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| # Copyright (c) SenseTime Research. All rights reserved. | |
| import numpy as np | |
| import PIL | |
| import PIL.Image | |
| import scipy | |
| import scipy.ndimage | |
| import dlib | |
| import copy | |
| from PIL import Image | |
| def get_landmark(img, detector, predictor): | |
| """get landmark with dlib | |
| :return: np.array shape=(68, 2) | |
| """ | |
| # detector = dlib.get_frontal_face_detector() | |
| # dets, _, _ = detector.run(img, 1, -1) | |
| dets = detector(img, 1) | |
| for k, d in enumerate(dets): | |
| shape = predictor(img, d.rect) | |
| t = list(shape.parts()) | |
| a = [] | |
| for tt in t: | |
| a.append([tt.x, tt.y]) | |
| lm = np.array(a) | |
| # face rect | |
| face_rect = [dets[0].rect.left(), dets[0].rect.top(), | |
| dets[0].rect.right(), dets[0].rect.bottom()] | |
| return lm, face_rect | |
| def align_face_for_insetgan(img, detector, predictor, output_size=256): | |
| """ | |
| :param img: numpy array rgb | |
| :return: PIL Image | |
| """ | |
| img_cp = copy.deepcopy(img) | |
| lm, face_rect = get_landmark(img, detector, predictor) | |
| lm_chin = lm[0: 17] # left-right | |
| lm_eyebrow_left = lm[17: 22] # left-right | |
| lm_eyebrow_right = lm[22: 27] # left-right | |
| lm_nose = lm[27: 31] # top-down | |
| lm_nostrils = lm[31: 36] # top-down | |
| lm_eye_left = lm[36: 42] # left-clockwise | |
| lm_eye_right = lm[42: 48] # left-clockwise | |
| lm_mouth_outer = lm[48: 60] # left-clockwise | |
| lm_mouth_inner = lm[60: 68] # left-clockwise | |
| # Calculate auxiliary vectors. | |
| eye_left = np.mean(lm_eye_left, axis=0) | |
| eye_right = np.mean(lm_eye_right, axis=0) | |
| eye_avg = (eye_left + eye_right) * 0.5 | |
| eye_to_eye = eye_right - eye_left | |
| mouth_left = lm_mouth_outer[0] | |
| mouth_right = lm_mouth_outer[6] | |
| mouth_avg = (mouth_left + mouth_right) * 0.5 | |
| eye_to_mouth = mouth_avg - eye_avg | |
| # Choose oriented crop rectangle. | |
| x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1] | |
| x /= np.hypot(*x) | |
| x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8) | |
| y = np.flipud(x) * [-1, 1] | |
| c = eye_avg + eye_to_mouth * 0.1 | |
| quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) | |
| qsize = np.hypot(*x) * 2 | |
| # read image | |
| # opencv to PIL | |
| img = PIL.Image.fromarray(img_cp) | |
| # img = PIL.Image.open(filepath) | |
| transform_size = output_size | |
| enable_padding = False | |
| # Shrink. | |
| # shrink = int(np.floor(qsize / output_size * 0.5)) | |
| # if shrink > 1: | |
| # rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink))) | |
| # img = img.resize(rsize, PIL.Image.ANTIALIAS) | |
| # quad /= shrink | |
| # qsize /= shrink | |
| # Crop. | |
| border = max(int(np.rint(qsize * 0.1)), 3) | |
| crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), | |
| int(np.ceil(max(quad[:, 1])))) | |
| # crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]), | |
| # min(crop[3] + border, img.size[1])) | |
| # img.save("debug/raw.jpg") | |
| if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]: | |
| img = img.crop(crop) | |
| quad -= crop[0:2] | |
| # img.save("debug/crop.jpg") | |
| # Pad. | |
| # pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), | |
| # int(np.ceil(max(quad[:, 1])))) | |
| # pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0), | |
| # max(pad[3] - img.size[1] + border, 0)) | |
| # if enable_padding and max(pad) > border - 4: | |
| # pad = np.maximum(pad, int(np.rint(qsize * 0.3))) | |
| # img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect') | |
| # h, w, _ = img.shape | |
| # y, x, _ = np.ogrid[:h, :w, :1] | |
| # mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]), | |
| # 1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3])) | |
| # blur = qsize * 0.02 | |
| # img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0) | |
| # img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0) | |
| # img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB') | |
| # quad += pad[:2] | |
| # Transform. | |
| # crop shape to transform shape | |
| # nw = | |
| # print(img.size, quad+0.5, np.bound((quad+0.5).flatten())) | |
| # assert False | |
| # img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR) | |
| # img.save("debug/transform.jpg") | |
| # if output_size < transform_size: | |
| img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS) | |
| # img.save("debug/resize.jpg") | |
| # print((quad+crop[0:2]).flatten()) | |
| # assert False | |
| # Return aligned image. | |
| return img, crop, face_rect | |
| def align_face_for_projector(img, detector, predictor, output_size): | |
| """ | |
