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import cv2 |
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import numpy as np |
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import os |
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import torch |
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from torchvision.transforms.functional import normalize |
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from facelib.detection import init_detection_model |
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from facelib.parsing import init_parsing_model |
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from facelib.utils.misc import img2tensor, imwrite, is_gray, bgr2gray |
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def get_largest_face(det_faces, h, w): |
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def get_location(val, length): |
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if val < 0: |
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return 0 |
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elif val > length: |
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return length |
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else: |
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return val |
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face_areas = [] |
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for det_face in det_faces: |
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left = get_location(det_face[0], w) |
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right = get_location(det_face[2], w) |
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top = get_location(det_face[1], h) |
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bottom = get_location(det_face[3], h) |
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face_area = (right - left) * (bottom - top) |
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face_areas.append(face_area) |
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largest_idx = face_areas.index(max(face_areas)) |
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return det_faces[largest_idx], largest_idx |
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def get_center_face(det_faces, h=0, w=0, center=None): |
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if center is not None: |
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center = np.array(center) |
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else: |
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center = np.array([w / 2, h / 2]) |
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center_dist = [] |
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for det_face in det_faces: |
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face_center = np.array([(det_face[0] + det_face[2]) / 2, (det_face[1] + det_face[3]) / 2]) |
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dist = np.linalg.norm(face_center - center) |
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center_dist.append(dist) |
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center_idx = center_dist.index(min(center_dist)) |
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return det_faces[center_idx], center_idx |
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class FaceRestoreHelper(object): |
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"""Helper for the face restoration pipeline (base class).""" |
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def __init__(self, |
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upscale_factor, |
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face_size=512, |
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crop_ratio=(1, 1), |
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det_model='retinaface_resnet50', |
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save_ext='png', |
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template_3points=False, |
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pad_blur=False, |
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use_parse=False, |
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device=None): |
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self.template_3points = template_3points |
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self.upscale_factor = int(upscale_factor) |
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self.crop_ratio = crop_ratio |
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assert (self.crop_ratio[0] >= 1 and self.crop_ratio[1] >= 1), 'crop ration only supports >=1' |
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self.face_size = (int(face_size * self.crop_ratio[1]), int(face_size * self.crop_ratio[0])) |
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if self.template_3points: |
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self.face_template = np.array([[192, 240], [319, 240], [257, 371]]) |
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else: |
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self.face_template = np.array([[192.98138, 239.94708], [318.90277, 240.1936], [256.63416, 314.01935], |
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[201.26117, 371.41043], [313.08905, 371.15118]]) |
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self.face_template = self.face_template * (face_size / 512.0) |
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if self.crop_ratio[0] > 1: |
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self.face_template[:, 1] += face_size * (self.crop_ratio[0] - 1) / 2 |
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if self.crop_ratio[1] > 1: |
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self.face_template[:, 0] += face_size * (self.crop_ratio[1] - 1) / 2 |
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self.save_ext = save_ext |
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self.pad_blur = pad_blur |
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if self.pad_blur is True: |
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self.template_3points = False |
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self.all_landmarks_5 = [] |
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self.det_faces = [] |
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self.affine_matrices = [] |
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self.inverse_affine_matrices = [] |
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self.cropped_faces = [] |
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self.restored_faces = [] |
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self.pad_input_imgs = [] |
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if device is None: |
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self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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else: |
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self.device = device |
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self.face_det = init_detection_model(det_model, half=False, device=self.device) |
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self.use_parse = use_parse |
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self.face_parse = init_parsing_model(model_name='parsenet', device=self.device) |
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def set_upscale_factor(self, upscale_factor): |
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self.upscale_factor = upscale_factor |
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def read_image(self, img): |
