322 lines
12 KiB
Python
322 lines
12 KiB
Python
|
import cv2
|
||
|
|
import numpy as np
|
||
|
|
import math
|
||
|
|
from PIL import Image
|
||
|
|
|
||
|
|
|
||
|
|
from pp_onnx.rec_postprocess import CTCLabelDecode
|
||
|
|
from pp_onnx.predict_base import PredictBase
|
||
|
|
|
||
|
|
class TextRecognizer(PredictBase):
|
||
|
|
def __init__(self, args):
|
||
|
|
self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")]
|
||
|
|
self.rec_batch_num = args.rec_batch_num
|
||
|
|
self.rec_algorithm = args.rec_algorithm
|
||
|
|
self.postprocess_op = CTCLabelDecode(character_dict_path=args.rec_char_dict_path, use_space_char=args.use_space_char)
|
||
|
|
|
||
|
|
# 初始化模型
|
||
|
|
self.rec_onnx_session = self.get_onnx_session(args.rec_model_dir, args.use_gpu)
|
||
|
|
self.rec_input_name = self.get_input_name(self.rec_onnx_session)
|
||
|
|
self.rec_output_name = self.get_output_name(self.rec_onnx_session)
|
||
|
|
|
||
|
|
|
||
|
|
def resize_norm_img(self, img, max_wh_ratio):
|
||
|
|
imgC, imgH, imgW = self.rec_image_shape
|
||
|
|
if self.rec_algorithm == 'NRTR' or self.rec_algorithm == 'ViTSTR':
|
||
|
|
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
|
||
|
|
# return padding_im
|
||
|
|
image_pil = Image.fromarray(np.uint8(img))
|
||
|
|
if self.rec_algorithm == 'ViTSTR':
|
||
|
|
img = image_pil.resize([imgW, imgH], Image.BICUBIC)
|
||
|
|
else:
|
||
|
|
img = image_pil.resize([imgW, imgH], Image.ANTIALIAS)
|
||
|
|
img = np.array(img)
|
||
|
|
norm_img = np.expand_dims(img, -1)
|
||
|
|
norm_img = norm_img.transpose((2, 0, 1))
|
||
|
|
if self.rec_algorithm == 'ViTSTR':
|
||
|
|
norm_img = norm_img.astype(np.float32) / 255.
|
||
|
|
else:
|
||
|
|
norm_img = norm_img.astype(np.float32) / 128. - 1.
|
||
|
|
return norm_img
|
||
|
|
elif self.rec_algorithm == 'RFL':
|
||
|
|
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
|
||
|
|
resized_image = cv2.resize(
|
||
|
|
img, (imgW, imgH), interpolation=cv2.INTER_CUBIC)
|
||
|
|
resized_image = resized_image.astype('float32')
|
||
|
|
resized_image = resized_image / 255
|
||
|
|
resized_image = resized_image[np.newaxis, :]
|
||
|
|
resized_image -= 0.5
|
||
|
|
resized_image /= 0.5
|
||
|
|
return resized_image
|
||
|
|
|
||
|
|
assert imgC == img.shape[2]
|
||
|
|
imgW = int((imgH * max_wh_ratio))
|
||
|
|
|
||
|
|
# import IPython
|
||
|
|
# IPython.embed(header="predict_rec.py L-56")
|
||
|
|
|
||
|
|
w = self.rec_onnx_session.get_inputs()[0].shape[3:][0]
|
||
|
|
if isinstance(w, int) and w>0:
|
||
|
|
imgW = w
|
||
|
|
# if w is not None and w > 0:
|
||
|
|
# imgW = w
|
||
|
|
|
||
|
|
|
||
|
|
h, w = img.shape[:2]
|
||
|
|
ratio = w / float(h)
|
||
|
|
if math.ceil(imgH * ratio) > imgW:
|
||
|
|
resized_w = imgW
|
||
|
|
else:
|
||
|
|
resized_w = int(math.ceil(imgH * ratio))
|
||
|
|
if self.rec_algorithm == 'RARE':
|
||
|
|
if resized_w > self.rec_image_shape[2]:
|
||
|
|
resized_w = self.rec_image_shape[2]
|
||
|
|
imgW = self.rec_image_shape[2]
|
||
|
|
resized_image = cv2.resize(img, (resized_w, imgH))
|
||
|
|
