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Solve this Puzzle in Python

Hi,

I would like to present you a very interesting puzzle. It was asked by maths teacher in high school ( not to solve in Python, but solve in pen and paper ). I could solve it back then ( where my "method" was not good and the way i found it is still a "mystery" to me. ). Even a few days back, I admit I couldn't find a method to solve this except brute forcing.

So I decided to apply brute-forcing for this puzzle in Python, and I asked this question in stackoverflow.com. But I was really surprised seeing the answers provided there. I would like to share this with you.

This is the puzzle :


A merchant has a 40 kg weight which he used in his shop. Once, it fell from his hands and was broken into 4 pieces. But surprisingly, now he can weigh any weight between 1 kg to 40 kg with the combination of these 4 pieces.
So question is, what are weights of those 4 pieces?
Try to solve this puzzle in Python. If you can't, find its answer here.
With Regards,
ARK.

Drawing Histogram in OpenCV-Python

Hi Friends,

Do you want to draw a histogram for an image as below?

Drawing Histogram in OpenCV-Python

See the histogram for above image for RGB channels.

Drawing Histogram in OpenCV-Python

The code:


import cv2
import numpy as np

img = cv2.imread('zzzyj.jpg')
h = np.zeros((300,256,3))

bins = np.arange(256).reshape(256,1)
color = [ (255,0,0),(0,255,0),(0,0,255) ]
for ch, col in enumerate(color):
hist_item = cv2.calcHist([img],[ch],None,[256],[0,256])
cv2.normalize(hist_item,hist_item,0,255,cv2.NORM_MINMAX)
hist=np.int32(np.around(hist_item))
pts = np.column_stack((bins,hist))
cv2.polylines(h,[pts],False,col)

h=np.flipud(h)

cv2.imshow('colorhist',h)
cv2.waitKey(0)

You can see the same code written using numpy functions on histogram here  : Drawing histogram in OpenCV- Python.

Don't forget to send your comments, doubts etc.

With Regards,
Abid Rahman K.











Simple Digit Recognition OCR in OpenCV-Python


Hi Friends, 

It is  a long since i have posted an article.

Now i present you a Simple Digit Recognition OCR using kNearestNeighbour features in OpenCV-Python.

It demonstrats how to train the data and recongnize digits from previously trained data.

The code is using new Python interface, cv2. ( OpenCV v 2.3+)

The code and explanation can be found here:
Test Image:

Simple Digit Recognition OCR in OpenCV-Python

Result image:

Simple Digit Recognition OCR in OpenCV-Python

Waiting for your feedback!!
Abid Rahman K.

Contour features


For more details on contours, visit :

1) Contours - 1 : Getting Started

2) Contours - 2 : Brotherhood

''' filename : contourfeatures.py

This sample calculates some useful parameters of a contour. This is an OpenCV implementation of regionprops function in matlab with some additional features.

Benefit : Learn to find different parameters of a contour region.
          Get familier with different contour functions in OpenCV.

Level : Beginner or Intermediate

Usage : python contourfeatures.py <image_file>

Abid Rahman 3/25/12 '''

import cv2
import numpy as np

class Contour:
    ''' Provides detailed parameter informations about a contour

        Create a Contour instant as follows: c = Contour(src_img, contour)
                where src_img should be grayscale image.

        Attributes:

