Measuring the distance between pixels on OpenCv with Python

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I'm a newbie with Open CV and computer vision so I humbly ask a question. With a pi camera I record a video and in real time I can recognize blue from other colors (I see blue as white and other colors as black).

I want to measure the length of the base of the area (because I have a white rectangle and a black rectangle). This two rectangle together create the square frame.

Excerpt of code:

# Take each frame
_, frame = cap.read(0)

# Convert BGR to HSV
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

# define range of blue color in HSV
lower_blue = np.array([80,30,30])
upper_blue = np.array([130,150,210])

# Threshold the HSV image to get only blue colors
mask = cv2.inRange(hsv, lower_blue, upper_blue)    
# applica filtri morfologici
mask = cv2.erode(mask, spotFilter)
mask = cv2.dilate(mask, maskMorph)
# ^Now the mask is a black and white image.
# ^Get height of the black region in this image

# 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)

Sep 11, 2018 in Python by bug_seeker
• 15,310 points
733 views

1 answer to this question.

0 votes

Assuming input frames will have "close to rectangle" shapes (where the following code works best), you have to use the findContours function to get the black region's boundary and boundingRectfunction to get it's dimensions.

mask = cv2.imread('mask.png') #The mask variable in your code
# plt.imshow(mask)
thresh_min,thresh_max = 127,255
ret,thresh = cv2.threshold(mask,thresh_min,thresh_max,0)
# findContours requires a monochrome image.
thresh_bw = cv2.cvtColor(thresh, cv2.COLOR_BGR2GRAY)
# findContours will find contours on bright areas (i.e. white areas), hence we'll need to invert the image first
thresh_bw_inv = cv2.bitwise_not(thresh_bw)

_, contours, hierarchy = cv2.findContours(thresh_bw_inv,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
# ^Gets all white contours

# Find the index of the largest contour
areas = [cv2.contourArea(c) for c in contours]
max_index = np.argmax(areas)
cnt=contours[max_index]

x,y,w,h = cv2.boundingRect(cnt)
#Draw the rectangle on original image here.
cv2.rectangle(mask,(x,y),(x+w,y+h),(0,255,0),2)
plt.imshow(mask)
print("Distances: vertical: %d, horizontal: %d" % (h,w))

enter image description here

answered Sep 11, 2018 by Priyaj
• 56,160 points

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