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import gradio as gr
import io
def solve_captcha(data_bytes: bytes = None, file_name: str = None):
import time
import os
import sys
import cv2
import numpy as np
import onnxruntime
real_tests = False
from config import characters, img_height, img_width, img_type, max_length, transpose_perm, OUTPUT_ONNX
sess = onnxruntime.InferenceSession(f"out.model.onnx")
name = sess.get_inputs()[0].name
def get_result(pred):
"""CTC decoder of the output tensor
https://distill.pub/2017/ctc/
https://en.wikipedia.org/wiki/Connectionist_temporal_classification
:return string, float
"""
accuracy = 1
last = None
ans = []
# pred - 3d tensor, we need 2d array - first element
for item in pred[0]:
# get index of element with max accuracy
char_ind = item.argmax()
# ignore duplicates and special characters
if char_ind != last and char_ind != 0 and char_ind != len(characters)+1:
# this element is a character - append it to answer
ans.append(characters[char_ind - 1])
# Get accuracy for current character and multiply global accuracy by it
accuracy *= item[char_ind]
last = char_ind
answ = "".join(ans)[:max_length]
return answ, accuracy
def decode_img(data_bytes: bytes):
# same actions, as for tensorflow
image = cv2.imdecode(np.asarray(bytearray(data_bytes), dtype=np.uint8), 1)
image: "np.ndarray" = image.astype(np.float32) / 255.
if image.shape != (img_height, img_width, 3):
image = cv2.resize(image, (img_width, img_height))
image = image.transpose(transpose_perm)
# Creating tensor ( adding 4d dimension )
image = np.array([image])
return image
def decode_img_array(nump_array):
# same actions, as for tensorflow
#image = cv2.imdecode(nump_array, 1)
image: "np.ndarray" = nump_array.astype(np.float32) / 255.
if image.shape != (img_height, img_width, 3):
image = cv2.resize(image, (img_width, img_height))
image = image.transpose(transpose_perm)
# Creating tensor ( adding 4d dimension )
image = np.array([image])
return image
def solve(data_bytes: bytes=None, file_name=None):
if file_name:
with open(file_name, 'rb') as F:
data_bytes = F.read()
if data_bytes is None:
print('[CAPTCHA RESOLVER NN] ПУСТОТА ВМЕСТО БАЙТОВ!')
return None
if isinstance(data_bytes,np.ndarray):
print('Img is ndarray')
img = decode_img_array(data_bytes)
else:
print('Img is bytes')
img_byte_arr = io.BytesIO()
data_bytes.save(img_byte_arr, format='PNG')
img_byte_arr = img_byte_arr.getvalue()
img = decode_img(data_bytes)
#print(img)
pred_onx = sess.run(None, {name: img})[0]
ans = get_result(pred_onx)
return ans
result = solve(data_bytes,file_name)
print ('solved', result)
return {"result":result[0], "predict":result[1]}
def image_classifier(inp):
if inp is None:
return "Не загружена картинка"
print('INPUT GOT>>>', inp)
#gradio img inp - numpy ndarray
result = solve_captcha(inp)
return result
#inputs=gr.Image(type="pil")
examples_names = ["1089","3569","5989","18769","73295","89238"]
examples = []
for n in examples_names:
examples.append("examples/"+n+".png")
demo = gr.Interface(fn=image_classifier, inputs=gr.Image(), outputs="textbox", examples=examples)
demo.launch(show_api = True) |