KHandy/khandy/image/align_and_crop.py

import cv2
import numpy as np

from .crop_or_pad import crop_or_pad as _crop_or_pad


def get_similarity_transform(src_pts, dst_pts):
    """Get similarity transform matrix from src_pts to dst_pts
    
    Args:
        src_pts: Kx2 np.array
            source points matrix, each row is a pair of coordinates (x, y)
        dst_pts: Kx2 np.array
            destination points matrix, each row is a pair of coordinates (x, y)
            
    Returns:
        xform_matrix: 3x3 np.array
            transform matrix from src_pts to dst_pts
    """
    src_pts = np.asarray(src_pts)
    dst_pts = np.asarray(dst_pts)
    assert src_pts.ndim == 2
    assert dst_pts.ndim == 2
    assert src_pts.shape[-1] == 2
    assert dst_pts.shape[-1] == 2
    
    npts = src_pts.shape[0]
    A = np.empty((npts * 2, 4))
    b = np.empty((npts * 2,))
    for k in range(npts):
        A[2 * k + 0] = [src_pts[k, 0], -src_pts[k, 1], 1, 0]
        A[2 * k + 1] = [src_pts[k, 1], src_pts[k, 0], 0, 1]
        b[2 * k + 0] = dst_pts[k, 0]
        b[2 * k + 1] = dst_pts[k, 1]
        
    x = np.linalg.lstsq(A, b)[0]
    xform_matrix = np.empty((3, 3))
    xform_matrix[0] = [x[0], -x[1], x[2]]
    xform_matrix[1] = [x[1], x[0], x[3]]
    xform_matrix[2] = [0, 0, 1]
    return xform_matrix
    
    
def align_and_crop(image, landmarks, std_landmarks, align_size, 
                   crop_size=None, crop_center=None,
                   return_transform_matrix=False):
    landmarks = np.asarray(landmarks)
    std_landmarks = np.asarray(std_landmarks)
    xform_matrix = get_similarity_transform(landmarks, std_landmarks)

    landmarks_ex = np.pad(landmarks, ((0,0),(0,1)), mode='constant', constant_values=1)
    dst_landmarks = np.dot(landmarks_ex, xform_matrix[:2,:].T)
    dst_image = cv2.warpAffine(image, xform_matrix[:2,:], dsize=align_size)
    if crop_size is not None:
        crop_center_ex = (crop_center[0], crop_center[1], 1)
        aligned_crop_center = np.dot(xform_matrix, crop_center_ex)
        dst_image = _crop_or_pad(dst_image, crop_size, aligned_crop_center)
        
        crop_begin_x = int(round(aligned_crop_center[0] - crop_size[0] / 2.0))
        crop_begin_y = int(round(aligned_crop_center[1] - crop_size[1] / 2.0))
        dst_landmarks -= np.asarray([[crop_begin_x, crop_begin_y]])
    if return_transform_matrix:
        return dst_image, dst_landmarks, xform_matrix
    else:
        return dst_image, dst_landmarks