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algorithm.py
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algorithm.py
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#!/usr/bin/python
# Import the required modules
import cv2, os
import numpy as np
from PIL import Image
import unicodedata
def predict_confidence(originalImagePath,tobeComparedImagePath,studentId):
print type(originalImagePath),type(tobeComparedImagePath),type(studentId)
originalImagePath = unicodedata.normalize('NFKD', originalImagePath).encode('ascii','ignore')
tobeComparedImagePath = unicodedata.normalize('NFKD', tobeComparedImagePath).encode('ascii','ignore')
studentId = unicodedata.normalize('NFKD', studentId).encode('ascii','ignore')
print type(originalImagePath),type(tobeComparedImagePath),type(studentId)
# 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()
# Path to the Yale Dataset
path = originalImagePath
# Call the get_images_and_labels function and get the face images and the
# corresponding labels
image_paths = [path]
print path
# 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')
#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
print faces
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(10)
#images, labels = get_images_and_labels(path)
#print path
cv2.destroyAllWindows()
# Perform the tranining
recognizer.train(images, np.array(labels))
# Append the images with the extension .sad into image_paths
image_paths = [tobeComparedImagePath]
# print image_paths
for image_path in image_paths:
print image_path
predict_image_pil = Image.open(image_path).convert('L')
predict_image = np.array(predict_image_pil, 'uint8')
faces = faceCascade.detectMultiScale(predict_image)
confidences = [];
print "faces : "
print faces
for (x, y, w, h) in faces:
nbr_predicted, conf = recognizer.predict(predict_image[y: y + h, x: x + w])
# print "Nbr_predicted %d" %nbr_predicted
# nbr_actual = int(os.path.split(image_path)[1].split(".")[0].replace("subject", ""))
# print "nbr_actual %d" %nbr_actual
#if nbr_actual == nbr_predicted:
print "is Correctly Recognized with confidence {}".format(conf)
confidences.append(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)
return confidences
#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 = [path]
#print path
# 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')
#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
#print faces
# 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(10)
# return the images list and labels list
# return images, labels
#predict_confidence("/home/sanket/Desktop/CMPE273PROJECT/Project/static/images/987654321/987654321-1", "/home/sanket/Desktop/CMPE273PROJECT/Project/static/images/987654321/987654321-2", "987654321")