"""
Copyright (c) Alibaba, Inc. and its affiliates.
"""
import os
import cv2
import numpy as np
import math
import traceback
from easydict import EasyDict as edict
import time
from iopaint.model.anytext.ocr_recog.RecModel import RecModel
import torch
import torch.nn.functional as F
def min_bounding_rect(img):
ret, thresh = cv2.threshold(img, 127, 255, 0)
contours, hierarchy = cv2.findContours(
thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
)
if len(contours) == 0:
print("Bad contours, using fake bbox...")
return np.array([[0, 0], [100, 0], [100, 100], [0, 100]])
max_contour = max(contours, key=cv2.contourArea)
rect = cv2.minAreaRect(max_contour)
box = cv2.boxPoints(rect)
box = np.int0(box)
# sort
x_sorted = sorted(box, key=lambda x: x[0])
left = x_sorted[:2]
right = x_sorted[2:]
left = sorted(left, key=lambda x: x[1])
(tl, bl) = left
right = sorted(right, key=lambda x: x[1])
(tr, br) = right
if tl[1] > bl[1]:
(tl, bl) = (bl, tl)
if tr[1] > br[1]:
(tr, br) = (br, tr)
return np.array([tl, tr, br, bl])
def create_predictor(model_dir=None, model_lang="ch", is_onnx=False):
model_file_path = model_dir
if model_file_path is not None and not os.path.exists(model_file_path):
raise ValueError("not find model file path {}".format(model_file_path))
if is_onnx:
import onnxruntime as ort
sess = ort.InferenceSession(
model_file_path, providers=["CPUExecutionProvider"]
) # 'TensorrtExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider'
return sess
else:
if model_lang == "ch":
n_class = 6625
elif model_lang == "en":
n_class = 97
else:
raise ValueError(f"Unsupported OCR recog model_lang: {model_lang}")
rec_config = edict(
in_channels=3,
backbone=edict(
type="MobileNetV1Enhance",
scale=0.5,
last_conv_stride=[1, 2],
last_pool_type="avg",
),
neck=edict(
type="SequenceEncoder",
encoder_type="svtr",
dims=64,
depth=2,
hidden_dims=120,
use_guide=True,
),
head=edict(
type="CTCHead",
fc_decay=0.00001,
out_channels=n_class,
return_feats=True,
),
)
rec_model = RecModel(rec_config)
if model_file_path is not None:
rec_model.load_state_dict(torch.load(model_file_path, map_location="cpu"))
rec_model.eval()
return rec_model.eval()
def _check_image_file(path):
img_end = {"jpg", "bmp", "png", "jpeg", "rgb", "tif", "tiff"}
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))
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
class TextRecognizer(object):
def __init__(self, args, predictor):
self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")]
self.rec_batch_num = args.rec_batch_num
self.predictor = predictor
self.chars = self.get_char_dict(args.rec_char_dict_path)
self.char2id = {x: i for i, x in enumerate(self.chars)}
self.is_onnx = not isinstance(self.predictor, torch.nn.Module)
self.use_fp16 = args.use_fp16
# img: CHW
def resize_norm_img(self, img, max_wh_ratio):
imgC, imgH, imgW = self.rec_image_shape
assert imgC == img.shape[0]
imgW = int((imgH * max_wh_ratio))
h, 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 = torch.nn.functional.interpolate(
img.unsqueeze(0),
size=(imgH, resized_w),
mode="bilinear",
align_corners=True,
)
resized_image /= 255.0
resized_image -= 0.5
resized_image /= 0.5
padding_im = torch.zeros((imgC, imgH, imgW), dtype=torch.float32).to(img.device)
padding_im[:, :, 0:resized_w] = resized_image[0]
return padding_im
# img_list: list of tensors with shape chw 0-255
def pred_imglist(self, img_list, show_debug=False, is_ori=False):
img_num = len(img_list)
assert img_num > 0
# Calculate the aspect ratio of all text bars
width_list = []
for img in img_list:
width_list.append(img.shape[2] / float(img.shape[1]))
# Sorting can speed up the recognition process
indices = torch.from_numpy(np.argsort(np.array(width_list)))
batch_num = self.rec_batch_num
preds_all = [None] * img_num
preds_neck_all = [None] * img_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
for ino in range(beg_img_no, end_img_no):
h, w = img_list[indices[ino]].shape[1:]
if h > w * 1.2:
img = img_list[indices[ino]]
img = torch.transpose(img, 1, 2).flip(dims=[1])
img_list[indices[ino]] = img
h, w = img.shape[1:]
