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import gradio as gr
import cv2
import numpy as np
import torch
import kornia as K
from kornia.core import Tensor
from kornia.contrib import FaceDetector, FaceDetectorResult, FaceKeypoint
def draw_keypoint(img: np.ndarray, det: FaceDetectorResult, kpt_type: FaceKeypoint) -> np.ndarray:
kpt = det.get_keypoint(kpt_type).int().tolist()
return cv2.circle(img, kpt, 2, (255, 0, 0), 2)
def detect(img_raw):
# preprocess
if img_raw is not None and len(img_raw.shape) == 3:
img = K.utils.image_to_tensor(img_raw, keepdim=False)
img = K.color.bgr_to_rgb(img.float())
# create the detector and find the faces !
face_detection = FaceDetector()
with torch.no_grad():
dets = face_detection(img)
dets = [FaceDetectorResult(o) for o in dets[0]]
img_vis = img_raw.copy()
vis_threshold = 0.8
for b in dets:
if b.score < vis_threshold:
continue
# Draw face bounding box
img_vis = cv2.rectangle(img_vis, b.top_left.int().tolist(), b.bottom_right.int().tolist(), (0, 255, 0), 4)
# Draw Keypoints
img_vis = draw_keypoint(img_vis, b, FaceKeypoint.EYE_LEFT)
img_vis = draw_keypoint(img_vis, b, FaceKeypoint.EYE_RIGHT)
img_vis = draw_keypoint(img_vis, b, FaceKeypoint.NOSE)
img_vis = draw_keypoint(img_vis, b, FaceKeypoint.MOUTH_LEFT)
img_vis = draw_keypoint(img_vis, b, FaceKeypoint.MOUTH_RIGHT)
return img_vis
title = "Kornia Face Detection"
description = "<p style='text-align: center'>This is a Gradio demo for Kornia's Face Detection.</p><p style='text-align: center'>To use it, simply upload your image, or click one of the examples to load them</p>"
article = "<p style='text-align: center'><a href='https://kornia.readthedocs.io/en/latest/' target='_blank'>Kornia Docs</a> | <a href='https://github.com/kornia/kornia' target='_blank'>Kornia Github Repo</a> | <a href='https://kornia.readthedocs.io/en/latest/applications/face_detection.html' target='_blank'>Kornia Face Detection Tutorial</a></p>"
examples = ['sample.jpg']
with gr.Blocks(title=title) as demo:
gr.Markdown(f"# {title}")
gr.Markdown(description)
with gr.Row():
input_image = gr.Image(type="numpy", label="Input Image")
output_image = gr.Image(type="numpy", label="Detected Faces")
gr.Examples(examples, inputs=input_image)
input_image.change(fn=detect, inputs=input_image, outputs=output_image)
gr.Markdown(article)
if __name__ == "__main__":
demo.launch() |