def one_pred(img_path):
ocr = PaddleOCR(use_angle_cls=True, lang="ch")
result = ocr.ocr(img_path, cls=True)
image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
scores = [line[1][1] for line in result]
im_show = draw_ocr(image,
boxes,
txts,
scores,
font_path='/doc/simfang.ttf')
im_show = Image.fromarray(im_show)
n = 0
stR = ""
for line in result:
#print(line[1])
n += 1
texts = line[1][0]
stR = stR + "," + str(texts)
#print(texts)
print(stR)
im_show.show()
return stR
def draw_server_result(image_file, res):
img = cv2.imread(image_file)
image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
if len(res) == 0:
return np.array(image)
keys = res[0].keys()
if 'text_region' not in keys: # for rec or clas, draw function is invalid
logger.info("draw function is invalid for rec or clas!")
return None
elif 'text' not in keys: # for ocr_det
logger.info("draw text boxes only!")
boxes = []
for dno in range(len(res)):
boxes.append(res[dno]['text_region'])
boxes = np.array(boxes)
draw_img = draw_boxes(image, boxes)
return draw_img
else: # for ocr_system
logger.info("draw boxes and texts!")
boxes = []
texts = []
scores = []
for dno in range(len(res)):
boxes.append(res[dno]['text_region'])
texts.append(res[dno]['text'])
scores.append(res[dno]['confidence'])
boxes = np.array(boxes)
scores = np.array(scores)
draw_img = draw_ocr(image,
boxes,
texts,
scores,
drop_score=0.5,
font_path="../../doc/simfang.ttf")
return draw_img
def img_word_translate(img_path):
ocr = PaddleOCR(use_angle_cls=True, lang="ch")
result = ocr.ocr(img_path, cls=True)
image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
scores = [line[1][1] for line in result]
im_show = draw_ocr(image, boxes, txts, scores, font_path='/doc/simfang.ttf')
im_show = Image.fromarray(im_show)
n = 0
stR = ""
for line in result:
# print(line[1])
n += 1
texts = line[1][0]
if re.findall('[\u3002\uff1b\uff0c\uff1a\u201c\u201d\uff08\uff09\u3001\uff1f\u300a\u300b]', texts):
stR = stR + str(texts)
else:
stR = stR + str(texts) + ","
# print(texts)
print(stR)
translate(stR, "en", "zh-CN")
im_show.show()
return stR
def one_pred(img_path):
ocr = PaddleOCR(use_angle_cls=True, lang="ch")
result = ocr.ocr(img_path, cls=False)
# print(result)
n = 0
stR = ""
phone = "" #电话
shopName = "" #店铺名
describe = "" #描述
otherinfo = "没有其他信息"
print("+++", result)
for line in result:
n += 1
texts = line[1][0]
if texts == "":
continue
if len(texts) < 2:
continue
r = test.pre(texts)
if len(texts) < 3:
continue
if r == "商铺名":
shopName = shopName + texts
continue
s = ms.pre(texts)
if s == "描述":
describe = describe + texts
continue
stR = stR + str(line[1][0]) + ":" + str(line[1][1]) + "\n"
phones = texts.split("1")
# print("++",phone)
for i in phones:
# print(i)
if re.match(r'^[1-9]\d{9}$', i):
a = "1" + i
# print(a)
phone = phone + ";" + a
else:
phone = " 图片中没有电话信息"
phone = phone[1:]
print("****", shopName, phone, describe, otherinfo)
#return im_show.show()
image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
scores = [line[1][1] for line in result]
im_show = draw_ocr(image,
boxes,
txts,
scores,
font_path='/doc/simfang.ttf')
im_show = Image.fromarray(im_show)
# im_show.show()
# im_show.save是保存识别后的图片
fanhui_img = 'result.jpg'