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"""img can be image path or cv2 loaded image.""" |
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if isinstance(img, str): |
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img = cv2.imread(img) |
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if np.max(img) > 256: |
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img = img / 65535 * 255 |
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if len(img.shape) == 2: |
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) |
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elif img.shape[2] == 4: |
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img = img[:, :, 0:3] |
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self.input_img = img |
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self.is_gray = is_gray(img, threshold=5) |
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if self.is_gray: |
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print('Grayscale input: True') |
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if min(self.input_img.shape[:2])<512: |
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f = 512.0/min(self.input_img.shape[:2]) |
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self.input_img = cv2.resize(self.input_img, (0,0), fx=f, fy=f, interpolation=cv2.INTER_LINEAR) |
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def get_face_landmarks_5(self, |
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only_keep_largest=False, |
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only_center_face=False, |
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resize=None, |
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blur_ratio=0.01, |
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eye_dist_threshold=None): |
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if resize is None: |
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scale = 1 |
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input_img = self.input_img |
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else: |
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h, w = self.input_img.shape[0:2] |
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scale = resize / min(h, w) |
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scale = max(1, scale) |
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h, w = int(h * scale), int(w * scale) |
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interp = cv2.INTER_AREA if scale < 1 else cv2.INTER_LINEAR |
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input_img = cv2.resize(self.input_img, (w, h), interpolation=interp) |
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with torch.no_grad(): |
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bboxes = self.face_det.detect_faces(input_img) |
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if bboxes is None or bboxes.shape[0] == 0: |
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return 0 |
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else: |
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bboxes = bboxes / scale |
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for bbox in bboxes: |
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eye_dist = np.linalg.norm([bbox[6] - bbox[8], bbox[7] - bbox[9]]) |
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if eye_dist_threshold is not None and (eye_dist < eye_dist_threshold): |
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continue |
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if self.template_3points: |
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landmark = np.array([[bbox[i], bbox[i + 1]] for i in range(5, 11, 2)]) |
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else: |
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landmark = np.array([[bbox[i], bbox[i + 1]] for i in range(5, 15, 2)]) |
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self.all_landmarks_5.append(landmark) |
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self.det_faces.append(bbox[0:5]) |
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if len(self.det_faces) == 0: |
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return 0 |
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if only_keep_largest: |
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h, w, _ = self.input_img.shape |
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self.det_faces, largest_idx = get_largest_face(self.det_faces, h, w) |
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self.all_landmarks_5 = [self.all_landmarks_5[largest_idx]] |
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elif only_center_face: |
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h, w, _ = self.input_img.shape |
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self.det_faces, center_idx = get_center_face(self.det_faces, h, w) |
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self.all_landmarks_5 = [self.all_landmarks_5[center_idx]] |
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if self.pad_blur: |
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self.pad_input_imgs = [] |
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for landmarks in self.all_landmarks_5: |
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eye_left = landmarks[0, :] |
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eye_right = landmarks[1, :] |
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eye_avg = (eye_left + eye_right) * 0.5 |
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mouth_avg = (landmarks[3, :] + landmarks[4, :]) * 0.5 |
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eye_to_eye = eye_right - eye_left |
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eye_to_mouth = mouth_avg - eye_avg |
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x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1] |
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x /= np.hypot(*x) |
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rect_scale = 1.5 |
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x *= max(np.hypot(*eye_to_eye) * 2.0 * rect_scale, np.hypot(*eye_to_mouth) * 1.8 * rect_scale) |
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y = np.flipud(x) * [-1, 1] |
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c = eye_avg + eye_to_mouth * 0.1 |
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quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) |
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qsize = np.hypot(*x) * 2 |
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border = max(int(np.rint(qsize * 0.1)), 3) |
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pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), |
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int(np.ceil(max(quad[:, 1])))) |
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pad = [ |
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max(-pad[0] + border, 1), |
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max(-pad[1] + border, 1), |
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max(pad[2] - self.input_img.shape[0] + border, 1), |
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max(pad[3] - self.input_img.shape[1] + border, 1) |
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] |
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if max(pad) > 1: |
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pad_img = np.pad(self.input_img, ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect') |
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landmarks[:, 0] += pad[0] |
