sumantrajoshi / face-recognition-using-deep-learning Goto Github PK

Hi

I want to train with my own dataset. How to do that. I also want to use pre trained model but still want to train with new dataset.

Thanks.

I followed your tutorial, I get the following error:
""
File "", line 1, in
File "C:\Program Files\JetBrains\PyCharm 2020.1\plugins\python\helpers\pydev_pydev_bundle\pydev_umd.py", line 197, in runfile
pydev_imports.execfile(filename, global_vars, local_vars) # execute the script
File "C:\Program Files\JetBrains\PyCharm 2020.1\plugins\python\helpers\pydev_pydev_imps_pydev_execfile.py", line 18, in execfile
exec(compile(contents+"\n", file, 'exec'), glob, loc)
File "C:/Users/alhus/Desktop/DeepFace Detecrion/main.py", line 371, in
input_embeddings = create_input_image_embeddings()
File "C:/Users/alhus/Desktop/DeepFace Detecrion/main.py", line 297, in create_input_image_embeddings
input_embeddings[person_name] = image_to_embedding(image_file, model)
File "C:/Users/alhus/Desktop/DeepFace Detecrion/main.py", line 257, in image_to_embedding
image = cv2.resize(image, (152, 152))
cv2.error: OpenCV(4.3.0) C:\projects\opencv-python\opencv\modules\imgproc\src\resize.cpp:3929: error: (-215:Assertion failed) !ssize.empty() in function 'cv::resize'

""

what is the reason for this error and how to fix it.
Thanks in advance.

code, below:

`
def image_to_embedding(image, model):
image = cv2.resize(image, (152, 152))
img = image[...,::-1]
img = np.around(np.transpose(img, (0,1,2))/255.0, decimals=12)
x_train = np.array([img])
embedding = model.predict_on_batch(x_train)
return embedding

def recognize_face(face_image, input_embeddings, model):
embedding = image_to_embedding(face_image, model)

minimum_distance = 200
name = None

# Loop over  names and encodings.
for (input_name, input_embedding) in input_embeddings.items():

    euclidean_distance = np.linalg.norm(embedding - input_embedding)

    print('Euclidean distance from %s is %s' % (input_name, euclidean_distance))

    if euclidean_distance < minimum_distance:
        minimum_distance = euclidean_distance
        name = input_name

if minimum_distance < 0.68:
    return str(name)
else:
    return None

import glob

def create_input_image_embeddings():
input_embeddings = {}

for file in glob.glob("C:\\Users\\alhus\\Desktop\\cr7 photos"):
    person_name = os.path.splitext(os.path.basename(file))[0]
    image_file = cv2.imread(file, 1)
    input_embeddings[person_name] = image_to_embedding(image_file, model)

return input_embeddings

def recognize_faces_in_cam(input_embeddings):
cv2.namedWindow("Face Recognizer")
vc = cv2.VideoCapture(0)

font = cv2.FONT_HERSHEY_SIMPLEX
face_cascade = face_detector

while vc.isOpened():
    _, frame = vc.read()
    img = frame
    height, width, channels = frame.shape

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = face_cascade.detectMultiScale(gray, 1.3, 5)

    # Loop through all the faces detected
    identities = []
    for (x, y, w, h) in faces:
        x1 = x
        y1 = y
        x2 = x + w
        y2 = y + h

        face_image = frame[max(0, y1):min(height, y2), max(0, x1):min(width, x2)]
        identity = recognize_face(face_image, input_embeddings, model)

        if identity is not None:
            img = cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 255, 255), 2)
            cv2.putText(img, str(identity), (x1 + 5, y1 - 5), font, 1, (255, 255, 255), 2)

    key = cv2.waitKey(100)
    cv2.imshow("Face Recognizer", img)

    if key == 27:  # exit on ESC
        break
vc.release()
cv2.destroyAllWindows()

cam = cv2.VideoCapture(0)

face_detector = myface_detector

count = 0
while(True):
ret, img = cam.read()
#gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_detector.detectMultiScale(img, 1.3, 5)
for (x,y,w,h) in faces:
x1 = x
y1 = y
x2 = x+w
y2 = y+h
cv2.rectangle(img, (x1,y1), (x2,y2), (255,255,255), 2)
count += 1
# Save the captured image into the datasets folder
cv2.imwrite("C:\Users\alhus\Desktop\cr7 photos" + str(count) + ".jpg", img[y1:y2,x1:x2])
cv2.imshow('image', img)
k = cv2.waitKey(200) & 0xff # Press 'ESC' for exiting video
if k == 27:
break
elif count >= 10: # Take 30 face sample and stop video
break
cam.release()
cv2.destroyAllWindows()

input_embeddings = create_input_image_embeddings()
recognize_faces_in_cam(input_embeddings)

`

replace “np.set_printoptions(threshold=np.nan)“ with ”np.set_printoptions(threshold=sys.maxsize)

it worked for me!

kernel keeps crashing while running the "# Lets run the face recognizer program :-)" section.