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| import logging | |
| import time | |
| import cv2 | |
| import numpy as np | |
| from .center_crop import center_crop | |
| from .face_detector import FaceDetector | |
| class VSNetModelPipeline: | |
| def __init__(self, model, face_detector: FaceDetector, background_resize=720, no_detected_resize=256): | |
| self.background_resize = background_resize | |
| self.no_detected_resize = no_detected_resize | |
| self.model = model | |
| self.face_detector = face_detector | |
| self.mask = self.create_circular_mask(face_detector.target_size, face_detector.target_size) | |
| def create_circular_mask(h, w, power=None, clipping_coef=0.85): | |
| center = (int(w / 2), int(h / 2)) | |
| Y, X = np.ogrid[:h, :w] | |
| dist_from_center = np.sqrt((X - center[0]) ** 2 + (Y - center[1]) ** 2) | |
| print(dist_from_center.max(), dist_from_center.min()) | |
| clipping_radius = min((h - center[0]), (w - center[1])) * clipping_coef | |
| max_size = max((h - center[0]), (w - center[1])) | |
| dist_from_center[dist_from_center < clipping_radius] = clipping_radius | |
| dist_from_center[dist_from_center > max_size] = max_size | |
| max_distance, min_distance = np.max(dist_from_center), np.min(dist_from_center) | |
| dist_from_center = 1 - (dist_from_center - min_distance) / (max_distance - min_distance) | |
| if power is not None: | |
| dist_from_center = np.power(dist_from_center, power) | |
| dist_from_center = np.stack([dist_from_center] * 3, axis=2) | |
| # mask = dist_from_center <= radius | |
| return dist_from_center | |
| def resize_size(image, size=720, always_apply=True): | |
| h, w, c = np.shape(image) | |
| if min(h, w) > size or always_apply: | |
| if h < w: | |
| h, w = int(size * h / w), size | |
| else: | |
| h, w = size, int(size * w / h) | |
| image = cv2.resize(image, (w, h), interpolation=cv2.INTER_AREA) | |
| return image | |
| def normalize(self, img): | |
| img = img.astype(np.float32) / 255 * 2 - 1 | |
| return img | |
| def denormalize(self, img): | |
| return (img + 1) / 2 | |
| def divide_crop(self, img, must_divided=32): | |
| h, w, _ = img.shape | |
| h = h // must_divided * must_divided | |
| w = w // must_divided * must_divided | |
| img = center_crop(img, h, w) | |
| return img | |
| def merge_crops(self, faces_imgs, crops, full_image): | |
| for face, crop in zip(faces_imgs, crops): | |
| x1, y1, x2, y2 = crop | |
| W, H = x2 - x1, y2 - y1 | |
| result_face = cv2.resize(face, (W, H), interpolation=cv2.INTER_LINEAR) | |
| face_mask = cv2.resize(self.mask, (W, H), interpolation=cv2.INTER_LINEAR) | |
| input_face = full_image[y1: y2, x1: x2] | |
| full_image[y1: y2, x1: x2] = (result_face * face_mask + input_face * (1 - face_mask)).astype(np.uint8) | |
| return full_image | |
| def __call__(self, img): | |
| return self.process_image(img) | |
| def process_image(self, img): | |
| img = self.resize_size(img, size=self.background_resize) | |
| img = self.divide_crop(img) | |
| face_crops, coords = self.face_detector(img) | |
| if len(face_crops) > 0: | |
| start_time = time.time() | |
| faces = self.normalize(face_crops) | |
| faces = faces.transpose(0, 3, 1, 2) | |
| out_faces = self.model(faces) | |
| out_faces = self.denormalize(out_faces) | |
| out_faces = out_faces.transpose(0, 2, 3, 1) | |
| out_faces = np.clip(out_faces * 255, 0, 255).astype(np.uint8) | |
| end_time = time.time() | |
| logging.info(f'Face FPS {1 / (end_time - start_time)}') | |
| else: | |
| out_faces = [] | |
| img = self.resize_size(img, size=self.no_detected_resize) | |
| img = self.divide_crop(img) | |
| start_time = time.time() | |
| full_image = self.normalize(img) | |
| full_image = np.expand_dims(full_image, 0).transpose(0, 3, 1, 2) | |
| full_image = self.model(full_image) | |
| full_image = self.denormalize(full_image) | |
| full_image = full_image.transpose(0, 2, 3, 1) | |
| full_image = np.clip(full_image * 255, 0, 255).astype(np.uint8) | |
| end_time = time.time() | |
| logging.info(f'Background FPS {1 / (end_time - start_time)}') | |
| result_image = self.merge_crops(out_faces, coords, full_image[0]) | |
| return result_image | |