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37 lines
1.2 KiB
Python
37 lines
1.2 KiB
Python
import gradio as gr
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import os, sys
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sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__), "utils"))
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from FCN8s_keras import FCN
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from PIL import Image
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import cv2
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import tensorflow as tf
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from drive import download_file_from_google_drive
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import numpy as np
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weights = os.path.join(file_folder, "face_seg_model_weights.h5")
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if not os.path.exists(weights):
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file_id = "1IerDF2DQqmJWqyvxYZOICJT1eThnG8WR"
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download_file_from_google_drive(file_id, weights)
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model1 = FCN()
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model1.load_weights(weights)
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def segment_face(inp):
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im = Image.fromarray(np.uint8(inp))
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im = im.resize((500, 500))
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in_ = np.array(im, dtype=np.float32)
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in_ = in_[:, :, ::-1]
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in_ -= np.array((104.00698793,116.66876762,122.67891434))
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in_ = in_[np.newaxis,:]
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out = model1.predict(in_)
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out_resized = cv2.resize(np.squeeze(out), (inp.shape[1], inp.shape[0]))
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out_resized_clipped = np.clip(out_resized.argmax(axis=2), 0, 1).astype(np.float64)
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result = (out_resized_clipped[:, :, np.newaxis] + 0.25)/1.25 * inp.astype(np.float64).astype(np.uint8)
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return result / 255
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iface = gr.Interface(segment_face, "webcam", "image", capture_session=True)
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if __name__ == "__main__":
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iface.launch() |