OpenCV is an open source and very powerful library when it comes to Image Processing. In the following post, I will do some basic Operations on Images using OpenCV
Before we start, we need to specify the prerequisites
- Google Collab or Jupyter Notebook
- Basic Knowledge of Python
These are the imports we require to start our journey
import sys import numpy as np import cv2 from google.colab.patches import cv2_imshow from PIL import Image import math
The next step we need to do is Import our image
img = cv2.imread("img1opencv.jpg", cv2.IMREAD_COLOR) cv2_imshow(img)
This would Import the Image on which we will perform the Operations
We need to find out the distance b/w 2 pixels
We do the following by finding colors at pixels and selecting a color (Note - The image is 3 Channel RGB image)
color1=np.array([169,169,169],dtype=np.uint8) color1List=np.where(np.all((npimg==color1),axis=-1)) color1List
This selects pixels with the color code (169,169,169).
Now we need a second pixel, so we pick another color from the image from that.
color2=np.array([34, 35, 39],dtype=np.uint8) color2List=np.where(np.all((npimg==color2),axis=-1)) color2List
Finally, we use the Euclidean formula to find the distance b/w 2 pixels.
dx2 = (color2List-color1List)**2 dy2 = (color2List-color1List)**2 distance = math.sqrt(dx2 + dy2) distance
Finding Image Negatives is very easy with OpenCV. It can be done by a simple command of
L = npimg.max() imgNeg = L-img cv2_imshow(imgNeg)
Power Law deals with Gamma levels of Images. It can be done by
for gamma in [0.1, 0.5, 1.2, 2.2]: gamma_corrected_img = np.array(255*(npimg / 255) ** gamma, dtype = 'uint8') cv2_imshow(gamma_corrected_img)
We will first start with Grey level slicing.
We do the following by first specifying a min and max threshold values. Let's assume our values to be 100 and 180.
T1 = 100 T2 = 180 h,w,c = img.shape img_thresh_back = np.zeros((h,w), dtype = int) for i in range(h): for j in range(w): if (T1 < npimg[i,j]).any() and (npimg[i,j] < T2).any(): img_thresh_back[i,j]= 255 else: img_thresh_back[i-1,j-1] = img[i-1,j-1] cv2_imshow(img_thresh_back)
Which results in:
Now, we will move on to Contrast Stretching:
Contrast Stretching applies stretches the contrast across the image, which results in the image to look like this:
The code to do this is
def pixelVal(pix, r1, s1, r2, s2): if (0 <= pix and pix <= r1): return (s1 / r1)*pix elif (r1 < pix and pix <= r2): return ((s2 - s1)/(r2 - r1)) * (pix - r1) + s1 else: return ((255 - s2)/(255 - r2)) * (pix - r2) + s2 r1 = 66 s1 = 0 r2 = 152 s2 = 255 pixelVal_vec = np.vectorize(pixelVal) contrast_stretched = pixelVal_vec(npimg, r1, s1, r2, s2) cv2_imshow(contrast_stretched)
This operation first requires us to crop the image in n*n size
We do so by
Now we create a cropped image using the shape to create a square matrix
crop_img = img[0:2581, 0:2581]
There are a lot of operations that can be done using this, including using the Average neighbor value, but I was facing difficulty doing that, so we will proceed with using the Minimum neighbor.
This can be done using
for i in crop_img: for j in i: min = np.min(j) j = min j = min j = min cv2_imshow(crop_img)
Now we move on to the most fun operations that can be done using OpenCV.
We will load a new image for this operation and resize both images to the same resolution.
I am going for 512 x 512
img1_resize = cv2.resize(img, (512,512)) img2_resize = cv2.resize(img2, (512,512))
Here img2 is the new image we loaded.
Now we can perform Addition on this image
imgAdd= cv2.addWeighted(img1_resize,0.5,img2_resize,0.5,0) cv2_imshow(imgAdd)
Now we move on to Division, dividing image 1 from image 2 and then image 2 from image 1
img1DivImg2 = cv2.divide(img1_resize, img2_resize) cv2_imshow(img1DivImg2)
img2DivImg1 = cv2.divide(img2_resize, img1_resize) cv2_imshow(img2DivImg1)
XOR and NOT operation can be performed easily on any image using OpenCV's inbuilt functions
img_xor = cv2.bitwise_xor(img1_resize, img2_resize, mask = None) cv2_imshow(img_xor)
And NOT operation can be done by
img_not = cv2.bitwise_not(img1_resize, img2_resize, mask = None) cv2_imshow(img_not)
Rotation is a very basic transformation which can easily be done by OpenCV.
rows,cols, height = img.shape M = cv2.getRotationMatrix2D(((cols-1)/2.0,(rows-1)/2.0),90,1) dst = cv2.warpAffine(img,M,(cols,rows)) cv2_imshow(dst)
Affine Transformation is a little tedious task which requires some extra operations to be performed before on the image.
It results in changing the perspective of the image.
srcTri = np.array( [[0, 0], [img.shape - 1, 0], [0, img.shape - 1]] ).astype(np.float32) dstTri = np.array( [[0, img.shape*0.33], [img.shape*0.85, img.shape*0.25], [img.shape*0.15, img.shape*0.7]] ).astype(np.float32) warp_mat = cv2.getAffineTransform(srcTri, dstTri) warp_dst = cv2.warpAffine(img, warp_mat, (img.shape, img.shape)) cv2_imshow(warp_dst)
Finding mean and variance of an image is very easy since an Image is nothing but a NumPy array
Therefore, we can do this by
mean = np.mean(img) mean variance = np.var(img) variance
We move on to our last operation for today, which is down sampling.
This is required to decrease the overall resolution of the image.
It can be done by
print(img.shape) img_down = cv2.pyrDown(img) print(img_down.shape)
The resultant image has a smaller shape, but the visible result won't be a noticeable change.
This concludes my post about OpenCV. I hope you did enjoy going through it, although it being a bit lengthy.
Here is a Google Collab link for all the operations performed here.
Google Collab Link