        c.area -- gives the area of the region
        c.parameter -- gives the perimeter of the region
        c.moments -- gives all values of moments as a dict
        c.centroid -- gives the centroid of the region as a tuple (x,y)
        c.bounding_box -- gives the bounding box parameters as a tuple => (x,y,width,height)
        c.bx,c.by,c.bw,c.bh -- corresponds to (x,y,width,height) of the bounding box
        c.aspect_ratio -- aspect ratio is the ratio of width to height
        c.equi_diameter -- equivalent diameter of the circle with same as area as that of region
        c.extent -- extent = contour area/bounding box area
        c.convex_hull -- gives the convex hull of the region
        c.convex_area -- gives the area of the convex hull
        c.solidity -- solidity = contour area / convex hull area
        c.center -- gives the center of the ellipse
        c.majoraxis_length -- gives the length of major axis
        c.minoraxis_length -- gives the length of minor axis
        c.orientation -- gives the orientation of ellipse
        c.eccentricity -- gives the eccentricity of ellipse
        c.filledImage -- returns the image where region is white and others are black
        c.filledArea -- finds the number of white pixels in filledImage
        c.convexImage -- returns the image where convex hull region is white and others are black
        c.pixelList -- array of indices of on-pixels in filledImage
        c.maxval -- corresponds to max intensity in the contour region
        c.maxloc -- location of max.intensity pixel location
        c.minval -- corresponds to min intensity in the contour region
        c.minloc -- corresponds to min.intensity pixel location
        c.meanval -- finds mean intensity in the contour region
        c.leftmost -- leftmost point of the contour
        c.rightmost -- rightmost point of the contour
        c.topmost -- topmost point of the contour
        c.bottommost -- bottommost point of the contour
        c.distance_image((x,y)) -- return the distance (x,y) from the contour.
        c.distance_image() -- return the distance image where distance to all points on image are calculated
        '''
    def __init__(self,img,cnt):
        self.img = img
        self.cnt = cnt
        self.size = len(cnt)

        # MAIN PARAMETERS

        #Contour.area - Area bounded by the contour region'''
        self.area = cv2.contourArea(self.cnt)

        # contour perimeter
        self.perimeter = cv2.arcLength(cnt,True)

        # centroid
        self.moments = cv2.moments(cnt)
        if self.moments['m00'] != 0.0:
            self.cx = self.moments['m10']/self.moments['m00']
            self.cy = self.moments['m01']/self.moments['m00']
            self.centroid = (self.cx,self.cy)
        else:
            self.centroid = "Region has zero area"

        # bounding box
        self.bounding_box=cv2.boundingRect(cnt)
        (self.bx,self.by,self.bw,self.bh) = self.bounding_box

        # aspect ratio
        self.aspect_ratio = self.bw/float(self.bh)

        # equivalent diameter
        self.equi_diameter = np.sqrt(4*self.area/np.pi)

        # extent = contour area/boundingrect area
        self.extent = self.area/(self.bw*self.bh)


        ### CONVEX HULL ###

        # convex hull
        self.convex_hull = cv2.convexHull(cnt)

        # convex hull area
        self.convex_area = cv2.contourArea(self.convex_hull)

        # solidity = contour area / convex hull area
        self.solidity = self.area/float(self.convex_area)


        ### ELLIPSE  ###

        self.ellipse = cv2.fitEllipse(cnt)

        # center, axis_length and orientation of ellipse
        (self.center,self.axes,self.orientation) = self.ellipse

        # length of MAJOR and minor axis
        self.majoraxis_length = max(self.axes)
        self.minoraxis_length = min(self.axes)

        # eccentricity = sqrt( 1 - (ma/MA)^2) --- ma= minor axis --- MA= major axis
        self.eccentricity = np.sqrt(1-(self.minoraxis_length/self.majoraxis_length)**2)


        ### CONTOUR APPROXIMATION ###

        self.approx = cv2.approxPolyDP(cnt,0.02*self.perimeter,True)


        ### EXTRA IMAGES ###

        # filled image :- binary image with contour region white and others black
        self.filledImage = np.zeros(self.img.shape[0:2],np.uint8)
        cv2.drawContours(self.filledImage,[self.cnt],0,255,-1)

        # area of filled image
        filledArea = cv2.countNonZero(self.filledImage)

        # pixelList - array of indices of contour region
        self.pixelList = np.transpose(np.nonzero(self.filledImage))

        # convex image :- binary image with convex hull region white and others black
        self.convexImage = np.zeros(self.img.shape[0:2],np.uint8)
        cv2.drawContours(self.convexImage,[self.convex_hull],0,255,-1)


        ### PIXEL PARAMETERS
     
        # mean value, minvalue, maxvalue
        self.minval,self.maxval,self.minloc,self.maxloc = cv2.minMaxLoc(self.img,mask = self.filledImage)
        self.meanval = cv2.mean(self.img,mask = self.filledImage)


