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import gradio as gr
import cv2
from utils.detect import create_mtcnn_net, MtcnnDetector
from utils.vision import vis_face
import argparse
from PIL import Image
import numpy as np
MIN_FACE_SIZE = 24
def parse_args():
parser = argparse.ArgumentParser(description='Test MTCNN',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--net', default='onet', help='which net to show', type=str)
parser.add_argument('--pnet_path', default="./model_store/pnet_epoch_20.pt",help='path to pnet model', type=str)
parser.add_argument('--rnet_path', default="./model_store/rnet_epoch_20.pt",help='path to rnet model', type=str)
parser.add_argument('--onet_path', default="./model_store/onet_epoch_20.pt",help='path to onet model', type=str)
parser.add_argument('--path', default="./img/mid.png",help='path to image', type=str)
parser.add_argument('--min_face_size', default=MIN_FACE_SIZE,help='min face size', type=int)
parser.add_argument('--use_cuda', default=False,help='use cuda', type=bool)
parser.add_argument('--thresh', default='[0.6, 0.7, 0.7]',help='thresh', type=str)
parser.add_argument('--save_name', default="result.jpg",help='save name', type=str)
parser.add_argument('--input_mode', default=1,help='image or video', type=int)
args = parser.parse_args()
return args
def greet(请上传待检测人脸图片):
args = parse_args()
thresh = [float(i) for i in (args.thresh).split('[')[1].split(']')[0].split(',')]
pnet, rnet, onet = create_mtcnn_net(p_model_path=args.pnet_path, r_model_path=args.rnet_path,o_model_path=args.onet_path, use_cuda=args.use_cuda)
mtcnn_detector = MtcnnDetector(pnet=pnet, rnet=rnet, onet=onet, min_face_size=args.min_face_size,threshold=thresh)
img = cv2.imread(请上传待检测人脸图片)
b,g,r = cv2.split(img)
img_bg = cv2.merge([r,g,b])
p_bboxs, r_bboxs, bboxs, landmarks = mtcnn_detector.detect_face(img)
save_name = args.save_name
fig = vis_face(img_bg, bboxs, landmarks, MIN_FACE_SIZE, save_name)
fig.canvas.draw()
# Get the RGB buffer from the figure
w, h = fig.canvas.get_width_height()
buf = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8)
buf.shape = (h, w, 3)
# canvas.tostring_rgb give pixmap in RGB mode.
# Roll the ALPHA channel to have it in RGBA mode
buf = np.roll(buf, 3, axis=2)
return Image.fromarray(buf)
iface = gr.Interface(fn=greet,
inputs=gr.Image(type="filepath"),
outputs="image")
iface.launch() |