import cv2filename = ‘your_image.jpg’W = 1000.oriimg = cv2.imread(filename,cv2.CV_LOAD_IMAGE_COLOR)height, width, depth = oriimg.shapeimgScale = W/widthnewX,newY = oriimg.shape[1]*imgScale, oriimg.shape[0]*imgScalenewimg = cv2.resize(oriimg,(int(newX),int(newY)))cv2.imshow(“Show by CV2”,newimg)cv2.waitKey(0)cv2.imwrite(“resizeimg.jpg”,newimg)
Find the Image shape:
oriimg.shape
It returns a tuple of height,width and channel(if image RGB color)ex:(height,width,channel) as (360,480,3)
Resize the Image:
Option 1:
Syntax:cv2.resize(image,(width,height))Example:img = cv2.resize(oriimg,(360,480))
Option 2(Factor with float value):
Syntax:cv2.resize(image,None,fx=int or float,fy=int or float)fx depends on widthfy depends on heightYou can put the second argument None or (0,0)Example:img = cv2.resize(oriimg,None,fx=0.5,fy=0.5)Note:0.5 means 50% of image to be scalling
Output:
Different interpolation methods are used to resize the image. It is same syntax but add one argument with key name interpolation.
Preferable interpolation methods are cv.INTER_AREA for shrinking and cv.INTER_CUBIC(slow) & cv.INTER_LINEAR for zooming. By default, interpolation method used is cv.INTER_LINEAR for all resizing purposes. You can resize an input image either of following methods:
Examples:img = cv2.resize(oriimg,None,fx=0.5,fy=0.5,interpolation=cv2.INTER_CUBIC)img = cv2.resize(oriimg,(200,300),interpolation=cv2.INTER_AREA)