        ### EXTREME POINTS ###

        # Finds the leftmost, rightmost, topmost and bottommost points
        self.leftmost = tuple(self.cnt[self.cnt[:,:,0].argmin()][0])
        self.rightmost = tuple(self.cnt[self.cnt[:,:,0].argmax()][0])
        self.topmost = tuple(self.cnt[self.cnt[:,:,1].argmin()][0])
        self.bottommost = tuple(self.cnt[self.cnt[:,:,1].argmax()][0])
        self.extreme = (self.leftmost,self.rightmost,self.topmost,self.bottommost)

    ### DISTANCE CALCULATION
 
    def distance_image(self,point=None):
     
        '''find the distance between a point and adjacent point on contour specified. Point should be a tuple or list (x,y)
            If no point is given, distance to all point is calculated and distance image is returned'''
        if type(point) == tuple:
            if len(point)==2:
                self.dist = cv2.pointPolygonTest(self.cnt,point,True)
                return self.dist
        else:
            dst = np.empty(self.img.shape)
            for i in xrange(self.img.shape[0]):
                for j in xrange(self.img.shape[1]):
                    dst.itemset(i,j,cv2.pointPolygonTest(self.cnt,(j,i),True))

            dst = dst+127
            dst = np.uint8(np.clip(dst,0,255))

            # plotting using palette method in numpy
            palette = []
            for i in xrange(256):
                if i<127:
                    palette.append([2*i,0,0])
                elif i==127:
                    palette.append([255,255,255])
                elif i>127:
                    l = i-128
                    palette.append([0,0,255-2*l])
            palette = np.array(palette,np.uint8)
            self.h2 = palette[dst]
            return self.h2


#### DEMO ######
if __name__=='__main__':

    import sys
    if len(sys.argv)>1:
        image = sys.argv[1]
    else:
        image = 'new.bmp'
        print "Usage : python contourfeatures.py <image_file>"
 
    im = cv2.imread(image)
    imgray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
    thresh = cv2.adaptiveThreshold(imgray,255,0,1,11,2)
    contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
    k = 1000
    for cnt in contours:

        # first shows the original image
        im2 = im.copy()
        c = Contour(imgray,cnt)
        print c.leftmost,c.rightmost
        cv2.putText(im2,'original image',(20,20), cv2.FONT_HERSHEY_PLAIN, 1.0,(0,255,0))      
        cv2.imshow('image',im2)
        if cv2.waitKey(k)==27:
            break
     
        im2 = im.copy()

        # Now shows original contours, approximated contours, convex hull
        cv2.drawContours(im2,[cnt],0,(0,255,0),4)
        string1 = 'green : original contour'
        cv2.putText(im2,string1,(20,20), cv2.FONT_HERSHEY_PLAIN, 1.0,(0,255,0))
        cv2.imshow('image',im2)
        if cv2.waitKey(k)==27:
            break
     
        approx = c.approx
        cv2.drawContours(im2,[approx],0,(255,0,0),2)
        string2 = 'blue : approximated contours'
        cv2.putText(im2,string2,(20,40), cv2.FONT_HERSHEY_PLAIN, 1.0,(0,255,0))
        cv2.imshow('image',im2)
        if cv2.waitKey(k)==27:
            break
     
        hull = c.convex_hull
        cv2.drawContours(im2,[hull],0,(0,0,255),2)
        string3 = 'red : convex hull'
        cv2.putText(im2,string3,(20,60), cv2.FONT_HERSHEY_PLAIN, 1.0,(0,255,0))
        cv2.imshow('image',im2)
        if cv2.waitKey(k)==27:
            break

        im2 = im.copy()

        # Now mark centroid and bounding box on image
        (cx,cy) = c.centroid
        cv2.circle(im2,(int(cx),int(cy)),5,(0,255,0),-1)
        cv2.putText(im2,'green : centroid',(20,20), cv2.FONT_HERSHEY_PLAIN, 1.0,(0,255,0))