im_show.save(fanhui_img)
return shopName, phone, describe, otherinfo, fanhui_img
def main(args):
image_file_list = get_image_file_list(args.image_dir)
text_sys = TextSystem(args)
is_visualize = True
tackle_img_num = 0
for image_file in image_file_list:
img = cv2.imread(image_file)
if img is None:
logger.info("error in loading image:{}".format(image_file))
continue
starttime = time.time()
tackle_img_num += 1
if not args.use_gpu and args.enable_mkldnn and tackle_img_num % 30 == 0:
text_sys = TextSystem(args)
dt_boxes, rec_res = text_sys(img)
elapse = time.time() - starttime
print("Predict time of %s: %.3fs" % (image_file, elapse))
dt_num = len(dt_boxes)
dt_boxes_final = []
txts_final = [] # add
for dno in range(dt_num):
text, score = rec_res[dno]
if score >= 0.5:
text_str = "%s, %.3f" % (text, score)
print(text_str)
dt_boxes_final.append(dt_boxes[dno])
txts_final.append(rec_res[dno][0]) # add
label_dic = dict(zip(txts_final, dt_boxes_final))
print(label_dic)
if is_visualize:
image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
boxes = dt_boxes
txts = [rec_res[i][0] for i in range(len(rec_res))]
scores = [rec_res[i][1] for i in range(len(rec_res))]
draw_img = draw_ocr(image,
boxes,
txts,
scores,
draw_txt=True,
drop_score=0.5)
draw_img_save = "./inference_results/"
if not os.path.exists(draw_img_save):
os.makedirs(draw_img_save)
cv2.imwrite(
os.path.join(draw_img_save, os.path.basename(image_file)),
draw_img[:, :, ::-1])
print("The visualized image saved in {}".format(
os.path.join(draw_img_save, os.path.basename(image_file))))
def one_pred(img_path):
ocr = PaddleOCR(use_angle_cls=True, lang="ch")
result = ocr.ocr(img_path, cls=True)
image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
scores = [line[1][1] for line in result]
im_show = draw_ocr(image,
boxes,
txts,
scores,
font_path='/doc/simfang.ttf')
im_show = Image.fromarray(im_show)
im_show.show()
from paddleocr import PaddleOCR
from tools.infer.utility import draw_ocr
ocr = PaddleOCR(
) # need to run only once to download and load model into memory
img_path = r'F:\chrome\zrbdata\data\images\33.jpg'
result = ocr.ocr(img_path, rec=False)
print("+++", result)
for line in result:
print(line)
# 显示结果
from PIL import Image
image = Image.open(img_path).convert('RGB')
im_show = draw_ocr(image,
result,
txts=None,
scores=None,
font_path='/path/to/PaddleOCR/doc/simfang.ttf')
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')
im_show.show()
def pre_save(img_path,save_path,csv_path,m_filePicker11=None):
ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
i = 0
names =[]
#abels = []
imgs=[]
one_qian_pic_path = ""
one_hou_pic_path = ""
#存放所有的图片路径
n_qian_pic_path = []
n_hou_pic_path = []
j=0
stR = ""
# 启动窗口
window.Show(True)
jindu_len = len(os.listdir(img_path)) + 2
print("共："+str(jindu_len-2))
window.m_gauge1.SetRange(jindu_len)
dangqian_jindu = 1
n=0
for img in os.listdir(img_path):
n+=1
window.m_staticText11.SetLabel("正在识别："+img_path+'/'+img)
print("当前正在识别"+str(dangqian_jindu)+"-->"+"正在识别："+img_path+'/'+img)
window.m_gauge1.SetValue(dangqian_jindu)
dangqian_jindu = dangqian_jindu + 1
#动态显示进度条
if dangqian_jindu==jindu_len:
window.m_gauge1.SetValue(dangqian_jindu)
print(img_path+'/'+img)
