I have a requirement where I have to deploy this model on Azure container registry and publish it as a Web Service. In order to do that, I need to save the OpenCV model as a pickle file so I can register the model on Azure.

Here is my code to detect faces:

import numpy as np import cv2 import matplotlib.pyplot as plt %matplotlib inline

load the cascade

face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml") image = cv2.imread('cricketteam.jpg')

Load the image

plt.figure(figsize=(12,8)) plt.imshow(image, cmap='gray') plt.show()

Convert into grayscale

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml") faces = faceCascade.detectMultiScale( gray, scaleFactor=1.3, minNeighbors=3, minSize=(30, 30) )

print("Found {0} Faces!".format(len(faces)))

for (x, y, w, h) in faces: cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)

status = cv2.imwrite('faces_detected.jpg', image)

print ("Image faces_detected.jpg written to filesystem: ",status)

facesdetected = cv2.imread('faces_detected.jpg')

Load the image

plt.figure(figsize=(12,8)) plt.imshow(image, cmap='gray') plt.show()

I have a requirement where I have to deploy this model on Azure container registry and publish it as a Web Service. In order to do that, I need to save the OpenCV model as a pickle file so I can register the model on Azure.

Here is my code to detect faces:

import numpy as np
import cv2
import matplotlib.pyplot as plt
%matplotlib inline
 
inline

# load the cascade
 
cascade
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
image = cv2.imread('cricketteam.jpg')
 
cv2.imread('cricketteam.jpg')

# Load the image
 
image 
plt.figure(figsize=(12,8))
plt.imshow(image, cmap='gray')
plt.show()
 
plt.show()

# Convert into grayscale
 
grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
 
cv2.COLOR_BGR2GRAY)

faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
faces = faceCascade.detectMultiScale(
        gray,
        scaleFactor=1.3,
        minNeighbors=3,
        minSize=(30, 30)
) 
 
 print("Found {0} Faces!".format(len(faces)))
 
Faces!".format(len(faces)))

for (x, y, w, h) in faces:
    cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
 
2)

status = cv2.imwrite('faces_detected.jpg', image)
 
image)

print ("Image faces_detected.jpg written to filesystem: ",status)
 
",status)

facesdetected = cv2.imread('faces_detected.jpg')
 
cv2.imread('faces_detected.jpg')
# Load the image
 
image 
plt.figure(figsize=(12,8))
plt.imshow(image, cmap='gray')
plt.show()plt.show()