color detection using opencv python
Question:
I am trying to run a script written using opencv in python which uses webcam to track colored objects (here the object is blue colored), which is also mentioned in opencv’s documentation here
import cv2
import numpy as np
cap = cv2.VideoCapture(0)
while(1):
# Take each frame
_, frame = cap.read()
# Convert BGR to HSV
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# define range of blue color in HSV
lower_blue = np.array([110,50,50])
upper_blue = np.array([130,255,255])
# Threshold the HSV image to get only blue colors
mask = cv2.inRange(hsv, lower_blue, upper_blue)
# Bitwise-AND mask and original image
res = cv2.bitwise_and(frame,frame, mask= mask)
cv2.imshow('frame',frame)
cv2.imshow('mask',mask)
cv2.imshow('res',res)
k = cv2.waitKey(5) & 0xFF
if k == 27:
break
cv2.destroyAllWindows()
But this code produces error :
OpenCV Error: Sizes of input arguments do not match (The lower bounary is neither an array of the same size and same type as src, nor a scalar) in inRange, file /build/buildd/opencv-2.4.2+dfsg/modules/core/src/arithm.cpp, line 2527
Traceback (most recent call last):
File "blue.py", line 19, in <module>
mask = cv2.inRange(hsv, lower_blue, upper_blue)
cv2.error: /build/buildd/opencv-2.4.2+dfsg/modules/core/src/arithm.cpp:2527: error: ( (-209) The lower bounary is neither an array of the same size and same type as src, nor a scalar in function inRange
I’ve tried solutions provided in related stackoverflow questions, but none of them helped.
What is the problem with the code ? why this error arises ?
I’m using opencv 2.4.2 & python 2.7 on ubuntu
Answers:
The range of blue color in HSV should be given as :
lower_blue = np.array([110, 50, 50], dtype=np.uint8)
upper_blue = np.array([130,255,255], dtype=np.uint8)
Here’s a HSV color threshold script to determine the lower and upper ranges instead of guess-and-checking
import cv2
import sys
import numpy as np
def nothing(x):
pass
# Load in image
image = cv2.imread('1.png')
# 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()
In order to detect the object based on color in OpenCV-python, I think this repo will help you check out this GitHub repo:
https://github.com/shashiben/Opencv/blob/master/Object%20Detection/object_detect_with_hsv.py
I did track the object based on HSV color using trackbar
and how to make the recognition of all shades of red and the red itself
how exactly will red be in hsv
I am trying to run a script written using opencv in python which uses webcam to track colored objects (here the object is blue colored), which is also mentioned in opencv’s documentation here
import cv2
import numpy as np
cap = cv2.VideoCapture(0)
while(1):
# Take each frame
_, frame = cap.read()
# Convert BGR to HSV
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# define range of blue color in HSV
lower_blue = np.array([110,50,50])
upper_blue = np.array([130,255,255])
# Threshold the HSV image to get only blue colors
mask = cv2.inRange(hsv, lower_blue, upper_blue)
# Bitwise-AND mask and original image
res = cv2.bitwise_and(frame,frame, mask= mask)
cv2.imshow('frame',frame)
cv2.imshow('mask',mask)
cv2.imshow('res',res)
k = cv2.waitKey(5) & 0xFF
if k == 27:
break
cv2.destroyAllWindows()
But this code produces error :
OpenCV Error: Sizes of input arguments do not match (The lower bounary is neither an array of the same size and same type as src, nor a scalar) in inRange, file /build/buildd/opencv-2.4.2+dfsg/modules/core/src/arithm.cpp, line 2527
Traceback (most recent call last):
File "blue.py", line 19, in <module>
mask = cv2.inRange(hsv, lower_blue, upper_blue)
cv2.error: /build/buildd/opencv-2.4.2+dfsg/modules/core/src/arithm.cpp:2527: error: ( (-209) The lower bounary is neither an array of the same size and same type as src, nor a scalar in function inRange
I’ve tried solutions provided in related stackoverflow questions, but none of them helped.
What is the problem with the code ? why this error arises ?
I’m using opencv 2.4.2 & python 2.7 on ubuntu
The range of blue color in HSV should be given as :
lower_blue = np.array([110, 50, 50], dtype=np.uint8)
upper_blue = np.array([130,255,255], dtype=np.uint8)
Here’s a HSV color threshold script to determine the lower and upper ranges instead of guess-and-checking
import cv2
import sys
import numpy as np
def nothing(x):
pass
# Load in image
image = cv2.imread('1.png')
# 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()
In order to detect the object based on color in OpenCV-python, I think this repo will help you check out this GitHub repo:
https://github.com/shashiben/Opencv/blob/master/Object%20Detection/object_detect_with_hsv.py
I did track the object based on HSV color using trackbar
and how to make the recognition of all shades of red and the red itself
how exactly will red be in hsv