i+=1
result = ocr.ocr(img_path+'/'+img, cls=True)
image = Image.open(img_path+'/'+img).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
scores = [line[1][1] for line in result]
im_show = draw_ocr(image, boxes, txts, scores, font_path='/doc/simfang.ttf')
im_show = Image.fromarray(im_show)
# im_show.show()
# im_show.save是保存识别后的图片
sp = save_path+"/"+img
im_show.save(sp)
print("图片保存的位置："+sp)
#存储所有的图片路径
n_qian_pic_path.append(img_path+'/'+img)
n_hou_pic_path.append(save_path+"/"+img)
if j==0:
one_hou_pic_path = save_path+"/"+img
one_qian_pic_path = img_path+'/'+img
j = j + 1
if str(result) == "None":
continue
for line in result:
texts = line[1][0]
if texts == "":
continue
if texts in names:
continue
texts = re.findall('[\u4e00-\u9fa50-9]+', texts, re.S) # 只要字符串中的中文，字母，数字
texts = "".join(texts)
label = test.pre(texts)
if len(texts) < 3:
label = "非商铺名"
if label == "商铺名":
#print(texts, " ：", label)
stR = stR + str(img) + ":" + str(texts) + "\n"
imgs.append(img)
#labels.append(label)
names.append(texts)
dataframe = pd.DataFrame({'图片名': imgs, '商铺名称': names})
if csv_path[-3:]=="xls":
dataframe.to_excel(csv_path, index=True)
elif csv_path[-3:]=="csv":
dataframe.to_csv(csv_path, index=True,sep=',')
else:
print("请选择csv或者xls文件")
stR = stR + "\n" + "共识别了" + str(n) + "张图片" + "\n" + "共检测出商铺名" + str(i) + "个\n"
dangqian_jindu = dangqian_jindu + 1
window.m_gauge1.SetValue(dangqian_jindu)
print("当前正在识别" + str(dangqian_jindu))
print(i)
return stR,one_qian_pic_path,one_hou_pic_path,n_qian_pic_path,n_hou_pic_path
def main(args):
image_file_list = get_image_file_list(args.image_dir)
templates = get_templates(args.template_dir)
text_sys = TextSystem(args)
is_visualize = True
tackle_img_num = 0
for image_file in image_file_list:
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
starttime = time.time()
tackle_img_num += 1
if not args.use_gpu and args.enable_mkldnn and tackle_img_num % 30 == 0:
text_sys = TextSystem(args)
dt_boxes, rec_res = text_sys(img)
elapse = time.time() - starttime
print("Predict time of %s: %.3fs" % (image_file, elapse))
drop_score = 0.5
match_results = text_match(templates,
dt_boxes,
rec_res,
drop_score=drop_score)
remove_invalid_results(templates,
match_results,
dt_boxes,
rec_res,
drop_score=drop_score)
ocr_results = []
for match_result in match_results:
ocr_result = {}
for partition_type, match_dict in match_result.items():
ocr_dict = {}
if partition_type == 'head':
continue
if partition_type == 'image_type':
ocr_dict[partition_type] = match_dict
continue
for partition_id, results in match_dict.items():
for result in results:
ocr_dict.setdefault(partition_id, [])
ocr_patition_dict = {
'text': result['text'],
# 'points': result['points'].tolist(),
'confidence': result['score']
}
ocr_dict[partition_id].append(ocr_patition_dict)
ocr_result[partition_type] = ocr_dict
ocr_results.append(ocr_result)
print('ocr_results:', ocr_results)
# dt_num = len(dt_boxes)
# for dno in range(dt_num):
# dt_box = dt_boxes[dno]
# text, score = rec_res[dno]
# if score >= drop_score:
# text_str = "%s, %.3f" % (text, score)
# print(text_str, dt_box)
# post_process(templates, text, dt_box)
if is_visualize:
image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
