OpenCV: minimum distance between arbitrarily large contour sets (Python)

Question

I am interested in calculating the average minimum distance between the elements of two sets of contours.

Here is an example image:

Here is my code:

    import cv2
    import numpy as np

def contours(layer):
    gray = cv2.cvtColor(layer, cv2.COLOR_BGR2GRAY)
    ret,binary = cv2.threshold(gray, 1,255,cv2.THRESH_BINARY) 
    image, contours, hierarchy =         cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
    drawn = cv2.drawContours(image,contours,-1,(150,150,150),3)
    return contours, drawn

def minDistance(contour, contourOther):
    distanceMin = 99999999
    for xA, yA in contour[0]:
        for xB, yB in contourOther[0]:
            distance = ((xB-xA)**2+(yB-yA)**2)**(1/2) # distance formula
            if (distance < distanceMin):
                distanceMin = distance
    return distanceMin

def cntDistanceCompare(contoursA, contoursB):
    cumMinDistList = []
    for contourA in contoursA:
        indMinDistList = []
        for contourB in contoursB:
            minDist = minDistance(contourA,contourB)
            indMinDistList.append(minDist)
        cumMinDistList.append(indMinDistList)
    l = cumMinDistList  
    return sum(l)/len(l) #returns mean distance

def maskBuilder(bgr,hl,hh,sl,sh,vl,vh):
    hsv = cv2.cvtColor(bgr, cv2.COLOR_BGR2HSV)
    lower_bound = np.array([hl,sl,vl],dtype=np.uint8)
    upper_bound = np.array([hh,sh,vh],dtype=np.uint8)
    return cv2.inRange(hsv, lower_bound,upper_bound)

img = cv2.imread("sample.jpg")
maskA=maskBuilder(img, 150,185, 40,220, 65,240) 
maskB=maskBuilder(img, 3,20, 50,180, 20,250)
layerA = cv2.bitwise_and(img, img, mask = maskA)
layerB = cv2.bitwise_and(img, img, mask = maskB)
contoursA = contours(layerA)[0]
contoursB = contours(layerA)[1]

print cntDistanceCompare(contoursA, contoursB)

As you can see from these images, masking and holding works (shown for the first set of contours):

The cntDistanceCompare () function passes through each contour of the set A and B, outputting the average minimum distance between the contours. Inside this function, minDistance () calculates from (x, y) points on each set of contours A and B the minimum Pythagorean distance (using the distance formula).

:   Traceback ( ):    "mindistance.py", 46,   cntDistanceCompare (, )    "mindistance.py", 26, cntDistanceCompare   minDist = minDistance (A, B)    "mindistance.py:, 15, minDistance    xB, yB . [0]:   TypeError: 'numpy.uint8'

, - , x, y , cv2.findContours().

Answer 1

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import cv2
import numpy as np

def contours(layer):
    gray = cv2.cvtColor(layer, cv2.COLOR_BGR2GRAY)
    ret,binary = cv2.threshold(gray, 1,255,cv2.THRESH_BINARY) 
    contours, hierarchy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
    #drawn = cv2.drawContours(image,contours,-1,(150,150,150),3)
    return contours #, drawn

def minDistance(contour, contourOther):
    distanceMin = 99999999
    for xA, yA in contour[0]:
        for xB, yB in contourOther[0]:
            distance = ((xB-xA)**2+(yB-yA)**2)**(1/2) # distance formula
            if (distance < distanceMin):
                distanceMin = distance
    return distanceMin

def cntDistanceCompare(contoursA, contoursB):
    cumMinDistList = []
    for contourA in contoursA:
        indMinDistList = []
        for contourB in contoursB:
            minDist = minDistance(contourA,contourB)
            indMinDistList.append(minDist)
        cumMinDistList.append(indMinDistList)
    l = cumMinDistList  
    return sum(l)/len(l) #returns mean distance

def maskBuilder(bgr,hl,hh,sl,sh,vl,vh):
    hsv = cv2.cvtColor(bgr, cv2.COLOR_BGR2HSV)
    lower_bound = np.array([hl,sl,vl],dtype=np.uint8)
    upper_bound = np.array([hh,sh,vh],dtype=np.uint8)
    return cv2.inRange(hsv, lower_bound,upper_bound)

def getContourCenters(contourData):
    contourCoordinates = []
    for contour in contourData:
        moments = cv2.moments(contour)
        contourX = int(moments['m10'] / float(moments['m00']))
        contourY = int(moments['m01'] / float(moments['m00']))
        contourCoordinates += [[contourX, contourY]]
    return contourCoordinates

img = cv2.imread("sample.jpg")
maskA=maskBuilder(img, 150,185, 40,220, 65,240) 
maskB=maskBuilder(img, 3,20, 50,180, 20,250)
layerA = cv2.bitwise_and(img, img, mask = maskA)
layerB = cv2.bitwise_and(img, img, mask = maskB)
contoursA = contours(layerA)
contoursB = contours(layerB)

print getContourCenters(contoursA)
print getContourCenters(contoursB)

#print cntDistanceCompare(contoursA, contoursB)

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