Finding red color in image using Python & OpenCV
Question:
I am trying to extract red color from an image. I have code that applies threshold to leave only values from specified range:
img=cv2.imread('img.bmp')
img_hsv=cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
lower_red = np.array([0,50,50]) #example value
upper_red = np.array([10,255,255]) #example value
mask = cv2.inRange(img_hsv, lower_red, upper_red)
img_result = cv2.bitwise_and(img, img, mask=mask)
But, as i checked, red can have Hue value in range, let’s say from 0 to 10, as well as in range from 170 to 180. Therefore, i would like to leave values from any of those two ranges. I tried setting threshold from 10 to 170 and using cv2.bitwise_not() function, but then i get all the white color as well. I think the best option would be to create a mask for each range and use them both, so I somehow have to join them together before proceeding.
Is there a way I could join two masks using OpenCV? Or is there some other way I could achieve my goal?
Edit. I came with not much elegant, but working solution:
image_result = np.zeros((image_height,image_width,3),np.uint8)
for i in range(image_height): #those are set elsewhere
for j in range(image_width): #those are set elsewhere
if img_hsv[i][j][1]>=50
and img_hsv[i][j][2]>=50
and (img_hsv[i][j][0] <= 10 or img_hsv[i][j][0]>=170):
image_result[i][j]=img_hsv[i][j]
It pretty much satisfies my needs, and OpenCV’s functions probably do pretty much the same, but if there’s a better way to do that(using some dedicated function and writing less code) please share it with me. 🙂
Answers:
I would just add the masks together, and use np.where to mask the original image.
img=cv2.imread("img.bmp")
img_hsv=cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# lower mask (0-10)
lower_red = np.array([0,50,50])
upper_red = np.array([10,255,255])
mask0 = cv2.inRange(img_hsv, lower_red, upper_red)
# upper mask (170-180)
lower_red = np.array([170,50,50])
upper_red = np.array([180,255,255])
mask1 = cv2.inRange(img_hsv, lower_red, upper_red)
# join my masks
mask = mask0+mask1
# set my output img to zero everywhere except my mask
output_img = img.copy()
output_img[np.where(mask==0)] = 0
# or your HSV image, which I *believe* is what you want
output_hsv = img_hsv.copy()
output_hsv[np.where(mask==0)] = 0
This should be much faster and much more readable than looping through each pixel of your image.
Play with this.
#blurring and smoothin
img1=cv2.imread('marathon.png',1)
hsv = cv2.cvtColor(img1,cv2.COLOR_BGR2HSV)
#lower red
lower_red = np.array([0,50,50])
upper_red = np.array([10,255,255])
#upper red
lower_red2 = np.array([170,50,50])
upper_red2 = np.array([180,255,255])
mask = cv2.inRange(hsv, lower_red, upper_red)
res = cv2.bitwise_and(img1,img1, mask= mask)
mask2 = cv2.inRange(hsv, lower_red2, upper_red2)
res2 = cv2.bitwise_and(img1,img1, mask= mask2)
img3 = res+res2
img4 = cv2.add(res,res2)
img5 = cv2.addWeighted(res,0.5,res2,0.5,0)
kernel = np.ones((15,15),np.float32)/225
smoothed = cv2.filter2D(res,-1,kernel)
smoothed2 = cv2.filter2D(img3,-1,kernel)
cv2.imshow('Original',img1)
cv2.imshow('Averaging',smoothed)
cv2.imshow('mask',mask)
cv2.imshow('res',res)
cv2.imshow('mask2',mask2)
cv2.imshow('res2',res2)
cv2.imshow('res3',img3)
cv2.imshow('res4',img4)
cv2.imshow('res5',img5)
cv2.imshow('smooth2',smoothed2)
cv2.waitKey(0)
cv2.destroyAllWindows()
To detect red, you can use a HSV color thresholder script to determine the lower/upper thresholds then cv2.bitwise_and() to obtain the mask. Using this input image,
We get this result and mask
Code
import numpy as np
import cv2
image = cv2.imread('1.jpg')
result = image.copy()
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
lower = np.array([155,25,0])
upper = np.array([179,255,255])
mask = cv2.inRange(image, lower, upper)
result = cv2.bitwise_and(result, result, mask=mask)
cv2.imshow('mask', mask)
cv2.imshow('result', result)
cv2.waitKey()
HSV color thresholder script with sliders, remember to change the image file path
import cv2
import sys
import numpy as np
def nothing(x):
pass
# Load in image
image = cv2.imread('1.jpg')
# Create a window
cv2.namedWindow('image')
# create trackbars for color change
cv2.createTrackbar('HMin','image',0,179,nothing) # Hue is from 0-179 for Opencv
cv2.createTrackbar('SMin','image',0,255,nothing)
cv2.createTrackbar('VMin','image',0,255,nothing)
cv2.createTrackbar('HMax','image',0,179,nothing)
cv2.createTrackbar('SMax','image',0,255,nothing)
cv2.createTrackbar('VMax','image',0,255,nothing)
