Spaces:
Running
Running
File size: 3,641 Bytes
278c80b 4b55707 278c80b 4b55707 278c80b 4b55707 278c80b 4b55707 a31338b 278c80b 4b55707 278c80b 4b55707 278c80b 4b55707 278c80b 4b55707 278c80b 4b55707 b780a4b 278c80b 4b55707 |
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 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 |
import os
import torch
import random
import warnings
import modelscope
import huggingface_hub
import gradio as gr
from PIL import Image
from model import Model
from torchvision import transforms
EN_US = os.getenv("LANG") != "zh_CN.UTF-8"
MODEL_DIR = (
huggingface_hub.snapshot_download(
"Genius-Society/svhn",
cache_dir="./__pycache__",
)
if EN_US
else modelscope.snapshot_download(
"Genius-Society/svhn",
cache_dir="./__pycache__",
)
)
ZH2EN = {
"上传图片": "Upload an image",
"状态栏": "Status",
"选择模型": "Select a model",
"识别结果": "Recognition result",
"门牌号识别": "Door Number Recognition",
}
def _L(zh_txt: str):
return ZH2EN[zh_txt] if EN_US else zh_txt
def infer(input_img: str, checkpoint_file: str):
status = "Success"
outstr = ""
try:
model = Model()
model.restore(f"{MODEL_DIR}/{checkpoint_file}")
with torch.no_grad():
transform = transforms.Compose(
[
transforms.Resize([64, 64]),
transforms.CenterCrop([54, 54]),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
]
)
image = Image.open(input_img)
image = image.convert("RGB")
image = transform(image)
images = image.unsqueeze(dim=0)
(
length_logits,
digit1_logits,
digit2_logits,
digit3_logits,
digit4_logits,
digit5_logits,
) = model.eval()(images)
length_prediction = length_logits.max(1)[1]
digit1_prediction = digit1_logits.max(1)[1]
digit2_prediction = digit2_logits.max(1)[1]
digit3_prediction = digit3_logits.max(1)[1]
digit4_prediction = digit4_logits.max(1)[1]
digit5_prediction = digit5_logits.max(1)[1]
output = [
digit1_prediction.item(),
digit2_prediction.item(),
digit3_prediction.item(),
digit4_prediction.item(),
digit5_prediction.item(),
]
for i in range(length_prediction.item()):
outstr += str(output[i])
except Exception as e:
status = f"{e}"
return status, outstr
def get_files(dir_path=MODEL_DIR, ext=".pth"):
files_and_folders = os.listdir(dir_path)
outputs = []
for file in files_and_folders:
if file.endswith(ext):
outputs.append(file)
return outputs
if __name__ == "__main__":
warnings.filterwarnings("ignore")
models = get_files()
images = get_files(f"{MODEL_DIR}/examples", ".png")
samples = []
for img in images:
samples.append(
[
f"{MODEL_DIR}/examples/{img}",
models[random.randint(0, len(models) - 1)],
]
)
gr.Interface(
fn=infer,
inputs=[
gr.Image(label=_L("上传图片"), type="filepath"),
gr.Dropdown(
label=_L("选择模型"),
choices=models,
value=models[0],
),
],
outputs=[
gr.Textbox(label=_L("状态栏"), show_copy_button=True),
gr.Textbox(label=_L("识别结果"), show_copy_button=True),
],
examples=samples,
title=_L("门牌号识别"),
flagging_mode="never",
cache_examples=False,
).launch(ssr_mode=False)
|