boxes = dt_boxes
txts = [rec_res[i][0] for i in range(len(rec_res))]
scores = [rec_res[i][1] for i in range(len(rec_res))]
draw_img = draw_ocr(image,
boxes,
txts,
scores,
draw_txt=True,
drop_score=drop_score)
draw_img_save = "./inference_results/"
if not os.path.exists(draw_img_save):
os.makedirs(draw_img_save)
cv2.imwrite(
os.path.join(draw_img_save, os.path.basename(image_file)),
draw_img[:, :, ::-1])
print("The visualized image saved in {}".format(
os.path.join(draw_img_save, os.path.basename(image_file))))
def one_pred(img_path):
ocr = PaddleOCR(use_angle_cls=True, lang="ch")
save_path = img_path
result = ocr.ocr(img_path, cls=True)
image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
scores = [line[1][1] for line in result]
im_show = draw_ocr(image,
boxes,
txts,
scores,
font_path='/doc/simfang.ttf')
im_show = Image.fromarray(im_show)
n_qian_pic_path = []
n_hou_pic_path = []
# print(result)
n = 0
stR = ""
name = ""
all = ""
phone = "" #电话
shopName = "" #店铺名
describe = "" #描述
otherinfo = "没有其他信息"
n_qian_pic_path.append(img_path + '/' + path[-3:])
n_hou_pic_path.append(save_path + "/" + path[-3:])
for line in result:
n += 1
texts = line[1][0]
if len(texts) < 4:
nm = name_test.pre(texts)
if nm == "姓名":
name = name + texts
nm1 = re.findall('[\u4e00-\u9fa5]+', texts[:3], re.S)
nm = name_test.pre(nm1)
if nm == "姓名":
name = name + str(nm1)
if texts[0] == "东":
a1 = texts
r = name_test.pre(texts)
if len(texts) < 2:
continue
if r == "商铺名":
shopName = shopName + texts
continue
if texts[0] == "孙":
a2 = re.findall('[\u4e00-\u9fa5]+', texts, re.S)
s = ms.pre(texts)
if s == "描述":
describe = describe + texts
continue
stR = stR + str(line[1][0]) + ":" + str(line[1][1]) + "\n"
phones = texts.split("1")
if texts[:2] == "地址":
a3 = texts
if texts[:2] == "电话":
a4 = re.findall('[0-9]+', texts, re.S)
if re.match(r'^[0-9]\d{9,12}$', texts):
phone = "1" + texts
# print("++",phone)
for i in phones:
# print(i)
if re.match(r'^[0-9]\d{9}$', i):
a = "1" + i
# print(a)
phone = phone + ";" + a
# 存储所有的图片路径
phone = phone[1:]
all = all + "\n公司名称：" + str(a1) + "\n姓名：" + "".join(
a2) + "\n手机：" + phone + "\n" + str(a3) + "\n电话：" + "".join(
a4[:2]) + ";" + "".join(a4[2:])
#print("+++",name)
print(all)
one_hou_pic_path = save_path + "/" + path[-3:]
one_qian_pic_path = img_path + '/' + path[-3:]
# print("****",shopName,phone,describe,otherinfo)
#return im_show.show()
im_show.show()
return shopName, phone, describe, otherinfo, one_qian_pic_path, one_hou_pic_path
def pre_save(img_path,save_path,csv_path,m_filePicker11=None):
ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory
i = 0
names =[]
#abels = []
imgs=[]
one_qian_pic_path = ""
one_hou_pic_path = ""
#存放所有的图片路径
n_qian_pic_path = []
n_hou_pic_path = []
j=0
stR = ""
# 启动窗口
window.Show(True)
jindu_len = len(os.listdir(img_path)) + 2
print("共："+str(jindu_len-2))
window.m_gauge1.SetRange(jindu_len)
dangqian_jindu = 1
n=0
aaa = []
for img in os.listdir(img_path):
n+=1
window.m_staticText11.SetLabel("正在识别："+img_path+'/'+img)
print("当前正在识别"+str(dangqian_jindu)+"-->"+"正在识别："+img_path+'/'+img)
window.m_gauge1.SetValue(dangqian_jindu)
dangqian_jindu = dangqian_jindu + 1
#动态显示进度条
if dangqian_jindu==jindu_len:
