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I'm trying to run a face recognition program in python using OpenCV but I'm getting the following error which I'm unable to resolve. Any help is greatly appreciated

This is the error

image description

Here is my code

image description

I'm trying to run a face recognition program in python using OpenCV but I'm getting the following error which I'm unable to resolve. Any help is greatly appreciated

This is the error

image description

Here is my code

image description image description image description image description image description image description

I'm trying to run a face recognition program in python using OpenCV but I'm getting the following error which I'm unable to resolve. Any help is greatly appreciated

This is the error

~~image description~~

Here is my the code

import cv2

import os

import numpy as np

subjects = ["", "Ramiz Raja", "Elvis Presley"]

function to detect face using OpenCV

def detect_face(img): gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

face_cascade = cv2.CascadeClassifier('C:\\Users\\darsh\\PycharmProjects\\helloworld\\opencv-files\\lbpcascade_frontalface.xml')

faces = face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5);

# if no faces are detected then return original img
if (len(faces) == 0):
    return None, None

# under the assumption that there will be only one face,
# extract the face area
(x, y, w, h) = faces[0]

# return only the face part of the image
return gray[y:y + w, x:x + h], faces[0]

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)

# list to hold all subject faces
faces = []
# list to hold labels for all subjects
labels = []

# let's go through each directory and read images within it
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;

    label = int(dir_name.replace("s", ""))

    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)

    for image_name in subject_images_names:

        # ignore system files like .DS_Store
        if image_name.startswith("."):
            continue;

        # build image description path
        # sample image description path = training-data/s1/1.pgm
        image_path = subject_dir_path + "\\" + image_name

        # read image
        image description = cv2.imread(image_path)

        # display an image description window to show the image
        cv2.imshow("Training on image...", cv2.resize(image, (400, 500)))
        cv2.waitKey(100)

        # detect face
        face, rect = detect_face(image)

        # ------STEP-4--------
        # for the purpose of this tutorial
        # 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

print("Preparing data...") faces, labels = prepare_training_data("C:\Users\darsh\PycharmProjects\helloworld\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.LBPHFaceRecognizer_create()

train our face recognizer of our training faces

face_recognizer.train(faces, np.array(labels))

def draw_rectangle(img, rect): (x, y, w, h) = rect cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)

def draw_text(img, text, x, y): cv2.putText(img, text, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0), 2)

def predict(test_img): # make a copy of the image ~~description~~ 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 descriptionusing our face recognizer
label, confidence = face_recognizer.predict(face)
# get name of respective label returned by face recognizer
label_text = subjects[label[0]]

# 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

print("Predicting images...")

load test images

test_img1 = cv2.imread("C:\Users\darsh\PycharmProjects\helloworld\test-data\test1.jpg") test_img2 = cv2.imread("C:\Users\darsh\PycharmProjects\helloworld\test-data\test2.jpg")

perform a prediction

predicted_img1 = predict(test_img1) predicted_img2 = predict(test_img2) print("Prediction complete")

display both images

cv2.imshow(subjects[1], cv2.resize(predicted_img1, (400, 500))) cv2.imshow(subjects[2], cv2.resize(predicted_img2, (400, 500))) cv2.waitKey(0) cv2.destroyAllWindows() cv2.waitKey(1) cv2.destroyAllWindows()

I'm trying to run a face recognition program in python using OpenCV but I'm getting the following error which I'm unable to resolve. Any help is greatly appreciated

This is the error

image description

Here is the code

import cv2 cv2 import os os import numpy as np np subjects = ["", "Ramiz Raja", "Elvis Presley"] Presley"] # function to detect face using OpenCV OpenCV def detect_face(img): gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) cv2.COLOR_BGR2GRAY) face_cascade = cv2.CascadeClassifier('C:\\Users\\darsh\\PycharmProjects\\helloworld\\opencv-files\\lbpcascade_frontalface.xml') faces = face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=5); # if no faces are detected then return original img if (len(faces) == 0): return None, None # under the assumption that there will be only one face, # extract the face area (x, y, w, h) = faces[0] # return only the face part of the image return gray[y:y + w, x:x + h], faces[0] 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) os.listdir(data_folder_path) # list to hold all subject faces faces = [] # list to hold labels for all subjects labels = [] # let's go through each directory and read images within it 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; label = int(dir_name.replace("s", "")) 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) 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...", cv2.resize(image, (400, 500))) cv2.waitKey(100) # detect face face, rect = detect_face(image) # ------STEP-4-------- # for the purpose of this tutorial # 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 print("Preparing data...") faces, labels = prepare_training_data("C:\Users\darsh\PycharmProjects\helloworld\training-data") prepare_training_data("C:\\Users\\darsh\\PycharmProjects\\helloworld\\training-data") print("Data prepared") prepared") # print total faces and labels labels print("Total faces: ", len(faces)) print("Total labels: ", len(labels)) len(labels)) # create our LBPH face recognizer recognizer face_recognizer = cv2.face.LBPHFaceRecognizer_create() cv2.face.LBPHFaceRecognizer_create() # train our face recognizer of our training faces faces face_recognizer.train(faces, np.array(labels)) np.array(labels)) def draw_rectangle(img, rect): (x, y, w, h) = rect cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2) 2) def draw_text(img, text, x, y): cv2.putText(img, text, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0), 2) 2) 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) detect_face(img) # predict the image using our face recognizer label, confidence = face_recognizer.predict(face) # get name of respective label returned by face recognizer label_text = subjects[label[0]] # 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 print("Predicting images...") images...") # load test images images test_img1 = cv2.imread("C:\Users\darsh\PycharmProjects\helloworld\test-data\test1.jpg") cv2.imread("C:\\Users\\darsh\\PycharmProjects\\helloworld\\test-data\\test1.jpg") test_img2 = cv2.imread("C:\Users\darsh\PycharmProjects\helloworld\test-data\test2.jpg") cv2.imread("C:\\Users\\darsh\\PycharmProjects\\helloworld\\test-data\\test2.jpg") # perform a prediction prediction predicted_img1 = predict(test_img1) predicted_img2 = predict(test_img2) print("Prediction complete") complete") # display both images images cv2.imshow(subjects[1], cv2.resize(predicted_img1, (400, 500))) cv2.imshow(subjects[2], cv2.resize(predicted_img2, (400, 500))) cv2.waitKey(0) cv2.destroyAllWindows() cv2.waitKey(1) cv2.destroyAllWindows()cv2.destroyAllWindows()