144 lines
5.3 KiB
Python
Executable File
144 lines
5.3 KiB
Python
Executable File
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
import os
|
|
import sys
|
|
|
|
__dir__ = os.path.dirname(os.path.abspath(__file__))
|
|
sys.path.append(__dir__)
|
|
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
|
|
|
|
import tools.infer.utility as utility
|
|
from ppocr.utils.utility import initial_logger
|
|
|
|
logger = initial_logger()
|
|
from ppocr.utils.utility import get_image_file_list, check_and_read_gif
|
|
import cv2
|
|
import copy
|
|
import numpy as np
|
|
import math
|
|
import time
|
|
|
|
|
|
class TextClassifier(object):
|
|
def __init__(self, args):
|
|
self.predictor, self.input_tensor, self.output_tensors = \
|
|
utility.create_predictor(args, mode="cls")
|
|
self.cls_image_shape = [int(v) for v in args.cls_image_shape.split(",")]
|
|
self.cls_batch_num = args.rec_batch_num
|
|
self.label_list = args.label_list
|
|
|
|
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
|
|
predict_time = 0
|
|
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()
|
|
starttime = time.time()
|
|
|
|
self.input_tensor.copy_from_cpu(norm_img_batch)
|
|
self.predictor.zero_copy_run()
|
|
|
|
prob_out = self.output_tensors[0].copy_to_cpu()
|
|
label_out = self.output_tensors[1].copy_to_cpu()
|
|
|
|
elapse = time.time() - starttime
|
|
predict_time += elapse
|
|
for rno in range(len(label_out)):
|
|
label_idx = label_out[rno]
|
|
score = prob_out[rno][label_idx]
|
|
label = self.label_list[label_idx]
|
|
cls_res[indices[beg_img_no + rno]] = [label, score]
|
|
if label == 180:
|
|
img_list[indices[beg_img_no + rno]] = cv2.rotate(
|
|
img_list[indices[beg_img_no + rno]], 1)
|
|
return img_list, cls_res, predict_time
|
|
|
|
|
|
def main(args):
|
|
image_file_list = get_image_file_list(args.image_dir)
|
|
text_classifier = TextClassifier(args)
|
|
valid_image_file_list = []
|
|
img_list = []
|
|
for image_file in image_file_list[:10]:
|
|
img, flag = check_and_read_gif(image_file)
|
|
if not flag:
|
|
img = cv2.imread(image_file)
|
|
if img is None:
|
|
logger.info("error in loading image:{}".format(image_file))
|
|
continue
|
|
valid_image_file_list.append(image_file)
|
|
img_list.append(img)
|
|
try:
|
|
img_list, cls_res, predict_time = text_classifier(img_list)
|
|
print(cls_res)
|
|
from matplotlib import pyplot as plt
|
|
for img, angle in zip(img_list, cls_res):
|
|
plt.title(str(angle))
|
|
plt.imshow(img)
|
|
plt.show()
|
|
except Exception as e:
|
|
print(e)
|
|
exit()
|
|
for ino in range(len(img_list)):
|
|
print("Predicts of %s:%s" % (valid_image_file_list[ino], cls_res[ino]))
|
|
print("Total predict time for %d images:%.3f" %
|
|
(len(img_list), predict_time))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main(utility.parse_args())
|