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import os
import cv2
import numpy as np
from openvino import Core
class CodecCTC:
def __init__(self, characters):
self.chars = ["[blank]"] + list(characters)
def decode(self, preds, top_k=10):
index, texts, nbest = 0, list(), list()
preds_index: np.ndarray = np.argmax(preds, 2)
preds_index = preds_index.transpose(1, 0)
preds_index_reshape = preds_index.reshape(-1)
preds_sizes = np.array([preds_index.shape[1]] * preds_index.shape[0])
for step in preds_sizes:
t = preds_index_reshape[index : index + step]
if t.shape[0] == 0:
continue
char_list = []
for i in range(step):
if t[i] == 0:
continue
# removing repeated characters and blank.
if i > 0 and t[i - 1] == t[i]:
continue
char_list.append(self.chars[t[i]])
# process n-best
probs = self.softmax(preds[i][0])
k_idx = np.argsort(-probs)[:top_k]
k_probs = probs[k_idx]
k_res = [dict(prob=p, char=self.chars[j]) for j, p in zip(k_idx, k_probs)]
nbest.append(k_res)
text = "".join(char_list)
texts.append(text)
index += step
return texts, nbest
def softmax(self, x):
e_x = np.exp(x - np.max(x))
return e_x / np.sum(e_x, axis=0)
class Recognizer:
def __init__(self, model_path, char_list_path):
core = Core()
self.model = core.read_model(model_path)
self.compiled_model = core.compile_model(self.model, "CPU")
self.infer_request = self.compiled_model.create_infer_request()
# (batch_size, channel, width, height)
_, _, self.inn_h, self.inn_w = self.model.inputs[0].shape
self.input_tensor_name = self.model.inputs[0].get_any_name()
self.output_tensor_name = self.model.outputs[0].get_any_name()
with open(char_list_path, "r", encoding="utf-8") as f:
char_list = "".join(line.strip("\n") for line in f)
self.codec = CodecCTC(char_list)
def __call__(self, inn_img):
inn_img = self.preprocess(inn_img, height=self.inn_h, width=self.inn_w)
inn_img = inn_img[None, :, :, :]
self.infer_request.infer(inputs={self.input_tensor_name: inn_img})
preds = self.infer_request.get_tensor(self.output_tensor_name).data[:]
result, nbest = self.codec.decode(preds)
return result, nbest
def preprocess(self, image, height, width):
src: np.ndarray = cv2.cvtColor(image, cv2.COLOR_RGBA2GRAY)
src = 255 - src
cv2.imwrite("a.png", src)
ratio = float(src.shape[1]) / float(src.shape[0])
tw = int(height * ratio)
rsz = cv2.resize(src, (tw, height), interpolation=cv2.INTER_AREA).astype(np.float32)
# [h,w] -> [c,h,w]
img = rsz[None, :, :]
_, h, w = img.shape
# right edge padding
pad_img = np.pad(img, ((0, 0), (0, height - h), (0, width - w)), mode="edge")
return pad_img
def main():
recog = Recognizer("model/model.xml", "model/char_list.txt")
target_dir = "."
file_list = [os.path.join(dn, fn) for dn, _, ff in os.walk(target_dir) for fn in ff]
file_list = sorted(file_list)
for fp in file_list:
if fp.endswith(".png"):
print(recog(fp))
if __name__ == "__main__":
main()
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