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POobject (Car) Detection and segmentation
 

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  1. POobject (Car) Detection and segmentation
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    <p>I am trying to segment cars from image that consist of only one car and an easy background like <br/><img src="https://i.stack.imgur.com/boq9B.png" alt="enter image description here"></p> <p><br/><img src="https://i.stack.imgur.com/8CtRz.png" alt="enter image description here"></p> <p><br/> but what I get from my implementation is this<br/> <img src="https://i.stack.imgur.com/PQGN3.png" alt="enter image description here"></p> <p><br/> and<br/> <img src="https://i.stack.imgur.com/LpD0h.png" alt="enter image description here"></p> <p><br/> respectively <br/></p> <p>but it works very easily on almost already segmented images like. <img src="https://i.stack.imgur.com/9YWsb.jpg" alt="enter image description here"></p> <p><br/> giving results like<br/> <img src="https://i.stack.imgur.com/14E7T.jpg" alt="enter image description here"></p> <p><br/></p> <p>The Code I am using is</p> <pre><code>import cv2 import numpy as np THRESH_TYPE=cv2.THRESH_BINARY_INV def show(name,obj): cv2.imshow(name,obj) cv2.moveWindow(name, 100, 100) cv2.waitKey(0) cv2.destroyAllWindows() def process_end(new): drawing = np.zeros(o.shape,np.uint8) # Image to draw the contours contours,hierarchy =cv2.findContours(new,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)#find connected borders for cnt in contours: color = np.random.randint(0,255,(3)).tolist() # Select a random color cv2.drawContours(drawing,[cnt],0,color,2) print "processing done" return drawing def process(name,path): global o print "Started!!! processing "+name ratio=1#change according to image size o=cv2.imread(path+name)#open image print type(o) show("original",o) w,h=o.shape[1]/ratio,o.shape[0]/ratio#resize ratio for width and height new=cv2.resize(o,(w,h))#resize image #show("Resized",new) new=cv2.cvtColor(new,cv2.COLOR_RGB2GRAY)#grey scale image show("grey",new) cv2.imwrite("grey.jpg",new) new1 = cv2.GaussianBlur(new,(5,5),0)#gaussians Blurs Image show("blurred1",new1) cv2.imwrite("gblur_"+name,new1)#save image new2 = cv2.medianBlur(new,7)#Median Blurs Image show("blurred2",new1) cv2.imwrite("mblur_"+name,new2)#save image #new=cv2.equalizeHist(new,)#do image histogram equalisation to better the contrast #show("hist equal otsu",new) ##cv2.imwrite("otsu_"+name,new)#save image a,new=cv2.threshold(new,0,255,THRESH_TYPE | cv2.THRESH_OTSU)#OTSU thresholding show("otsu",new) cv2.imwrite("otsu_"+name,new)#save image return new,name new,name=process("car9.jpg","C:\\Users\\XOR\\Desktop\\file\\")#Change the Name and path accordingly new=cv2.Canny(new, 100,200)#canny edge detection technique show("canny",new) cv2.imwrite("canny_"+name,new)#save image new=process_end(new) show("blobed",new) cv2.imwrite("blob_"+name,new)#save image new=cv2.Sobel(new,-1,1,0,3,BORDER_WRAP) show("sobel",new) cv2.imwrite("sobel_"+name,new)#save image </code></pre> <p>I have tried the watershed algorithm (on matlab) too but it also doesn't help. I'm looking for a way to segment the first two images that gives a result similar to third one.</p>
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    1. COcould you please check my profile and email me. I solved another one of your problems, but you took it down yesterday as i was posting the solution! (substring problem)
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