# Set default value for MAX HSV trackbars.
cv2.setTrackbarPos('HMax', 'image', 179)
cv2.setTrackbarPos('SMax', 'image', 255)
cv2.setTrackbarPos('VMax', 'image', 255)
# Initialize to check if HSV min/max value changes
hMin = sMin = vMin = hMax = sMax = vMax = 0
phMin = psMin = pvMin = phMax = psMax = pvMax = 0
output = image
wait_time = 33
while(1):
# get current positions of all trackbars
hMin = cv2.getTrackbarPos('HMin','image')
sMin = cv2.getTrackbarPos('SMin','image')
vMin = cv2.getTrackbarPos('VMin','image')
hMax = cv2.getTrackbarPos('HMax','image')
sMax = cv2.getTrackbarPos('SMax','image')
vMax = cv2.getTrackbarPos('VMax','image')
# Set minimum and max HSV values to display
lower = np.array([hMin, sMin, vMin])
upper = np.array([hMax, sMax, vMax])
# Create HSV Image and threshold into a range.
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, lower, upper)
output = cv2.bitwise_and(image,image, mask= mask)
# Print if there is a change in HSV value
if( (phMin != hMin) | (psMin != sMin) | (pvMin != vMin) | (phMax != hMax) | (psMax != sMax) | (pvMax != vMax) ):
print("(hMin = %d , sMin = %d, vMin = %d), (hMax = %d , sMax = %d, vMax = %d)" % (hMin , sMin , vMin, hMax, sMax , vMax))
phMin = hMin
psMin = sMin
pvMin = vMin
phMax = hMax
psMax = sMax
pvMax = vMax
# Display output image
cv2.imshow('image',output)
# Wait longer to prevent freeze for videos.
if cv2.waitKey(wait_time) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()
I am trying to extract red color from an image. I have code that applies threshold to leave only values from specified range:
img=cv2.imread('img.bmp')
img_hsv=cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
lower_red = np.array([0,50,50]) #example value
upper_red = np.array([10,255,255]) #example value
mask = cv2.inRange(img_hsv, lower_red, upper_red)
img_result = cv2.bitwise_and(img, img, mask=mask)
But, as i checked, red can have Hue value in range, let’s say from 0 to 10, as well as in range from 170 to 180. Therefore, i would like to leave values from any of those two ranges. I tried setting threshold from 10 to 170 and using cv2.bitwise_not() function, but then i get all the white color as well. I think the best option would be to create a mask for each range and use them both, so I somehow have to join them together before proceeding.
Is there a way I could join two masks using OpenCV? Or is there some other way I could achieve my goal?
Edit. I came with not much elegant, but working solution:
image_result = np.zeros((image_height,image_width,3),np.uint8)
for i in range(image_height): #those are set elsewhere
for j in range(image_width): #those are set elsewhere
if img_hsv[i][j][1]>=50
and img_hsv[i][j][2]>=50
and (img_hsv[i][j][0] <= 10 or img_hsv[i][j][0]>=170):
image_result[i][j]=img_hsv[i][j]
It pretty much satisfies my needs, and OpenCV’s functions probably do pretty much the same, but if there’s a better way to do that(using some dedicated function and writing less code) please share it with me. 🙂
I would just add the masks together, and use np.where to mask the original image.
img=cv2.imread("img.bmp")
img_hsv=cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# lower mask (0-10)
lower_red = np.array([0,50,50])
upper_red = np.array([10,255,255])
mask0 = cv2.inRange(img_hsv, lower_red, upper_red)
# upper mask (170-180)
lower_red = np.array([170,50,50])
upper_red = np.array([180,255,255])
mask1 = cv2.inRange(img_hsv, lower_red, upper_red)
# join my masks
mask = mask0+mask1
# set my output img to zero everywhere except my mask
output_img = img.copy()
output_img[np.where(mask==0)] = 0
# or your HSV image, which I *believe* is what you want
output_hsv = img_hsv.copy()
output_hsv[np.where(mask==0)] = 0
This should be much faster and much more readable than looping through each pixel of your image.
