Training set

[maclory]

Training set

[Steve-Tod]

Hi, we crop the face with keypoints. The code is like this:

def bbox_generator(anno, img_root, expand_scale=0.3):
    split = anno.split()
    img_name = split[0]
    full_w, full_h = Image.open(os.path.join(img_root, img_name)).size
    # here split[1:] should be
    x_min, y_min, w, h = [int(x) for x in split[1:]]
    
    # center_crop
    if w > h:
        dx = (w - h) / 2
        x_min += dx
        w = h
    else:
        dy = (h - w) / 2
        y_min += dy
        h = w

    expand_x = int(w * expand_scale / 2)
    expand_y = int(h * expand_scale / 2)
    new_x_min = max(0, x_min - expand_x)
    new_y_min = max(0, y_min - expand_y)
    x_max = min(new_x_min + w + 2 * expand_x, full_w)
    y_max = min(new_y_min + h + 2 * expand_y, full_h)
    return img_name, (new_x_min, new_y_min, x_max, y_max)

For bicubic sampling, we use img.resize, where img is a PIL.Image.

[aiden013]

And please tell me what kind of method do you use to implement bicubic sampling.

[aiden013]

Hi, we crop the face with keypoints. The code is like this:

def bbox_generator(anno, img_root, expand_scale=0.3):
    split = anno.split()
    img_name = split[0]
    full_w, full_h = Image.open(os.path.join(img_root, img_name)).size
    # here split[1:] should be
    x_min, y_min, w, h = [int(x) for x in split[1:]]
    
    # center_crop
    if w > h:
        dx = (w - h) / 2
        x_min += dx
        w = h
    else:
        dy = (h - w) / 2
        y_min += dy
        h = w

    expand_x = int(w * expand_scale / 2)
    expand_y = int(h * expand_scale / 2)
    new_x_min = max(0, x_min - expand_x)
    new_y_min = max(0, y_min - expand_y)
    x_max = min(new_x_min + w + 2 * expand_x, full_w)
    y_max = min(new_y_min + h + 2 * expand_y, full_h)
    return img_name, (new_x_min, new_y_min, x_max, y_max)

For bicubic sampling, we use img.resize, where img is a PIL.Image.

Thank you very much. I wish you all the best.