2022-04-06 动态界定图像HSV颜色范围

有时候通过颜色范围处理图像时，如果对颜色范围界限要求不高时，可以参考一些常规颜色范围表就够了，但是有时候图像中颜色比较丰富，有时候颜色分量会与肉眼看到的不一样，基础颜色的范围并不能很好的区分出图像中的颜色，所以需要针对该场景下的兔相机进行范围缩放。在调试过程中如果改变一个值运行一次程序的效率是很低的，所以可以使用动态的方式改变HSV范围值来界定图像中的颜色范围。

笔者找到一个滑块找颜色的程序，在输入位置改成你的图像即可。这个程序中的UI不是很理想，图像显示不能缩放，后面有时间再研究一下修改图像为全尺寸显示会好很多。

from __future__ import division
import cv2
import numpy as np

from src.com.hf.opencv import u


def nothing(*arg):
    pass

# Initial HSV GUI slider values to load on program start.
icol = (0, 0, 0, 255, 255, 255)  # Green
# icol = (18, 0, 196, 36, 255, 255)  # Yellow
# icol = (89, 0, 0, 125, 255, 255)  # Blue
# icol = (0, 100, 80, 10, 255, 255)   # Red
cv2.namedWindow('colorTest')
# Lower range colour sliders.
cv2.createTrackbar('lowHue', 'colorTest', icol[0], 255, nothing)
cv2.createTrackbar('lowSat', 'colorTest', icol[1], 255, nothing)
cv2.createTrackbar('lowVal', 'colorTest', icol[2], 255, nothing)
# Higher range colour sliders.
cv2.createTrackbar('highHue', 'colorTest', icol[3], 255, nothing)
cv2.createTrackbar('highSat', 'colorTest', icol[4], 255, nothing)
cv2.createTrackbar('highVal', 'colorTest', icol[5], 255, nothing)

# Raspberry pi file path example.
# frame = cv2.imread('/home/pi/python3/opencv/color-test/colour-circles-test.jpg')
# Windows file path example.
frame = cv2.imread('images/Image_20220401090044200(1).png')

while True:
    # Get HSV values from the GUI sliders.
    lowHue = cv2.getTrackbarPos('lowHue', 'colorTest')
    lowSat = cv2.getTrackbarPos('lowSat', 'colorTest')
    lowVal = cv2.getTrackbarPos('lowVal', 'colorTest')
    highHue = cv2.getTrackbarPos('highHue', 'colorTest')
    highSat = cv2.getTrackbarPos('highSat', 'colorTest')
    highVal = cv2.getTrackbarPos('highVal', 'colorTest')

    # Show the original image.
    # cv2.imshow('frame', frame)

    # Blur methods available, comment or uncomment to try different blur methods.
    frameBGR = cv2.GaussianBlur(frame, (7, 7), 0)
    # frameBGR = cv2.medianBlur(frameBGR, 7)
    # frameBGR = cv2.bilateralFilter(frameBGR, 15 ,75, 75)
    """kernal = np.ones((15, 15), np.float32)/255
    frameBGR = cv2.filter2D(frameBGR, -1, kernal)"""

    # Show blurred image.
    # cv2.imshow('blurred', frameBGR)

    # HSV (Hue, Saturation, Value).
    # Convert the frame to HSV colour model.
    hsv = cv2.cvtColor(frameBGR, cv2.COLOR_BGR2HSV)

    # HSV values to define a colour range.
    colorLow = np.array([lowHue, lowSat, lowVal])
    colorHigh = np.array([highHue, highSat, highVal])
    mask = cv2.inRange(hsv, colorLow, colorHigh)
    # Show the first mask
    # cv2.imshow('mask-plain', mask)

    kernal = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (7, 7))
    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernal)
    mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernal)

    # Show morphological transformation mask
    # cv2.imshow('mask', mask)

    # Put mask over top of the original image.
    result = cv2.bitwise_and(frame, frame, mask=mask)

    # Show final output image
    # cv2.imshow('colorTest', result)
    u.show('colorTest', result)

    k = cv2.waitKey(5) & 0xFF
    if k == 27:
        break

cv2.destroyAllWindows()