resized_image = resized_image.astype('float32')
|
||
|
|
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 resize_norm_img_vl(self, img, image_shape):
|
||
|
|
|
||
|
|
imgC, imgH, imgW = image_shape
|
||
|
|
img = img[:, :, ::-1] # bgr2rgb
|
||
|
|
resized_image = cv2.resize(
|
||
|
|
img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
|
||
|
|
resized_image = resized_image.astype('float32')
|
||
|
|
resized_image = resized_image.transpose((2, 0, 1)) / 255
|
||
|
|
return resized_image
|
||
|
|
|
||
|
|
def resize_norm_img_srn(self, img, image_shape):
|
||
|
|
imgC, imgH, imgW = image_shape
|
||
|
|
|
||
|
|
img_black = np.zeros((imgH, imgW))
|
||
|
|
im_hei = img.shape[0]
|
||
|
|
im_wid = img.shape[1]
|
||
|
|
|
||
|
|
if im_wid <= im_hei * 1:
|
||
|
|
img_new = cv2.resize(img, (imgH * 1, imgH))
|
||
|
|
elif im_wid <= im_hei * 2:
|
||
|
|
img_new = cv2.resize(img, (imgH * 2, imgH))
|
||
|
|
elif im_wid <= im_hei * 3:
|
||
|
|
img_new = cv2.resize(img, (imgH * 3, imgH))
|
||
|
|
else:
|
||
|
|
img_new = cv2.resize(img, (imgW, imgH))
|
||
|
|
|
||
|
|
img_np = np.asarray(img_new)
|
||
|
|
img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
|
||
|
|
img_black[:, 0:img_np.shape[1]] = img_np
|
||
|
|
img_black = img_black[:, :, np.newaxis]
|
||
|
|
|
||
|
|
row, col, c = img_black.shape
|
||
|
|
c = 1
|
||
|
|
|
||
|
|
return np.reshape(img_black, (c, row, col)).astype(np.float32)
|
||
|
|
|
||
|
|
def srn_other_inputs(self, image_shape, num_heads, max_text_length):
|
||
|
|
|
||
|
|
imgC, imgH, imgW = image_shape
|
||
|
|
feature_dim = int((imgH / 8) * (imgW / 8))
|
||
|
|
|
||
|
|
encoder_word_pos = np.array(range(0, feature_dim)).reshape(
|
||
|
|
(feature_dim, 1)).astype('int64')
|
||
|
|
gsrm_word_pos = np.array(range(0, max_text_length)).reshape(
|
||
|
|
(max_text_length, 1)).astype('int64')
|
||
|
|
|
||
|
|
gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length))
|
||
|
|
gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape(
|
||
|
|
[-1, 1, max_text_length, max_text_length])
|
||
|
|
gsrm_slf_attn_bias1 = np.tile(
|
||
|
|
gsrm_slf_attn_bias1,
|
||
|
|
[1, num_heads, 1, 1]).astype('float32') * [-1e9]
|
||
|
|
|
||
|
|
gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape(
|
||
|
|
[-1, 1, max_text_length, max_text_length])
|
||
|
|
gsrm_slf_attn_bias2 = np.tile(
|
||
|
|
gsrm_slf_attn_bias2,
|
||
|
|
[1, num_heads, 1, 1]).astype('float32') * [-1e9]
|
||
|
|
|
||
|
|
encoder_word_pos = encoder_word_pos[np.newaxis, :]
|
||
|
|
gsrm_word_pos = gsrm_word_pos[np.newaxis, :]
|
||
|
|
|
||
|
|
return [
|
||
|
|
encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
|
||
|
|
gsrm_slf_attn_bias2
|
||
|
|
]
|
||
|
|
|
||
|
|
def process_image_srn(self, img, image_shape, num_heads, max_text_length):
|
||
|
|
norm_img = self.resize_norm_img_srn(img, image_shape)
|
||
|
|
norm_img = norm_img[np.newaxis, :]
|
||
|
|
|
||
|
|
[encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \
|
||
|
|
self.srn_other_inputs(image_shape, num_heads, max_text_length)
|
||
|
|
|
||
|
|