window.m_gauge1.SetValue(dangqian_jindu)
print(img_path+'/'+img)
i += 1
result = ocr.ocr(img_path+'/'+img, cls=True)
image = Image.open(img_path+'/'+img).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
scores = [line[1][1] for line in result]
im_show = draw_ocr(image, boxes, txts, scores, font_path='/doc/simfang.ttf')
im_show = Image.fromarray(im_show)
# im_show.show()
# im_show.save是保存识别后的图片
#存储所有的图片路径
n_qian_pic_path.append(img_path+'/'+img)
n_hou_pic_path.append(save_path+"/"+img)
for line in result:
if line[1][0] =="":
continue
if len(line) < 6:
continue
# print("+++",line)
car_board = one_pred(str(line[1][0]))
aaa.append(car_board)
#print("***",car_board)
if j == 0:
one_hou_pic_path = save_path+"/"+img
one_qian_pic_path = img_path+'/'+img
j = j + 1
if str(result) == "None":
continue
dataframe = pd.DataFrame({'图片名': imgs, '车牌号': names})
if csv_path[-3:]=="xls":
dataframe.to_excel(csv_path, index=True)
elif csv_path[-3:]=="csv":
dataframe.to_csv(csv_path, index=True,sep=',')
else:
print("请选择csv或者xls文件")
for i in aaa:
if i != None:
stR = stR + str(i) + "\n"
dangqian_jindu = dangqian_jindu + 1
window.m_gauge1.SetValue(dangqian_jindu)
print("当前正在识别" + str(dangqian_jindu))
print(i)
print(stR)
return stR,one_qian_pic_path,one_hou_pic_path,n_qian_pic_path,n_hou_pic_path
from paddleocr import PaddleOCR
from tools.infer.utility import draw_ocr
# 模型路径下必须含有model和params文件
ocr = PaddleOCR(det_model_dir='{your_det_model_dir}',
rec_model_dir='{your_rec_model_dir}',
rec_char_dict_path='{your_rec_char_dict_path}',
cls_model_dir='{your_cls_model_dir}',
use_angle_cls=True)
img_path = 'PaddleOCR/doc/imgs/11.jpg'
result = ocr.ocr(img_path, cls=True)
for line in result:
print(line)
# 显示结果
from PIL import Image
image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
scores = [line[1][1] for line in result]
im_show = draw_ocr(image,
boxes,
txts,
scores,
font_path='/path/to/PaddleOCR/doc/simfang.ttf')
im_show = Image.fromarray(im_show)
im_show.save('result.jpg')
def one_pred(img_path):
ocr = PaddleOCR(use_angle_cls=True, lang="ch")
result = ocr.ocr(img_path, cls=True)
image = Image.open(img_path).convert('RGB')
boxes = [line[0] for line in result]
txts = [line[1][0] for line in result]
scores = [line[1][1] for line in result]
im_show = draw_ocr(image,
boxes,
txts,
scores,
font_path='/doc/simfang.ttf')
im_show = Image.fromarray(im_show)
# im_show.show()
# im_show.save是保存识别后的图片
im_show.save("img/result_img/result.jpg")
n = 0
stR = ""
phone = "" #电话
shopName = "" #店铺名
describe = "" #描述
for line in result:
texts = line[1][0]
if texts == "":
continue
if len(texts) < 2:
continue
# print(line[1])
n += 1
stR = stR + str(texts) + ":" + str(texts) + "\n"
texts = re.findall('[\u4e00-\u9fa50-9]+', texts,
re.S) # 只要字符串中的中文，字母，数字
texts = "".join(texts)
if len(texts) < 3:
continue
r = test.pre(texts)
if r == "商铺名":
shopName = shopName + texts + ','
continue
s = ms.pre(texts)
if s == "描述":
describe = describe + texts
continue
# print("+++",texts)
if re.match(r'^[1-9]\d{11}$', texts) or len(texts) == 11:
phone = str(texts)
continue
else:
phones = texts.split("1")
# print("++",phone)
for i in phones:
# print(i)
if re.match(r'^[1-9]\d{7,11}$', i):
a = "1" + i
