# 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