Face Detection and blurring

Face detection
In this tutorial we will show how to use the Kornia Face Detection and how we can blurring these detected faces.

Edgar Riba


November 30, 2021

Open in google colab

!pip install kornia
!pip install kornia-rs
import io

import requests

def download_image(url: str, filename: str = "") -> str:
    filename = url.split("/")[-1] if len(filename) == 0 else filename
    # Download
    bytesio = io.BytesIO(requests.get(url).content)
    # Save file
    with open(filename, "wb") as outfile:

    return filename

url = "https://raw.githubusercontent.com/kornia/data/main/crowd.jpg"

Import the needed libraries

import cv2
import kornia as K
import matplotlib.pyplot as plt
import numpy as np
import torch
from kornia.contrib import FaceDetector, FaceDetectorResult

# select the device and type
device = torch.device("cpu")  # use 'cuda:0'
dtype = torch.float32

Read the image with kornia

# load the image (face detector expects a image in rage 0-255 (8 bits))
img = K.io.load_image("crowd.jpg", K.io.ImageLoadType.RGB8, device=device)[None, ...].to(dtype=dtype)  # BxCxHxW
img_vis = K.tensor_to_image(img.byte())  # to later visualize
plt.figure(figsize=(8, 8))

Create the FaceDetector object and apply to the image

# create the detector and find the faces !
face_detection = FaceDetector().to(device, dtype)

with torch.no_grad():
    dets = face_detection(img)

# to decode later the detections
dets = [FaceDetectorResult(o) for o in dets]

Create a function to crop the faces from the original image and apply blurring using the gaussian_blurd2d operator.

Alternatively, explore other blur operator in kornia.filters.

# blurring paramters
k: int = 21  # kernel_size
s: float = 35.0  # sigma

def apply_blur_face(img: torch.Tensor, img_vis: np.ndarray, x1, y1, x2, y2):
    # crop the face
    roi = img[..., y1:y2, x1:x2]

    # apply blurring and put back to the visualisation image
    roi = K.filters.gaussian_blur2d(roi, (k, k), (s, s))
    img_vis[y1:y2, x1:x2] = K.tensor_to_image(roi)

Let draw the detections and save/visualize the image

for b in dets:
    # draw face bounding box around each detected face
    top_left = b.top_left.int().tolist()
    bottom_right = b.bottom_right.int().tolist()
    scores = b.score.tolist()

    for score, tp, br in zip(scores, top_left, bottom_right):
        x1, y1 = tp
        x2, y2 = br

        if score < 0.7:
            continue  # skip detection with low score
        img_vis = cv2.rectangle(img_vis, (x1, y1), (x2, y2), (0, 255, 0), 2)

        # blur the detected faces
        apply_blur_face(img, img_vis, x1, y1, x2, y2)

plt.figure(figsize=(8, 8))

Play with the Real Time Demo

You can achieve 60 FPS in CPU using a standard WebCam.

See: https://github.com/kornia/kornia/blob/master/examples/face_detection/main_video.py

from IPython.display import YouTubeVideo