        (x,y,w,h) = c.bounding_box
        cv2.rectangle(im2,(x,y),(x+w,y+h),(0,0,255))
        cv2.putText(im2,'red : bounding rectangle',(20,40), cv2.FONT_HERSHEY_PLAIN, 1.0,(0,255,0))

        (center , axis, angle) = c.ellipse
        cx,cy = int(center[0]),int(center[1])
        ax1,ax2 = int(axis[0]),int(axis[1])
        orientation = int(angle)
        cv2.ellipse(im2,(cx,cy),(ax1,ax2),orientation,0,360,(255,255,255),3)
        cv2.putText(im2,'white : fitting ellipse',(20,60), cv2.FONT_HERSHEY_PLAIN, 1.0,(255,255,255))

        cv2.circle(im2,c.leftmost,5,(0,255,0),-1)
        cv2.circle(im2,c.rightmost,5,(0,255,0))
        cv2.circle(im2,c.topmost,5,(0,0,255),-1)
        cv2.circle(im2,c.bottommost,5,(0,0,255))
        cv2.imshow('image',im2)
        if cv2.waitKey(k)==27:
            break

     
        # Now shows the filled image, convex image, and distance image
        filledimage = c.filledImage
        cv2.putText(filledimage,'filledImage',(20,20), cv2.FONT_HERSHEY_PLAIN, 1.0,255)
        cv2.imshow('image',filledimage)
        if cv2.waitKey(k)==27:
            break

        conveximage = c.convexImage
        cv2.putText(conveximage,'convexImage',(20,20), cv2.FONT_HERSHEY_PLAIN, 1.0,255)
        cv2.imshow('image',conveximage)
        if cv2.waitKey(k)==27:
            break

        distance_image = c.distance_image()
        cv2.imshow('image',distance_image)
        cv2.putText(distance_image,'distance_image',(20,20), cv2.FONT_HERSHEY_PLAIN, 1.0,(255,255,255))
        if cv2.waitKey(k)==27:
            break
     
cv2.destroyAllWindows()
     

Welcome to OpenCV-Python !!!

Welcome to OpenCV-Python !!!
Welcome to OpenCV-Python !!!
This site provides some tutorials and examples on OpenCV-Python for absolute beginners.

Why this blog?

I started learning OpenCV a few months back. Before that, I used MATLAB for all my image processing purposes. The experience of shifting from MATLAB to OpenCV was amazing. Since i am a Python fan, I decided to use Python-bindings of OpenCV.

But as a beginner, I found a main problem in front of me. There are plenty of OpenCV tutorials available online for beginners, but most of them uses C++ for example codes. I couldn't find a single tutorial in Python for OpenCV. I could find a lot of OpenCV code in Python scattered online, but none of them was good for a beginner to start with. The best one I could find was none other than OpenCV-Python documentation. But it only lists the functions, not the examples using them.

Finally, I decided to start with C++ tutorials itself. I started to convert those codes from C++ to Python with the help of OpenCV-Python documentation. I decided to start a site or blog which shows the route i travelled to learn OpenCV so that it could be really helpful to the beginners. And here is the result.

Remember, I am also a beginner in OpenCV and I am still learning its basics. Here, what I am doing is, to give you only the outline ideas for a beginner to start with. For in-depth understanding, surely, I am not the right person to explain. But I know some good places where you can find them and I will share it with you as far as possible.

Some basic assumptions...

Here, I have made some assumptions...

1. You already know Python very well.
                Remember, this site only provides tutorial on OpenCV only, not on Python. So make sure, you have studied Python before beginning this site.
                Also, if you are good in C++, I would recommend you to use OpenCV in C++ itself. OpenCV can be used in C also.

2. You are using Python 2.7, OpenCV 2.4 and its python-bindings.
               
               I would recommend you to use Linux. It may be a little difficult at the beginning. But as time passes, you will enjoy it. You will feel the freedom of open-source.

I am always waiting for your suggestions and comments...

Feel free to talk for a better output...

You can contact me at: abidrahman2@gmail.com

Hope you will enjoy here...


Regards,
Abid K.
Welcome to OpenCV-Python !!!

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