Using other face detection algorithm

[jeanbverroken]

Hello, I have a small issue with the code.
First of all, thanks so much for providing us with this code!

In the demo.py the code looks as follows:

    #Get BGR in order for the detector to work.
    input_img = cv2.cvtColor(img.astype('uint8'), cv2.COLOR_GRAY2BGR)
    detected = detector(input_img, 1)
    img_h, img_w, _ = np.shape(input_img)
    faces = np.empty((len(detected), img_size, img_size, 3))
    for i, d in enumerate(detected):
        x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, d.bottom() + 1, d.width(), d.height()
        xw1 = max(int(x1 - 0.4 * w), 0)
        yw1 = max(int(y1 - 0.4 * h), 0)
        xw2 = min(int(x2 + 0.4 * w), img_w - 1)
        yw2 = min(int(y2 + 0.4 * h), img_h - 1)
        cv2.rectangle(input_img, (x1, y1), (x2, y2), (255, 0, 0), 2)
        faces[i, :, :, :] = cv2.resize(input_img[yw1:yw2 + 1, xw1:xw2 + 1, :], (img_size, img_size))
    
    # predict ages and genders of the detected faces
    results = model.predict(faces)

However, is there any way to use the model.predict(faces) on the input image?
This is because in an earlier stage, I already detected and extracted the faces of the subject that I am working with. This is the input image that I am trying to feed in. This means it is a time loss to do this again, and sometimes the faces can't be detected due to it already being cropped.

When I use the model.predict on the original image, I of course get an error; being:

Error when checking : expected input_11 to have 4 dimensions, but got array with shape (224, 224, 3)

Which is expected. However, is there a simple way to get past this?

The algorithm I am workng with has as output:

cut_image = img[y_m-he:y_m+he,x_m-wi:x_m+wi]
cut_image = cv2.resize(cut_image,(width,height), interpolation = cv2.INTER_AREA)

Thanks so much

[yu4u]

In the demo.py, the shape of the input to model.predict should be (batch_size, 64, 64, 3).
Therefore, the following code will work:

cut_image = cv2.resize(cut_image, (64, 64), interpolation = cv2.INTER_AREA)
cut_image = np.expand_dims(cut_image, axis=0)

[jeanbverroken]

Thanks!