import cv2 """ brief: face alignment with FFHQ method (https://github.com/NVlabs/ffhq-dataset) author: lzhbrian (https://lzhbrian.me) date: 2020.1.5 note: code is heavily borrowed from https://github.com/NVlabs/ffhq-dataset http://dlib.net/face_landmark_detection.py.html requirements: apt install cmake conda install Pillow numpy scipy pip install dlib # download face landmark model from: # http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2 """ import numpy as np from PIL import Image import dlib class Croper: def __init__(self, path_of_lm): # download model from: http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2 self.predictor = dlib.shape_predictor(path_of_lm) def get_landmark(self, img_np): """get landmark with dlib :return: np.array shape=(68, 2) """ detector = dlib.get_frontal_face_detector() dets = detector(img_np, 1) # print("Number of faces detected: {}".format(len(dets))) # for k, d in enumerate(dets): if len(dets) == 0: return None d = dets[0] # Get the landmarks/parts for the face in box d. shape = self.predictor(img_np, d) # print("Part 0: {}, Part 1: {} ...".format(shape.part(0), shape.part(1))) t = list(shape.parts()) a = [] for tt in t: a.append([tt.x, tt.y]) lm = np.array(a) # lm is a shape=(68,2) np.array return lm def align_face(self, img, lm, output_size=1024): """ :param filepath: str :return: PIL Image """ 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] # Addition of binocular difference and double mouth difference 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 # 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, Image.ANTIALIAS) quad /= shrink qsize /= shrink else: rsize = (int(np.rint(float(img.size[0]))), int(np.rint(float(img.size[1])))) # 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 = Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB') # quad += pad[:2] # Transform. quad = (quad + 0.5).flatten() lx = max(min(quad[0], quad[2]), 0) ly = max(min(quad[1], quad[7]), 0) rx = min(max(quad[4], quad[6]), img.size[0]) ry = min(max(quad[3], quad[5]), img.size[0]) # img = img.transform((transform_size, transform_size), Image.QUAD, (quad + 0.5).flatten(), # Image.BILINEAR) # if output_size < transform_size: # img = img.resize((output_size, output_size), Image.ANTIALIAS) # Save aligned image. return rsize, crop, [lx, ly, rx, ry] def crop(self, img_np_list, still=False, xsize=512): # first frame for all video img_np = img_np_list[0] lm = self.get_landmark(img_np) if lm is None: raise 'can not detect the landmark from source image' rsize, crop, quad = self.align_face(img=Image.fromarray(img_np), lm=lm, output_size=xsize) clx, cly, crx, cry = crop lx, ly, rx, ry = quad lx, ly, rx, ry = int(lx), int(ly), int(rx), int(ry) for _i in range(len(img_np_list)): _inp = img_np_list[_i] _inp = cv2.resize(_inp, (rsize[0], rsize[1])) _inp = _inp[cly:cry, clx:crx] # cv2.imwrite('test1.jpg', _inp) if not still: _inp = _inp[ly:ry, lx:rx] # cv2.imwrite('test2.jpg', _inp) img_np_list[_i] = _inp return img_np_list, crop, quad