# wh_ratio = w * 1.0 / h
# max_wh_ratio = max(max_wh_ratio, wh_ratio) # comment to not use different ratio
for ino in range(beg_img_no, end_img_no):
norm_img = self.resize_norm_img(img_list[indices[ino]], max_wh_ratio)
if self.use_fp16:
norm_img = norm_img.half()
norm_img = norm_img.unsqueeze(0)
norm_img_batch.append(norm_img)
norm_img_batch = torch.cat(norm_img_batch, dim=0)
if show_debug:
for i in range(len(norm_img_batch)):
_img = norm_img_batch[i].permute(1, 2, 0).detach().cpu().numpy()
_img = (_img + 0.5) * 255
_img = _img[:, :, ::-1]
file_name = f"{indices[beg_img_no + i]}"
file_name = file_name + "_ori" if is_ori else file_name
cv2.imwrite(file_name + ".jpg", _img)
if self.is_onnx:
input_dict = {}
input_dict[self.predictor.get_inputs()[0].name] = (
norm_img_batch.detach().cpu().numpy()
)
outputs = self.predictor.run(None, input_dict)
preds = {}
preds["ctc"] = torch.from_numpy(outputs[0])
preds["ctc_neck"] = [torch.zeros(1)] * img_num
else:
preds = self.predictor(norm_img_batch)
for rno in range(preds["ctc"].shape[0]):
preds_all[indices[beg_img_no + rno]] = preds["ctc"][rno]
preds_neck_all[indices[beg_img_no + rno]] = preds["ctc_neck"][rno]
return torch.stack(preds_all, dim=0), torch.stack(preds_neck_all, dim=0)
def get_char_dict(self, character_dict_path):
character_str = []
with open(character_dict_path, "rb") as fin:
lines = fin.readlines()
for line in lines:
line = line.decode("utf-8").strip("\n").strip("\r\n")
character_str.append(line)
dict_character = list(character_str)
dict_character = ["sos"] + dict_character + [" "] # eos is space
return dict_character
def get_text(self, order):
char_list = [self.chars[text_id] for text_id in order]
return "".join(char_list)
def decode(self, mat):
text_index = mat.detach().cpu().numpy().argmax(axis=1)
ignored_tokens = [0]
selection = np.ones(len(text_index), dtype=bool)
selection[1:] = text_index[1:] != text_index[:-1]
for ignored_token in ignored_tokens:
selection &= text_index != ignored_token
return text_index[selection], np.where(selection)[0]
def get_ctcloss(self, preds, gt_text, weight):
if not isinstance(weight, torch.Tensor):
weight = torch.tensor(weight).to(preds.device)
ctc_loss = torch.nn.CTCLoss(reduction="none")
log_probs = preds.log_softmax(dim=2).permute(1, 0, 2) # NTC-->TNC
targets = []
target_lengths = []
for t in gt_text:
targets += [self.char2id.get(i, len(self.chars) - 1) for i in t]
target_lengths += [len(t)]
targets = torch.tensor(targets).to(preds.device)
target_lengths = torch.tensor(target_lengths).to(preds.device)
input_lengths = torch.tensor([log_probs.shape[0]] * (log_probs.shape[1])).to(
preds.device
)
loss = ctc_loss(log_probs, targets, input_lengths, target_lengths)
loss = loss / input_lengths * weight
return loss
def main():
rec_model_dir = "./ocr_weights/ppv3_rec.pth"
predictor = create_predictor(rec_model_dir)
args = edict()
args.rec_image_shape = "3, 48, 320"
args.rec_char_dict_path = "./ocr_weights/ppocr_keys_v1.txt"
args.rec_batch_num = 6
text_recognizer = TextRecognizer(args, predictor)
image_dir = "./test_imgs_cn"
gt_text = ["韩国小馆"] * 14
image_file_list = get_image_file_list(image_dir)
valid_image_file_list = []
img_list = []
for image_file in image_file_list:
img = cv2.imread(image_file)
if img is None:
print("error in loading image:{}".format(image_file))
continue
valid_image_file_list.append(image_file)
img_list.append(torch.from_numpy(img).permute(2, 0, 1).float())
try:
tic = time.time()
times = []
for i in range(10):
preds, _ = text_recognizer.pred_imglist(img_list) # get text
preds_all = preds.softmax(dim=2)
times += [(time.time() - tic) * 1000.0]
tic = time.time()
print(times)
print(np.mean(times[1:]) / len(preds_all))
weight = np.ones(len(gt_text))
loss = text_recognizer.get_ctcloss(preds, gt_text, weight)
for i in range(len(valid_image_file_list)):
pred = preds_all[i]
order, idx = text_recognizer.decode(pred)
text = text_recognizer.get_text(order)
print(
f'{valid_image_file_list[i]}: pred/gt="{text}"/"{gt_text[i]}", loss={loss[i]:.2f}'
)
except Exception as E:
print(traceback.format_exc(), E)
if __name__ == "__main__":
main()