gsrm_slf_attn_bias1 = gsrm_slf_attn_bias1.astype(np.float32)
|
||
|
|
gsrm_slf_attn_bias2 = gsrm_slf_attn_bias2.astype(np.float32)
|
||
|
|
encoder_word_pos = encoder_word_pos.astype(np.int64)
|
||
|
|
gsrm_word_pos = gsrm_word_pos.astype(np.int64)
|
||
|
|
|
||
|
|
return (norm_img, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
|
||
|
|
gsrm_slf_attn_bias2)
|
||
|
|
|
||
|
|
def resize_norm_img_sar(self, img, image_shape,
|
||
|
|
width_downsample_ratio=0.25):
|
||
|
|
imgC, imgH, imgW_min, imgW_max = image_shape
|
||
|
|
h = img.shape[0]
|
||
|
|
w = img.shape[1]
|
||
|
|
valid_ratio = 1.0
|
||
|
|
# make sure new_width is an integral multiple of width_divisor.
|
||
|
|
width_divisor = int(1 / width_downsample_ratio)
|
||
|
|
# resize
|
||
|
|
ratio = w / float(h)
|
||
|
|
resize_w = math.ceil(imgH * ratio)
|
||
|
|
if resize_w % width_divisor != 0:
|
||
|
|
resize_w = round(resize_w / width_divisor) * width_divisor
|
||
|
|
if imgW_min is not None:
|
||
|
|
resize_w = max(imgW_min, resize_w)
|
||
|
|
if imgW_max is not None:
|
||
|
|
valid_ratio = min(1.0, 1.0 * resize_w / imgW_max)
|
||
|
|
resize_w = min(imgW_max, resize_w)
|
||
|
|
resized_image = cv2.resize(img, (resize_w, imgH))
|
||
|
|
resized_image = resized_image.astype('float32')
|
||
|
|
# norm
|
||
|
|
if 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
|
||
|
|
resize_shape = resized_image.shape
|
||
|
|
padding_im = -1.0 * np.ones((imgC, imgH, imgW_max), dtype=np.float32)
|
||
|
|
padding_im[:, :, 0:resize_w] = resized_image
|
||
|
|
pad_shape = padding_im.shape
|
||
|
|
|
||
|
|
return padding_im, resize_shape, pad_shape, valid_ratio
|
||
|
|
|
||
|
|
def resize_norm_img_spin(self, img):
|
||
|
|
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
|
||
|
|
# return padding_im
|
||
|
|
img = cv2.resize(img, tuple([100, 32]), cv2.INTER_CUBIC)
|
||
|
|
img = np.array(img, np.float32)
|
||
|
|
img = np.expand_dims(img, -1)
|
||
|
|
img = img.transpose((2, 0, 1))
|
||
|
|
mean = [127.5]
|
||
|
|
std = [127.5]
|
||
|
|
mean = np.array(mean, dtype=np.float32)
|
||
|
|
std = np.array(std, dtype=np.float32)
|
||
|
|
mean = np.float32(mean.reshape(1, -1))
|
||
|
|
stdinv = 1 / np.float32(std.reshape(1, -1))
|
||
|
|
img -= mean
|
||
|
|
img *= stdinv
|
||
|
|
return img
|
||
|
|
|
||
|
|
def resize_norm_img_svtr(self, img, image_shape):
|
||
|
|
|
||
|
|
imgC, imgH, imgW = image_shape
|
||
|
|
resized_image = cv2.resize(
|
||
|
|
img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
|
||
|
|
resized_image = resized_image.astype('float32')
|
||
|
|
resized_image = resized_image.transpose((2, 0, 1)) / 255
|
||
|
|
resized_image -= 0.5
|
||
|
|
resized_image /= 0.5
|
||
|
|
return resized_image
|
||
|
|
|
||
|
|
def resize_norm_img_abinet(self, img, image_shape):
|
||
|
|
|
||
|
|
imgC, imgH, imgW = image_shape
|
||
|
|
|
||
|
|
resized_image = cv2.resize(
|
||
|
|
img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
|
||
|
|
resized_image = resized_image.astype('float32')
|
||
|
|
resized_image = resized_image / 255.
