I want to use OpenCV2.0 and Python2.6 to show resized images. I used and adopted this example but unfortunately, this code is for OpenCV2.1 and does not seem to be working o
def rescale_by_height(image, target_height, method=cv2.INTER_LANCZOS4):
"""Rescale `image` to `target_height` (preserving aspect ratio)."""
w = int(round(target_height * image.shape[1] / image.shape[0]))
return cv2.resize(image, (w, target_height), interpolation=method)
def rescale_by_width(image, target_width, method=cv2.INTER_LANCZOS4):
"""Rescale `image` to `target_width` (preserving aspect ratio)."""
h = int(round(target_width * image.shape[0] / image.shape[1]))
return cv2.resize(image, (target_width, h), interpolation=method)
If you wish to use CV2, you need to use the resize
function.
For example, this will resize both axes by half:
small = cv2.resize(image, (0,0), fx=0.5, fy=0.5)
and this will resize the image to have 100 cols (width) and 50 rows (height):
resized_image = cv2.resize(image, (100, 50))
Another option is to use scipy
module, by using:
small = scipy.misc.imresize(image, 0.5)
There are obviously more options you can read in the documentation of those functions (cv2.resize, scipy.misc.imresize).
Update:
According to the SciPy documentation:
imresize
is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.
Use skimage.transform.resize instead.
Note that if you're looking to resize by a factor, you may actually want skimage.transform.rescale.
You could use the GetSize function to get those information, cv.GetSize(im) would return a tuple with the width and height of the image. You can also use im.depth and img.nChan to get some more information.
And to resize an image, I would use a slightly different process, with another image instead of a matrix. It is better to try to work with the same type of data:
size = cv.GetSize(im)
thumbnail = cv.CreateImage( ( size[0] / 10, size[1] / 10), im.depth, im.nChannels)
cv.Resize(im, thumbnail)
Hope this helps ;)
Julien
There are two ways to resize an image. The new size can be specified:
Manually;
height, width = src.shape[:2]
dst = cv2.resize(src, (2*width, 2*height), interpolation = cv2.INTER_CUBIC)
By a scaling factor.
dst = cv2.resize(src, None, fx = 2, fy = 2, interpolation = cv2.INTER_CUBIC)
,
where fx is the scaling factor along the horizontal axis and fy along the vertical axis.
To shrink an image, it will generally look best with INTER_AREA interpolation, whereas to enlarge an image, it will generally look best with INTER_CUBIC (slow) or INTER_LINEAR (faster but still looks OK).
import cv2
img = cv2.imread('YOUR_PATH_TO_IMG')
height, width = img.shape[:2]
max_height = 300
max_width = 300
# only shrink if img is bigger than required
if max_height < height or max_width < width:
# get scaling factor
scaling_factor = max_height / float(height)
if max_width/float(width) < scaling_factor:
scaling_factor = max_width / float(width)
# resize image
img = cv2.resize(img, None, fx=scaling_factor, fy=scaling_factor, interpolation=cv2.INTER_AREA)
cv2.imshow("Shrinked image", img)
key = cv2.waitKey()
import cv2 as cv
im = cv.imread(path)
height, width = im.shape[:2]
thumbnail = cv.resize(im, (round(width / 10), round(height / 10)), interpolation=cv.INTER_AREA)
cv.imshow('exampleshq', thumbnail)
cv.waitKey(0)
cv.destroyAllWindows()
Here's a function to upscale or downscale an image by desired width or height while maintaining aspect ratio
# Resizes a image and maintains aspect ratio
def maintain_aspect_ratio_resize(image, width=None, height=None, inter=cv2.INTER_AREA):
# Grab the image size and initialize dimensions
dim = None
(h, w) = image.shape[:2]
# Return original image if no need to resize
if width is None and height is None:
return image
# We are resizing height if width is none
if width is None:
# Calculate the ratio of the height and construct the dimensions
r = height / float(h)
dim = (int(w * r), height)
# We are resizing width if height is none
else:
# Calculate the ratio of the width and construct the dimensions
r = width / float(w)
dim = (width, int(h * r))
# Return the resized image
return cv2.resize(image, dim, interpolation=inter)
Usage
import cv2
image = cv2.imread('1.png')
cv2.imshow('width_100', maintain_aspect_ratio_resize(image, width=100))
cv2.imshow('width_300', maintain_aspect_ratio_resize(image, width=300))
cv2.waitKey()
Using this example image
Simply downscale to width=100
(left) or upscale to width=300
(right)