#import OpenCV module
import cv2
import os
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline#function to detect face
def detect_face (img):#convert the test image to gray image
gray = cv2.cvtColor (img, cv2.COLOR_BGR2GRAY)#load OpenCV face detector
face_cas = cv2.CascadeClassifier (‘-File name.xml-‘)
faces = face_cas.detectMultiScale (gray, scaleFactor=1.3, minNeighbors=4);#if no faces are detected then return image
if (len (faces) == 0):
return None, None#extract the face
faces [0]=(x, y, w, h)#return only the face part
return gray[y: y+w, x: x+h], faces [0]#this function will read all persons’ training images, detect face #from each image
#and will return two lists of exactly same size, one list
def prepare_training_data(data_folder_path):#——STEP-1——–
#get the directories (one directory for each subject) in data folder
dirs = os.listdir(data_folder_path)
faces = []labels = []for dir_name in dirs:#our subject directories start with letter ‘s’ so
#ignore any non-relevant directories if any
if not dir_name.startswith(“s”):
continue;#——STEP-2——–
#extract label number of subject from dir_name
#format of dir name = slabel
#, so removing letter ‘s’ from dir_name will give us label
label = int(dir_name.replace(“s”, “”))#build path of directory containin images for current subject subject
#sample subject_dir_path = “training-data/s1”
subject_dir_path = data_folder_path + “/” + dir_name#get the images names that are inside the given subject directory
subject_images_names = os.listdir(subject_dir_path)#——STEP-3——–
#go through each image name, read image,
#detect face and add face to list of faces
for image_name in subject_images_names:#ignore system files like .DS_Store
if image_name.startswith(“.”):
continue;#build image path
#sample image path = training-data/s1/1.pgm
image_path = subject_dir_path + “/” + image_name#read image
image = cv2.imread(image_path)#display an image window to show the image
cv2.imshow(“Training on image…”, image)
cv2.waitKey(100)#detect face
face, rect = detect_face(image)#——STEP-4——–
#we will ignore faces that are not detected
if face is not None:#add face to list of faces
faces.append(face)#add label for this face
labels.append(label)
cv2.destroyAllWindows()
cv2.waitKey(1)
cv2.destroyAllWindows()
return faces, labels#let’s first prepare our training data
#data will be in two lists of same size
#one list will contain all the faces
#and other list will contain respective labels for each face
print(“Preparing data…”)
faces, labels = prepare_training_data(“training-data”)
print(“Data prepared”)#print total faces and labels
print(“Total faces: “, len(faces))
print(“Total labels: “, len(labels))#create our LBPH face recognizer
face_recognizer = cv2.face.createLBPHFaceRecognizer()#train our face recognizer of our training faces
face_recognizer.train(faces, np.array(labels))#function to draw rectangle on image
#according to given (x, y) coordinates and
#given width and heigh
def draw_rectangle(img, rect):
(x, y, w, h) = rect
cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)#function to draw text on give image starting from
#passed (x, y) coordinates.
def draw_text(img, text, x, y):
cv2.putText(img, text, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0), 2)#this function recognizes the person in image passed
#and draws a rectangle around detected face with name of the subject
def predict(test_img):#make a copy of the image as we don’t want to chang original image
img = test_img.copy()#detect face from the image
face, rect = detect_face(img)#predict the image using our face recognizer
label= face_recognizer.predict(face)#get name of respective label returned by face recognizer
label_text = subjects[label]#draw a rectangle around face detected
draw_rectangle(img, rect)#draw name of predicted person
draw_text(img, label_text, rect[0], rect[1]-5)
return img#load test images
test_img1 = cv2.imread(“test-data/test1.jpg”)
test_img2 = cv2.imread(“test-data/test2.jpg”)#perform a prediction
predicted_img1 = predict(test_img1)
predicted_img2 = predict(test_img2)
print(“Prediction complete”)#create a figure of 2 plots (one for each test image)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))#display test image1 result
ax1.imshow(cv2.cvtColor(predicted_img1, cv2.COLOR_BGR2RGB))#display test image2 result
ax2.imshow(cv2.cvtColor(predicted_img2, cv2.COLOR_BGR2RGB))#display both images
cv2.imshow(“Tom cruise test”, predicted_img1)
cv2.imshow(“Shahrukh Khan test”, predicted_img2)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.waitKey(1)
cv2.destroyAllWindows()

مترجم: عارف ارازپور