|
||
|
|
|
||
|
|
mean = np.array([0.485, 0.456, 0.406])
|
||
|
|
std = np.array([0.229, 0.224, 0.225])
|
||
|
|
resized_image = (
|
||
|
|
resized_image - mean[None, None, ...]) / std[None, None, ...]
|
||
|
|
resized_image = resized_image.transpose((2, 0, 1))
|
||
|
|
resized_image = resized_image.astype('float32')
|
||
|
|
|
||
|
|
return resized_image
|
||
|
|
|
||
|
|
def norm_img_can(self, img, image_shape):
|
||
|
|
|
||
|
|
img = cv2.cvtColor(
|
||
|
|
img, cv2.COLOR_BGR2GRAY) # CAN only predict gray scale image
|
||
|
|
|
||
|
|
if self.inverse:
|
||
|
|
img = 255 - img
|
||
|
|
|
||
|
|
if self.rec_image_shape[0] == 1:
|
||
|
|
h, w = img.shape
|
||
|
|
_, imgH, imgW = self.rec_image_shape
|
||
|
|
if h < imgH or w < imgW:
|
||
|
|
padding_h = max(imgH - h, 0)
|
||
|
|
padding_w = max(imgW - w, 0)
|
||
|
|
img_padded = np.pad(img, ((0, padding_h), (0, padding_w)),
|
||
|
|
'constant',
|
||
|
|
constant_values=(255))
|
||
|
|
img = img_padded
|
||
|
|
|
||
|
|
img = np.expand_dims(img, 0) / 255.0 # h,w,c -> c,h,w
|
||
|
|
img = img.astype('float32')
|
||
|
|
|
||
|
|
return img
|
||
|
|
|
||
|
|
def __call__(self, 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 recognition process
|
||
|
|
indices = np.argsort(np.array(width_list))
|
||
|
|
rec_res = [['', 0.0]] * img_num
|
||
|
|
batch_num = self.rec_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 = []
|
||
|
|
imgC, imgH, imgW = self.rec_image_shape[:3]
|
||
|
|
max_wh_ratio = imgW / imgH
|
||
|
|
# 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]],
|
||
|
|
max_wh_ratio)
|
||
|
|
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()
|
||
|
|
|
||
|
|
# img = img[:, :, ::-1].transpose(2, 0, 1)
|
||
|
|
# img = img[:, :, ::-1]
|
||
|
|
# img = img.transpose(2, 0, 1)
|
||
|
|
# img = img.astype(np.float32)
|
||
|
|
# img = np.expand_dims(img, axis=0)
|
||
|
|
# print(img.shape)
|
||
|
|
|
||
|
|
input_feed = self.get_input_feed(self.rec_input_name, norm_img_batch)
|
||
|
|
|
||
|
|
# import IPython
|
||
|
|
# IPython.embed(header='L-303')
|
||
|
|
|
||
|
|
outputs = self.rec_onnx_session.run(self.rec_output_name, input_feed=input_feed)
|
||
|
|
|
||
|
|
preds = outputs[0]
|
||
|
|
|
||
|
|
rec_result = self.postprocess_op(preds)
|
||
|
|
for rno in range(len(rec_result)):
|
||
|
|
rec_res[indices[beg_img_no + rno]] = rec_result[rno]
|
||
|
|
|
||
|
|
return rec_res
|