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| import cv2 | |
| import gradio as gr | |
| from PIL import Image | |
| import numpy as np | |
| import torch | |
| import kornia as K | |
| from kornia.contrib import FaceDetector, FaceDetectorResult | |
| import time | |
| device = torch.device('cpu') | |
| face_detection = FaceDetector().to(device) | |
| def scale_image(img: np.ndarray, size: int) -> np.ndarray: | |
| h, w = img.shape[:2] | |
| scale = 1. * size / w | |
| return cv2.resize(img, (int(w * scale), int(h * scale))) | |
| def apply_blur_face(img: torch.Tensor, img_vis: np.ndarray, det: FaceDetectorResult): | |
| # crop the face | |
| x1, y1 = det.xmin.int(), det.ymin.int() | |
| x2, y2 = det.xmax.int(), det.ymax.int() | |
| roi = img[..., y1:y2, x1:x2] | |
| #print(roi.shape) | |
| if roi.shape[-1]==0 or roi.shape[-2]==0: | |
| return | |
| # apply blurring and put back to the visualisation image | |
| roi = K.filters.gaussian_blur2d(roi, (21, 21), (100., 100.)) | |
| roi = K.color.rgb_to_bgr(roi) | |
| img_vis[y1:y2, x1:x2] = K.tensor_to_image(roi) | |
| def run(image): | |
| image.thumbnail((1280, 1280)) | |
| img_raw = np.array(image) | |
| # preprocess | |
| img = K.image_to_tensor(img_raw, keepdim=False).to(device) | |
| img = K.color.bgr_to_rgb(img.float()) | |
| with torch.no_grad(): | |
| dets = face_detection(img) | |
| dets = [FaceDetectorResult(o) for o in dets] | |
| img_vis = img_raw.copy() | |
| for b in dets: | |
| if b.score < 0.5: | |
| continue | |
| apply_blur_face(img, img_vis, b) | |
| return Image.fromarray(img_vis) | |
| if __name__ == "__main__": | |
| start = time.time() | |
| for _ in range(100): | |
| image = Image.open("./images/crowd.jpeg") | |
| _ = run(image) | |
| print('It took', (time.time()-start)/100, 'seconds.') |