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landmarks[:, 1] += pad[1] |
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h, w, _ = pad_img.shape |
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y, x, _ = np.ogrid[:h, :w, :1] |
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mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], |
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np.float32(w - 1 - x) / pad[2]), |
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1.0 - np.minimum(np.float32(y) / pad[1], |
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np.float32(h - 1 - y) / pad[3])) |
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blur = int(qsize * blur_ratio) |
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if blur % 2 == 0: |
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blur += 1 |
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blur_img = cv2.boxFilter(pad_img, 0, ksize=(blur, blur)) |
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pad_img = pad_img.astype('float32') |
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pad_img += (blur_img - pad_img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0) |
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pad_img += (np.median(pad_img, axis=(0, 1)) - pad_img) * np.clip(mask, 0.0, 1.0) |
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pad_img = np.clip(pad_img, 0, 255) |
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self.pad_input_imgs.append(pad_img) |
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else: |
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self.pad_input_imgs.append(np.copy(self.input_img)) |
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return len(self.all_landmarks_5) |
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def align_warp_face(self, save_cropped_path=None, border_mode='constant'): |
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"""Align and warp faces with face template. |
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""" |
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if self.pad_blur: |
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assert len(self.pad_input_imgs) == len( |
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self.all_landmarks_5), f'Mismatched samples: {len(self.pad_input_imgs)} and {len(self.all_landmarks_5)}' |
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for idx, landmark in enumerate(self.all_landmarks_5): |
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affine_matrix = cv2.estimateAffinePartial2D(landmark, self.face_template, method=cv2.LMEDS)[0] |
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self.affine_matrices.append(affine_matrix) |
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if border_mode == 'constant': |
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border_mode = cv2.BORDER_CONSTANT |
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elif border_mode == 'reflect101': |
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border_mode = cv2.BORDER_REFLECT101 |
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elif border_mode == 'reflect': |
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border_mode = cv2.BORDER_REFLECT |
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if self.pad_blur: |
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input_img = self.pad_input_imgs[idx] |
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else: |
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input_img = self.input_img |
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cropped_face = cv2.warpAffine( |
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input_img, affine_matrix, self.face_size, borderMode=border_mode, borderValue=(135, 133, 132)) |
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self.cropped_faces.append(cropped_face) |
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if save_cropped_path is not None: |
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path = os.path.splitext(save_cropped_path)[0] |
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save_path = f'{path}_{idx:02d}.{self.save_ext}' |
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imwrite(cropped_face, save_path) |
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def get_inverse_affine(self, save_inverse_affine_path=None): |
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"""Get inverse affine matrix.""" |
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for idx, affine_matrix in enumerate(self.affine_matrices): |
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inverse_affine = cv2.invertAffineTransform(affine_matrix) |
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inverse_affine *= self.upscale_factor |
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self.inverse_affine_matrices.append(inverse_affine) |
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if save_inverse_affine_path is not None: |
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path, _ = os.path.splitext(save_inverse_affine_path) |
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save_path = f'{path}_{idx:02d}.pth' |
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torch.save(inverse_affine, save_path) |
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def add_restored_face(self, face): |
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if self.is_gray: |
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face = bgr2gray(face) |
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self.restored_faces.append(face) |
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def paste_faces_to_input_image(self, save_path=None, upsample_img=None, draw_box=False, face_upsampler=None): |
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h, w, _ = self.input_img.shape |
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h_up, w_up = int(h * self.upscale_factor), int(w * self.upscale_factor) |
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if upsample_img is None: |
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upsample_img = cv2.resize(self.input_img, (w_up, h_up), interpolation=cv2.INTER_LINEAR) |
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else: |
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upsample_img = cv2.resize(upsample_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4) |
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assert len(self.restored_faces) == len( |
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self.inverse_affine_matrices), ('length of restored_faces and affine_matrices are different.') |
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inv_mask_borders = [] |
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for restored_face, inverse_affine in zip(self.restored_faces, self.inverse_affine_matrices): |
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if face_upsampler is not None: |
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restored_face = face_upsampler.enhance(restored_face, outscale=self.upscale_factor)[0] |
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inverse_affine /= self.upscale_factor |
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inverse_affine[:, 2] *= self.upscale_factor |
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face_size = (self.face_size[0]*self.upscale_factor, self.face_size[1]*self.upscale_factor) |
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else: |
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if self.upscale_factor > 1: |
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extra_offset = 0.5 * self.upscale_factor |
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else: |
