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| import numpy as np | |
| import scipy.spatial as spatial | |
| def bilinear_interpolate(img, coords): | |
| """ Interpolates over every image channel | |
| http://en.wikipedia.org/wiki/Bilinear_interpolation | |
| :param img: max 3 channel image | |
| :param coords: 2 x _m_ array. 1st row = xcoords, 2nd row = ycoords | |
| :returns: array of interpolated pixels with same shape as coords | |
| """ | |
| int_coords = np.int32(coords) | |
| x0, y0 = int_coords | |
| dx, dy = coords - int_coords | |
| # 4 Neighbour pixels | |
| q11 = img[y0, x0] | |
| q21 = img[y0, x0+1] | |
| q12 = img[y0+1, x0] | |
| q22 = img[y0+1, x0+1] | |
| btm = q21.T * dx + q11.T * (1 - dx) | |
| top = q22.T * dx + q12.T * (1 - dx) | |
| inter_pixel = top * dy + btm * (1 - dy) | |
| return inter_pixel.T | |
| def grid_coordinates(points): | |
| """ x,y grid coordinates within the ROI of supplied points | |
| :param points: points to generate grid coordinates | |
| :returns: array of (x, y) coordinates | |
| """ | |
| xmin = np.min(points[:, 0]) | |
| xmax = np.max(points[:, 0]) + 1 | |
| ymin = np.min(points[:, 1]) | |
| ymax = np.max(points[:, 1]) + 1 | |
| return np.asarray([(x, y) for y in range(ymin, ymax) | |
| for x in range(xmin, xmax)], np.uint32) | |
| def process_warp(src_img, result_img, tri_affines, dst_points, delaunay): | |
| """ | |
| Warp each triangle from the src_image only within the | |
| ROI of the destination image (points in dst_points). | |
| """ | |
| roi_coords = grid_coordinates(dst_points) | |
| # indices to vertices. -1 if pixel is not in any triangle | |
| roi_tri_indices = delaunay.find_simplex(roi_coords) | |
| for simplex_index in range(len(delaunay.simplices)): | |
| coords = roi_coords[roi_tri_indices == simplex_index] | |
| num_coords = len(coords) | |
| out_coords = np.dot(tri_affines[simplex_index], | |
| np.vstack((coords.T, np.ones(num_coords)))) | |
| x, y = coords.T | |
| result_img[y, x] = bilinear_interpolate(src_img, out_coords) | |
| return None | |
| def triangular_affine_matrices(vertices, src_points, dest_points): | |
| """ | |
| Calculate the affine transformation matrix for each | |
| triangle (x,y) vertex from dest_points to src_points | |
| :param vertices: array of triplet indices to corners of triangle | |
| :param src_points: array of [x, y] points to landmarks for source image | |
| :param dest_points: array of [x, y] points to landmarks for destination image | |
| :returns: 2 x 3 affine matrix transformation for a triangle | |
| """ | |
| ones = [1, 1, 1] | |
| for tri_indices in vertices: | |
| src_tri = np.vstack((src_points[tri_indices, :].T, ones)) | |
| dst_tri = np.vstack((dest_points[tri_indices, :].T, ones)) | |
| mat = np.dot(src_tri, np.linalg.inv(dst_tri))[:2, :] | |
| yield mat | |
| def warp_image(src_img, src_points, dest_points, dest_shape, dtype=np.uint8): | |
| # Resultant image will not have an alpha channel | |
| num_chans = 3 | |
| src_img = src_img[:, :, :3] | |
| rows, cols = dest_shape[:2] | |
| result_img = np.zeros((rows, cols, num_chans), dtype) | |
| delaunay = spatial.Delaunay(dest_points) | |
| tri_affines = np.asarray(list(triangular_affine_matrices( | |
| delaunay.simplices, src_points, dest_points))) | |
| process_warp(src_img, result_img, tri_affines, dest_points, delaunay) | |
| return result_img | |
| def test_local(): | |
| from functools import partial | |
| import cv2 | |
| import scipy.misc | |
| import locator | |
| import aligner | |
| from matplotlib import pyplot as plt | |
| # Load source image | |
| face_points_func = partial(locator.face_points, '../data') | |
| base_path = '../females/Screenshot 2015-03-04 17.11.12.png' | |
| src_path = '../females/BlDmB5QCYAAY8iw.jpg' | |
| src_img = cv2.imread(src_path) | |
| # Define control points for warps | |
| src_points = face_points_func(src_path) | |
| base_img = cv2.imread(base_path) | |
| base_points = face_points_func(base_path) | |
| size = (600, 500) | |
| src_img, src_points = aligner.resize_align(src_img, src_points, size) | |
| base_img, base_points = aligner.resize_align(base_img, base_points, size) | |
| result_points = locator.weighted_average_points(src_points, base_points, 0.2) | |
| # Perform transform | |
| dst_img1 = warp_image(src_img, src_points, result_points, size) | |
| dst_img2 = warp_image(base_img, base_points, result_points, size) | |
| import blender | |
| ave = blender.weighted_average(dst_img1, dst_img2, 0.6) | |
| mask = blender.mask_from_points(size, result_points) | |
| blended_img = blender.poisson_blend(dst_img1, dst_img2, mask) | |
| plt.subplot(2, 2, 1) | |
| plt.imshow(ave) | |
| plt.subplot(2, 2, 2) | |
| plt.imshow(dst_img1) | |
| plt.subplot(2, 2, 3) | |
| plt.imshow(dst_img2) | |
| plt.subplot(2, 2, 4) | |
| plt.imshow(blended_img) | |
| plt.show() | |
| if __name__ == "__main__": | |
| test_local() | |