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recog.py
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#!/usr/bin/python
# Import the required modules
import cv2, os
import numpy as np
from PIL import Image
# For face detection we will use the Haar Cascade provided by OpenCV.
cascadePath = "haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath)
# For face recognition we will the the LBPH Face Recognizer
recognizer = cv2.createLBPHFaceRecognizer()
def get_images_and_labels(path):
# Append all the absolute image paths in a list image_paths
# We will not read the image with the .sad extension in the training set
# Rather, we will use them to test our accuracy of the training
image_paths = [os.path.join(path, f) for f in os.listdir(path) if not f.endswith('.sad')]
# images will contains face images
images = []
# labels will contains the label that is assigned to the image
labels = []
for image_path in image_paths:
# Read the image and convert to grayscale
image_pil = Image.open(image_path).convert('L')
# Convert the image format into numpy array
image = np.array(image_pil, 'uint8')
# Get the label of the image
nbr = int(os.path.split(image_path)[1].split(".")[0].replace("subject", ""))
# Detect the face in the image
faces = faceCascade.detectMultiScale(image)
# If face is detected, append the face to images and the label to labels
for (x, y, w, h) in faces:
images.append(image[y: y + h, x: x + w])
labels.append(nbr)
cv2.imshow("Adding faces to traning set...", image[y: y + h, x: x + w])
cv2.waitKey(50)
# return the images list and labels list
return images, labels
# Path to the Yale Dataset
path = './yalefaces'
# Call the get_images_and_labels function and get the face images and the
# corresponding labels
images, labels = get_images_and_labels(path)
cv2.destroyAllWindows()
# Perform the tranining
recognizer.train(images, np.array(labels))
# # Append the images with the extension .sad into image_paths
# image_paths = [os.path.join(path, f) for f in os.listdir(path) if f.endswith('.sad')]
# for image_path in image_paths:
# predict_image_pil = Image.open(image_path).convert('L')
# predict_image = np.array(predict_image_pil, 'uint8')
# faces = faceCascade.detectMultiScale(predict_image)
# for (x, y, w, h) in faces:
# nbr_predicted, conf = recognizer.predict(predict_image[y: y + h, x: x + w])
# nbr_actual = int(os.path.split(image_path)[1].split(".")[0].replace("subject", ""))
# if nbr_actual == nbr_predicted:
# print "{} is Correctly Recognized with confidence {}".format(nbr_actual, conf)
# else:
# print "{} is Incorrect Recognized as {}".format(nbr_actual, nbr_predicted)
# cv2.imshow("Recognizing Face", predict_image[y: y + h, x: x + w])
# cv2.waitKey(1000)
predict_image_pil = Image.open('./detected_faces/face_detected1.png').convert('L')
predict_image = np.array(predict_image_pil, 'uint8')
faces = faceCascade.detectMultiScale(predict_image)
for (x, y, w, h) in faces:
nbr_predicted, conf = recognizer.predict(predict_image[y: y + h, x: x + w])
if conf < 55 :
print "{} is Correctly Recognized with confidence {}".format(nbr_predicted, conf)
else:
print "face_detected1.png not identified"
cv2.imshow("Recognizing Face", predict_image[y: y + h, x: x + w])
cv2.waitKey(1000)