Spaces:
Runtime error
Runtime error
File size: 1,809 Bytes
d8f7d4a 4221e68 d8f7d4a e27d92f d8f7d4a e27d92f d8f7d4a e27d92f d8f7d4a e27d92f d8f7d4a e27d92f d8f7d4a e27d92f d8f7d4a e27d92f d8f7d4a 289b883 e27d92f 289b883 e27d92f d8f7d4a 289b883 d8f7d4a d503311 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
import os
import copy
import time
import cv2 as cv
import numpy as np
import onnxruntime
from PIL import Image
import gradio
def run_inference(onnx_session, input_size, image):
# リサイズ
temp_image = copy.deepcopy(image)
resize_image = cv.resize(temp_image, dsize=(input_size, input_size))
x = cv.cvtColor(resize_image, cv.COLOR_BGR2RGB)
# 前処理
x = np.array(x, dtype=np.float32)
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
x = (x / 255 - mean) / std
x = x.transpose(2, 0, 1).astype('float32')
x = x.reshape(-1, 3, input_size, input_size)
# 推論
input_name = onnx_session.get_inputs()[0].name
output_name = onnx_session.get_outputs()[0].name
onnx_result = onnx_session.run([output_name], {input_name: x})
# 後処理
onnx_result = np.array(onnx_result).squeeze()
min_value = np.min(onnx_result)
max_value = np.max(onnx_result)
onnx_result = (onnx_result - min_value) / (max_value - min_value)
onnx_result *= 255
onnx_result = onnx_result.astype('uint8')
return onnx_result
# Load model
onnx_session = onnxruntime.InferenceSession("u2net.onnx")
def create_rgba(mode, image):
out = run_inference(
onnx_session,
320,
image,
)
resize_image = cv.resize(out, dsize=(image.shape[1], image.shape[0]))
if mode == "binary":
resize_image[resize_image > 255] = 255
resize_image[resize_image < 125] = 0
mask = Image.fromarray(resize_image)
rgba_image = Image.fromarray(image).convert('RGBA')
rgba_image.putalpha(mask)
return rgba_image
inputs = [gradio.inputs.Radio(["binary", "smooth"]), gradio.inputs.Image()]
outputs = gradio.outputs.Image()
gradio.Interface(fn=create_rgba, inputs=inputs, outputs=outputs).launch()
|