kornia_benchmark.py

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.')