dataset_process/croper.py

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