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45ad2ac | 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 | #!/usr/bin/env python3
"""用训练好的位置保留模型识别验证码。输出格式兼容 harness。
预处理: 灰度->开运算去噪->RGB 160x64 /255 -> (1,3,64,160) -> (1,4,10) -> 4位数字
"""
import json
import os
import sys
import cv2
import numpy as np
import onnxruntime as ort
from PIL import Image
MODEL = os.path.expanduser("~/sp_captcha_assets/models/captcha_1000.onnx")
_sess = None
def _get_sess():
global _sess
if _sess is None:
_sess = ort.InferenceSession(MODEL, providers=["CPUExecutionProvider"])
return _sess
def predict(path):
img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
k = np.ones((2, 2), np.uint8)
denoised = cv2.morphologyEx(img, cv2.MORPH_OPEN, k)
im = Image.fromarray(denoised).convert("RGB").resize((160, 64), Image.BILINEAR)
a = np.asarray(im, dtype=np.float32) / 255.0
x = a.transpose(2, 0, 1)[None] # (1,3,64,160)
out = _get_sess().run(None, {"input": x})[0] # (1,4,10)
return "".join(str(int(out[0, p].argmax())) for p in range(4))
def main():
out = {}
for f in sys.argv[1:]:
try:
out[f] = {"onnx": predict(f)}
except Exception as e:
out[f] = {"onnx": "", "err": str(e)}
print(json.dumps(out, ensure_ascii=False))
if __name__ == "__main__":
main()
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