Python 3.7.6、PyCharm、OpenCV（最新）、FLIR Lepton 3.5 相机/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,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

解决方法

在没有相机的情况下给出答案是很有挑战性的。
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。
如果你想抓取不止一帧，你应该循环执行。
normed = cv2.normalize(frame,None,65535,cv2.NORM_MINMAX)
返回范围 [0,65535] 内的 uint16 值。
由 uint8 转换为 np.uint8(normed) 时出现溢出。
我建议标准化到范围 [0,255]。
您还可以选择结果类型为 uint8：
```
 normed = cv2.normalize(frame,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,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：

这是带有着色的代码示例（使用“铁黑”颜色图）：

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 = [
        255,253,251,249,247,245,243,241,239,237,235,233,231,229,227,225,223,221,219,217,215,213,211,209,207,205,203,201,199,197,195,193,191,189,187,185,183,181,179,177,175,173,171,169,167,165,163,161,159,157,155,153,151,149,147,145,143,141,139,137,135,133,131,129,126,124,122,120,118,116,114,112,110,108,106,104,102,100,98,96,94,92,90,88,86,84,82,80,78,76,74,72,70,68,66,64,62,60,58,56,54,52,50,48,46,44,42,40,38,36,34,32,30,28,26,24,22,20,18,16,14,12,10,8,6,4,2,9,31,45,53,17,67,19,21,23,89,25,27,103,29,111,41,121,51,123,61,125,71,127,128,81,130,91,132,101,134,136,3,138,144,158,5,140,172,7,186,13,194,196,198,200,95,37,212,85,214,43,216,75,218,49,69,55,59,224,57,47,226,228,39,230,232,234,99,236,238,109,240,119,242,244,152,156,246,160,248,178,15,182,250,192,252,206,210,254,220,24]

    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] = blue
    lut[:,1] = green
    lut[:,2] = red

    return lut

# Generate color map - used for colorizing the video frame.
colorMap = generateColourMap()


image_counter = 0
video = cv2.VideoCapture(0,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,frame = video.read()

注意：
红外传感器不是彩色传感器。
为框架着色使用“假色” - 着色可用于 eustatic 目的。
“假色”没有物理意义。
对红外图像进行着色的方法有很多种，并没有“标准着色”方法。

flir opencv python