Choosing the Correct Upper and Lower Hsv Boundaries For Color Detection With'Cv::Inrange' (Opencv)

Choosing the correct upper and lower HSV boundaries for color detection with`cv::inRange` (OpenCV)

Problem 1 : Different applications use different scales for HSV. For example gimp uses H = 0-360, S = 0-100 and V = 0-100. But OpenCV uses H: 0-179, S: 0-255, V: 0-255. Here i got a hue value of 22 in gimp. So I took half of it, 11, and defined range for that. ie (5,50,50) - (15,255,255).

Problem 2: And also, OpenCV uses BGR format, not RGB. So change your code which converts RGB to HSV as follows:

cv.CvtColor(frame, frameHSV, cv.CV_BGR2HSV)

Now run it. I got an output as follows:

Hope that is what you wanted. There are some false detections, but they are small, so you can choose biggest contour which is your lid.

EDIT:

As Karl Philip told in his comment, it would be good to add new code. But there is change of only a single line. So, I would like to add the same code implemented in new cv2 module, so users can compare the easiness and flexibility of new cv2 module.

import cv2
import numpy as np

img = cv2.imread('sof.jpg')

ORANGE_MIN = np.array([5, 50, 50],np.uint8)
ORANGE_MAX = np.array([15, 255, 255],np.uint8)

hsv_img = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)

frame_threshed = cv2.inRange(hsv_img, ORANGE_MIN, ORANGE_MAX)
cv2.imwrite('output2.jpg', frame_threshed)

It gives the same result as above. But code is much more simpler.

How to define boundaries for HSV color in OpenCV

If you are trying to segment only the rusty part and not the other ones i tried some code as follow.

import cv2
import numpy as np

img = cv2.imread("rust1.png",1)
img_hsv=cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

lower_bound = np.array([120, 80, 0],dtype="uint8")
upper_bound = np.array([255, 255, 80],dtype="uint8")
mask0 = cv2.inRange(img_hsv, lower_bound, upper_bound)
cv2.imshow("mask",mask0)
output_img1 = cv2.bitwise_and(img,img,mask=mask0)

lower_bound = np.array([100,50,50])
upper_bound = np.array([205,205,88])
mask1 = cv2.inRange(img_hsv, lower_bound, upper_bound)
output_img2 = cv2.bitwise_and(img,img,mask=mask1)
cv2.imshow("mask1",mask1)

final = cv2.bitwise_or(output_img1, output_img2)

cv2.imshow("rust",np.hstack([img,final]))[![enter image description here][1]][1]

How to get low and high values of hsv color in python opencv

Try below code:

import cv2
import numpy as np

img = cv2.imread("jacket.jpg")

hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

# mask of green (36,25,25) ~ (86, 255,255)
mask = cv2.inRange(hsv, (36, 25, 25), (70, 255,255))

green = cv2.bitwise_and(img,img, mask= mask)    

cv2.imshow('Image', green)
cv2.waitKey(0)
cv2.destroyAllWindowss()

output:

Check this stackoverflow discussion on how to correctly select the upper and lower hsv values for color detection.

Choosing the correct upper and lower HSV boundaries for color detection with`cv::inRange` (OpenCV)

Problem 1 : Different applications use different scales for HSV. For example gimp uses H = 0-360, S = 0-100 and V = 0-100. But OpenCV uses H: 0-179, S: 0-255, V: 0-255. Here i got a hue value of 22 in gimp. So I took half of it, 11, and defined range for that. ie (5,50,50) - (15,255,255).

Problem 2: And also, OpenCV uses BGR format, not RGB. So change your code which converts RGB to HSV as follows:

cv.CvtColor(frame, frameHSV, cv.CV_BGR2HSV)

Now run it. I got an output as follows:

Hope that is what you wanted. There are some false detections, but they are small, so you can choose biggest contour which is your lid.

EDIT:

As Karl Philip told in his comment, it would be good to add new code. But there is change of only a single line. So, I would like to add the same code implemented in new cv2 module, so users can compare the easiness and flexibility of new cv2 module.

import cv2
import numpy as np

img = cv2.imread('sof.jpg')

ORANGE_MIN = np.array([5, 50, 50],np.uint8)
ORANGE_MAX = np.array([15, 255, 255],np.uint8)

hsv_img = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)

frame_threshed = cv2.inRange(hsv_img, ORANGE_MIN, ORANGE_MAX)
cv2.imwrite('output2.jpg', frame_threshed)

It gives the same result as above. But code is much more simpler.

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