Commit43dfad1

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edited hough transform tutorial

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`@@ -8,4 +8,8 @@ To run this:`
`8`	`8`	`- To detect lines of live camera:`
`9`	`9`	```
`10`	`10`	`python live_shape_detector.py`
	`11`	+ ```
	`12`	+- To detect circles on `coins.jpg` image, consider:
	`13`	+ ```
	`14`	`+ python circle_detector.py coins.jpg`
`11`	`15`	```

Lines changed: 32 additions & 0 deletions

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`@@ -0,0 +1,32 @@`
	`1`	`+importcv2`
	`2`	`+importnumpyasnp`
	`3`	`+importmatplotlib.pyplotasplt`
	`4`	`+importsys`
	`5`	`+`
	`6`	`+# load the image`
	`7`	`+img=cv2.imread(sys.argv[1])`
	`8`	`+# convert BGR to RGB to be suitable for showing using matplotlib library`
	`9`	`+img=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)`
	`10`	`+# make a copy of the original image`
	`11`	`+cimg=img.copy()`
	`12`	`+# convert image to grayscale`
	`13`	`+img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)`
	`14`	`+# apply a blur using the median filter`
	`15`	`+img=cv2.medianBlur(img,5)`
	`16`	`+# finds the circles in the grayscale image using the Hough transform`
	`17`	`+circles=cv2.HoughCircles(image=img,method=cv2.HOUGH_GRADIENT,dp=0.9,`
	`18`	`+minDist=80,param1=110,param2=39,maxRadius=70)`
	`19`	`+`
	`20`	`+forco,iinenumerate(circles[0, :],start=1):`
	`21`	`+# draw the outer circle in green`
	`22`	`+cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2)`
	`23`	`+# draw the center of the circle in red`
	`24`	`+cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3)`
	`25`	`+`
	`26`	`+# print the number of circles detected`
	`27`	`+print("Number of circles detected:",co)`
	`28`	`+# save the image, convert to BGR to save with proper colors`
	`29`	`+# cv2.imwrite("coins_circles_detected.png", cimg)`
	`30`	`+# show the image`
	`31`	`+plt.imshow(cimg)`
	`32`	`+plt.show()`

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