mirror of
https://github.com/gradio-app/gradio.git
synced 2024-12-21 02:19:59 +08:00
39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
import gradio as gr
|
|
import os, sys
|
|
file_folder = os.path.dirname(os.path.abspath(__file__))
|
|
sys.path.insert(0, os.path.join(file_folder, "utils"))
|
|
from FCN8s_keras import FCN
|
|
from PIL import Image
|
|
import cv2
|
|
import tensorflow as tf
|
|
from drive import download_file_from_google_drive
|
|
import numpy as np
|
|
|
|
weights = os.path.join(file_folder, "face_seg_model_weights.h5")
|
|
if not os.path.exists(weights):
|
|
file_id = "1IerDF2DQqmJWqyvxYZOICJT1eThnG8WR"
|
|
download_file_from_google_drive(file_id, weights)
|
|
|
|
model1 = FCN()
|
|
model1.load_weights(weights)
|
|
|
|
|
|
def segment_face(inp):
|
|
im = Image.fromarray(np.uint8(inp))
|
|
im = im.resize((500, 500))
|
|
in_ = np.array(im, dtype=np.float32)
|
|
in_ = in_[:, :, ::-1]
|
|
in_ -= np.array((104.00698793,116.66876762,122.67891434))
|
|
in_ = in_[np.newaxis,:]
|
|
out = model1.predict(in_)
|
|
|
|
out_resized = cv2.resize(np.squeeze(out), (inp.shape[1], inp.shape[0]))
|
|
out_resized_clipped = np.clip(out_resized.argmax(axis=2), 0, 1).astype(np.float64)
|
|
result = (out_resized_clipped[:, :, np.newaxis] + 0.25)/1.25 * inp.astype(np.float64).astype(np.uint8)
|
|
return result / 255
|
|
|
|
|
|
iface = gr.Interface(segment_face, gr.inputs.Image(source="webcam", tool=None), "image", capture_session=True)
|
|
|
|
if __name__ == "__main__":
|
|
iface.launch() |