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Python OpenCV Basic Operations on images

In this article, we will learn about essential Python OpenCV Basic Operations on images. We have used Dilation and Erosion on the image given below. Here we will also learn how to perform Face Detection on an image using Cascading Classifier.

Dilation and Erosion Operation on images

Opencv Dilation and Erosion Operations
In [52]:
import cv2
                import numpy as np
                import matplotlib.pyplot as plt
                img = cv2.imread('images.jpeg')
                img= cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
                plt.figure(figsize=(20, 20))
                plt.subplot(3, 2, 1)
                plt.title("Original")
                plt.imshow(img)
                kernel = np.ones((5,5), np.uint8)
                erosion = cv2.erode(img, kernel, iterations = 1)
                plt.subplot(3, 2, 2)
                plt.title("Erosion")
                plt.imshow(erosion)
                dilation = cv2.dilate(img, kernel, iterations = 1)
                plt.subplot(3, 2, 3)
                plt.title("Dilation")
                plt.imshow(dilation)
Out[52]:
<matplotlib.image.AxesImage at 0x7f12a880ff28>
Opencv Dilation Operation on image

Plot a histogram from an image.

Opencv Histogram image
In [53]:
img = cv.imread('WaldoBeach.jpg')
img= cv.cvtColor(img, cv.COLOR_BGR2RGB)
mask = np.zeros(img.shape[:2], np.uint8)
mask[100:300, 100:400] = 255
masked_img = cv.bitwise_and(img,img,mask = mask)
hist_full = cv.calcHist([img],[0],None,[256],[0,256])
hist_mask = cv.calcHist([img],[0],mask,[256],[0,256])
plt.figure(figsize=(20, 20))
plt.subplot(221), plt.imshow(img, 'gray')
plt.subplot(222), plt.imshow(mask,'gray')
plt.subplot(223), plt.imshow(masked_img, 'gray')
plt.subplot(224), plt.plot(hist_full)
plt.plot(hist_mask)
plt.xlim([0,256])
plt.show()
Opencv Histogram image

Using Affine transformation on a given image.

Image Affine Transformation
In [54]:
img = cv.imread('images.jpeg')
img= cv.cvtColor(img, cv.COLOR_BGR2RGB)
pts1 = np.float32([[50,50],[200,50],[50,200]])
pts2 = np.float32([[10,100],[200,50],[100,250]])
plt.figure(figsize=(20, 20))
M = cv.getAffineTransform(pts1,pts2)
dst = cv.warpAffine(img,M,(cols,rows))
plt.subplot(121),plt.imshow(img),plt.title('Input')
plt.subplot(122),plt.imshow(dst),plt.title('Output')
plt.show()
Image Affine Transformation

Using Cascade Classifier for face detection.

Cascade Classifier for Face detection
In [70]:
import cv2
from matplotlib.patches import Rectangle
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
img = cv2.imread('images.jpeg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img= cv.cvtColor(img, cv.COLOR_BGR2RGB)
faces= face_cascade.detectMultiScale(gray, 1.1, 5)
for (x,y,w,h) in faces:
    ax = plt.gca()
    ax.add_patch( Rectangle((x,y),w,h,fc ='none',ec ='b',lw = 4) )
plt.imshow(img,cmap = 'gray')
plt.title('Sachin tendulkar'), plt.xticks([]), plt.yticks([])
plt.show()
Cascade Classifier for Face detection

Using Cascade Classifier for face detection and eyes detection.

Cascade Classifier for Face Detection and Eyes Detection
In [94]:
import numpy as np
import cv2

face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('haarcascade_eye_tree_eyeglasses.xml')

img = cv2.imread('images.jpeg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
img= cv.cvtColor(img, cv.COLOR_BGR2RGB)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x,y,w,h) in faces:
     cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
     roi_gray = gray[y:y+h, x:x+w]
     roi_color = img[y:y+h, x:x+w]
     eyes = eye_cascade.detectMultiScale(roi_gray)
     for (ex,ey,ew,eh) in eyes:
         cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)
plt.imshow(img,cmap = 'gray')
plt.title('Sachin tendulkar'), plt.xticks([]), plt.yticks([])
plt.show()
Cascade Classifier for Face Detection and Eyes Detection
Conclusion

From the above content we came to know about how many more operations can be used on an image using open cv and how these operations make a huge difference in their appearance. I also came to know how cascade classifier can detect any part of the body through the classifier that is used with the cascading and how we can plot a histogram through an image.

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