Update:
I make a HSV
colormap. It's more easy and accurate
to find the color range using this map than before.
And maybe I should change use (40, 40,40) ~ (70, 255,255) in hsv
to find the green
.
Original answer:
- Convert to
HSV
color-space,
- Use
cv2.inRange(hsv, hsv_lower, hsv_higher)
to get the green mask.
We use the range (in hsv)
: (36,0,0) ~ (86,255,255)
for this sunflower
.
The source image:
The masked green regions:
More steps:
The core source code:
import cv2
import numpy as np
## Read
img = cv2.imread("sunflower.jpg")
## convert to hsv
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
## mask of green (36,25,25) ~ (86, 255,255)
# mask = cv2.inRange(hsv, (36, 25, 25), (86, 255,255))
mask = cv2.inRange(hsv, (36, 25, 25), (70, 255,255))
## slice the green
imask = mask>0
green = np.zeros_like(img, np.uint8)
green[imask] = img[imask]
## save
cv2.imwrite("green.png", green)
Similar:
- Choosing the correct upper and lower HSV boundaries for color detection with`cv::inRange` (OpenCV)
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