import os
import sys
from PIL import Image
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, "../..")))
os.environ["FLAGS_allocator_strategy"] = "auto_growth"
import math
import time
import cv2
import numpy as np
import utility
from postprocess import build_post_process
def _check_image_file(path):
img_end = {"jpg", "bmp", "png", "jpeg", "rgb", "tif", "tiff", "gif"}
return any([path.lower().endswith(e) for e in img_end])
def get_image_file_list(img_file):
imgs_lists = []
if img_file is None or not os.path.exists(img_file):
raise Exception("not found any img file in {}".format(img_file))
img_end = {"jpg", "bmp", "png", "jpeg", "rgb", "tif", "tiff", "gif"}
if os.path.isfile(img_file) and _check_image_file(img_file):
imgs_lists.append(img_file)
elif os.path.isdir(img_file):
for single_file in os.listdir(img_file):
file_path = os.path.join(img_file, single_file)
if os.path.isfile(file_path) and _check_image_file(file_path):
imgs_lists.append(file_path)
if len(imgs_lists) == 0:
raise Exception("not found any img file in {}".format(img_file))
imgs_lists = sorted(imgs_lists)
return imgs_lists
def check_and_read_gif(img_path):
if os.path.basename(img_path)[-3:] in ["gif", "GIF"]:
gif = cv2.VideoCapture(img_path)
ret, frame = gif.read()
if not ret:
return None, False
if len(frame.shape) == 2 or frame.shape[-1] == 1:
frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
imgvalue = frame[:, :, ::-1]
return imgvalue, True
return None, False
class TextRecognizer(object):
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
postprocess_params = {
"name": "CTCLabelDecode",
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char,
}
if self.rec_algorithm == "SRN":
postprocess_params = {
"name": "SRNLabelDecode",
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char,
}
elif self.rec_algorithm == "RARE":
postprocess_params = {
"name": "AttnLabelDecode",
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char,
}
elif self.rec_algorithm == "NRTR":
postprocess_params = {
"name": "NRTRLabelDecode",
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char,
}
elif self.rec_algorithm == "SAR":
postprocess_params = {
"name": "SARLabelDecode",
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char,
}
self.postprocess_op = build_post_process(postprocess_params)
(
self.predictor,
self.input_tensor,
self.output_tensors,
self.config,
) = utility.create_predictor(args, "rec")
self.use_onnx = args.use_onnx
def resize_norm_img(self, img, max_wh_ratio):
imgC, imgH, imgW = self.rec_image_shape
if self.rec_algorithm == "NRTR":
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# return padding_im
image_pil = Image.fromarray(np.uint8(img))
img = image_pil.resize([100, 32], Image.ANTIALIAS)
img = np.array(img)
norm_img = np.expand_dims(img, -1)
norm_img = norm_img.transpose((2, 0, 1))
return norm_img.astype(np.float32) / 128.0 - 1.0
assert imgC == img.shape[2]
imgW = int((imgH * max_wh_ratio))
if self.use_onnx:
w = self.input_tensor.shape[3:][0]
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_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_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 __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
st = time.time()
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
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):
if self.rec_algorithm == "SAR":
norm_img, _, _, valid_ratio = self.resize_norm_img_sar(
img_list[indices[ino]], self.rec_image_shape
)
norm_img = norm_img[np.newaxis, :]
valid_ratio = np.expand_dims(valid_ratio, axis=0)
valid_ratios = []
valid_ratios.append(valid_ratio)
norm_img_batch.append(norm_img)
elif self.rec_algorithm == "SRN":
norm_img = self.process_image_srn(
img_list[indices[ino]], self.rec_image_shape, 8, 25
)
encoder_word_pos_list = []
gsrm_word_pos_list = []
gsrm_slf_attn_bias1_list = []
gsrm_slf_attn_bias2_list = []
encoder_word_pos_list.append(norm_img[1])
gsrm_word_pos_list.append(norm_img[2])
gsrm_slf_attn_bias1_list.append(norm_img[3])
gsrm_slf_attn_bias2_list.append(norm_img[4])
norm_img_batch.append(norm_img[0])
elif self.rec_algorithm == "SVTR":
norm_img = self.resize_norm_img_svtr(
img_list[indices[ino]], self.rec_image_shape
)
norm_img = norm_img[np.newaxis, :]
norm_img_batch.append(norm_img)
else:
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()
if self.rec_algorithm == "SRN":
encoder_word_pos_list = np.concatenate(encoder_word_pos_list)
gsrm_word_pos_list = np.concatenate(gsrm_word_pos_list)
gsrm_slf_attn_bias1_list = np.concatenate(gsrm_slf_attn_bias1_list)
gsrm_slf_attn_bias2_list = np.concatenate(gsrm_slf_attn_bias2_list)
inputs = [
norm_img_batch,
encoder_word_pos_list,
gsrm_word_pos_list,
gsrm_slf_attn_bias1_list,
gsrm_slf_attn_bias2_list,
]
if self.use_onnx:
input_dict = {}
input_dict[self.input_tensor.name] = norm_img_batch
outputs = self.predictor.run(self.output_tensors, input_dict)
preds = {"predict": outputs[2]}
else:
input_names = self.predictor.get_input_names()
for i in range(len(input_names)):
input_tensor = self.predictor.get_input_handle(input_names[i])
input_tensor.copy_from_cpu(inputs[i])
self.predictor.run()
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
preds = {"predict": outputs[2]}
elif self.rec_algorithm == "SAR":
valid_ratios = np.concatenate(valid_ratios)
inputs = [
norm_img_batch,
valid_ratios,
]
if self.use_onnx:
input_dict = {}
input_dict[self.input_tensor.name] = norm_img_batch
outputs = self.predictor.run(self.output_tensors, input_dict)
preds = outputs[0]
else:
input_names = self.predictor.get_input_names()
for i in range(len(input_names)):
input_tensor = self.predictor.get_input_handle(input_names[i])
input_tensor.copy_from_cpu(inputs[i])
self.predictor.run()
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
preds = outputs[0]
else:
if self.use_onnx:
input_dict = {}
input_dict[self.input_tensor.name] = norm_img_batch
outputs = self.predictor.run(self.output_tensors, input_dict)
preds = outputs[0]
else:
self.input_tensor.copy_from_cpu(norm_img_batch)
self.predictor.run()
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
if len(outputs) != 1:
preds = outputs
else:
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, time.time() - st
def main(args):
image_file_list = get_image_file_list(args.image_dir)
text_recognizer = TextRecognizer(args)
valid_image_file_list = []
img_list = []
# warmup 2 times
if args.warmup:
img = np.random.uniform(0, 255, [48, 320, 3]).astype(np.uint8)
for i in range(2):
res = text_recognizer([img] * int(args.rec_batch_num))
for image_file in image_file_list:
img = cv2.imread(image_file)
valid_image_file_list.append(image_file)
img_list.append(img)
for i in range(10):
t0 = time.time()
rec_res, _ = text_recognizer(img_list)
print((time.time() - t0) * 1000)
for ino in range(len(img_list)):
print("Predicts of {}:{}".format(valid_image_file_list[ino], rec_res[ino]))
if __name__ == "__main__":
main(utility.parse_args())