| | import cv2 |
| | import numpy as np |
| | from skimage import transform as trans |
| |
|
| |
|
| | arcface_dst = np.array( |
| | [[38.2946, 51.6963], [73.5318, 51.5014], [56.0252, 71.7366], |
| | [41.5493, 92.3655], [70.7299, 92.2041]], |
| | dtype=np.float32) |
| |
|
| | def estimate_norm(lmk, image_size=112,mode='arcface'): |
| | assert lmk.shape == (5, 2) |
| | assert image_size%112==0 or image_size%128==0 |
| | if image_size%112==0: |
| | ratio = float(image_size)/112.0 |
| | diff_x = 0 |
| | else: |
| | ratio = float(image_size)/128.0 |
| | diff_x = 8.0*ratio |
| | dst = arcface_dst * ratio |
| | dst[:,0] += diff_x |
| | tform = trans.SimilarityTransform() |
| | tform.estimate(lmk, dst) |
| | M = tform.params[0:2, :] |
| | return M |
| |
|
| | def norm_crop(img, landmark, image_size=112, mode='arcface'): |
| | M = estimate_norm(landmark, image_size, mode) |
| | warped = cv2.warpAffine(img, M, (image_size, image_size), borderValue=0.0) |
| | return warped |
| |
|
| | def norm_crop2(img, landmark, image_size=112, mode='arcface'): |
| | M = estimate_norm(landmark, image_size, mode) |
| | warped = cv2.warpAffine(img, M, (image_size, image_size), borderValue=0.0) |
| | return warped, M |
| |
|
| | def square_crop(im, S): |
| | if im.shape[0] > im.shape[1]: |
| | height = S |
| | width = int(float(im.shape[1]) / im.shape[0] * S) |
| | scale = float(S) / im.shape[0] |
| | else: |
| | width = S |
| | height = int(float(im.shape[0]) / im.shape[1] * S) |
| | scale = float(S) / im.shape[1] |
| | resized_im = cv2.resize(im, (width, height)) |
| | det_im = np.zeros((S, S, 3), dtype=np.uint8) |
| | det_im[:resized_im.shape[0], :resized_im.shape[1], :] = resized_im |
| | return det_im, scale |
| |
|
| |
|
| | def transform(data, center, output_size, scale, rotation): |
| | scale_ratio = scale |
| | rot = float(rotation) * np.pi / 180.0 |
| | |
| | t1 = trans.SimilarityTransform(scale=scale_ratio) |
| | cx = center[0] * scale_ratio |
| | cy = center[1] * scale_ratio |
| | t2 = trans.SimilarityTransform(translation=(-1 * cx, -1 * cy)) |
| | t3 = trans.SimilarityTransform(rotation=rot) |
| | t4 = trans.SimilarityTransform(translation=(output_size / 2, |
| | output_size / 2)) |
| | t = t1 + t2 + t3 + t4 |
| | M = t.params[0:2] |
| | cropped = cv2.warpAffine(data, |
| | M, (output_size, output_size), |
| | borderValue=0.0) |
| | return cropped, M |
| |
|
| |
|
| | def trans_points2d(pts, M): |
| | new_pts = np.zeros(shape=pts.shape, dtype=np.float32) |
| | for i in range(pts.shape[0]): |
| | pt = pts[i] |
| | new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32) |
| | new_pt = np.dot(M, new_pt) |
| | |
| | new_pts[i] = new_pt[0:2] |
| |
|
| | return new_pts |
| |
|
| |
|
| | def trans_points3d(pts, M): |
| | scale = np.sqrt(M[0][0] * M[0][0] + M[0][1] * M[0][1]) |
| | |
| | new_pts = np.zeros(shape=pts.shape, dtype=np.float32) |
| | for i in range(pts.shape[0]): |
| | pt = pts[i] |
| | new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32) |
| | new_pt = np.dot(M, new_pt) |
| | |
| | new_pts[i][0:2] = new_pt[0:2] |
| | new_pts[i][2] = pts[i][2] * scale |
| |
|
| | return new_pts |
| |
|
| |
|
| | def trans_points(pts, M): |
| | if pts.shape[1] == 2: |
| | return trans_points2d(pts, M) |
| | else: |
| | return trans_points3d(pts, M) |
| |
|
| |
|