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extra_offset = 0 |
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inverse_affine[:, 2] += extra_offset |
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face_size = self.face_size |
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inv_restored = cv2.warpAffine(restored_face, inverse_affine, (w_up, h_up)) |
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mask = np.ones(face_size, dtype=np.float32) |
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inv_mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up)) |
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inv_mask_erosion = cv2.erode( |
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inv_mask, np.ones((int(2 * self.upscale_factor), int(2 * self.upscale_factor)), np.uint8)) |
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pasted_face = inv_mask_erosion[:, :, None] * inv_restored |
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total_face_area = np.sum(inv_mask_erosion) |
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if draw_box: |
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h, w = face_size |
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mask_border = np.ones((h, w, 3), dtype=np.float32) |
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border = int(1400/np.sqrt(total_face_area)) |
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mask_border[border:h-border, border:w-border,:] = 0 |
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inv_mask_border = cv2.warpAffine(mask_border, inverse_affine, (w_up, h_up)) |
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inv_mask_borders.append(inv_mask_border) |
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w_edge = int(total_face_area**0.5) // 20 |
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erosion_radius = w_edge * 2 |
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inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8)) |
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blur_size = w_edge * 2 |
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inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0) |
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if len(upsample_img.shape) == 2: |
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upsample_img = upsample_img[:, :, None] |
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inv_soft_mask = inv_soft_mask[:, :, None] |
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if self.use_parse: |
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face_input = cv2.resize(restored_face, (512, 512), interpolation=cv2.INTER_LINEAR) |
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face_input = img2tensor(face_input.astype('float32') / 255., bgr2rgb=True, float32=True) |
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normalize(face_input, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) |
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face_input = torch.unsqueeze(face_input, 0).to(self.device) |
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with torch.no_grad(): |
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out = self.face_parse(face_input)[0] |
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out = out.argmax(dim=1).squeeze().cpu().numpy() |
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parse_mask = np.zeros(out.shape) |
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MASK_COLORMAP = [0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 255, 0, 0, 0] |
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for idx, color in enumerate(MASK_COLORMAP): |
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parse_mask[out == idx] = color |
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parse_mask = cv2.GaussianBlur(parse_mask, (101, 101), 11) |
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parse_mask = cv2.GaussianBlur(parse_mask, (101, 101), 11) |
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thres = 10 |
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parse_mask[:thres, :] = 0 |
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parse_mask[-thres:, :] = 0 |
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parse_mask[:, :thres] = 0 |
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parse_mask[:, -thres:] = 0 |
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parse_mask = parse_mask / 255. |
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parse_mask = cv2.resize(parse_mask, face_size) |
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parse_mask = cv2.warpAffine(parse_mask, inverse_affine, (w_up, h_up), flags=3) |
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inv_soft_parse_mask = parse_mask[:, :, None] |
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fuse_mask = (inv_soft_parse_mask<inv_soft_mask).astype('int') |
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inv_soft_mask = inv_soft_parse_mask*fuse_mask + inv_soft_mask*(1-fuse_mask) |
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if len(upsample_img.shape) == 3 and upsample_img.shape[2] == 4: |
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alpha = upsample_img[:, :, 3:] |
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upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img[:, :, 0:3] |
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upsample_img = np.concatenate((upsample_img, alpha), axis=2) |
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else: |
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upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img |
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if np.max(upsample_img) > 256: |
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upsample_img = upsample_img.astype(np.uint16) |
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else: |
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upsample_img = upsample_img.astype(np.uint8) |
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if draw_box: |
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img_color = np.ones([*upsample_img.shape], dtype=np.float32) |
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img_color[:,:,0] = 0 |
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img_color[:,:,1] = 255 |
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img_color[:,:,2] = 0 |
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for inv_mask_border in inv_mask_borders: |
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upsample_img = inv_mask_border * img_color + (1 - inv_mask_border) * upsample_img |
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if save_path is not None: |
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path = os.path.splitext(save_path)[0] |
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save_path = f'{path}.{self.save_ext}' |
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imwrite(upsample_img, save_path) |
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return upsample_img |
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def clean_all(self): |
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self.all_landmarks_5 = [] |
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self.restored_faces = [] |
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self.affine_matrices = [] |
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self.cropped_faces = [] |
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self.inverse_affine_matrices = [] |
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self.det_faces = [] |
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self.pad_input_imgs = [] |