87 lines
3.3 KiB
Python
87 lines
3.3 KiB
Python
|
import cv2
|
||
|
|
import copy
|
||
|
|
import numpy as np
|
||
|
|
import math
|
||
|
|
|
||
|
|
from pp_onnx.cls_postprocess import ClsPostProcess
|
||
|
|
from pp_onnx.predict_base import PredictBase
|
||
|
|
|
||
|
|
class TextClassifier(PredictBase):
|
||
|
|
def __init__(self, args):
|
||
|
|
self.cls_image_shape = [int(v) for v in args.cls_image_shape.split(",")]
|
||
|
|
self.cls_batch_num = args.cls_batch_num
|
||
|
|
self.cls_thresh = args.cls_thresh
|
||
|
|
self.postprocess_op = ClsPostProcess(label_list=args.label_list)
|
||
|
|
|
||
|
|
# 初始化模型
|
||
|
|
self.cls_onnx_session = self.get_onnx_session(args.cls_model_dir, args.use_gpu)
|
||
|
|
self.cls_input_name = self.get_input_name(self.cls_onnx_session)
|
||
|
|
self.cls_output_name = self.get_output_name(self.cls_onnx_session)
|
||
|
|
|
||
|
|
def resize_norm_img(self, img):
|
||
|
|
imgC, imgH, imgW = self.cls_image_shape
|
||
|
|
h = img.shape[0]
|
||
|
|
w = img.shape[1]
|
||
|
|
ratio = w / float(h)
|
||
|
|
if math.ceil(imgH * ratio) > imgW:
|
||
|
|
resized_w = imgW
|
||
|
|
else:
|
||
|
|
resized_w = int(math.ceil(imgH * ratio))
|
||
|
|
resized_image = cv2.resize(img, (resized_w, imgH))
|
||
|
|
resized_image = resized_image.astype('float32')
|
||
|
|
if self.cls_image_shape[0] == 1:
|
||
|
|
resized_image = resized_image / 255
|
||
|
|
resized_image = resized_image[np.newaxis, :]
|
||
|
|
else:
|
||
|
|
resized_image = resized_image.transpose((2, 0, 1)) / 255
|
||
|
|
resized_image -= 0.5
|
||
|
|
resized_image /= 0.5
|
||
|
|
padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
|
||
|
|
padding_im[:, :, 0:resized_w] = resized_image
|
||
|
|
return padding_im
|
||
|
|
|
||
|
|
def __call__(self, img_list):
|
||
|
|
img_list = copy.deepcopy(img_list)
|
||
|
|
img_num = len(img_list)
|
||
|
|
# Calculate the aspect ratio of all text bars
|
||
|
|
width_list = []
|
||
|
|
for img in img_list:
|
||
|
|
width_list.append(img.shape[1] / float(img.shape[0]))
|
||
|
|
# Sorting can speed up the cls process
|
||
|
|
indices = np.argsort(np.array(width_list))
|
||
|
|
|
||
|
|
cls_res = [['', 0.0]] * img_num
|
||
|
|
batch_num = self.cls_batch_num
|
||
|
|
|
||
|
|
for beg_img_no in range(0, img_num, batch_num):
|
||
|
|
|
||
|
|
end_img_no = min(img_num, beg_img_no + batch_num)
|
||
|
|
norm_img_batch = []
|
||
|
|
max_wh_ratio = 0
|
||
|
|
|
||
|
|
for ino in range(beg_img_no, end_img_no):
|
||
|
|
h, w = img_list[indices[ino]].shape[0:2]
|
||
|
|
wh_ratio = w * 1.0 / h
|
||
|
|
max_wh_ratio = max(max_wh_ratio, wh_ratio)
|
||
|
|
for ino in range(beg_img_no, end_img_no):
|
||
|
|
norm_img = self.resize_norm_img(img_list[indices[ino]])
|
||
|
|
norm_img = norm_img[np.newaxis, :]
|
||
|
|
norm_img_batch.append(norm_img)
|
||
|
|
norm_img_batch = np.concatenate(norm_img_batch)
|
||
|
|
norm_img_batch = norm_img_batch.copy()
|
||
|
|
|
||
|
|
input_feed = self.get_input_feed(self.cls_input_name, norm_img_batch)
|
||
|
|
outputs = self.cls_onnx_session.run(self.cls_output_name, input_feed=input_feed)
|
||
|
|
|
||
|
|
prob_out = outputs[0]
|
||
|
|
|
||
|
|
cls_result = self.postprocess_op(prob_out)
|
||
|
|
for rno in range(len(cls_result)):
|
||
|
|
label, score = cls_result[rno]
|
||
|
|
cls_res[indices[beg_img_no + rno]] = [label, score]
|
||
|
|
if '180' in label and score > self.cls_thresh:
|
||
|
|
img_list[indices[beg_img_no + rno]] = cv2.rotate(
|
||
|
|
img_list[indices[beg_img_no + rno]], 1)
|
||
|
|
return img_list, cls_res
|
||
|
|
|