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python - 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，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，

  1. 我正在尝试转换为 RGB888 图像数据，以便流具有“颜色”。
  2. 为什么流视频是静态模式，不像普通的嵌入式笔记本网络摄像头那样流视频？

我已经尝试过其他人共享的代码/脚本，甚至是 FLIR 应用说明中的示例代码，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, 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

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标签： pythonopencvflir

解决方案

在没有相机的情况下给出答案具有挑战性。
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。
    如果要抓取多于一帧，则应循环执行。

  • normed = cv2.normalize(frame, None, 0, 65535, cv2.NORM_MINMAX)
    返回uint16[0, 65535] 范围内的值。
    转换为uint8by时出现溢出np.uint8(normed)。
    我建议标准化到范围 [0, 255]。
    您还可以选择结果的类型为uint8：

     normed = cv2.normalize(frame, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)

这是完整的更新代码（未经测试）：

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：
在此处输入图像描述

这是带有着色的代码示例（使用“Iron Black”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, 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()

注意：
IR 传感器不是彩色传感器。
为框架着色使用“假色”——着色可用于安定目的。
“假颜色”没有物理意义。
有很多方法可以对 IR 图像进行着色，并且没有“标准着色”方法。

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