Play with this.
#blurring and smoothin
img1=cv2.imread('marathon.png',1)
hsv = cv2.cvtColor(img1,cv2.COLOR_BGR2HSV)
#lower red
lower_red = np.array([0,50,50])
upper_red = np.array([10,255,255])
#upper red
lower_red2 = np.array([170,50,50])
upper_red2 = np.array([180,255,255])
mask = cv2.inRange(hsv, lower_red, upper_red)
res = cv2.bitwise_and(img1,img1, mask= mask)
mask2 = cv2.inRange(hsv, lower_red2, upper_red2)
res2 = cv2.bitwise_and(img1,img1, mask= mask2)
img3 = res+res2
img4 = cv2.add(res,res2)
img5 = cv2.addWeighted(res,0.5,res2,0.5,0)
kernel = np.ones((15,15),np.float32)/225
smoothed = cv2.filter2D(res,-1,kernel)
smoothed2 = cv2.filter2D(img3,-1,kernel)
cv2.imshow('Original',img1)
cv2.imshow('Averaging',smoothed)
cv2.imshow('mask',mask)
cv2.imshow('res',res)
cv2.imshow('mask2',mask2)
cv2.imshow('res2',res2)
cv2.imshow('res3',img3)
cv2.imshow('res4',img4)
cv2.imshow('res5',img5)
cv2.imshow('smooth2',smoothed2)
cv2.waitKey(0)
cv2.destroyAllWindows()
To detect red, you can use a HSV color thresholder script to determine the lower/upper thresholds then cv2.bitwise_and() to obtain the mask. Using this input image,
We get this result and mask
Code
import numpy as np
import cv2
image = cv2.imread('1.jpg')
result = image.copy()
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
lower = np.array([155,25,0])
upper = np.array([179,255,255])
mask = cv2.inRange(image, lower, upper)
result = cv2.bitwise_and(result, result, mask=mask)
cv2.imshow('mask', mask)
cv2.imshow('result', result)
cv2.waitKey()
HSV color thresholder script with sliders, remember to change the image file path
import cv2
import sys
import numpy as np
def nothing(x):
pass
# Load in image
image = cv2.imread('1.jpg')
# Create a window
cv2.namedWindow('image')
# create trackbars for color change
cv2.createTrackbar('HMin','image',0,179,nothing) # Hue is from 0-179 for Opencv
cv2.createTrackbar('SMin','image',0,255,nothing)
cv2.createTrackbar('VMin','image',0,255,nothing)
cv2.createTrackbar('HMax','image',0,179,nothing)
cv2.createTrackbar('SMax','image',0,255,nothing)
cv2.createTrackbar('VMax','image',0,255,nothing)
# Set default value for MAX HSV trackbars.
cv2.setTrackbarPos('HMax', 'image', 179)
cv2.setTrackbarPos('SMax', 'image', 255)
cv2.setTrackbarPos('VMax', 'image', 255)
# Initialize to check if HSV min/max value changes
hMin = sMin = vMin = hMax = sMax = vMax = 0
phMin = psMin = pvMin = phMax = psMax = pvMax = 0
output = image
wait_time = 33
while(1):
# get current positions of all trackbars
hMin = cv2.getTrackbarPos('HMin','image')
sMin = cv2.getTrackbarPos('SMin','image')
vMin = cv2.getTrackbarPos('VMin','image')
hMax = cv2.getTrackbarPos('HMax','image')
sMax = cv2.getTrackbarPos('SMax','image')
vMax = cv2.getTrackbarPos('VMax','image')
# Set minimum and max HSV values to display
lower = np.array([hMin, sMin, vMin])
upper = np.array([hMax, sMax, vMax])
# Create HSV Image and threshold into a range.
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, lower, upper)
output = cv2.bitwise_and(image,image, mask= mask)
# Print if there is a change in HSV value
if( (phMin != hMin) | (psMin != sMin) | (pvMin != vMin) | (phMax != hMax) | (psMax != sMax) | (pvMax != vMax) ):
print("(hMin = %d , sMin = %d, vMin = %d), (hMax = %d , sMax = %d, vMax = %d)" % (hMin , sMin , vMin, hMax, sMax , vMax))
phMin = hMin
psMin = sMin
pvMin = vMin
phMax = hMax
psMax = sMax
pvMax = vMax
# Display output image
cv2.imshow('image',output)
# Wait longer to prevent freeze for videos.
if cv2.waitKey(wait_time) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()