# print(a)
phone = phone + a + ";"
continue
stR = stR + "\n" + "共识别出" + str(
n
) + "段文本" + "\n其中商铺名为：" + shopName # + "\n电话为：" +str(phone) # + "\n描述为："+describe
print(stR)
return stR
def main(args):
image_file_list = get_image_file_list(args.image_dir)
text_sys = TextSystem(args)
is_visualize = True
tackle_img_num = 0
for image_file in image_file_list:
img = cv2.imread(image_file)
# print(image_file)
if img is None:
logger.info("error in loading image:{}".format(image_file))
continue
starttime = time.time()
tackle_img_num += 1
if not args.use_gpu and args.enable_mkldnn and tackle_img_num % 30 == 0:
text_sys = TextSystem(args)
dt_boxes, rec_res = text_sys(img)
elapse = time.time() - starttime
print("Predict time of %s: %.3fs" % (image_file, elapse))
dt_num = len(dt_boxes)
dt_boxes_final = []
txts_final = [] # add
for dno in range(dt_num):
text, score = rec_res[dno]
if score >= 0.5:
text_str = "%s, %.3f" % (text, score)
# print(text_str)
dt_boxes_final.append(dt_boxes[dno])
txts_final.append(rec_res[dno][0]) # add
'''
file_info_list = {}
file_all = json.loads(json.dumps(file_info_list))
file_list = []
mark_out = []
image_result = dict()
image_result['fileUrl'] = './data/img'
for i in range(len(txts_final)):
label_dic = dict()
label_dic['MarkLabel'] = txts_final[i]
label_dic['Points'] = dt_boxes_final[i]
mark_out.append(label_dic)
image_result['MarkOut'] = mark_out
file_list.append(image_result)
file_all['fileInfoList'] = file_list
with open("./data.json", "w", encoding='utf8') as f:
json.dump(file_all, f, ensure_ascii=False, cls=NpEncoder, indent=4)
'''
image_info = {}
image_label_info = json.loads(json.dumps(image_info))
image_label_info['version'] = '4.5.6'
image_label_info['flags'] = {}
shapes = []
# image_result['fileUrl'] = './data/img'
for i in range(len(txts_final)):
label_dic = dict()
label_dic['label'] = txts_final[i]
label_dic['points'] = dt_boxes_final[i]
label_dic['group_id'] = 1
label_dic['shape_type'] = 'polygon'
label_dic['flags'] = {}
shapes.append(label_dic)
image_label_info['shapes'] = shapes
img_name = os.path.basename(image_file)
dir_name = './doc/imgs/'
image_path = dir_name + img_name
image_label_info['imagePath'] = img_name # can be modified
with open(image_path, 'rb') as f:
base64_data = base64.b64encode(f.read())
img_base64 = base64_data.decode()
image_label_info['imageData'] = img_base64
image_label_info['imageHeight'] = np.array(img).shape[0]
image_label_info['imageWidth'] = np.array(img).shape[1]
with open(dir_name + img_name.split('.')[0] + ".json",
"w",
encoding='utf8') as f:
json.dump(image_label_info,
f,
ensure_ascii=False,
cls=NpEncoder,
indent=2)
if is_visualize:
image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
boxes = dt_boxes
txts = [rec_res[i][0] for i in range(len(rec_res))]
scores = [rec_res[i][1] for i in range(len(rec_res))]
draw_img = draw_ocr(image,
boxes,
txts,
scores,
draw_txt=True,
drop_score=0.5)
draw_img_save = "./inference_results/"
if not os.path.exists(draw_img_save):
os.makedirs(draw_img_save)
cv2.imwrite(
os.path.join(draw_img_save, os.path.basename(image_file)),
draw_img[:, :, ::-1])
print("The visualized image saved in {}".format(
os.path.join(draw_img_save, os.path.basename(image_file))))