| :param filepath: str | |
| :return: PIL Image | |
| """ | |
| img_cp = copy.deepcopy(img) | |
| lm, face_rect = get_landmark(img, detector, predictor) | |
| lm_chin = lm[0: 17] # left-right | |
| lm_eyebrow_left = lm[17: 22] # left-right | |
| lm_eyebrow_right = lm[22: 27] # left-right | |
| lm_nose = lm[27: 31] # top-down | |
| lm_nostrils = lm[31: 36] # top-down | |
| lm_eye_left = lm[36: 42] # left-clockwise | |
| lm_eye_right = lm[42: 48] # left-clockwise | |
| lm_mouth_outer = lm[48: 60] # left-clockwise | |
| lm_mouth_inner = lm[60: 68] # left-clockwise | |
| # Calculate auxiliary vectors. | |
| eye_left = np.mean(lm_eye_left, axis=0) | |
| eye_right = np.mean(lm_eye_right, axis=0) | |
| eye_avg = (eye_left + eye_right) * 0.5 | |
| eye_to_eye = eye_right - eye_left | |
| mouth_left = lm_mouth_outer[0] | |
| mouth_right = lm_mouth_outer[6] | |
| mouth_avg = (mouth_left + mouth_right) * 0.5 | |
| eye_to_mouth = mouth_avg - eye_avg | |
| # Choose oriented crop rectangle. | |
| x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1] | |
| x /= np.hypot(*x) | |
| x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8) | |
| y = np.flipud(x) * [-1, 1] | |
| c = eye_avg + eye_to_mouth * 0.1 | |
| quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) | |
| qsize = np.hypot(*x) * 2 | |
| # read image | |
| img = PIL.Image.fromarray(img_cp) | |
| transform_size = output_size | |
| enable_padding = True | |
| # Shrink. | |
| shrink = int(np.floor(qsize / output_size * 0.5)) | |
| if shrink > 1: | |
| rsize = (int(np.rint(float(img.size[0]) / shrink)), | |
| int(np.rint(float(img.size[1]) / shrink))) | |
| img = img.resize(rsize, PIL.Image.ANTIALIAS) | |
| quad /= shrink | |
| qsize /= shrink | |
| # Crop. | |
| border = max(int(np.rint(qsize * 0.1)), 3) | |
| crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), | |
| int(np.ceil(max(quad[:, 1])))) | |
| crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]), | |
| min(crop[3] + border, img.size[1])) | |
| if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]: | |
| img = img.crop(crop) | |
| quad -= crop[0:2] | |
| # Pad. | |
| pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), | |
| int(np.ceil(max(quad[:, 1])))) | |
| pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0), | |
| max(pad[3] - img.size[1] + border, 0)) | |
| if enable_padding and max(pad) > border - 4: | |
| pad = np.maximum(pad, int(np.rint(qsize * 0.3))) | |
| img = np.pad(np.float32(img), | |
| ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect') | |
| h, w, _ = img.shape | |
| y, x, _ = np.ogrid[:h, :w, :1] | |
| mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]), | |
| 1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3])) | |
| blur = qsize * 0.02 | |
| img += (scipy.ndimage.gaussian_filter(img, | |
| [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0) | |
| img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0) | |
| img = PIL.Image.fromarray( | |
| np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB') | |
| quad += pad[:2] | |
| # Transform. | |
| img = img.transform((transform_size, transform_size), | |
| PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR) | |
| if output_size < transform_size: | |
| img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS) | |
| # Return aligned image. | |
| return img | |
| def reverse_quad_transform(image, quad_to_map_to, alpha): | |
| # forward mapping, for simplicity | |
| result = Image.new("RGBA", image.size) | |
| result_pixels = result.load() | |
| width, height = result.size | |
| for y in range(height): | |
| for x in range(width): | |
| result_pixels[x, y] = (0, 0, 0, 0) | |
| p1 = (quad_to_map_to[0], quad_to_map_to[1]) | |
| p2 = (quad_to_map_to[2], quad_to_map_to[3]) | |
| p3 = (quad_to_map_to[4], quad_to_map_to[5]) | |
| p4 = (quad_to_map_to[6], quad_to_map_to[7]) | |
| p1_p2_vec = (p2[0] - p1[0], p2[1] - p1[1]) | |
| p4_p3_vec = (p3[0] - p4[0], p3[1] - p4[1]) | |
| for y in range(height): | |
| for x in range(width): | |
| pixel = image.getpixel((x, y)) | |
| y_percentage = y / float(height) | |
| x_percentage = x / float(width) | |
| # interpolate vertically | |
| pa = (p1[0] + p1_p2_vec[0] * y_percentage, | |
| p1[1] + p1_p2_vec[1] * y_percentage) | |
| pb = (p4[0] + p4_p3_vec[0] * y_percentage, | |
| p4[1] + p4_p3_vec[1] * y_percentage) | |
| pa_to_pb_vec = (pb[0] - pa[0], pb[1] - pa[1]) | |
| # interpolate horizontally | |
| p = (pa[0] + pa_to_pb_vec[0] * x_percentage, | |
| pa[1] + pa_to_pb_vec[1] * x_percentage) | |
| try: | |
| result_pixels[p[0], p[1]] = ( | |
| pixel[0], pixel[1], pixel[2], min(int(alpha * 255), pixel[3])) | |
| except Exception: | |
| pass | |
| return result | |